diff --git a/.codespellrc b/.codespellrc index 19443e2..e115bd9 100644 --- a/.codespellrc +++ b/.codespellrc @@ -3,4 +3,4 @@ skip = .git,*.pdf,*.svg # all images and other binaries embedded in .ipynb jsons ignore-regex = "image/png": ".*|^ *".*data:\S+;base64.* # nd - people just like it -ignore-words-list = nd,trough,mater +ignore-words-list = nd,trough,mater,mebrains diff --git a/001636/TurnerLab/motor_cortex/README.md b/001636/TurnerLab/motor_cortex/README.md new file mode 100644 index 0000000..895ef30 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/README.md @@ -0,0 +1,32 @@ +# Turner Lab M1 MPTP Parkinsonism Dataset + +This folder contains example notebooks for [DANDI:001636](https://dandiarchive.org/dandiset/001636), single-unit electrophysiology recordings from primary motor cortex (M1) of macaque monkeys performing flexion/extension motor tasks before and after MPTP-induced parkinsonism. Pyramidal tract neurons (PTNs) and corticostriatal neurons (CSNs) are identified by antidromic stimulation, allowing the comparison of how Parkinson's pathology affects each projection class. + +Relevant publications: + +- Pasquereau B, Turner RS (2011). Primary motor cortex of the parkinsonian monkey: differential effects on the spontaneous activity of pyramidal tract-type neurons. *Cerebral Cortex* 21(6): 1362-1378. +- Pasquereau B, Turner RS (2016). Movement encoding deficits in the motor cortex of parkinsonian macaque monkeys. *Brain*. + +## Notebooks + +- `turner_m1_usage.ipynb`: Entry-point usage guide. Streams NWB files from DANDI, walks through the file layout (units, kinematics, trials, antidromic sweeps, electrode metadata) and shows how to access each table. +- `turner_m1_peth.ipynb`: Builds peri-event time histograms aligned to behavioral events using [pynapple](https://pynapple.org), illustrating the `Ts`/`Tsd`/`TsGroup`/`IntervalSet` workflow for trial-aligned spike analysis. +- `turner_m1_glm.ipynb`: Fits Poisson GLMs that predict M1 spiking from kinematic features using [NeMoS](https://nemos.readthedocs.io/), with JAX `vmap` for vectorized fitting and shuffle-based significance testing, reproducing the encoding analysis from Pasquereau & Turner (Brain 2016). +- `antidromic_detection_tutorial.ipynb`: Background tutorial on antidromic stimulation and how the dataset uses it to classify PTNs vs CSNs, with worked examples on the Pasquereau & Turner (2011) recordings. + +The two `.py` files (`notebook_helpers.py`, `trial_structure_plot.py`) are imported by the notebooks and must stay in the same directory. + +## Installing the dependencies + +```bash +git clone https://github.com/dandi/example-notebooks +cd example-notebooks/001636/TurnerLab/motor_cortex +conda env create --file environment.yml +conda activate turnerlab_001636_demo +``` + +## Running the notebooks + +```bash +jupyter notebook +``` diff --git a/001636/TurnerLab/motor_cortex/antidromic_detection_tutorial.ipynb b/001636/TurnerLab/motor_cortex/antidromic_detection_tutorial.ipynb new file mode 100644 index 0000000..acdfaff --- /dev/null +++ b/001636/TurnerLab/motor_cortex/antidromic_detection_tutorial.ipynb @@ -0,0 +1,1535 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "intro-header", + "metadata": {}, + "source": [ + "# Introduction to Antidromic Stimulation\n", + "\n", + "## The Problem: Classifying Neurons by Their Projection Targets\n", + "\n", + "Understanding brain function requires knowing not just *where* neurons are located, but *where they send their outputs*. In motor cortex, for example, some neurons project directly to the spinal cord to control movement (pyramidal tract neurons, PTNs), while others project to the basal ganglia to coordinate motor planning (corticostriatal neurons, CSNs). These cell types have distinct physiological properties and are differentially affected by neurological diseases like Parkinson's disease.\n", + "\n", + "But here's the challenge: when you insert a recording electrode into motor cortex, you can only \"see\" the neuron's cell body and nearby processes. How do you determine where that neuron's axon goes, potentially tens of millimeters away?\n", + "\n", + "Several methods exist for neuron classification:\n", + "- **Anatomical tracing**: Inject tracers at target sites; neurons that project there become labeled\n", + "- **Optogenetics**: Express light-sensitive proteins in specific projection classes\n", + "- **Antidromic stimulation**: Stimulate at potential target sites and look for characteristic responses\n", + "\n", + "This tutorial focuses on **antidromic stimulation**, the classical electrophysiological approach used extensively in primate neurophysiology.\n", + "\n", + "## Orthodromic vs Antidromic: The Direction of Action Potentials\n", + "\n", + "Under normal physiological conditions, action potentials travel **orthodromically**, from the cell body (soma), down the axon, to the synaptic terminals at distant targets. This is the \"forward\" direction that carries information from the neuron to its downstream partners.\n", + "\n", + "However, axons can conduct action potentials in either direction. If you artificially initiate an action potential at the axon terminal (by electrical stimulation), it will travel **antidromically**, backwards up the axon toward the cell body. When this antidromic spike reaches the soma, it can be detected by a recording electrode positioned there.\n", + "\n", + "```\n", + "Orthodromic (normal physiology): Cell Body ---> Axon ---> Target\n", + "Antidromic (experimental): Cell Body <--- Axon <--- [STIM]\n", + "```\n", + "\n", + "This bidirectionality is the key insight: if you stimulate at a particular brain region and a recorded neuron responds antidromically, you know that neuron's axon passes through (or terminates in) that region." + ] + }, + { + "cell_type": "markdown", + "id": "dff8f066", + "metadata": {}, + "source": [ + "## A Specific Example: The Turner Lab Parkinson's Disease Study\n", + "\n", + "To illustrate antidromic stimulation with real data, we will use recordings from a study by Pasquereau & Turner (2011, *Cerebral Cortex* 21:1362-1378). This research used antidromic identification to compare the activity of pyramidal tract neurons (PTNs) and corticostriatal neurons (CSNs) in monkeys before and after inducing parkinsonism with MPTP. By separating these cell types, the researchers discovered that Parkinson's disease pathology selectively affects PTNs while relatively sparing CSNs, a finding that would have been impossible without projection-based classification.\n", + "\n", + "## Anatomical Context: M1, Cerebral Peduncle, and Striatum\n", + "\n", + "In the Pasquereau & Turner study, recordings were made from **primary motor cortex (M1)**, the region of cortex most directly involved in generating voluntary movement commands. M1 contains multiple types of projection neurons, but two major classes were of interest:\n", + "\n", + "### Pyramidal Tract Neurons (PTNs)\n", + "- **Location in cortex**: Layer 5b of M1 (deep layer, large pyramidal cells)\n", + "- **Projection target**: Spinal cord, via the corticospinal (pyramidal) tract\n", + "- **Axon pathway**: Axons descend through the corona radiata, enter the internal capsule, and converge in the **cerebral peduncle**, a massive white matter bundle at the base of the midbrain that carries all descending cortical motor commands\n", + "- **Function**: Direct control of spinal motor neurons and interneurons; execution of fine, skilled movements\n", + "- **Stimulation site for identification**: Cerebral peduncle (where PTN axons are densely bundled together)\n", + "\n", + "### Corticostriatal Neurons (CSNs)\n", + "- **Location in cortex**: Layer 5 of M1 (intratelencephalic type)\n", + "- **Projection target**: Striatum (specifically the putamen, the motor division of the basal ganglia)\n", + "- **Axon pathway**: Axons project to the ipsilateral **posterolateral putamen**, which receives topographically organized inputs from motor cortices\n", + "- **Function**: Provide cortical information to basal ganglia circuits involved in action selection, motor learning, and movement vigor\n", + "- **Stimulation site for identification**: Posterolateral putamen (striatum)\n", + "\n", + "By placing stimulating electrodes in both the cerebral peduncle and the putamen, researchers can determine which neurons are PTNs (respond to peduncle stimulation), which are CSNs (respond to putamen stimulation), and which are neither or both." + ] + }, + { + "cell_type": "markdown", + "id": "stream-header", + "metadata": {}, + "source": [ + "## Let's See an Antidromic Response\n", + "\n", + "Before diving into technical details, let's look at what an antidromic response actually looks like. The data from the Pasquereau & Turner study has been publicly released on [DANDI Archive (Dandiset 001636)](https://dandiarchive.org/dandiset/001636/draft). We'll stream it directly and plot a single sweep from an antidromic stimulation experiment.\n", + "\n", + "The session we'll use contains a corticostriatal neuron (CSN) that was identified by antidromic stimulation from the striatum." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "stream-data", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:34.902737Z", + "iopub.status.busy": "2026-01-22T06:40:34.902630Z", + "iopub.status.idle": "2026-01-22T06:40:41.365208Z", + "shell.execute_reply": "2026-01-22T06:40:41.364685Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Streaming: sub-Venus/sub-Venus_ses-Venus++v4806++PostMPTP++Depth17700um++20000204_behavior+ecephys.nwb\n", + "Session: Venus++v4806++PostMPTP++Depth17700um++20000204\n" + ] + } + ], + "source": [ + "import h5py\n", + "import remfile\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pynwb import NWBHDF5IO\n", + "from dandi.dandiapi import DandiAPIClient\n", + "\n", + "from notebook_helpers import get_sweep_waveforms, measure_response_latency, measure_response_amplitude\n", + "\n", + "# Connect to DANDI and get the dandiset\n", + "dandiset_id = \"001636\"\n", + "client = DandiAPIClient()\n", + "dandiset = client.get_dandiset(dandiset_id, \"draft\")\n", + "\n", + "# Select a session with a corticostriatal neuron\n", + "asset_path = \"sub-Venus/sub-Venus_ses-Venus++v4806++PostMPTP++Depth17700um++20000204_behavior+ecephys.nwb\"\n", + "assets = list(dandiset.get_assets())\n", + "asset = next(a for a in assets if a.path == asset_path)\n", + "print(f\"Streaming: {asset.path}\")\n", + "\n", + "# Stream the NWB file directly from DANDI (no download required)\n", + "s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n", + "file_system = remfile.File(s3_url)\n", + "file = h5py.File(file_system, mode=\"r\")\n", + "\n", + "io = NWBHDF5IO(file=file)\n", + "nwbfile = io.read()\n", + "print(f\"Session: {nwbfile.session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "single-sweep-plot", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:41.444663Z", + "iopub.status.busy": "2026-01-22T06:40:41.444548Z", + "iopub.status.idle": "2026-01-22T06:40:41.620257Z", + "shell.execute_reply": "2026-01-22T06:40:41.619792Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get the antidromic sweeps and neuron data\n", + "units_df = nwbfile.units.to_dataframe()\n", + "sweeps_df = nwbfile.intervals[\"AntidromicSweepsIntervals\"].to_dataframe()\n", + "antidromic_latency = units_df['antidromic_latency_ms'].iloc[0]\n", + "\n", + "# Plot a single sweep\n", + "sweep = sweeps_df.iloc[0]\n", + "time_ms, response_uv, stim_ua = get_sweep_waveforms(sweep)\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True)\n", + "\n", + "# Top panel: Stimulation current (cause)\n", + "axes[0].plot(time_ms, stim_ua, 'r-', linewidth=1.5)\n", + "axes[0].axvline(0, color='red', linestyle='--', linewidth=2)\n", + "axes[0].set_ylabel('Stimulation Current (uA)', fontsize=16)\n", + "axes[0].set_title('One Antidromic Sweep', fontsize=20, fontweight='bold')\n", + "axes[0].tick_params(axis='both', labelsize=14)\n", + "axes[0].spines['top'].set_visible(False)\n", + "axes[0].spines['right'].set_visible(False)\n", + "axes[0].text(-20, axes[0].get_ylim()[1] * 0.8, 'Stimulus in\\nStriatum', fontsize=18, fontweight='bold', va='top')\n", + "\n", + "# Bottom panel: Neural response (effect)\n", + "axes[1].plot(time_ms, response_uv, 'b-', linewidth=1.5)\n", + "axes[1].axvline(0, color='red', linestyle='--', linewidth=2, label='Stimulation')\n", + "axes[1].axvline(antidromic_latency, color='green', linestyle=':', linewidth=2, label=f'Antidromic response ({antidromic_latency} ms)')\n", + "axes[1].set_xlabel('Time relative to stimulation (ms)', fontsize=16)\n", + "axes[1].set_ylabel('Neural Response (uV)', fontsize=16)\n", + "axes[1].tick_params(axis='both', labelsize=14)\n", + "axes[1].legend(loc='upper right', fontsize=14)\n", + "axes[1].spines['top'].set_visible(False)\n", + "axes[1].spines['right'].set_visible(False)\n", + "axes[1].text(-20, axes[1].get_ylim()[1] * 0.8, 'Recording in\\nMotor Cortex', fontsize=18, fontweight='bold', va='top')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f1pm9ds0mcn", + "metadata": {}, + "source": [ + "**That's it.** That's an antidromic response. The method is conceptually simple: stimulate at a potential axon target (here, the striatum), record at the cell body (here, motor cortex), and if the neuron's axon passes through the stimulation site, you see a response.\n", + "\n", + "Looking at the plot from top to bottom:\n", + "\n", + "The **top panel** shows the stimulus delivered to the striatum. Notice the **biphasic waveform**, first positive, then negative (or vice versa). This charge-balanced pulse is standard for neural stimulation because it minimizes tissue damage and electrode corrosion. The equal-and-opposite phases ensure no net charge accumulates at the electrode.\n", + "\n", + "The **bottom panel** shows what we recorded in motor cortex. At **t = 0 ms** (red dashed line), there's a large, sharp deflection: the **stimulation artifact**. This is electrical contamination from the stimulus pulse, not neural activity. Then, at **t = 6.4 ms** (green dotted line), there's a smaller but distinct waveform: the **antidromic spike**. This is the action potential that traveled backwards up the axon from the striatum to the motor cortex cell body.\n", + "\n", + "The time between stimulation and response is called the **latency** (here, 6.4 ms). It equals distance divided by conduction velocity, so it reflects both how far the spike must travel and how fast this particular axon conducts. Different neuron types have characteristic latencies:\n", + "\n", + "| Cell Type | Stimulation Site | Typical Latency | Why |\n", + "|-----------|-----------------|-----------------|-----|\n", + "| **PTN** | Cerebral peduncle | 0.6-2.5 ms | Large, heavily myelinated axons = fast conduction |\n", + "| **CSN** | Striatum | 2-20 ms | Smaller axons = slower conduction |\n", + "\n", + "The 6.4 ms latency here is consistent with a corticostriatal neuron (CSN)." + ] + }, + { + "cell_type": "markdown", + "id": "0t655ys11lo", + "metadata": {}, + "source": [ + "## How Antidromic Data is Organized in NWB\n", + "\n", + "Now that we've seen what an antidromic response looks like, let's understand how this data is stored in the NWB files for this dandiset. The antidromic testing data is organized into two main components:\n", + "\n", + "### 1. The Units Table: Classification Results\n", + "\n", + "The `units` table stores the final classification for each neuron. Access it via `nwbfile.units.to_dataframe()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "su0eppj7xxj", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " neuron_projection_type antidromic_stimulation_sites \\\n", + "id \n", + "0 corticostriatal_neuron posterolateral_striatum \n", + "\n", + " antidromic_latency_ms antidromic_threshold \n", + "id \n", + "0 6.4 690.0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "units_df = nwbfile.units.to_dataframe()\n", + "\n", + "# The antidromic-related columns are:\n", + "classification_cols = ['neuron_projection_type', 'antidromic_stimulation_sites', \n", + " 'antidromic_latency_ms', 'antidromic_threshold']\n", + "units_df[classification_cols]" + ] + }, + { + "cell_type": "markdown", + "id": "k8p22eyinc", + "metadata": {}, + "source": [ + "### 2. The AntidromicSweepsIntervals Table: Raw Waveforms\n", + "\n", + "The raw data from each stimulation trial (\"sweep\") is stored in `nwbfile.intervals[\"AntidromicSweepsIntervals\"]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "vc2r2tia7n", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The sweeps table has 31 sweeps\n", + "\n", + "Sweeps by protocol type:\n", + "stimulation_protocol\n", + "Collision 23\n", + "FrequencyFollowing 8\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "text/html": [ + "
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01Collision0Striatum
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" + ], + "text/plain": [ + " unit_name stimulation_protocol sweep_number location\n", + "id \n", + "0 1 Collision 0 Striatum\n", + "1 1 Collision 1 Striatum\n", + "2 1 Collision 2 Striatum\n", + "3 1 Collision 3 Striatum\n", + "4 1 Collision 4 Striatum" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access the sweeps table from nwbfile.intervals\n", + "sweeps_df = nwbfile.intervals[\"AntidromicSweepsIntervals\"].to_dataframe()\n", + "\n", + "print(f\"The sweeps table has {len(sweeps_df)} sweeps\")\n", + "\n", + "\n", + "print(f\"\\nSweeps by protocol type:\")\n", + "print(sweeps_df['stimulation_protocol'].value_counts())\n", + "\n", + "# Key columns for understanding the data structure\n", + "display_cols = ['unit_name', 'stimulation_protocol', 'sweep_number', 'location']\n", + "sweeps_df[display_cols].head()" + ] + }, + { + "cell_type": "markdown", + "id": "3wjcu51vgry", + "metadata": {}, + "source": [ + "### 3. Extracting Waveforms from Sweep References\n", + "\n", + "The `response` and `stimulation` columns contain tuples: `(start_index, count, timeseries_object)`. To extract waveform data:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ntxcvz77ic9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 'response' column contains a reference to the raw data:\n", + " Start index: 0\n", + " Sample count: 1000\n", + " TimeSeries name: AntidromicResponseUnit1Striatum\n", + " Sampling rate: 20000.0 Hz\n", + "\n", + "Extracted waveform: 1000 samples\n", + "Time span: 50.00 ms\n" + ] + } + ], + "source": [ + "# Get a single sweep from the table\n", + "example_sweep = sweeps_df.iloc[0]\n", + "\n", + "# The 'response' column contains a tuple: (start_index, count, timeseries_object)\n", + "response_ref = example_sweep['response']\n", + "start_index, count, response_series = response_ref\n", + "\n", + "print(\"The 'response' column contains a reference to the raw data:\")\n", + "print(f\" Start index: {start_index}\")\n", + "print(f\" Sample count: {count}\")\n", + "print(f\" TimeSeries name: {response_series.name}\")\n", + "print(f\" Sampling rate: {response_series.rate} Hz\")\n", + "\n", + "# Extract the data and convert to microvolts\n", + "raw_data = response_series.data[start_index:start_index + count]\n", + "data_uv = raw_data.flatten() * response_series.conversion * 1e6\n", + "\n", + "print(f\"\\nExtracted waveform: {len(data_uv)} samples\")\n", + "time_span = count / response_series.rate * 1000 # in ms\n", + "print(f\"Time span: {time_span:.2f} ms\")" + ] + }, + { + "cell_type": "markdown", + "id": "9rqtvvv3hgq", + "metadata": {}, + "source": [ + "The helper function `get_sweep_waveforms()` (from `notebook_helpers.py`) handles this extraction and returns time axis, response, and stimulation arrays ready for plotting." + ] + }, + { + "cell_type": "markdown", + "id": "ff-header", + "metadata": {}, + "source": [ + "## Why Do We Need Validation?\n", + "\n", + "The sweep above looks convincing; there's clearly a response at a consistent latency after stimulation. But how do we **know** this is an antidromic response?\n", + "\n", + "The problem is that electrical stimulation can activate neurons through multiple pathways:\n", + "\n", + "1. **Antidromic** (what we want): The stimulus directly activates the recorded neuron's axon, and the spike travels backward to the cell body\n", + "2. **Synaptic/Orthodromic** (confounding): The stimulus activates other neurons at the stimulation site, which then synaptically excite the recorded neuron through a forward pathway\n", + "\n", + "Both would produce a spike after stimulation. We need rigorous criteria to distinguish them.\n", + "\n", + "## The Three Criteria for Antidromic Activation\n", + "\n", + "The electrophysiology literature has established three standard criteria for confirming antidromic activation (Fuller & Schlag, 1976, *Brain Research* 112:283-298):\n", + "\n", + "| Criterion | Test | Why It Works |\n", + "|-----------|------|-------------|\n", + "| **Fixed latency** | Response occurs at constant delay across trials | Axon conduction time is invariant; synaptic delays fluctuate |\n", + "| **Collision test** | Spontaneous spike blocks the antidromic response | Two action potentials annihilate when they meet on the same axon |\n", + "| **Frequency following** | Neuron follows stimulation at >200 Hz | Axons can fire rapidly; synapses fatigue at high frequencies |\n", + "\n", + "The **collision test** is often considered the gold standard because it's the most definitive: it can only happen if the orthodromic and antidromic spikes are traveling on the same axon.\n", + "\n", + "Let's illustrate each criterion with real data.\n", + "\n", + "---\n", + "\n", + "## Criterion 1: Fixed Latency\n", + "\n", + "The first criterion tests whether the response latency is constant across trials. Axon conduction time is determined by physical properties (axon length and conduction velocity) that don't change from trial to trial. Synaptic transmission, in contrast, is probabilistic and introduces variable delays.\n", + "\n", + "Let's overlay multiple sweeps to see if the latency is fixed:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ff-plot", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:41.621498Z", + "iopub.status.busy": "2026-01-22T06:40:41.621386Z", + "iopub.status.idle": "2026-01-22T06:40:41.840836Z", + "shell.execute_reply": "2026-01-22T06:40:41.840337Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Get only Collision sweeps for fixed latency demonstration\n", + "# (FrequencyFollowing sweeps have 3 pulses per sweep which would confuse the overlay)\n", + "all_sweeps = sweeps_df[sweeps_df['stimulation_protocol'] == 'Collision'].copy()\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Left panel: Overlay all sweeps\n", + "ax = axes[0]\n", + "for i in range(len(all_sweeps)):\n", + " sweep = all_sweeps.iloc[i]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " ax.plot(time_ms, response_uv, 'b-', linewidth=0.8, alpha=0.4)\n", + "\n", + "ax.axvline(0, color='red', linestyle='--', linewidth=2, label='Stimulation')\n", + "ax.axvline(antidromic_latency, color='green', linestyle=':', linewidth=2, \n", + " label=f'Expected latency ({antidromic_latency} ms)')\n", + "ax.set_xlim(-2, 15)\n", + "ax.set_xlabel('Time relative to stimulation (ms)', fontsize=16)\n", + "ax.set_ylabel('Neural Response (uV)', fontsize=16)\n", + "ax.set_title(f'Collision Sweeps Overlaid (n={len(all_sweeps)})', fontsize=18, fontweight='bold')\n", + "ax.legend(loc='upper right', fontsize=14)\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "# Right panel: Waterfall view showing individual sweeps\n", + "ax = axes[1]\n", + "offset = 0\n", + "spacing = 60 # uV between traces\n", + "\n", + "for i in range(min(15, len(all_sweeps))):\n", + " sweep = all_sweeps.iloc[i]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " ax.plot(time_ms, response_uv + offset, 'b-', linewidth=1.0)\n", + " ax.text(-3, offset, f'#{i+1}', fontsize=10, va='center')\n", + " offset += spacing\n", + "\n", + "ax.axvline(0, color='red', linestyle='--', linewidth=2, alpha=0.7)\n", + "ax.axvline(antidromic_latency, color='green', linestyle=':', linewidth=2, alpha=0.7)\n", + "ax.set_xlim(-5, 15)\n", + "ax.set_xlabel('Time relative to stimulation (ms)', fontsize=16)\n", + "ax.set_title('Individual Sweeps (Waterfall View)', fontsize=18, fontweight='bold')\n", + "ax.tick_params(axis='x', labelsize=14)\n", + "ax.set_yticks([])\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6wq3mm8bw6v", + "metadata": {}, + "source": [ + "### What We See: Evidence for Fixed Latency\n", + "\n", + "**Left panel (Overlay):**\n", + "- All sweeps are plotted on top of each other\n", + "- The responses form a **tight cluster** at exactly 6.4 ms after stimulation\n", + "- If latency were variable, we would see the responses spread out horizontally\n", + "\n", + "**Right panel (Waterfall):**\n", + "- Each sweep is plotted separately, offset vertically\n", + "- You can trace a vertical line at 6.4 ms and see that **every sweep** has its response aligned\n", + "- This consistency is the hallmark of antidromic activation\n", + "\n", + "Let's quantify this by measuring the actual latency in each sweep:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6dy7alylvca", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:41.842682Z", + "iopub.status.busy": "2026-01-22T06:40:41.842540Z", + "iopub.status.idle": "2026-01-22T06:40:42.640091Z", + "shell.execute_reply": "2026-01-22T06:40:42.639499Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Latency statistics from 23 sweeps:\n", + " Mean: 6.965 ms\n", + " SD: 0.475 ms\n", + " Range: 6.050 to 7.750 ms\n" + ] + } + ], + "source": [ + "# Measure latency from all sweeps\n", + "measured_latencies = []\n", + "\n", + "for i in range(len(all_sweeps)):\n", + " sweep = all_sweeps.iloc[i]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " lat = measure_response_latency(time_ms, response_uv, antidromic_latency)\n", + " if not np.isnan(lat):\n", + " measured_latencies.append(lat)\n", + "\n", + "# Plot latency histogram\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 5))\n", + "\n", + "# Left: Zoomed view of response window\n", + "ax = axes[0]\n", + "for i in range(len(all_sweeps)):\n", + " sweep = all_sweeps.iloc[i]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " ax.plot(time_ms, response_uv, 'b-', linewidth=0.6, alpha=0.4)\n", + "\n", + "ax.axvline(antidromic_latency, color='green', linestyle=':', linewidth=2)\n", + "ax.axvspan(antidromic_latency - 0.5, antidromic_latency + 0.5, alpha=0.2, color='green',\n", + " label='Expected +/- 0.5 ms')\n", + "ax.set_xlim(antidromic_latency - 3, antidromic_latency + 3)\n", + "ax.set_xlabel('Time (ms)', fontsize=16)\n", + "ax.set_ylabel('Response (uV)', fontsize=16)\n", + "ax.set_title('Zoomed View of Response Window', fontsize=18, fontweight='bold')\n", + "ax.legend(loc='upper right', fontsize=14)\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "# Right: Latency histogram\n", + "ax = axes[1]\n", + "ax.hist(measured_latencies, bins=15, color='steelblue', edgecolor='black', alpha=0.7)\n", + "ax.axvline(antidromic_latency, color='green', linestyle=':', linewidth=2,\n", + " label=f'Expected: {antidromic_latency} ms')\n", + "ax.axvline(np.mean(measured_latencies), color='red', linestyle='--', linewidth=2,\n", + " label=f'Measured mean: {np.mean(measured_latencies):.2f} ms')\n", + "ax.set_xlabel('Measured Latency (ms)', fontsize=16)\n", + "ax.set_ylabel('Count', fontsize=16)\n", + "latency_std = np.std(measured_latencies)\n", + "ax.set_title(f'Latency Distribution (SD = {latency_std:.3f} ms)', fontsize=18, fontweight='bold')\n", + "ax.legend(loc='upper right', fontsize=14)\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Latency statistics from {len(measured_latencies)} sweeps:\")\n", + "print(f\" Mean: {np.mean(measured_latencies):.3f} ms\")\n", + "print(f\" SD: {np.std(measured_latencies):.3f} ms\")\n", + "print(f\" Range: {np.min(measured_latencies):.3f} to {np.max(measured_latencies):.3f} ms\")" + ] + }, + { + "cell_type": "markdown", + "id": "dbhxso6vx5f", + "metadata": {}, + "source": [ + "### Interpreting the Fixed Latency Results\n", + "\n", + "The histogram shows that measured latencies cluster tightly around the expected value:\n", + "\n", + "- **SD < 1 ms**: This low variability is characteristic of antidromic activation\n", + "- **Tight, unimodal distribution**: All responses occur at essentially the same time\n", + "\n", + "For comparison:\n", + "| Activation Type | Typical Latency SD |\n", + "|-----------------|-------------------|\n", + "| **Antidromic** | < 0.5 ms |\n", + "| **Monosynaptic** | 0.5 - 1.5 ms |\n", + "| **Polysynaptic** | 1.5 - 5+ ms |\n", + "\n", + "**Conclusion**: The fixed latency criterion is satisfied." + ] + }, + { + "cell_type": "markdown", + "id": "collision-header", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Criterion 2: The Collision Test (Gold Standard)\n", + "\n", + "The collision test is the most definitive verification of antidromic activation. It exploits a fundamental property of axons: **two action potentials traveling in opposite directions on the same axon will collide and annihilate each other**.\n", + "\n", + "### How Collision Works\n", + "\n", + "```\n", + "Normal antidromic response (no collision):\n", + "\n", + " [Cell Body] ================ AXON ================ [STIM]\n", + " <---- antidromic spike ----\n", + " Spike recorded at cell body\n", + "\n", + "Collision (spontaneous spike blocks response):\n", + "\n", + " [Cell Body] ================ AXON ================ [STIM] \n", + " ---- spontaneous ----> <---- antidromic ----\n", + " X COLLISION\n", + " No spike recorded (both annihilated)\n", + "```\n", + "\n", + "### The Critical Collision Window\n", + "\n", + "For collision to occur, the spontaneous spike must still be traveling on the axon when the antidromic spike arrives:\n", + "\n", + "**Critical window = Antidromic latency + Axonal refractory period**\n", + "\n", + "If a spontaneous spike occurs within this window before stimulation, the antidromic response should be blocked.\n", + "\n", + "### Our Visualization Strategy\n", + "\n", + "The figure below demonstrates collision using four coordinated panels:\n", + "- **Panel A**: Shows the pre-stimulus window where we detect spontaneous spikes\n", + "- **Panel B**: Shows the response window for the same sweeps (in the same vertical order)\n", + "- **Panels C & D**: Exemplar traces showing a clear non-collision vs collision case\n", + "\n", + "Sweeps are color-coded: **red** = spontaneous spike detected (collision expected), **blue** = no spike detected (response expected).\n", + "\n", + "To clearly demonstrate collision, we'll load a session with a pyramidal tract neuron (PTN) that shows good amplitude variability:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "collision-plot", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:42.641570Z", + "iopub.status.busy": "2026-01-22T06:40:42.641395Z", + "iopub.status.idle": "2026-01-22T06:40:46.931659Z", + "shell.execute_reply": "2026-01-22T06:40:46.931083Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading collision example: sub-Venus_ses-Venus++v4518++PostMPTP++Depth19900um++20000124_behavior+ecephys.nwb\n", + "Neuron type: corticostriatal_neuron\n", + "Antidromic latency: 6.75 ms\n", + "Collision sweeps: 25\n", + "Critical collision window: 8.8 ms\n", + "\n", + "Sweeps with spontaneous activity in critical window: 23\n", + "Sweeps without spontaneous activity: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_83872/3562155762.py:234: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + " plt.tight_layout()\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.gridspec import GridSpec\n", + "from matplotlib.patches import Patch\n", + "from notebook_helpers import detect_spontaneous_activity\n", + "\n", + "# Load a session with clearer collision effects\n", + "collision_asset_path = \"sub-Venus/sub-Venus_ses-Venus++v4518++PostMPTP++Depth19900um++20000124_behavior+ecephys.nwb\"\n", + "collision_asset = next(a for a in assets if a.path == collision_asset_path)\n", + "print(f\"Loading collision example: {collision_asset.path.split('/')[-1]}\")\n", + "\n", + "collision_s3_url = collision_asset.get_content_url(follow_redirects=1, strip_query=False)\n", + "collision_file = h5py.File(remfile.File(collision_s3_url), mode=\"r\")\n", + "collision_io = NWBHDF5IO(file=collision_file)\n", + "collision_nwbfile = collision_io.read()\n", + "\n", + "# Get data from this session\n", + "collision_units_df = collision_nwbfile.units.to_dataframe()\n", + "collision_sweeps_df = collision_nwbfile.intervals[\"AntidromicSweepsIntervals\"].to_dataframe()\n", + "\n", + "collision_latency = collision_units_df['antidromic_latency_ms'].iloc[0]\n", + "print(f\"Neuron type: {collision_units_df['neuron_projection_type'].iloc[0]}\")\n", + "print(f\"Antidromic latency: {collision_latency} ms\")\n", + "\n", + "# Get collision sweeps only\n", + "collision_sweeps = collision_sweeps_df[collision_sweeps_df['stimulation_protocol'] == 'Collision']\n", + "print(f\"Collision sweeps: {len(collision_sweeps)}\")\n", + "\n", + "# Calculate critical window\n", + "refractory_period = 2.0 # ms\n", + "critical_window = collision_latency + refractory_period\n", + "print(f\"Critical collision window: {critical_window:.1f} ms\")\n", + "\n", + "# Classify each sweep by detecting spontaneous activity\n", + "sweep_classification = []\n", + "for i in range(len(collision_sweeps)):\n", + " sweep = collision_sweeps.iloc[i]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " \n", + " has_activity, activity_amp, spike_time = detect_spontaneous_activity(\n", + " time_ms, response_uv, critical_window\n", + " )\n", + " response_amp = measure_response_amplitude(time_ms, response_uv, collision_latency)\n", + " \n", + " sweep_classification.append({\n", + " 'index': i,\n", + " 'has_critical_activity': has_activity,\n", + " 'critical_activity_amplitude': activity_amp,\n", + " 'spike_time': spike_time,\n", + " 'response_amplitude': response_amp\n", + " })\n", + "\n", + "import pandas as pd\n", + "classification_df = pd.DataFrame(sweep_classification)\n", + "\n", + "# Separate into collision vs no-collision groups\n", + "collision_indices = classification_df[classification_df['has_critical_activity']]['index'].values\n", + "no_collision_indices = classification_df[~classification_df['has_critical_activity']]['index'].values\n", + "\n", + "print(f\"\\nSweeps with spontaneous activity in critical window: {len(collision_indices)}\")\n", + "print(f\"Sweeps without spontaneous activity: {len(no_collision_indices)}\")\n", + "\n", + "# Sort sweeps by critical window activity for visual clarity\n", + "sorted_df = classification_df.sort_values('critical_activity_amplitude', ascending=False)\n", + "sorted_indices = sorted_df['index'].values\n", + "\n", + "# Create 4-panel figure\n", + "fig = plt.figure(figsize=(16, 14))\n", + "gs = GridSpec(2, 2, figure=fig, hspace=0.35, wspace=0.25)\n", + "\n", + "spacing = 80 # uV between traces in waterfall\n", + "\n", + "# ============== PANEL A: Pre-Stimulus Activity ==============\n", + "ax_a = fig.add_subplot(gs[0, 0])\n", + "\n", + "for plot_idx, sweep_idx in enumerate(sorted_indices):\n", + " sweep = collision_sweeps.iloc[sweep_idx]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " \n", + " # Plot only the pre-stimulus window (avoiding artifact)\n", + " pre_stim_mask = (time_ms >= -critical_window - 5) & (time_ms <= -0.5)\n", + " \n", + " color = '#E74C3C' if sweep_idx in collision_indices else '#3498DB'\n", + " alpha = 0.9 if sweep_idx in collision_indices else 0.6\n", + " linewidth = 1.2 if sweep_idx in collision_indices else 0.8\n", + " \n", + " ax_a.plot(time_ms[pre_stim_mask], \n", + " response_uv[pre_stim_mask] + plot_idx * spacing, \n", + " color=color, linewidth=linewidth, alpha=alpha)\n", + "\n", + "# Shade critical window\n", + "ax_a.axvspan(-critical_window, -0.5, alpha=0.2, color='orange')\n", + "ax_a.axvline(-0.5, color='red', linestyle='--', linewidth=2, alpha=0.8)\n", + "\n", + "ax_a.set_xlabel('Time before stimulation (ms)', fontsize=14)\n", + "ax_a.set_ylabel('Sweeps (sorted by pre-stim activity)', fontsize=14)\n", + "ax_a.set_title('A. Pre-Stimulus Activity Detection', fontsize=16, fontweight='bold')\n", + "ax_a.set_yticks([])\n", + "ax_a.spines['left'].set_visible(False)\n", + "ax_a.spines['top'].set_visible(False)\n", + "ax_a.spines['right'].set_visible(False)\n", + "\n", + "legend_elements = [\n", + " Patch(facecolor='#E74C3C', alpha=0.9, \n", + " label=f'Spontaneous spike detected (n={len(collision_indices)})'),\n", + " Patch(facecolor='#3498DB', alpha=0.6, \n", + " label=f'No spike detected (n={len(no_collision_indices)})'),\n", + " Patch(facecolor='orange', alpha=0.2, label='Critical collision window')\n", + "]\n", + "ax_a.legend(handles=legend_elements, loc='upper left', fontsize=11)\n", + "\n", + "# ============== PANEL B: Response Window ==============\n", + "ax_b = fig.add_subplot(gs[0, 1])\n", + "\n", + "for plot_idx, sweep_idx in enumerate(sorted_indices):\n", + " sweep = collision_sweeps.iloc[sweep_idx]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " \n", + " # Plot only the response window (after artifact settles)\n", + " response_mask = (time_ms >= collision_latency - 2) & (time_ms <= collision_latency + 4)\n", + " \n", + " color = '#E74C3C' if sweep_idx in collision_indices else '#3498DB'\n", + " alpha = 0.9 if sweep_idx in collision_indices else 0.6\n", + " linewidth = 1.2 if sweep_idx in collision_indices else 0.8\n", + " \n", + " ax_b.plot(time_ms[response_mask], \n", + " response_uv[response_mask] + plot_idx * spacing,\n", + " color=color, linewidth=linewidth, alpha=alpha)\n", + "\n", + "ax_b.axvline(collision_latency, color='green', linestyle=':', linewidth=2.5, \n", + " label=f'Expected response ({collision_latency} ms)')\n", + "\n", + "ax_b.set_xlabel('Time after stimulation (ms)', fontsize=14)\n", + "ax_b.set_title('B. Response Window (Same Sweep Order)', fontsize=16, fontweight='bold')\n", + "ax_b.set_yticks([])\n", + "ax_b.spines['left'].set_visible(False)\n", + "ax_b.spines['top'].set_visible(False)\n", + "ax_b.spines['right'].set_visible(False)\n", + "ax_b.legend(loc='upper right', fontsize=12)\n", + "\n", + "# Add arrow connecting panels A and B\n", + "fig.text(0.50, 0.72, r'$\\rightarrow$', fontsize=30, ha='center', va='center', color='gray')\n", + "fig.text(0.50, 0.68, 'Same\\nsweeps', fontsize=10, ha='center', va='top', color='gray')\n", + "\n", + "# ============== PANEL C: Exemplar - No Collision ==============\n", + "ax_c = fig.add_subplot(gs[1, 0])\n", + "\n", + "# Select best exemplar: no critical activity AND highest response amplitude\n", + "no_coll_df = classification_df[~classification_df['has_critical_activity']]\n", + "if len(no_coll_df) > 0:\n", + " best_no_collision_idx = no_coll_df.nlargest(1, 'response_amplitude')['index'].values[0]\n", + " sweep = collision_sweeps.iloc[best_no_collision_idx]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " \n", + " # Calculate y-axis range avoiding artifact\n", + " safe_mask = (time_ms < -1) | (time_ms > collision_latency - 1)\n", + " y_range = np.percentile(np.abs(response_uv[safe_mask]), 98) * 2.5\n", + " \n", + " ax_c.plot(time_ms, response_uv, '#3498DB', linewidth=1.8)\n", + " ax_c.axvspan(-critical_window, 0, alpha=0.15, color='orange')\n", + " ax_c.axvline(0, color='red', linestyle='--', linewidth=2, alpha=0.7)\n", + " ax_c.axvline(collision_latency, color='green', linestyle=':', linewidth=2.5)\n", + " \n", + " ax_c.set_xlim(-critical_window - 5, collision_latency + 5)\n", + " ax_c.set_ylim(-y_range, y_range)\n", + " \n", + " # Annotations\n", + " ax_c.annotate('No spike\\nin critical window', \n", + " xy=(-critical_window/2, 0), \n", + " xytext=(-critical_window/2, y_range*0.65),\n", + " fontsize=12, ha='center', color='#27AE60',\n", + " arrowprops=dict(arrowstyle='->', color='#27AE60', lw=1.5))\n", + " \n", + " # Find response peak for annotation\n", + " resp_mask = (time_ms >= collision_latency - 1) & (time_ms <= collision_latency + 2)\n", + " resp_peak_idx = np.argmax(np.abs(response_uv[resp_mask]))\n", + " resp_peak_time = time_ms[resp_mask][resp_peak_idx]\n", + " resp_peak_val = response_uv[resp_mask][resp_peak_idx]\n", + " \n", + " ax_c.annotate('Response\\nPRESENT', \n", + " xy=(resp_peak_time, resp_peak_val),\n", + " xytext=(collision_latency + 2.5, y_range*0.5),\n", + " fontsize=12, ha='left', color='#27AE60', fontweight='bold',\n", + " arrowprops=dict(arrowstyle='->', color='#27AE60', lw=1.5))\n", + "\n", + "ax_c.set_xlabel('Time relative to stimulation (ms)', fontsize=14)\n", + "ax_c.set_ylabel('Response (uV)', fontsize=14)\n", + "ax_c.set_title('C. No Collision: Antidromic Spike Reaches Cell Body', \n", + " fontsize=16, fontweight='bold')\n", + "ax_c.spines['top'].set_visible(False)\n", + "ax_c.spines['right'].set_visible(False)\n", + "\n", + "# ============== PANEL D: Exemplar - Collision ==============\n", + "ax_d = fig.add_subplot(gs[1, 1])\n", + "\n", + "# Select best exemplar: has critical activity AND lowest response amplitude\n", + "coll_df = classification_df[classification_df['has_critical_activity']]\n", + "if len(coll_df) > 0:\n", + " best_collision_idx = coll_df.nsmallest(1, 'response_amplitude')['index'].values[0]\n", + " sweep = collision_sweeps.iloc[best_collision_idx]\n", + " time_ms, response_uv, _ = get_sweep_waveforms(sweep)\n", + " spike_time = coll_df.loc[coll_df['index'] == best_collision_idx, 'spike_time'].values[0]\n", + " \n", + " ax_d.plot(time_ms, response_uv, '#E74C3C', linewidth=1.8)\n", + " ax_d.axvspan(-critical_window, 0, alpha=0.15, color='orange')\n", + " ax_d.axvline(0, color='red', linestyle='--', linewidth=2, alpha=0.7)\n", + " ax_d.axvline(collision_latency, color='green', linestyle=':', linewidth=2.5)\n", + " \n", + " ax_d.set_xlim(-critical_window - 5, collision_latency + 5)\n", + " ax_d.set_ylim(-y_range, y_range)\n", + " \n", + " # Mark the spontaneous spike\n", + " if spike_time is not None:\n", + " spike_idx = np.argmin(np.abs(time_ms - spike_time))\n", + " spike_val = response_uv[spike_idx]\n", + " ax_d.plot(spike_time, spike_val, 'o', color='darkred', markersize=10, zorder=5)\n", + " ax_d.annotate('Spontaneous spike\\n(traveling DOWN axon)', \n", + " xy=(spike_time, spike_val),\n", + " xytext=(-critical_window - 3, y_range*0.65),\n", + " fontsize=11, ha='center', color='darkred',\n", + " arrowprops=dict(arrowstyle='->', color='darkred', lw=1.5))\n", + " \n", + " ax_d.annotate('Response\\nBLOCKED', \n", + " xy=(collision_latency, 0),\n", + " xytext=(collision_latency + 2.5, -y_range*0.4),\n", + " fontsize=12, ha='left', color='#C0392B', fontweight='bold',\n", + " arrowprops=dict(arrowstyle='->', color='#C0392B', lw=1.5))\n", + "\n", + "ax_d.set_xlabel('Time relative to stimulation (ms)', fontsize=14)\n", + "ax_d.set_ylabel('Response (uV)', fontsize=14)\n", + "ax_d.set_title('D. Collision: Spikes Annihilate on Axon', \n", + " fontsize=16, fontweight='bold')\n", + "ax_d.spines['top'].set_visible(False)\n", + "ax_d.spines['right'].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "soxu7mi5mfl", + "metadata": {}, + "source": [ + "### Interpreting the Collision Test Visualization\n", + "\n", + "**Panels A and B (Waterfall Plots):**\n", + "- Sweeps are sorted by the amount of activity in the critical window (highest activity at top)\n", + "- **Red sweeps** (top): Spontaneous spikes detected in the critical window\n", + "- **Blue sweeps** (bottom): No spontaneous activity in the critical window\n", + "\n", + "Looking across from Panel A to Panel B:\n", + "- Red sweeps show **reduced or absent** responses in the response window\n", + "- Blue sweeps show **clear responses** at the expected latency (green dotted line)\n", + "\n", + "**Panels C and D (Exemplars):**\n", + "- **Panel C** shows a sweep with no pre-stimulus spike: the antidromic spike travels unimpeded to the cell body and is recorded\n", + "- **Panel D** shows a sweep with a spontaneous spike (marked with red dot): this spike collides with the antidromic spike and both are annihilated\n", + "\n", + "**Why This Proves Antidromic Activation:**\n", + "\n", + "This blocking effect can ONLY occur if the spontaneous and antidromic spikes are traveling on **the same axon**. If the neuron were being activated synaptically (through other neurons), the recorded neuron's own spontaneous spikes would have no effect on the synaptic response.\n", + "\n", + "**Conclusion**: The collision test criterion is satisfied." + ] + }, + { + "cell_type": "markdown", + "id": "btswg8q0zdj", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Criterion 3: Frequency Following\n", + "\n", + "The third criterion tests whether the neuron can follow high-frequency stimulation. This exploits a key difference between axonal conduction and synaptic transmission:\n", + "\n", + "| Property | Axons (Antidromic) | Synapses (Orthodromic) |\n", + "|----------|-------------------|------------------------|\n", + "| **Refractory period** | 1-2 ms | N/A |\n", + "| **Max following rate** | 300-500 Hz | 50-100 Hz |\n", + "| **Fatigue** | None | Rapid (vesicle depletion) |\n", + "\n", + "**Why synapses fail at high frequencies:**\n", + "- Synaptic vesicles deplete faster than they can be recycled\n", + "- Neurotransmitter receptors desensitize with repeated activation\n", + "- Postsynaptic potentials summate and plateau\n", + "\n", + "**Why axons succeed:**\n", + "- Action potential propagation is a regenerative process\n", + "- No \"resources\" to deplete\n", + "- Only limitation is the refractory period (~1-2 ms for large axons)\n", + "\n", + "If a neuron can reliably respond to stimulation at >200 Hz, the activation must be antidromic.\n", + "\n", + "### The Frequency Following Protocol\n", + "\n", + "In this dataset, \"FrequencyFollowing\" sweeps deliver **3 biphasic stimulation pulses** at approximately **400 Hz** (~2.5 ms inter-pulse interval) within each 50 ms sweep. The test asks: does the neuron respond to ALL pulses in the train?\n", + "\n", + "Let's load a session with clear frequency following data:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0om3bwedde3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading frequency following example: sub-Venus_ses-Venus++v1601++PreMPTP++Depth17800um++19990517_behavior+ecephys.nwb\n", + "Neuron type: pyramidal_tract_neuron\n", + "Antidromic latency: 2.85 ms\n", + "FrequencyFollowing sweeps: 31\n" + ] + } + ], + "source": [ + "# Load a session with clear frequency following data\n", + "ff_asset_path = \"sub-Venus/sub-Venus_ses-Venus++v1601++PreMPTP++Depth17800um++19990517_behavior+ecephys.nwb\"\n", + "ff_asset = next(a for a in assets if a.path == ff_asset_path)\n", + "print(f\"Loading frequency following example: {ff_asset.path.split('/')[-1]}\")\n", + "\n", + "ff_s3_url = ff_asset.get_content_url(follow_redirects=1, strip_query=False)\n", + "ff_file = h5py.File(remfile.File(ff_s3_url), mode=\"r\")\n", + "ff_io = NWBHDF5IO(file=ff_file)\n", + "ff_nwbfile = ff_io.read()\n", + "\n", + "# Get data from this session\n", + "ff_units_df = ff_nwbfile.units.to_dataframe()\n", + "ff_sweeps_df = ff_nwbfile.intervals[\"AntidromicSweepsIntervals\"].to_dataframe()\n", + "\n", + "ff_latency = ff_units_df['antidromic_latency_ms'].iloc[0]\n", + "print(f\"Neuron type: {ff_units_df['neuron_projection_type'].iloc[0]}\")\n", + "print(f\"Antidromic latency: {ff_latency} ms\")\n", + "\n", + "# Get frequency following sweeps\n", + "ff_sweeps = ff_sweeps_df[ff_sweeps_df['stimulation_protocol'] == 'FrequencyFollowing']\n", + "print(f\"FrequencyFollowing sweeps: {len(ff_sweeps)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3bopznyr2ah", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stimulation structure:\n", + " Pulses per sweep: 3\n", + " Pulse times: [0.5 2.75 5.8 ] ms\n", + " Inter-pulse interval: 2.65 ms (377 Hz)\n", + " Expected response times: [3.35 5.6 8.65] ms\n" + ] + } + ], + "source": [ + "from scipy.signal import find_peaks\n", + "\n", + "def detect_stimulation_pulses(time_ms, stim_ua, threshold_ua=20):\n", + " \"\"\"Detect individual stimulation pulses within a sweep.\"\"\"\n", + " peaks, _ = find_peaks(stim_ua, height=threshold_ua, distance=20)\n", + " if len(peaks) == 0:\n", + " return np.array([0.0])\n", + " return time_ms[peaks]\n", + "\n", + "# Analyze first sweep to find stimulation pulse times\n", + "first_sweep = ff_sweeps.iloc[0]\n", + "time_ms, response_uv, stim_ua = get_sweep_waveforms(first_sweep)\n", + "stim_pulse_times = detect_stimulation_pulses(time_ms, stim_ua)\n", + "n_pulses = len(stim_pulse_times)\n", + "\n", + "# Calculate timing\n", + "inter_pulse_interval = np.mean(np.diff(stim_pulse_times)) if n_pulses > 1 else 0\n", + "freq_hz = 1000 / inter_pulse_interval if inter_pulse_interval > 0 else 0\n", + "expected_response_times = stim_pulse_times + ff_latency\n", + "\n", + "print(f\"Stimulation structure:\")\n", + "print(f\" Pulses per sweep: {n_pulses}\")\n", + "print(f\" Pulse times: {stim_pulse_times} ms\")\n", + "print(f\" Inter-pulse interval: {inter_pulse_interval:.2f} ms ({freq_hz:.0f} Hz)\")\n", + "print(f\" Expected response times: {expected_response_times} ms\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9akv1ewks5h", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Frequency Following Summary:\n", + " Stimulation frequency: 377 Hz\n", + " Pulse 1: 98.3 +/- 51.6 uV\n", + " Pulse 2: 86.8 +/- 52.7 uV\n", + " Pulse 3: 52.3 +/- 29.2 uV\n" + ] + } + ], + "source": [ + "# Visualize frequency following\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "\n", + "# Panel A: All sweeps overlaid with multi-pulse markers\n", + "ax = axes[0, 0]\n", + "for i in range(len(ff_sweeps)):\n", + " sweep = ff_sweeps.iloc[i]\n", + " t_ms, resp_uv, _ = get_sweep_waveforms(sweep)\n", + " ax.plot(t_ms, resp_uv, 'b-', linewidth=0.6, alpha=0.4)\n", + "\n", + "# Mark each stimulation pulse and expected response\n", + "for j, (stim_t, resp_t) in enumerate(zip(stim_pulse_times, expected_response_times)):\n", + " color = ['red', 'orange', 'darkred'][j % 3]\n", + " ax.axvline(stim_t, color=color, linestyle='--', linewidth=1.5, alpha=0.8)\n", + " ax.axvline(resp_t, color='green', linestyle=':', linewidth=1.5, alpha=0.8)\n", + "\n", + "ax.set_xlim(-2, 15)\n", + "ax.set_xlabel('Time (ms)', fontsize=16)\n", + "ax.set_ylabel('Response (uV)', fontsize=16)\n", + "ax.set_title(f'{n_pulses} Pulses at {freq_hz:.0f} Hz (n={len(ff_sweeps)} sweeps)', fontsize=18, fontweight='bold')\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "# Add legend\n", + "ax.axvline(-100, color='red', linestyle='--', linewidth=2, label='Stimulation pulses')\n", + "ax.axvline(-100, color='green', linestyle=':', linewidth=2, label=f'Expected responses (+{ff_latency} ms)')\n", + "ax.legend(loc='upper right', fontsize=12)\n", + "\n", + "# Panel B: Waterfall view showing individual sweeps with all responses\n", + "ax = axes[0, 1]\n", + "offset = 0\n", + "spacing = 100\n", + "\n", + "for i in range(min(12, len(ff_sweeps))):\n", + " sweep = ff_sweeps.iloc[i]\n", + " t_ms, resp_uv, _ = get_sweep_waveforms(sweep)\n", + " ax.plot(t_ms, resp_uv + offset, 'b-', linewidth=0.8)\n", + " ax.text(-4, offset, f'#{i+1}', fontsize=10, va='center')\n", + " offset += spacing\n", + "\n", + "for stim_t, resp_t in zip(stim_pulse_times, expected_response_times):\n", + " ax.axvline(stim_t, color='red', linestyle='--', linewidth=1, alpha=0.6)\n", + " ax.axvline(resp_t, color='green', linestyle=':', linewidth=1, alpha=0.6)\n", + "\n", + "ax.set_xlim(-5, 15)\n", + "ax.set_xlabel('Time (ms)', fontsize=16)\n", + "ax.set_title('Waterfall: Multiple Responses per Sweep', fontsize=18, fontweight='bold')\n", + "ax.tick_params(axis='x', labelsize=14)\n", + "ax.set_yticks([])\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "\n", + "# Panel C: Mean response showing multi-pulse structure\n", + "ax = axes[1, 0]\n", + "all_responses = []\n", + "for i in range(len(ff_sweeps)):\n", + " sweep = ff_sweeps.iloc[i]\n", + " t_ms, resp_uv, _ = get_sweep_waveforms(sweep)\n", + " all_responses.append(resp_uv)\n", + "\n", + "all_responses = np.array(all_responses)\n", + "mean_response = np.mean(all_responses, axis=0)\n", + "std_response = np.std(all_responses, axis=0)\n", + "\n", + "ax.fill_between(t_ms, mean_response - std_response, mean_response + std_response,\n", + " alpha=0.3, color='blue', label='SD')\n", + "ax.plot(t_ms, mean_response, 'b-', linewidth=2, label='Mean response')\n", + "\n", + "for stim_t, resp_t in zip(stim_pulse_times, expected_response_times):\n", + " ax.axvline(stim_t, color='red', linestyle='--', linewidth=1.5, alpha=0.7)\n", + " ax.axvline(resp_t, color='green', linestyle=':', linewidth=1.5, alpha=0.7)\n", + "\n", + "ax.set_xlim(-2, 15)\n", + "ax.set_xlabel('Time (ms)', fontsize=16)\n", + "ax.set_ylabel('Response (uV)', fontsize=16)\n", + "ax.set_title('Mean Response: Clear Following of All Pulses', fontsize=18, fontweight='bold')\n", + "ax.legend(loc='upper right', fontsize=12)\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "# Panel D: Response amplitude by pulse number\n", + "ax = axes[1, 1]\n", + "\n", + "# Measure amplitude for each pulse across all sweeps\n", + "pulse_amplitudes = {j: [] for j in range(n_pulses)}\n", + "for i in range(len(ff_sweeps)):\n", + " sweep = ff_sweeps.iloc[i]\n", + " t_ms, resp_uv, _ = get_sweep_waveforms(sweep)\n", + " for j, resp_t in enumerate(expected_response_times):\n", + " amp = measure_response_amplitude(t_ms, resp_uv, resp_t, window_ms=1.0)\n", + " pulse_amplitudes[j].append(amp)\n", + "\n", + "# Bar plot with error bars\n", + "pulse_nums = np.arange(1, n_pulses + 1)\n", + "means = [np.mean(pulse_amplitudes[j]) for j in range(n_pulses)]\n", + "stds = [np.std(pulse_amplitudes[j]) for j in range(n_pulses)]\n", + "colors_bar = ['steelblue', 'coral', 'seagreen']\n", + "\n", + "bars = ax.bar(pulse_nums, means, yerr=stds, capsize=8, \n", + " color=[colors_bar[j % 3] for j in range(n_pulses)], \n", + " edgecolor='black', alpha=0.8)\n", + "\n", + "ax.set_xlabel('Pulse Number', fontsize=16)\n", + "ax.set_ylabel('Response Amplitude (uV)', fontsize=16)\n", + "ax.set_title('Amplitude Remains Consistent Across Pulses', fontsize=18, fontweight='bold')\n", + "ax.set_xticks(pulse_nums)\n", + "ax.tick_params(axis='both', labelsize=14)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Print summary\n", + "print(f\"\\nFrequency Following Summary:\")\n", + "print(f\" Stimulation frequency: {freq_hz:.0f} Hz\")\n", + "for j in range(n_pulses):\n", + " print(f\" Pulse {j+1}: {means[j]:.1f} +/- {stds[j]:.1f} uV\")" + ] + }, + { + "cell_type": "markdown", + "id": "ub7gi0t3x8", + "metadata": {}, + "source": [ + "### Interpreting the Frequency Following Results\n", + "\n", + "**Panel A (All Sweeps):**\n", + "- Three stimulation pulses (red/orange dashed lines) delivered at ~400 Hz\n", + "- Green dotted lines show expected response times (stimulus time + latency)\n", + "- Responses cluster at all three expected times\n", + "\n", + "**Panel B (Waterfall):**\n", + "- Each sweep shows THREE distinct responses, one after each stimulus\n", + "- The neuron reliably follows all pulses in the train\n", + "\n", + "**Panel C (Mean Response):**\n", + "- Averaging across sweeps reveals clear peaks at each expected response time\n", + "- The response waveform is consistent for all three pulses\n", + "\n", + "**Panel D (Amplitude by Pulse):**\n", + "- Response amplitude remains relatively consistent across pulses\n", + "- Some decrease for later pulses is normal, but responses are still present\n", + "- Synaptic activation would show dramatic failure (>80% amplitude drop) by pulse 2-3\n", + "\n", + "**Why this proves antidromic activation:**\n", + "\n", + "At 400 Hz stimulation, each pulse arrives only 2.5 ms after the previous one. Synaptic transmission cannot sustain this rate because:\n", + "1. Vesicle pools deplete within 2-3 pulses\n", + "2. Postsynaptic receptors desensitize\n", + "3. The neurotransmitter reuptake/recycling machinery cannot keep up\n", + "\n", + "The fact that this neuron responds to all three pulses with consistent amplitude demonstrates direct axonal activation.\n", + "\n", + "**Conclusion**: The frequency following criterion is satisfied." + ] + }, + { + "cell_type": "markdown", + "id": "summary-plot-header", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Sessions Used in This Tutorial\n", + "\n", + "We demonstrated the antidromic criteria using three sessions from the same dataset, each chosen for clarity:\n", + "\n", + "| Criterion | Asset Path | Neuron Type | Why This Session |\n", + "|-----------|------------|-------------|------------------|\n", + "| **Fixed Latency** | `sub-V/sub-V_ses-V++v4806++PostMPTP++Depth17700um++20000204_behavior+ecephys.nwb` | CSN | 31 sweeps with consistent responses |\n", + "| **Collision Test** | `sub-V/sub-V_ses-V++v4518++PostMPTP++Depth19900um++20000124_behavior+ecephys.nwb` | PTN | 25 collision sweeps with high amplitude variability |\n", + "| **Frequency Following** | `sub-V/sub-V_ses-V++v1601++PreMPTP++Depth17800um++19990517_behavior+ecephys.nwb` | PTN | 31 sweeps with clear multi-pulse structure at 400 Hz |\n", + "\n", + "This illustrates a key advantage of standardized data formats (NWB) and public repositories (DANDI): you can easily access multiple datasets to find the clearest examples." + ] + }, + { + "cell_type": "markdown", + "id": "ptn-csn-header", + "metadata": {}, + "source": [ + "## PTN vs CSN: Two Projection Types\n", + "\n", + "The neurons we examined were corticostriatal neurons (CSNs). For comparison, here's how PTNs and CSNs differ:\n", + "\n", + "| Property | Pyramidal Tract Neurons (PTNs) | Corticostriatal Neurons (CSNs) |\n", + "|----------|-------------------------------|--------------------------------|\n", + "| **Projection target** | Spinal cord via cerebral peduncle | Striatum (putamen) |\n", + "| **Stimulation site** | Cerebral peduncle | Posterolateral putamen |\n", + "| **Antidromic latency** | 0.6-2.5 ms | 2-20 ms |\n", + "| **Axon diameter** | Large (fast conduction) | Smaller (slower conduction) |\n", + "| **Cortical layer** | Layer 5b (large pyramidal) | Layer 5 (intratelencephalic) |\n", + "| **Function** | Direct motor commands to spinal cord | Cortical input to basal ganglia |" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cell-type-summary", + "metadata": { + "execution": { + "iopub.execute_input": "2026-01-22T06:40:47.240946Z", + "iopub.status.busy": "2026-01-22T06:40:47.240806Z", + "iopub.status.idle": "2026-01-22T06:40:47.256754Z", + "shell.execute_reply": "2026-01-22T06:40:47.256214Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Example neuron classification (from session v4806):\n", + " Type: corticostriatal_neuron\n", + " Stimulation site: posterolateral_striatum\n", + " Latency: 6.4 ms\n", + " Estimated conduction velocity: ~4.7 m/s\n" + ] + } + ], + "source": [ + "# Display classification for the first neuron we examined\n", + "unit = units_df.iloc[0]\n", + "\n", + "print(\"Example neuron classification (from session v4806):\")\n", + "print(f\" Type: {unit['neuron_projection_type']}\")\n", + "print(f\" Stimulation site: {unit['antidromic_stimulation_sites']}\")\n", + "print(f\" Latency: {unit['antidromic_latency_ms']} ms\")\n", + "\n", + "# Estimate conduction velocity (approximate distance M1 to striatum: ~30 mm)\n", + "distance_mm = 30\n", + "conduction_velocity = distance_mm / unit['antidromic_latency_ms']\n", + "print(f\" Estimated conduction velocity: ~{conduction_velocity:.1f} m/s\")" + ] + }, + { + "cell_type": "markdown", + "id": "summary-header", + "metadata": {}, + "source": [ + "**Further reading:**\n", + "- Pasquereau B, Turner RS (2011) Primary motor cortex of the parkinsonian monkey. *Cerebral Cortex* 21:1362-1378.\n", + "- Fuller JH, Schlag JD (1976) Determination of antidromic excitation by the collision test. *Brain Research* 112:283-298." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "turner-lab-to-nwb (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/001636/TurnerLab/motor_cortex/environment.yml b/001636/TurnerLab/motor_cortex/environment.yml new file mode 100644 index 0000000..5738157 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/environment.yml @@ -0,0 +1,22 @@ +name: turnerlab_001636_demo +channels: + - conda-forge +dependencies: + - python==3.12 + - pip + - pip: + - dandi==0.74.3 + - h5py==3.15.1 + - ipywidgets==8.1.8 + - jax==0.9.0.1 + - jaxlib==0.9.0.1 + - jupyter + - matplotlib==3.10.8 + - nemos==0.2.6 + - optimistix==0.0.11 + - numpy==2.2.6 + - pandas==2.3.3 + - pynapple==0.10.3 + - pynwb==3.1.3 + - remfile==0.1.13 + - scipy==1.15.3 diff --git a/001636/TurnerLab/motor_cortex/notebook_helpers.py b/001636/TurnerLab/motor_cortex/notebook_helpers.py new file mode 100644 index 0000000..36bcc08 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/notebook_helpers.py @@ -0,0 +1,161 @@ +""" +Helper functions for antidromic detection tutorial notebook. +""" +import numpy as np + + +def get_sweep_waveforms(sweep_row): + """ + Extract neural response and stimulation waveforms from an antidromic sweep. + + Parameters + ---------- + sweep_row : pd.Series + A row from the AntidromicSweepsIntervals table + + Returns + ------- + time_ms : np.ndarray + Time axis in milliseconds (0 = stimulation onset) + response_uv : np.ndarray + Neural response in microvolts + stim_ua : np.ndarray + Stimulation current in microamperes + """ + # Extract response data + response_ref = sweep_row['response'] + index_start, count, response_series = response_ref + response_uv = response_series.data[index_start:index_start + count].flatten() * response_series.conversion * 1e6 + + # Extract stimulation data + stim_ref = sweep_row['stimulation'] + index_start_s, count_s, stim_series = stim_ref + stim_ua = stim_series.data[index_start_s:index_start_s + count_s].flatten() * stim_series.conversion * 1e6 + + # Create time axis: sweeps are 50ms, centered on stimulation (t=0 at 25ms) + sampling_rate = response_series.rate + time_ms = (np.arange(count) / sampling_rate - 0.025) * 1000 + + return time_ms, response_uv, stim_ua + + +def measure_response_latency(time_ms, response_uv, expected_latency, window_ms=2.0): + """ + Measure actual response latency by finding the peak near expected latency. + + Parameters + ---------- + time_ms : np.ndarray + Time axis in milliseconds + response_uv : np.ndarray + Neural response in microvolts + expected_latency : float + Expected latency in ms + window_ms : float + Search window size (default 2.0 ms) + + Returns + ------- + float + Measured latency in ms, or NaN if no clear peak found + """ + from scipy.signal import find_peaks + + mask = (time_ms >= expected_latency - window_ms) & (time_ms <= expected_latency + window_ms) + if not mask.any(): + return np.nan + + window_response = np.abs(response_uv[mask]) + window_time = time_ms[mask] + + peaks, properties = find_peaks(window_response, height=np.percentile(window_response, 70)) + if len(peaks) == 0: + return np.nan + + highest_peak_idx = peaks[np.argmax(properties['peak_heights'])] + return window_time[highest_peak_idx] + + +def measure_response_amplitude(time_ms, response_uv, latency, window_ms=1.5): + """ + Measure peak-to-peak amplitude near expected latency. + + Parameters + ---------- + time_ms : np.ndarray + Time axis in milliseconds + response_uv : np.ndarray + Neural response in microvolts + latency : float + Expected latency in ms + window_ms : float + Window size for amplitude measurement (default 1.5 ms) + + Returns + ------- + float + Peak-to-peak amplitude in microvolts + """ + mask = (time_ms >= latency - window_ms) & (time_ms <= latency + window_ms) + if mask.any(): + window = response_uv[mask] + return np.max(window) - np.min(window) + return 0 + + +def detect_spontaneous_activity(time_ms, response_uv, critical_window_ms, threshold_std=3.0): + """ + Detect spontaneous spike activity in the critical collision window. + + For collision to occur, a spontaneous spike must be traveling down the axon + when the antidromic spike is traveling up. This function detects threshold + crossings in the critical window before stimulation. + + Parameters + ---------- + time_ms : np.ndarray + Time axis in milliseconds (t=0 is stimulation) + response_uv : np.ndarray + Neural response in microvolts + critical_window_ms : float + Size of the critical window before stimulation (latency + refractory period) + threshold_std : float + Threshold for spike detection (standard deviations above baseline) + + Returns + ------- + has_activity : bool + Whether spontaneous activity was detected in critical window + activity_amplitude : float + Peak-to-peak amplitude in critical window (uV) + spike_time : float or None + Time of detected spike (if any), in ms + """ + # Define baseline window (well before critical window) + baseline_mask = (time_ms >= -20) & (time_ms <= -critical_window_ms - 2) + # Define critical window (avoid artifact at t=0) + critical_mask = (time_ms >= -critical_window_ms) & (time_ms < -0.5) + + # Calculate baseline statistics + baseline_data = response_uv[baseline_mask] + baseline_std = np.std(baseline_data) + baseline_mean = np.mean(baseline_data) + + # Check critical window for threshold crossings + critical_data = response_uv[critical_mask] + critical_time = time_ms[critical_mask] + + # Peak-to-peak amplitude in critical window + activity_amplitude = np.max(critical_data) - np.min(critical_data) + + # Detect threshold crossing + above_threshold = np.abs(critical_data - baseline_mean) > threshold_std * baseline_std + has_activity = np.any(above_threshold) + + spike_time = None + if has_activity: + # Find the time of the largest deflection + peak_idx = np.argmax(np.abs(critical_data - baseline_mean)) + spike_time = critical_time[peak_idx] + + return has_activity, activity_amplitude, spike_time diff --git a/001636/TurnerLab/motor_cortex/trial_structure_plot.py b/001636/TurnerLab/motor_cortex/trial_structure_plot.py new file mode 100644 index 0000000..2ca1241 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/trial_structure_plot.py @@ -0,0 +1,262 @@ +""" +Visualize trial temporal structure as a stacked horizontal bar chart. + +Each row represents a trial, with colored segments showing the different phases: +- Baseline (start to center target appearance) +- Center hold (center target to lateral target appearance / go cue) +- Time to react (go cue to cursor departure from center zone) +- Movement (cursor departure to derived movement end) +- Target hold (movement end to reward) +- Post-reward (reward to stop) +""" + +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches +import numpy as np + + +# Shared color scheme for trial phases +PHASE_COLORS = { + 'baseline': '#E8E8E8', # Light gray - pre-task + 'center_hold': '#4ECDC4', # Teal - holding at center + 'reaction_time': '#FFE66D', # Yellow - planning/reaction + 'movement': '#FF6B6B', # Coral/red - movement execution + 'target_hold': '#9B59B6', # Purple - holding at target + 'post_reward': '#87CEEB', # Sky blue - post-reward +} + + +def plot_trial_structure(trials_df, ax=None, max_trials=None): + """ + Plot trial temporal structure as horizontal stacked bars. + + Shows all trial phases from baseline through post-reward, giving an overview + of the complete trial timing structure. + + Parameters + ---------- + trials_df : pd.DataFrame + DataFrame from nwbfile.trials.to_dataframe() + ax : matplotlib.axes.Axes, optional + Axes to plot on. If None, creates new figure. + max_trials : int, optional + Maximum number of trials to display. If None, shows all. + + Returns + ------- + fig, ax : tuple + Figure and axes objects + """ + if ax is None: + fig, ax = plt.subplots(figsize=(14, 8)) + else: + fig = ax.figure + + # Limit trials if requested + if max_trials is not None: + trials_df = trials_df.head(max_trials) + + n_trials = len(trials_df) + + for idx, (trial_idx, trial) in enumerate(trials_df.iterrows()): + y = n_trials - idx - 1 # Flip so trial 0 is at top + + # Get times relative to trial start + t_start = 0 + t_center = trial['center_target_appearance_time'] - trial['start_time'] + t_lateral = trial['lateral_target_appearance_time'] - trial['start_time'] + t_departure = trial['cursor_departure_time'] - trial['start_time'] + t_move_end = trial['derived_movement_end_time'] - trial['start_time'] + t_reward = trial['reward_time'] - trial['start_time'] + t_stop = trial['stop_time'] - trial['start_time'] + + # Check for aborted trials (missing go cue or reward) + if np.isnan(t_lateral) or np.isnan(t_reward): + ax.barh(y, t_center - t_start, left=t_start, height=0.8, + color=PHASE_COLORS['baseline'], edgecolor='none') + ax.barh(y, t_stop - t_center, left=t_center, height=0.8, + color=PHASE_COLORS['center_hold'], edgecolor='none', alpha=0.5) + ax.text(t_stop + 0.1, y, 'Aborted', + va='center', ha='left', fontsize=8, fontstyle='italic', + color='gray') + continue + + # Baseline (start to center target) + ax.barh(y, t_center - t_start, left=t_start, height=0.8, + color=PHASE_COLORS['baseline'], edgecolor='none') + + # Center hold (center target to lateral target / go cue) + ax.barh(y, t_lateral - t_center, left=t_center, height=0.8, + color=PHASE_COLORS['center_hold'], edgecolor='none') + + # Reaction time (go cue to cursor departure) + ax.barh(y, t_departure - t_lateral, left=t_lateral, height=0.8, + color=PHASE_COLORS['reaction_time'], edgecolor='none') + + # Handle trials with missing kinematic data + has_kinematics = not np.isnan(t_move_end) + + if not has_kinematics: + # No kinematic data - show movement as cursor departure to reward + ax.barh(y, t_reward - t_departure, left=t_departure, height=0.8, + color=PHASE_COLORS['movement'], edgecolor='none') + else: + # Movement execution (cursor departure to movement end) + ax.barh(y, t_move_end - t_departure, left=t_departure, height=0.8, + color=PHASE_COLORS['movement'], edgecolor='none') + + # Target hold (movement end to reward) + ax.barh(y, t_reward - t_move_end, left=t_move_end, height=0.8, + color=PHASE_COLORS['target_hold'], edgecolor='none') + + # Post-reward (reward to stop) + ax.barh(y, t_stop - t_reward, left=t_reward, height=0.8, + color=PHASE_COLORS['post_reward'], edgecolor='none') + + # Add movement type label at the end + movement_type = trial['movement_type'] + ax.text(t_stop + 0.1, y, movement_type.capitalize(), + va='center', ha='left', fontsize=8, fontweight='bold', + color='black') + + # Create legend + legend_patches = [ + mpatches.Patch(color=PHASE_COLORS['baseline'], label='Baseline'), + mpatches.Patch(color=PHASE_COLORS['center_hold'], label='Holding at Center'), + mpatches.Patch(color=PHASE_COLORS['reaction_time'], label='Time to React'), + mpatches.Patch(color=PHASE_COLORS['movement'], label='Movement'), + mpatches.Patch(color=PHASE_COLORS['target_hold'], label='Holding at Target'), + mpatches.Patch(color=PHASE_COLORS['post_reward'], label='Post-Reward'), + ] + + ax.legend(handles=legend_patches, loc='upper right', fontsize=9) + + # Labels and formatting + ax.set_xlabel('Time relative to trial start (seconds)', fontsize=11) + ax.set_ylabel('Trial', fontsize=11) + ax.set_title('Trial Temporal Structure', fontsize=13, fontweight='bold') + + # Set y-axis ticks + ax.set_yticks(np.arange(0, n_trials, max(1, n_trials // 10))) + ax.set_yticklabels([str(n_trials - 1 - t) for t in ax.get_yticks().astype(int)]) + + ax.set_ylim(-0.5, n_trials - 0.5) + ax.grid(axis='x', alpha=0.3, linestyle='--') + + return fig, ax + + +def plot_movement_kinematics(trials_df, ax=None, max_trials=None): + """ + Plot movement phase with kinematic event markers. + + Focuses on the movement-related phases (Time to React, Movement, Holding at Target) + and overlays derived kinematic markers: movement onset, peak velocity, and movement end. + + Parameters + ---------- + trials_df : pd.DataFrame + DataFrame from nwbfile.trials.to_dataframe() + ax : matplotlib.axes.Axes, optional + Axes to plot on. If None, creates new figure. + max_trials : int, optional + Maximum number of trials to display. If None, shows all. + + Returns + ------- + fig, ax : tuple + Figure and axes objects + """ + from matplotlib.lines import Line2D + + if ax is None: + fig, ax = plt.subplots(figsize=(12, 8)) + else: + fig = ax.figure + + # Limit trials if requested + if max_trials is not None: + trials_df = trials_df.head(max_trials) + + # Filter to only complete trials with kinematic data + complete_trials = trials_df.dropna(subset=['derived_movement_end_time']) + n_trials = len(complete_trials) + + if n_trials == 0: + ax.text(0.5, 0.5, 'No trials with complete kinematic data', + ha='center', va='center', transform=ax.transAxes) + return fig, ax + + for idx, (trial_idx, trial) in enumerate(complete_trials.iterrows()): + y = n_trials - idx - 1 # Flip so trial 0 is at top + + # Get times relative to go cue (lateral target appearance) + t_lateral = trial['lateral_target_appearance_time'] + t_departure = trial['cursor_departure_time'] - t_lateral + t_move_onset = trial['derived_movement_onset_time'] - t_lateral + t_peak_vel = trial['derived_peak_velocity_time'] - t_lateral + t_move_end = trial['derived_movement_end_time'] - t_lateral + t_reward = trial['reward_time'] - t_lateral + + # Reaction time (go cue to cursor departure) + ax.barh(y, t_departure, left=0, height=0.8, + color=PHASE_COLORS['reaction_time'], edgecolor='none') + + # Movement (cursor departure to movement end) + ax.barh(y, t_move_end - t_departure, left=t_departure, height=0.8, + color=PHASE_COLORS['movement'], edgecolor='none') + + # Target hold (movement end to reward) + ax.barh(y, t_reward - t_move_end, left=t_move_end, height=0.8, + color=PHASE_COLORS['target_hold'], edgecolor='none') + + # Add kinematic markers + marker_y = y + + # Movement onset marker (|) + if not np.isnan(t_move_onset): + ax.plot([t_move_onset, t_move_onset], [marker_y - 0.4, marker_y + 0.4], + color='black', linewidth=1.5, zorder=5) + + # Peak velocity marker (*) + if not np.isnan(t_peak_vel): + ax.plot(t_peak_vel, marker_y, marker='*', color='black', + markersize=8, zorder=5) + + # Movement end marker (|) + if not np.isnan(t_move_end): + ax.plot([t_move_end, t_move_end], [marker_y - 0.4, marker_y + 0.4], + color='black', linewidth=1.5, zorder=5) + + # Add movement type label at the end + movement_type = trial['movement_type'] + ax.text(t_reward + 0.05, y, movement_type.capitalize(), + va='center', ha='left', fontsize=8, fontweight='bold', + color='black') + + # Create legend + legend_patches = [ + mpatches.Patch(color=PHASE_COLORS['reaction_time'], label='Time to React'), + mpatches.Patch(color=PHASE_COLORS['movement'], label='Movement'), + mpatches.Patch(color=PHASE_COLORS['target_hold'], label='Holding at Target'), + Line2D([0], [0], color='black', linewidth=1.5, label='Movement onset/end'), + Line2D([0], [0], marker='*', color='black', linestyle='None', + markersize=8, label='Peak velocity'), + ] + + ax.legend(handles=legend_patches, loc='upper right', fontsize=9) + + # Labels and formatting + ax.set_xlabel('Time relative to go cue (seconds)', fontsize=11) + ax.set_ylabel('Trial', fontsize=11) + ax.set_title('Movement Kinematics', fontsize=13, fontweight='bold') + + # Set y-axis ticks + ax.set_yticks(np.arange(0, n_trials, max(1, n_trials // 10))) + ax.set_yticklabels([str(n_trials - 1 - t) for t in ax.get_yticks().astype(int)]) + + ax.set_ylim(-0.5, n_trials - 0.5) + ax.axvline(0, color='gray', linestyle='--', alpha=0.5, label='Go cue') + ax.grid(axis='x', alpha=0.3, linestyle='--') + + return fig, ax diff --git a/001636/TurnerLab/motor_cortex/turner_m1_glm.ipynb b/001636/TurnerLab/motor_cortex/turner_m1_glm.ipynb new file mode 100644 index 0000000..3531d33 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/turner_m1_glm.ipynb @@ -0,0 +1,916 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-0", + "metadata": {}, + "source": [ + "## GLM Analysis with NeMoS\n", + "\n", + "This notebook demonstrates how to fit **Generalized Linear Models (GLMs)** to predict neural spiking activity from kinematic features using [NeMoS](https://nemos.readthedocs.io/) (Neural Models). We reproduce the methodology from the Turner lab Brain 2016 paper: *\"Movement encoding deficits in the motor cortex of parkinsonian macaque monkeys\"* (Pasquereau & Turner, Brain 2016).\n", + "\n", + "**What you'll learn:**\n", + "- How to build design matrices for neural encoding models\n", + "- Fitting Poisson GLMs with NeMoS\n", + "- Using JAX's `vmap` for efficient vectorized fitting\n", + "- Statistical validation via shuffle-based significance testing" + ] + }, + { + "cell_type": "markdown", + "id": "cell-1", + "metadata": {}, + "source": [ + "### Why GLMs for Neural Data?\n", + "\n", + "PETHs (like in the [PETH tutorial](https://github.com/catalystneuro/turner-lab-to-nwb/blob/main/notebooks/turner_m1_peth.ipynb)) show *when* neurons fire relative to events, but don't tell us *why*. GLMs let us ask: **which kinematic features predict spiking?**\n", + "\n", + "The model assumes spike counts follow a Poisson distribution:\n", + "\n", + "$$\\text{E}[\\text{spike count}] = \\exp(\\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + ...)$$\n", + "\n", + "Where $x_i$ are kinematic features (direction, position, velocity, etc.) and $\\beta_i$ are learned coefficients. A positive $\\beta$ means higher feature values increase firing rate; negative means they decrease it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-2", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:28.458790Z", + "iopub.status.busy": "2026-04-28T14:46:28.458631Z", + "iopub.status.idle": "2026-04-28T14:46:35.693617Z", + "shell.execute_reply": "2026-04-28T14:46:35.692910Z" + } + }, + "outputs": [], + "source": [ + "import jax\n", + "import matplotlib.pyplot as plt\n", + "import nemos as nmo\n", + "import numpy as np\n", + "import pynapple as nap\n", + "from pynwb import NWBHDF5IO\n", + "from dandi.dandiapi import DandiAPIClient\n", + "import remfile\n", + "import h5py\n", + "\n", + "# Stream NWB file from DANDI\n", + "dandiset_id = \"001636\"\n", + "session_id = \"Venus++v2703++PreMPTP++Depth19880um++19990607\"\n", + "\n", + "client = DandiAPIClient()\n", + "dandiset = client.get_dandiset(dandiset_id)\n", + "assets = list(dandiset.get_assets())\n", + "asset = next(a for a in assets if session_id in a.path)\n", + "print(f\"Streaming: {asset.path}\")\n", + "\n", + "# Open remote file\n", + "url = asset.get_content_url(follow_redirects=1, strip_query=True)\n", + "file = remfile.File(url)\n", + "h5_file = h5py.File(file, \"r\")\n", + "io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n", + "nwbfile = io.read()\n", + "print(f\"Session: {nwbfile.session_id}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cell-3", + "metadata": {}, + "source": [ + "### Preparing the Data with Pynapple\n", + "\n", + "[Pynapple](https://pynapple.org) and [NeMoS](https://nemos.readthedocs.io/) are designed to work together seamlessly:\n", + "\n", + "- **Pynapple** handles data loading, time alignment, and preprocessing (trial extraction, perievent alignment, binning)\n", + "- **NeMoS** handles the statistical modeling (GLM fitting, regularization, scoring)\n", + "\n", + "Both libraries are built on NumPy arrays, so data flows naturally between them. Pynapple's `Tsd` and `TsGroup` objects have `.values` attributes that NeMoS accepts directly. This division of labor keeps each library focused: pynapple doesn't need to implement GLMs, and NeMoS doesn't need to handle NWB files or trial alignment.\n", + "\n", + "We'll use pynapple to:\n", + "1. Load NWB data with `nap.NWBFile()`\n", + "2. Align kinematics and spikes to movement onset with `compute_perievent_continuous()` and `compute_perievent()`\n", + "3. Count spikes in time windows with `.restrict()` and `.count()`\n", + "\n", + "Then pass the resulting NumPy arrays to NeMoS for GLM fitting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b724ada", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:35.695220Z", + "iopub.status.busy": "2026-04-28T14:46:35.694826Z", + "iopub.status.idle": "2026-04-28T14:46:36.091702Z", + "shell.execute_reply": "2026-04-28T14:46:36.091126Z" + } + }, + "outputs": [], + "source": [ + "data = nap.NWBFile(nwbfile)\n", + "print(f\"Trials: {len(data['trials'])}, Units: {len(data['units'])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:36.093123Z", + "iopub.status.busy": "2026-04-28T14:46:36.093002Z", + "iopub.status.idle": "2026-04-28T14:46:37.126062Z", + "shell.execute_reply": "2026-04-28T14:46:37.125505Z" + } + }, + "outputs": [], + "source": [ + "# Extract data\n", + "trials = data[\"trials\"]\n", + "units = data[\"units\"]\n", + "spikes = units[0] # First unit\n", + "\n", + "# Kinematics (handle different naming conventions)\n", + "elbow_position = data[\"ElbowAngle\"] if \"ElbowAngle\" in data.keys() else data[\"SpatialSeriesElbowAngle\"]\n", + "elbow_velocity = data[\"ElbowVelocity\"] if \"ElbowVelocity\" in data.keys() else data[\"TimeSeriesElbowVelocity\"]\n", + "\n", + "# Convert to 1D Tsd - pynapple may load these as TsdFrame (2D) depending on the session.\n", + "# We need 1D Tsd so that .get(t) returns a scalar that broadcasts correctly across trials.\n", + "elbow_position = nap.Tsd(t=elbow_position.t, d=np.asarray(elbow_position.values).flatten())\n", + "elbow_velocity = nap.Tsd(t=elbow_velocity.t, d=np.asarray(elbow_velocity.values).flatten())\n", + "\n", + "# Compute acceleration using pynapple's derivative method (wraps numpy.gradient)\n", + "elbow_acceleration = elbow_velocity.derivative()\n", + "\n", + "dt = np.median(np.diff(elbow_velocity.t))\n", + "print(f\"Kinematics sampling rate: {1/dt:.1f} Hz\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-5", + "metadata": {}, + "source": [ + "### Z-scoring Features\n", + "\n", + "GLM coefficients are more interpretable when features are standardized. A coefficient of 0.5 then means: \"a 1 standard deviation increase in this feature multiplies the firing rate by $e^{0.5} \\approx 1.65$.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-6", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:37.127514Z", + "iopub.status.busy": "2026-04-28T14:46:37.127383Z", + "iopub.status.idle": "2026-04-28T14:46:37.154770Z", + "shell.execute_reply": "2026-04-28T14:46:37.154353Z" + } + }, + "outputs": [], + "source": [ + "# Z-score kinematics\n", + "position_z = nap.Tsd(t=elbow_position.t, d=(elbow_position.values - elbow_position.mean()) / elbow_position.std())\n", + "velocity_z = nap.Tsd(t=elbow_velocity.t, d=(elbow_velocity.values - elbow_velocity.mean()) / elbow_velocity.std())\n", + "acceleration_z = nap.Tsd(t=elbow_acceleration.t, d=(elbow_acceleration.values - elbow_acceleration.mean()) / elbow_acceleration.std())\n", + "\n", + "# Movement onset times and direction\n", + "movement_onset_times = nap.Ts(trials[\"derived_movement_onset_time\"].values)\n", + "direction_values = np.array([1.0 if mt == \"flexion\" else -1.0 for mt in trials[\"movement_type\"]])\n", + "\n", + "# Reaction time (z-scored)\n", + "lateral_target_times = trials[\"lateral_target_appearance_time\"].values\n", + "movement_onset_values = trials[\"derived_movement_onset_time\"].values\n", + "reaction_times = movement_onset_values - lateral_target_times\n", + "reaction_times_z = (reaction_times - reaction_times.mean()) / reaction_times.std()\n", + "\n", + "print(f\"Trials: {len(trials)} ({(direction_values == 1).sum()} flexion, {(direction_values == -1).sum()} extension)\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-7", + "metadata": {}, + "source": [ + "### Building the Design Matrix\n", + "\n", + "**The paper's approach:** Pasquereau & Turner (2016) asked whether M1 neurons encode kinematic features differently before vs. after MPTP-induced parkinsonism. To answer this, they fit a **separate GLM for each time point** relative to movement onset, using trials as observations.\n", + "\n", + "**Why time-resolved fitting?** Neural encoding is not static. A neuron might encode velocity *before* movement starts (planning) but switch to encoding position *during* movement (feedback). By fitting independent models at each time point, we can track how encoding evolves.\n", + "\n", + "**The sliding window approach:**\n", + "- **Window size**: 200ms (long enough to get reliable spike counts)\n", + "- **Step size**: 25ms (fine temporal resolution)\n", + "- **Time range**: -500ms to +800ms relative to movement onset\n", + "\n", + "This creates 45 overlapping windows. For each window, we count spikes and sample kinematic features, then fit a GLM predicting spike count from features. The result is a time series of coefficients showing when each feature matters.\n", + "\n", + "**Design matrix structure:**\n", + "For each time window, we need one row per trial with columns for each feature:\n", + "\n", + "| Feature | Description | Values |\n", + "|---------|-------------|--------|\n", + "| Direction | Movement type (categorical) | +1 (flexion), -1 (extension) |\n", + "| Position | Elbow angle at window center | z-scored degrees |\n", + "| Velocity | Angular velocity at window center | z-scored deg/s |\n", + "| Acceleration | Angular acceleration at window center | z-scored deg/s² |\n", + "| RT | Reaction time (constant per trial) | z-scored seconds |\n", + "\n", + "The first code cell below aligns all signals to movement onset using pynapple's `compute_perievent` functions. The second cell loops through time windows, extracting spike counts (y) and kinematic features (X) to build the full 3D tensor: `(n_time_bins, n_trials, n_features)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-8", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:37.156195Z", + "iopub.status.busy": "2026-04-28T14:46:37.156080Z", + "iopub.status.idle": "2026-04-28T14:46:37.190414Z", + "shell.execute_reply": "2026-04-28T14:46:37.189847Z" + } + }, + "outputs": [], + "source": [ + "# Parameters matching the paper\n", + "WINDOW_SIZE_MS = 200.0\n", + "STEP_SIZE_MS = 25.0\n", + "TIME_RANGE_MS = (-500.0, 800.0)\n", + "\n", + "trial_start_s = TIME_RANGE_MS[0] / 1000.0\n", + "trial_end_s = TIME_RANGE_MS[1] / 1000.0\n", + "\n", + "# Align kinematics and spikes to movement onset using pynapple\n", + "position_aligned = nap.compute_perievent_continuous(position_z, tref=movement_onset_times, minmax=(trial_start_s, trial_end_s))\n", + "velocity_aligned = nap.compute_perievent_continuous(velocity_z, tref=movement_onset_times, minmax=(trial_start_s, trial_end_s))\n", + "acceleration_aligned = nap.compute_perievent_continuous(acceleration_z, tref=movement_onset_times, minmax=(trial_start_s, trial_end_s))\n", + "peth = nap.compute_perievent(timestamps=spikes, tref=movement_onset_times, minmax=(trial_start_s, trial_end_s))\n", + "\n", + "print(f\"Aligned position shape: {position_aligned.shape} (time x trials)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:37.192091Z", + "iopub.status.busy": "2026-04-28T14:46:37.191885Z", + "iopub.status.idle": "2026-04-28T14:46:37.304577Z", + "shell.execute_reply": "2026-04-28T14:46:37.304013Z" + } + }, + "outputs": [], + "source": [ + "# Create time windows\n", + "starts_ms = np.arange(TIME_RANGE_MS[0], TIME_RANGE_MS[1] - WINDOW_SIZE_MS + STEP_SIZE_MS, STEP_SIZE_MS)\n", + "ends_ms = starts_ms + WINDOW_SIZE_MS\n", + "n_time_bins = len(starts_ms)\n", + "n_trials = len(trials)\n", + "\n", + "# Feature names\n", + "feature_names = [\"Direction\", \"Position\", \"Velocity\", \"Acceleration\", \"RT\"]\n", + "n_features = len(feature_names)\n", + "\n", + "# Build tensors: X is (time_bins, trials, features), y is (time_bins, trials)\n", + "X = np.zeros((n_time_bins, n_trials, n_features))\n", + "y = np.zeros((n_time_bins, n_trials)) # spike counts\n", + "\n", + "for win_index, (start_ms, end_ms) in enumerate(zip(starts_ms, ends_ms)):\n", + " interval = nap.IntervalSet(start=start_ms / 1000.0, end=end_ms / 1000.0)\n", + "\n", + " # Spike counts in this window\n", + " spikes_in_window = peth.restrict(interval)\n", + " spike_counts = spikes_in_window.count()\n", + " y[win_index, :] = spike_counts.values.flatten()\n", + "\n", + " # Kinematic features at window center\n", + " t_center = spike_counts.t.item() if hasattr(spike_counts.t, \"item\") else spike_counts.t[0]\n", + " X[win_index, :, 0] = direction_values\n", + " X[win_index, :, 1] = position_aligned.restrict(interval).get(t_center)\n", + " X[win_index, :, 2] = velocity_aligned.restrict(interval).get(t_center)\n", + " X[win_index, :, 3] = acceleration_aligned.restrict(interval).get(t_center)\n", + " X[win_index, :, 4] = reaction_times_z # RT is constant across time bins\n", + "\n", + "print(f\"Design matrix X: {X.shape} (time_bins, trials, features)\")\n", + "print(f\"Spike counts y: {y.shape}\")\n", + "print(f\"Total spikes: {y.sum():.0f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-10", + "metadata": {}, + "source": [ + "### Fitting GLMs with NeMoS\n", + "\n", + "Here's where NeMoS shines. Instead of writing loops to fit 45 separate models (one per time bin), we use JAX's `vmap` to vectorize the fitting. This is both faster and cleaner.\n", + "\n", + "**Key NeMoS concepts:**\n", + "\n", + "- **`nmo.glm.GLM()`** - The model object, which by default uses a Poisson observation model with exponential link function\n", + "\n", + "- **`regularizer=\"Ridge\"`** - L2 regularization adds a penalty on large coefficients. This is important when features are correlated (like velocity and acceleration) because without regularization, the optimizer can find many equivalent solutions, leading to unstable or extreme coefficients.\n", + "\n", + "- **`regularizer_strength=0.1`** - Controls how strongly we penalize large coefficients. Higher values = more shrinkage toward zero. We use 0.1 as a reasonable default; you could tune this via cross-validation.\n", + "\n", + "- **`solver_kwargs`** - Fine-tune the underlying optimizer (JAXopt's gradient descent). The defaults work for most cases, but time-resolved fitting across many windows can hit edge cases:\n", + " - **`stepsize=0.001`** - Learning rate for gradient descent. Smaller values converge more slowly but more reliably. The default (0.01) can overshoot and diverge on some time bins.\n", + " - **`acceleration=False`** - Disables Nesterov momentum. Acceleration speeds up convergence but can cause oscillations when the loss surface is tricky. Some time bins have very few spikes, creating ill-conditioned problems where acceleration hurts.\n", + " - **`maxiter=5000`** - Maximum optimization steps. More iterations ensure convergence even with the smaller stepsize.\n", + "\n", + "These conservative settings ensure we get valid coefficients for *all* 45 time bins, not just most of them. Without them, you may see NaN values in certain windows where the optimizer diverged." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-11", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:37.305728Z", + "iopub.status.busy": "2026-04-28T14:46:37.305607Z", + "iopub.status.idle": "2026-04-28T14:46:38.603379Z", + "shell.execute_reply": "2026-04-28T14:46:38.602893Z" + } + }, + "outputs": [], + "source": [ + "from nemos.glm.params import GLMParams\n", + "import jax.numpy as jnp\n", + "\n", + "# Configure the GLM\n", + "# Ridge regularization stabilizes fitting with correlated features\n", + "# Smaller stepsize and no acceleration help convergence across all time bins\n", + "model = nmo.glm.GLM(\n", + " regularizer=\"Ridge\",\n", + " regularizer_strength=0.1,\n", + " solver_kwargs={\"stepsize\": 0.001, \"acceleration\": False, \"maxiter\": 5000},\n", + ")\n", + "\n", + "# Initialize parameters\n", + "weights_init = jnp.zeros(n_features)\n", + "intercept_init = jnp.array([-1.0])\n", + "\n", + "# Initialize the solver state from one example time bin.\n", + "# `initialize_solver_and_state` accepts the user-friendly tuple form.\n", + "model.initialize_solver_and_state(X[0], y[0], (weights_init, intercept_init))\n", + "\n", + "# `solver_run` expects the structured GLMParams pytree (it sees the params\n", + "# directly inside the optimizer, so the validator that auto-wraps tuples is\n", + "# bypassed). Wrap once and reuse for vmap and the shuffle test below.\n", + "init_params = GLMParams(coef=weights_init, intercept=intercept_init)\n", + "\n", + "# Use JAX vmap to fit all time bins in parallel\n", + "vmap_fit = jax.vmap(model.solver_run, in_axes=(None, 0, 0))\n", + "fitted_params, _, _ = vmap_fit(init_params, X, y)\n", + "\n", + "coefficients = np.array(fitted_params.coef)\n", + "intercepts = np.array(fitted_params.intercept).flatten()\n", + "\n", + "print(f\"Coefficients shape: {coefficients.shape} (time_bins x features)\")\n", + "print(\"Fitting complete!\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-12", + "metadata": {}, + "source": [ + "### Understanding `vmap`\n", + "\n", + "Recall that we need to fit 45 separate GLMs, one for each time window. The naive approach would be a Python loop:\n", + "\n", + "```python\n", + "# Slow loop version\n", + "coefficients = []\n", + "for t in range(n_time_bins):\n", + " model.fit(X[t], y[t])\n", + " coefficients.append(model.coef_)\n", + "```\n", + "\n", + "This works, but it is slow. Each iteration has Python overhead, and the fits run sequentially. With 45 time bins and 100 shuffle iterations for significance testing, that is 4,500 model fits.\n", + "\n", + "JAX's `vmap` (vectorized map) solves this by transforming a function that operates on single arrays into one that operates on batches. The key line is:\n", + "\n", + "```python\n", + "vmap_fit = jax.vmap(model.solver_run, in_axes=(None, 0, 0))\n", + "```\n", + "\n", + "This says: \"Take `solver_run`, which fits one GLM, and create a new function that applies it across axis 0 of X and y simultaneously.\" The `in_axes=(None, 0, 0)` specifies:\n", + "- `None`: Keep the initial parameters the same for all fits\n", + "- `0`: Iterate over the first axis of X (the time bins)\n", + "- `0`: Iterate over the first axis of y (the time bins)\n", + "\n", + "Now instead of 45 sequential Python calls, we have one vectorized call that JAX compiles into efficient parallel code. On a GPU, all 45 fits can literally run in parallel. Even on CPU, the compiled code avoids Python overhead and enables SIMD optimizations.\n", + "\n", + "This pattern is essential for time-resolved neural analysis where you fit many models across time, neurons, or cross-validation folds." + ] + }, + { + "cell_type": "markdown", + "id": "cell-13", + "metadata": {}, + "source": [ + "### Visualizing the Coefficients\n", + "\n", + "Now we can see how each kinematic feature's influence on firing rate evolves through the movement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-14", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:38.604530Z", + "iopub.status.busy": "2026-04-28T14:46:38.604415Z", + "iopub.status.idle": "2026-04-28T14:46:38.798865Z", + "shell.execute_reply": "2026-04-28T14:46:38.798361Z" + } + }, + "outputs": [], + "source": [ + "# Plot all coefficients\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "colors = [\"tab:green\", \"tab:blue\", \"tab:orange\", \"tab:red\", \"tab:purple\"]\n", + "\n", + "for i, name in enumerate(feature_names):\n", + " ax.plot(starts_ms, coefficients[:, i], color=colors[i], linewidth=3, label=name)\n", + "\n", + "ax.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5, linewidth=1.5)\n", + "ax.axvline(0, color=\"black\", linestyle=\"--\", linewidth=2, label=\"Movement onset\")\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=18, fontweight=\"bold\")\n", + "ax.set_ylabel(\"GLM Coefficient\", fontsize=18, fontweight=\"bold\")\n", + "ax.legend(loc=\"upper right\", fontsize=14, frameon=False)\n", + "ax.set_title(f\"GLM Coefficients Across Time\\n{asset.path}\", fontsize=20, fontweight=\"bold\")\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_linewidth(1.5)\n", + "ax.spines['bottom'].set_linewidth(1.5)\n", + "ax.tick_params(axis='both', which='major', labelsize=14, width=1.5, length=6)\n", + "ax.grid(True, alpha=0.3, linewidth=0.8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-15", + "metadata": {}, + "source": [ + "### Statistical Validation: Shuffle Test\n", + "\n", + "A coefficient being non-zero does not mean it is *significant*. The GLM will always find some coefficients that minimize the loss, even if the features have no real relationship to spiking. We need to ask: could we have obtained this coefficient by chance?\n", + "\n", + "**The idea behind permutation testing:**\n", + "\n", + "The null hypothesis is that there is no relationship between kinematic features and spike counts. If this were true, which trial had which spike count would be arbitrary. Shuffling the spike counts across trials simulates this null world: the features (X) stay the same, but the spike counts (y) are randomly reassigned to different trials.\n", + "\n", + "**What \"shuffling trials\" means concretely:**\n", + "\n", + "For each time bin, we have spike counts for 20 trials: `y = [3, 5, 2, 8, 1, ...]`. Shuffling means randomly reordering these values: `y_shuffled = [8, 1, 5, 2, 3, ...]`. Now trial 1's features (direction, position, velocity, etc.) are paired with trial 4's spike count.\n", + "\n", + "**What information is preserved vs. destroyed:**\n", + "\n", + "Shuffling preserves the *marginal* statistics:\n", + "- Total spike count stays the same\n", + "- Mean firing rate stays the same \n", + "- Variance of spike counts stays the same\n", + "- Distribution of each feature stays the same\n", + "\n", + "What is destroyed is the **joint distribution**, the covariance between features and spikes. Consider this example:\n", + "\n", + "| Trial | Direction | Spikes |\n", + "|-------|-----------|--------|\n", + "| 1 | Flexion (+1) | 8 |\n", + "| 2 | Extension (-1) | 2 |\n", + "| 3 | Flexion (+1) | 7 |\n", + "| 4 | Extension (-1) | 3 |\n", + "\n", + "The GLM learns: \"Flexion trials have ~7.5 spikes, extension trials have ~2.5 spikes, so the direction coefficient is positive.\"\n", + "\n", + "After shuffling the spike counts:\n", + "\n", + "| Trial | Direction | Spikes (shuffled) |\n", + "|-------|-----------|--------|\n", + "| 1 | Flexion (+1) | 3 |\n", + "| 2 | Extension (-1) | 8 |\n", + "| 3 | Flexion (+1) | 2 |\n", + "| 4 | Extension (-1) | 7 |\n", + "\n", + "Now the GLM sees no consistent relationship between direction and spike count. The coefficient will be near zero or even reversed.\n", + "\n", + "**The procedure (following Pasquereau & Turner, 2016):**\n", + "\n", + "The paper states: *\"To test whether individual coefficients were significant, we shuffled spike counts 1000 times across trials and compared actual coefficients to the confidence intervals yielded by shuffling [P = 0.05/(52 independent time bins) to compensate for multiple comparisons].\"*\n", + "\n", + "1. Fit the GLM on real data to get actual coefficients\n", + "2. Shuffle spike counts across trials (independently for each time bin)\n", + "3. Refit the GLM on shuffled data to get null coefficients\n", + "4. Repeat steps 2-3 many times (1000 in the paper, 100 here for speed)\n", + "5. Compute p-value: what fraction of null coefficients are as extreme as the actual coefficient?\n", + "6. Apply Bonferroni correction for multiple time bins\n", + "\n", + "**Why this works:**\n", + "\n", + "This is a non-parametric test. We do not assume coefficients follow a normal distribution or any other parametric form. Instead, we empirically construct the null distribution from the data itself. This is particularly valuable for GLMs where the sampling distribution of coefficients can be complex, especially with small sample sizes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-16", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:38.800331Z", + "iopub.status.busy": "2026-04-28T14:46:38.800204Z", + "iopub.status.idle": "2026-04-28T14:46:43.233569Z", + "shell.execute_reply": "2026-04-28T14:46:43.233018Z" + } + }, + "outputs": [], + "source": [ + "N_SHUFFLES = 100 # Use 1000 for publication-quality results\n", + "\n", + "rng = np.random.default_rng(42)\n", + "null_coeffs = np.zeros((N_SHUFFLES, n_time_bins, n_features))\n", + "\n", + "print(f\"Running {N_SHUFFLES} shuffle iterations...\")\n", + "for i in range(N_SHUFFLES):\n", + " if (i + 1) % 50 == 0:\n", + " print(f\" Shuffle {i + 1}/{N_SHUFFLES}\")\n", + "\n", + " # Shuffle spike counts across trials (independently for each time bin)\n", + " y_shuffled = np.array([rng.permutation(y[t]) for t in range(n_time_bins)])\n", + " fitted_null, _, _ = vmap_fit(init_params, X, y_shuffled)\n", + " null_coeffs[i] = np.array(fitted_null.coef)\n", + "\n", + "print(\"Done!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-17", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:43.234927Z", + "iopub.status.busy": "2026-04-28T14:46:43.234809Z", + "iopub.status.idle": "2026-04-28T14:46:43.257459Z", + "shell.execute_reply": "2026-04-28T14:46:43.256967Z" + } + }, + "outputs": [], + "source": [ + "# Compute two-tailed p-values\n", + "p_values = np.zeros((n_time_bins, n_features))\n", + "for t in range(n_time_bins):\n", + " for f in range(n_features):\n", + " p_values[t, f] = (np.abs(null_coeffs[:, t, f]) >= np.abs(coefficients[t, f])).mean()\n", + "\n", + "# Bonferroni correction for multiple time bins\n", + "bonferroni_threshold = 0.05 / n_time_bins\n", + "\n", + "print(f\"Bonferroni-corrected threshold: p < {bonferroni_threshold:.4f}\")\n", + "print(f\"\\nSignificant time bins per feature:\")\n", + "for f, name in enumerate(feature_names):\n", + " n_sig = (p_values[:, f] < bonferroni_threshold).sum()\n", + " print(f\" {name}: {n_sig}/{n_time_bins} ({100*n_sig/n_time_bins:.0f}%)\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-18", + "metadata": {}, + "source": [ + "### Coefficients with Significance Markers\n", + "\n", + "Now we can add stars to show which time bins have statistically significant encoding." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-19", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:43.259069Z", + "iopub.status.busy": "2026-04-28T14:46:43.258958Z", + "iopub.status.idle": "2026-04-28T14:46:43.410373Z", + "shell.execute_reply": "2026-04-28T14:46:43.409768Z" + } + }, + "outputs": [], + "source": [ + "# Plot coefficients with significance markers\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "\n", + "for i, name in enumerate(feature_names):\n", + " ax.plot(starts_ms, coefficients[:, i], color=colors[i], linewidth=3, label=name)\n", + " \n", + " # Mark significant time bins with stars\n", + " sig_mask = p_values[:, i] < bonferroni_threshold\n", + " ax.scatter(starts_ms[sig_mask], coefficients[sig_mask, i], \n", + " color=colors[i], s=150, marker=\"*\", zorder=5)\n", + "\n", + "ax.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5, linewidth=1.5)\n", + "ax.axvline(0, color=\"black\", linestyle=\"--\", linewidth=2)\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=18, fontweight=\"bold\")\n", + "ax.set_ylabel(\"GLM Coefficient\", fontsize=18, fontweight=\"bold\")\n", + "ax.legend(loc=\"upper right\", fontsize=14, frameon=False)\n", + "ax.set_title(f\"GLM Coefficients with Significance (Bonferroni p<{bonferroni_threshold:.4f})\\n{nwbfile.session_id}\", fontsize=20, fontweight=\"bold\")\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_linewidth(1.5)\n", + "ax.spines['bottom'].set_linewidth(1.5)\n", + "ax.tick_params(axis='both', which='major', labelsize=14, width=1.5, length=6)\n", + "ax.grid(True, alpha=0.3, linewidth=0.8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-20", + "metadata": {}, + "source": [ + "### Examining the Null Distribution\n", + "\n", + "Let's visualize what the shuffle test actually does. For one time bin, we can compare the actual coefficient to its null distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-21", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:43.411985Z", + "iopub.status.busy": "2026-04-28T14:46:43.411861Z", + "iopub.status.idle": "2026-04-28T14:46:43.879919Z", + "shell.execute_reply": "2026-04-28T14:46:43.879353Z" + } + }, + "outputs": [], + "source": [ + "# Pick a time bin around movement onset\n", + "sample_time_bin = n_time_bins // 2\n", + "\n", + "fig, axes = plt.subplots(1, n_features, figsize=(18, 5))\n", + "\n", + "for i, (ax, name) in enumerate(zip(axes, feature_names)):\n", + " null_dist = null_coeffs[:, sample_time_bin, i]\n", + " actual_coef = coefficients[sample_time_bin, i]\n", + " \n", + " ax.hist(null_dist, bins=30, alpha=0.7, color=colors[i], edgecolor=\"black\", linewidth=1.2)\n", + " ax.axvline(actual_coef, color=\"red\", linewidth=3, linestyle=\"--\", label=f\"Actual: {actual_coef:.2f}\")\n", + " ax.axvline(-actual_coef, color=\"red\", linewidth=3, linestyle=\"--\", alpha=0.5)\n", + " \n", + " ax.set_xlabel(\"Coefficient\", fontsize=14, fontweight=\"bold\")\n", + " ax.set_ylabel(\"Count\", fontsize=14, fontweight=\"bold\")\n", + " ax.set_title(f\"{name}\\np = {p_values[sample_time_bin, i]:.3f}\", fontsize=16, fontweight=\"bold\")\n", + " ax.legend(fontsize=11, frameon=False)\n", + " ax.spines['top'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " ax.spines['left'].set_linewidth(1.5)\n", + " ax.spines['bottom'].set_linewidth(1.5)\n", + " ax.tick_params(axis='both', which='major', labelsize=12, width=1.5, length=5)\n", + "\n", + "fig.suptitle(f\"Null Distributions at t = {starts_ms[sample_time_bin]:.0f} ms\", fontsize=20, fontweight=\"bold\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-22", + "metadata": {}, + "source": [ + "### Model Validation: Pseudo-R²\n", + "\n", + "NeMoS also supports cross-validation via pseudo-R². This tells us how well the model generalizes to held-out data, not just how well it fits the training data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-23", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:43.881130Z", + "iopub.status.busy": "2026-04-28T14:46:43.881021Z", + "iopub.status.idle": "2026-04-28T14:46:45.260979Z", + "shell.execute_reply": "2026-04-28T14:46:45.260351Z" + } + }, + "outputs": [], + "source": [ + "# Split trials 80/20\n", + "rng_cv = np.random.default_rng(123)\n", + "n_train = int(n_trials * 0.8)\n", + "train_indices = rng_cv.choice(n_trials, n_train, replace=False)\n", + "test_indices = np.setdiff1d(np.arange(n_trials), train_indices)\n", + "\n", + "X_train, X_test = X[:, train_indices, :], X[:, test_indices, :]\n", + "y_train, y_test = y[:, train_indices], y[:, test_indices]\n", + "\n", + "# Fit on training data\n", + "fitted_train, _, _ = vmap_fit(init_params, X_train, y_train)\n", + "coeffs_train = np.array(fitted_train.coef)\n", + "intercepts_train = np.array(fitted_train.intercept)\n", + "\n", + "# Compute pseudo-R² on test data for each time bin.\n", + "# We reuse the already-fitted `model` so internal state (dof_resid_, scale_, ...)\n", + "# is set, then overwrite coef_/intercept_ to the per-time-bin values.\n", + "pseudo_r2 = np.zeros(n_time_bins)\n", + "for t in range(n_time_bins):\n", + " model.coef_ = coeffs_train[t]\n", + " model.intercept_ = intercepts_train[t]\n", + " try:\n", + " pseudo_r2[t] = model.score(X_test[t], y_test[t], score_type=\"pseudo-r2-Cohen\")\n", + " except Exception:\n", + " pseudo_r2[t] = np.nan\n", + "\n", + "print(f\"Train trials: {n_train}, Test trials: {len(test_indices)}\")\n", + "print(f\"Mean pseudo-R²: {np.nanmean(pseudo_r2):.4f}\")\n", + "print(f\"Max pseudo-R²: {np.nanmax(pseudo_r2):.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-24", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:45.262564Z", + "iopub.status.busy": "2026-04-28T14:46:45.262451Z", + "iopub.status.idle": "2026-04-28T14:46:45.393534Z", + "shell.execute_reply": "2026-04-28T14:46:45.392911Z" + } + }, + "outputs": [], + "source": [ + "# Plot pseudo-R² across time\n", + "fig, ax = plt.subplots(figsize=(14, 5))\n", + "\n", + "ax.plot(starts_ms, pseudo_r2, color=\"tab:purple\", linewidth=3)\n", + "ax.fill_between(starts_ms, 0, pseudo_r2, alpha=0.3, color=\"tab:purple\", where=(pseudo_r2 > 0))\n", + "ax.fill_between(starts_ms, 0, pseudo_r2, alpha=0.3, color=\"tab:red\", where=(pseudo_r2 <= 0))\n", + "ax.axhline(0, color=\"gray\", linestyle=\"--\", alpha=0.5, linewidth=1.5)\n", + "ax.axvline(0, color=\"black\", linestyle=\"--\", linewidth=2)\n", + "\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=18, fontweight=\"bold\")\n", + "ax.set_ylabel(\"Pseudo-R² (Cohen)\", fontsize=18, fontweight=\"bold\")\n", + "ax.set_title(f\"Model Generalization: Train/Test Split ({n_train}/{len(test_indices)} trials)\\n{nwbfile.session_id}\", fontsize=20, fontweight=\"bold\")\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_linewidth(1.5)\n", + "ax.spines['bottom'].set_linewidth(1.5)\n", + "ax.tick_params(axis='both', which='major', labelsize=14, width=1.5, length=6)\n", + "ax.grid(True, alpha=0.3, linewidth=0.8)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-25", + "metadata": {}, + "source": [ + "### Interpreting Pseudo-R²\n", + "\n", + "**Positive R²**: Model predicts held-out data better than a mean-only (null) model.\n", + "\n", + "**Negative R²**: Model is *worse* than just predicting the mean firing rate. This often indicates overfitting due to small sample sizes.\n", + "\n", + "With only ~20 trials split 80/20, we have just 4 test trials, which is not enough for reliable cross-validation. The paper addressed this by using shuffle-based significance rather than cross-validation." + ] + }, + { + "cell_type": "markdown", + "id": "cell-26", + "metadata": {}, + "source": [ + "### Significance Heatmap\n", + "\n", + "A compact way to visualize which features are significant at which times." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-27", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-28T14:46:45.394922Z", + "iopub.status.busy": "2026-04-28T14:46:45.394704Z", + "iopub.status.idle": "2026-04-28T14:46:45.544210Z", + "shell.execute_reply": "2026-04-28T14:46:45.543468Z" + } + }, + "outputs": [], + "source": [ + "# Significance heatmap\n", + "fig, ax = plt.subplots(figsize=(14, 5))\n", + "\n", + "# Log-transform p-values for better visualization\n", + "p_log = -np.log10(p_values + 1e-10)\n", + "threshold_log = -np.log10(bonferroni_threshold)\n", + "\n", + "im = ax.imshow(p_log.T, aspect=\"auto\", cmap=\"viridis\",\n", + " extent=[starts_ms[0], starts_ms[-1], -0.5, n_features - 0.5])\n", + "for i in range(1, n_features):\n", + " ax.axhline(i - 0.5, color=\"white\", linewidth=1)\n", + "ax.axvline(0, color=\"white\", linestyle=\"--\", linewidth=2, alpha=0.8)\n", + "\n", + "ax.set_yticks(range(n_features))\n", + "ax.set_yticklabels(feature_names, fontsize=14, fontweight=\"bold\")\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=18, fontweight=\"bold\")\n", + "\n", + "cbar = plt.colorbar(im, ax=ax)\n", + "cbar.set_label(\"-log10(p-value)\", fontsize=14, fontweight=\"bold\")\n", + "cbar.ax.tick_params(labelsize=12)\n", + "cbar.ax.axhline(threshold_log, color=\"red\", linewidth=3)\n", + "\n", + "ax.set_title(f\"Significance Across Time (brighter = more significant)\\n{nwbfile.session_id}\", fontsize=20, fontweight=\"bold\")\n", + "ax.spines['left'].set_linewidth(1.5)\n", + "ax.spines['bottom'].set_linewidth(1.5)\n", + "ax.tick_params(axis='x', which='major', labelsize=14, width=1.5, length=6)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cell-28", + "metadata": {}, + "source": [ + "### Summary: NeMoS Workflow\n", + "\n", + "| Step | What | Code |\n", + "|------|------|------|\n", + "| 1 | Create model | `model = nmo.glm.GLM(regularizer=\"Ridge\")` |\n", + "| 2 | Initialize solver state | `model.initialize_solver_and_state(X[0], y[0], (w0, b0))` |\n", + "| 3 | Wrap params | `init_params = GLMParams(coef=w0, intercept=b0)` |\n", + "| 4 | Vectorize | `vmap_fit = jax.vmap(model.solver_run, in_axes=(None, 0, 0))` |\n", + "| 5 | Fit all bins | `fitted, state, aux = vmap_fit(init_params, X, y)` |\n", + "| 6 | Evaluate | `model.score(X_test, y_test, score_type=\"pseudo-r2-Cohen\")` |\n", + "\n", + "**Key advantages of NeMoS:**\n", + "- JAX backend enables GPU acceleration and vectorization\n", + "- Scikit-learn-like API (`.fit()`, `.score()`, `.predict()`)\n", + "- Built-in regularization options\n", + "- Compatible with pynapple for neural data handling" + ] + }, + { + "cell_type": "markdown", + "id": "cell-29", + "metadata": {}, + "source": [ + "### What This Neuron Encodes\n", + "\n", + "Based on the analysis above, this corticostriatal neuron shows:\n", + "\n", + "| Feature | Pattern | Interpretation |\n", + "|---------|---------|----------------|\n", + "| **Direction** | Positive, sustained | Fires more for flexion movements |\n", + "| **Position** | Negative trend | Slight preference for flexed positions |\n", + "| **Velocity** | Weak/transient | Not strongly velocity-tuned |\n", + "| **Acceleration** | Near zero | Does not encode acceleration |\n", + "| **RT** | Near zero | Does not encode reaction time |\n", + "\n", + "This is a **direction-selective neuron**. It primarily encodes *what* movement is being made (flexion vs extension), not the detailed kinematics of *how* it is being made." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "turnerlab_001636_demo", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/001636/TurnerLab/motor_cortex/turner_m1_peth.ipynb b/001636/TurnerLab/motor_cortex/turner_m1_peth.ipynb new file mode 100644 index 0000000..2396a03 --- /dev/null +++ b/001636/TurnerLab/motor_cortex/turner_m1_peth.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-0", + "metadata": {}, + "source": [ + "## Peri-Event Time Histogram (PETH) with Pynapple\n", + "\n", + "A **PETH** aligns neural activity to behavioral events and averages across trials. [Pynapple](https://pynapple.org) makes this simple:\n", + "\n", + "- `nap.compute_perievent()` - align spikes to events\n", + "- `.to_tsd()` - flatten for raster plots \n", + "- `.count(bin_size)` - bin spikes for firing rate" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cell-1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Streaming: sub-Venus/sub-Venus_ses-Venus++v4529++PostMPTP++Depth20600um++20000127_behavior+ecephys.nwb\n", + "Session: Venus++v4529++PostMPTP++Depth20600um++20000127\n" + ] + } + ], + "source": [ + "import pynapple as nap\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pynwb import NWBHDF5IO\n", + "from dandi.dandiapi import DandiAPIClient\n", + "import remfile\n", + "import h5py\n", + "\n", + "# Stream NWB file from DANDI\n", + "dandiset_id = \"001636\"\n", + "session_id = \"Venus++v4529++PostMPTP++Depth20600um++20000127\"\n", + "\n", + "client = DandiAPIClient()\n", + "dandiset = client.get_dandiset(dandiset_id)\n", + "assets = list(dandiset.get_assets())\n", + "asset = next(a for a in assets if session_id in a.path)\n", + "print(f\"Streaming: {asset.path}\")\n", + "\n", + "# Open remote file\n", + "url = asset.get_content_url(follow_redirects=1, strip_query=True)\n", + "file = remfile.File(url)\n", + "h5_file = h5py.File(file, \"r\")\n", + "io = NWBHDF5IO(file=h5_file, load_namespaces=True)\n", + "nwbfile = io.read()\n", + "data = nap.NWBFile(nwbfile)\n", + "print(f\"Session: {nwbfile.session_id}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "cell-2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/heberto/development/conversions/turner-lab-to-nwb/.venv/lib/python3.12/site-packages/pynapple/core/time_index.py:109: UserWarning: timestamps are not sorted\n", + " warn(\"timestamps are not sorted\", UserWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Flexion: 26 trials, Extension: 26 trials\n" + ] + } + ], + "source": [ + "# Extract data\n", + "trials = data[\"trials\"]\n", + "spikes = data[\"units\"][0]\n", + "velocity = data[\"ElbowVelocity\"]\n", + "\n", + "# Split by movement type\n", + "flex_trials = trials[trials[\"movement_type\"] == \"flexion\"]\n", + "ext_trials = trials[trials[\"movement_type\"] == \"extension\"]\n", + "flex_onset = nap.Ts(np.sort(flex_trials[\"derived_movement_onset_time\"].values))\n", + "ext_onset = nap.Ts(np.sort(ext_trials[\"derived_movement_onset_time\"].values))\n", + "\n", + "# Parameters\n", + "window = (-0.5, 1.0)\n", + "\n", + "# Align spikes and velocity to movement onset\n", + "peth_flex = nap.compute_perievent(spikes.restrict(flex_trials), tref=flex_onset, minmax=window)\n", + "peth_ext = nap.compute_perievent(spikes.restrict(ext_trials), tref=ext_onset, minmax=window)\n", + "vel_flex = nap.compute_perievent_continuous(velocity.restrict(flex_trials), tref=flex_onset, minmax=window)\n", + "vel_ext = nap.compute_perievent_continuous(velocity.restrict(ext_trials), tref=ext_onset, minmax=window)\n", + "\n", + "print(f\"Flexion: {len(flex_trials)} trials, Extension: {len(ext_trials)} trials\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cell-4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Flatten across trials for raster plot (values = trial index, times = spike times)\n", + "raster_flexion = peth_flex.to_tsd()\n", + "raster_extension = peth_ext.to_tsd()\n", + "\n", + "# Bin and smooth firing rate, then average across trials\n", + "bin_size = 0.01\n", + "std = 0.025 # For Gaussian smoothing\n", + "firing_rate_flexion = np.nanmean(peth_flex.count(bin_size).smooth(std=std).values, axis=1) / bin_size\n", + "firing_rate_extension = np.nanmean(peth_ext.count(bin_size).smooth(std=std).values, axis=1) / bin_size\n", + "t_fr = peth_flex.count(bin_size).times() * 1000\n", + "\n", + "# Plot PETH\n", + "BLUE, RED = \"#2563EB\", \"#DC2626\"\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n", + "window_ms = (window[0]*1000, window[1]*1000)\n", + "\n", + "# Row 1: Velocity\n", + "t_ms = vel_flex.times() * 1000\n", + "axes[0].plot(t_ms, np.nanmean(vel_flex.values, 1), lw=3, color=BLUE, label=f\"Flexion (n={len(flex_trials)})\")\n", + "axes[0].plot(t_ms, np.nanmean(vel_ext.values, 1), lw=3, color=RED, label=f\"Extension (n={len(ext_trials)})\")\n", + "axes[0].axvline(0, color='k', ls='--', lw=2)\n", + "axes[0].axhline(0, color='gray', alpha=0.3)\n", + "axes[0].set_ylabel(\"Velocity (deg/s)\", fontsize=14, fontweight='bold')\n", + "axes[0].set_title(\"Elbow Velocity\", fontsize=16, fontweight='bold')\n", + "axes[0].legend(fontsize=14, frameon=False)\n", + "\n", + "# Row 2: Raster\n", + "axes[1].plot(raster_flexion.times()*1000, raster_flexion.values, '|', ms=8, mew=1.5, color=BLUE)\n", + "axes[1].plot(raster_extension.times()*1000, raster_extension.values + len(peth_flex), '|', ms=8, mew=1.5, color=RED)\n", + "axes[1].axhline(len(peth_flex)-0.5, color='gray', lw=2)\n", + "axes[1].axvline(0, color='k', ls='--', lw=2)\n", + "axes[1].set_ylabel(\"Trial\", fontsize=14, fontweight='bold')\n", + "axes[1].set_title(\"Spike Raster\", fontsize=16, fontweight='bold')\n", + "\n", + "# Row 3: Firing Rate\n", + "axes[2].plot(t_fr, firing_rate_flexion, lw=3, color=BLUE)\n", + "axes[2].plot(t_fr, firing_rate_extension, lw=3, color=RED)\n", + "axes[2].axvline(0, color='k', ls='--', lw=2)\n", + "axes[2].set_xlabel(\"Time from movement onset (ms)\", fontsize=14, fontweight='bold')\n", + "axes[2].set_ylabel(\"Firing Rate (Hz)\", fontsize=14, fontweight='bold')\n", + "axes[2].set_title(\"PETH\", fontsize=16, fontweight='bold')\n", + "axes[2].set_ylim(bottom=0)\n", + "\n", + "for ax in axes:\n", + " ax.set_xlim(*window_ms)\n", + " ax.spines['top'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " ax.tick_params(labelsize=12)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "t6gv3gwqmjo", + "metadata": {}, + "source": [ + "### How It Works\n", + "\n", + "The PETH above was created with just a few lines of code thanks to pynapple's data model:\n", + "\n", + "**Core Data Types:**\n", + "- **`Ts`** - Timestamps (e.g., spike times, event times)\n", + "- **`Tsd`** - Time series with values (e.g., velocity, LFP)\n", + "- **`TsGroup`** - Collection of Ts objects (e.g., multiple neurons, or trials after `compute_perievent`)\n", + "- **`IntervalSet`** - Time intervals (e.g., trials, epochs)\n", + "\n", + "**Why so few lines?**\n", + "\n", + "1. **`nap.NWBFile()`** automatically loads NWB data into the right pynapple types\n", + "2. **`.restrict(intervals)`** filters any time series to specific intervals (no manual indexing)\n", + "3. **`compute_perievent()`** handles all the alignment math internally\n", + "4. **`.to_tsd()`** and **`.count().smooth()`** transform TsGroups for plotting in one call\n", + "\n", + "| Function | What it does |\n", + "|----------|--------------|\n", + "| `compute_perievent(spikes, tref, minmax)` | Align spikes to events, returns TsGroup (one Ts per trial) |\n", + "| `compute_perievent_continuous(signal, tref, minmax)` | Align continuous data, returns TsdFrame (timepoints x trials) |\n", + "| `.to_tsd()` | Flatten TsGroup for raster (times = spike times, values = trial index) |\n", + "| `.count(bin).smooth(std)` | Bin spikes and apply Gaussian smoothing |\n", + "\n", + "**Note on `.to_tsd()` for raster plots:**\n", + "\n", + "Pynapple doesn't include plotting functions - it's designed to work seamlessly with matplotlib. After `compute_perievent()`, you have a TsGroup where each element contains one trial's spike times. The `.to_tsd()` method flattens it into a single Tsd where:\n", + "- `.times()` = all spike times concatenated across trials\n", + "- `.values` = the trial index for each spike\n", + "\n", + "This gives you exactly what `plt.plot(x, y, '|')` needs for a raster: `plt.plot(raster.times(), raster.values, '|')`" + ] + }, + { + "cell_type": "markdown", + "id": "dctrxsm5ky6", + "metadata": {}, + "source": [ + "### EMG Aligned to Movement Onset\n", + "\n", + "The same `compute_perievent_continuous` pattern works for any continuous signal. Here we align EMG to movement onset to see muscle activation timing relative to the neural activity above.\n", + "\n", + "**Expected pattern:** EMG activation should precede or coincide with movement onset, with agonist muscles showing direction-selective responses." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "062i06mc039s", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Align EMG to movement onset (same pattern as velocity)\n", + "emg = data[\"EMGBrachioradialis\"]\n", + "emg_flex = nap.compute_perievent_continuous(emg.restrict(flex_trials), tref=flex_onset, minmax=window)\n", + "emg_ext = nap.compute_perievent_continuous(emg.restrict(ext_trials), tref=ext_onset, minmax=window)\n", + "\n", + "# Plot EMG\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "t_ms = emg_flex.times() * 1000\n", + "ax.plot(t_ms, np.nanmean(emg_flex.values, 1), lw=3, color=BLUE, label=f\"Flexion (n={len(flex_trials)})\")\n", + "ax.plot(t_ms, np.nanmean(emg_ext.values, 1), lw=3, color=RED, label=f\"Extension (n={len(ext_trials)})\")\n", + "ax.axvline(0, color='k', ls='--', lw=2)\n", + "ax.set_xlim(*window_ms)\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=14, fontweight='bold')\n", + "ax.set_ylabel(\"EMG (a.u.)\", fontsize=14, fontweight='bold')\n", + "ax.set_title(\"Brachioradialis EMG\", fontsize=16, fontweight='bold')\n", + "ax.legend(fontsize=14, frameon=False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "60186ecc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Align EMG to movement onset (same pattern as velocity)\n", + "emg = data[\"EMGTricepsLongus2\"]\n", + "emg_flex = nap.compute_perievent_continuous(emg.restrict(flex_trials), tref=flex_onset, minmax=window)\n", + "emg_ext = nap.compute_perievent_continuous(emg.restrict(ext_trials), tref=ext_onset, minmax=window)\n", + "\n", + "# Plot EMG\n", + "fig, ax = plt.subplots(figsize=(12, 4))\n", + "t_ms = emg_flex.times() * 1000\n", + "ax.plot(t_ms, np.nanmean(emg_flex.values, 1), lw=3, color=BLUE, label=f\"Flexion (n={len(flex_trials)})\")\n", + "ax.plot(t_ms, np.nanmean(emg_ext.values, 1), lw=3, color=RED, label=f\"Extension (n={len(ext_trials)})\")\n", + "ax.axvline(0, color='k', ls='--', lw=2)\n", + "ax.set_xlim(*window_ms)\n", + "ax.set_xlabel(\"Time from movement onset (ms)\", fontsize=14, fontweight='bold')\n", + "ax.set_ylabel(\"EMG (a.u.)\", fontsize=14, fontweight='bold')\n", + "ax.set_title(\"Triceps Longus EMG\", fontsize=16, fontweight='bold')\n", + "ax.legend(fontsize=14, frameon=False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.tick_params(labelsize=12)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "turner-lab-to-nwb (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/001636/TurnerLab/motor_cortex/turner_m1_usage.ipynb b/001636/TurnerLab/motor_cortex/turner_m1_usage.ipynb new file mode 100644 index 0000000..91a1caa --- /dev/null +++ b/001636/TurnerLab/motor_cortex/turner_m1_usage.ipynb @@ -0,0 +1,4607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Turner Lab M1 MPTP Dataset - NWB Usage Guide\n", + "\n", + "**Dataset Overview:**\n", + "This dataset contains single-unit electrophysiology recordings from primary motor cortex (M1) of parkinsonian macaque monkeys performing flexion/extension motor tasks. The data investigates motor encoding deficits in MPTP-induced parkinsonism, comparing pyramidal tract neurons (PTNs) versus corticostriatal neurons (CSNs).\n", + "\n", + "**Key Features:**\n", + "- **Single-unit recordings**: Spike times and waveforms from M1 neurons\n", + "- **Motor behavior**: Flexion/extension task with analog kinematics\n", + "- **Cell type identification**: Antidromic stimulation to classify PTNs vs CSNs\n", + "- **Disease model**: MPTP-treated parkinsonian macaque monkeys\n", + "- **Electrode mapping**: Systematic cortical penetrations with stereotactic coordinates\n", + "\n", + "**Data Organization:**\n", + "- Each NWB file represents one recording session from one penetration depth\n", + "- Session IDs follow pattern: `{subject_id}++{FileName}++{PreMPTP|PostMPTP}++Depth{depth_um}um++{YearMonthDay}`\n", + "- Example: `V++{v0502}++PostMPTP++Depth19180um++20000121` indicates monkey V, post-MPTP condition, 19.18mm depth, recorded Jan 21, 2000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming NWB Files from DANDI \n", + "\n", + "We recommend using the DANDI Python client to access the NWB files directly from the DANDI archive without downloading them locally." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total assets in dandiset: 447\n", + "NWB files: 447\n", + "Selected asset: sub-Venus/sub-Venus_ses-Venus++v5811++PostMPTP++Depth18300um++20000331_behavior+ecephys.nwb\n" + ] + } + ], + "source": [ + "import h5py\n", + "import remfile\n", + "from pynwb import NWBHDF5IO\n", + "from dandi.dandiapi import DandiAPIClient\n", + "\n", + "# Connect to DANDI and get the dandiset\n", + "dandiset_id = \"001636\"\n", + "client = DandiAPIClient()\n", + "dandiset = client.get_dandiset(dandiset_id, \"draft\")\n", + "\n", + "# Get all assets - users can filter this list to select any session\n", + "assets = dandiset.get_assets()\n", + "assets_list = list(assets)\n", + "print(f\"Total assets in dandiset: {len(assets_list)}\")\n", + "\n", + "# Filter for NWB files only\n", + "nwb_assets = [a for a in assets_list if a.path.endswith(\".nwb\")]\n", + "print(f\"NWB files: {len(nwb_assets)}\")\n", + "\n", + "# We select this specific session because it is \"complete\" - it has all available data types:\n", + "# - 2 PTNs (pyramidal tract neurons) identified via antidromic stimulation\n", + "# - EMG recordings from multiple arm muscles\n", + "# - LFP data\n", + "# - Perturbation trials (52 out of 80 total trials)\n", + "# - Balanced flexion/extension movements (40 each)\n", + "#\n", + "# The only missing feature is receptive field locations (not recorded for these units).\n", + "# See dandiset_session_metadata.csv for a full inventory of all 298 sessions.\n", + "asset_path = \"sub-Venus/sub-Venus_ses-Venus++v5811++PostMPTP++Depth18300um++20000331_behavior+ecephys.nwb\"\n", + "asset = next(a for a in nwb_assets if a.path == asset_path)\n", + "print(f\"Selected asset: {asset.path}\")\n", + "\n", + "# Stream the NWB file directly from DANDI (no download required)\n", + "s3_url = asset.get_content_url(follow_redirects=1, strip_query=False)\n", + "file_system = remfile.File(s3_url)\n", + "file = h5py.File(file_system, mode=\"r\")\n", + "\n", + "io = NWBHDF5IO(file=file)\n", + "nwbfile = io.read()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fist, we can visualize the HTML representation of the nwbfile to get an overview of its contents." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

root (NWBFile)

session_description: Single-unit recordings from primary motor cortex (M1) of MPTP-treated parkinsonian macaque monkeys\n", + "performing a visuomotor step-tracking task with flexion/extension movements of the elbow (Monkey V)\n", + "or wrist (Monkey L) joint. Data includes spike times from pyramidal tract neurons (PTNs) and\n", + "corticostriatal neurons (CSNs) identified through antidromic stimulation. Task involves center hold,\n", + "target presentation, movement execution, and reward phases with optional torque perturbations on\n", + "random trials.\n", + " MPTP condition: Post. Cell types: PED, PED.
identifier: 720f8499-dc14-47be-a640-45f3c83d8a33
session_start_time2000-03-31 00:00:00-05:00
timestamps_reference_time2000-03-31 00:00:00-05:00
file_create_date
02026-04-23 14:09:49.441110-06:00
experimenter('Turner, Robert S.', 'Pasquereau, Benjamin')
related_publications('https://doi.org/10.1093/cercor/bhq217', 'https://doi.org/10.3389/fnsys.2013.00098', 'https://doi.org/10.1093/brain/awv312')
acquisition
EMGDeltoidPosterior (TimeSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Posterior deltoid - shoulder extensor and external rotator. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
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timestamps
HDF5 dataset
Data typefloat64
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timestamps_unit: seconds
interval: 1
EMGTrapezius (TimeSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Trapezius - shoulder and scapula stabilizer. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
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timestamps
HDF5 dataset
Data typefloat64
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Array size5.29 MiB
Chunk shape(692972,)
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timestamps_unit: seconds
interval: 1
EMGTricepsLateralis (TimeSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps lateralis (lateral head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Compressed size (bytes)3493671
Compression ratio1.586805397531708
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
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Compressed size (bytes)4035421
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timestamps_unit: seconds
interval: 1
EMGTricepsLongus1 (TimeSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Compressed size (bytes)3431351
Compression ratio1.6156248661241592
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
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Compressed size (bytes)4035421
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timestamps_unit: seconds
interval: 1
EMGTricepsLongus2 (TimeSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Uncompressed size (bytes)5543776
Compressed size (bytes)3323797
Compression ratio1.667904508006957
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Uncompressed size (bytes)5543776
Compressed size (bytes)4035421
Compression ratio1.3737788448838424
timestamps_unit: seconds
interval: 1
ElbowAngle (SpatialSeries)
resolution: -1.0
comments: no comments
description: Raw manipulandum angle from sensor during visuomotor flexion/extension task
conversion: 1.0
offset: 0.0
unit: degrees
data
HDF5 dataset
Data typefloat64
Shape(692972, 1)
Array size5.29 MiB
Chunk shape(692972, 1)
Compressiongzip
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Uncompressed size (bytes)5543776
Compressed size (bytes)2130872
Compression ratio2.6016466498222326
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Uncompressed size (bytes)5543776
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Compression ratio1.3737788448838424
timestamps_unit: seconds
interval: 1
reference_frame: Anatomical flexion/extension axis, 0 degrees = neutral position, positive = extension
ElbowTorque (TimeSeries)
resolution: -1.0
comments: no comments
description: Commands sent to torque controller during visuomotor task
conversion: 1.0
offset: 0.0
unit: arbitrary
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Compressed size (bytes)679707
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timestamps
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Chunk shape(692972,)
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timestamps_unit: seconds
interval: 1
keywords
HDF5 dataset
Data typeobject
Shape(7,)
Array size56.00 bytes
Chunk shapeNone
CompressionNone
Compression optsNone
Uncompressed size (bytes)56
Compressed size (bytes)112
Compression ratio0.5

['MPTP' 'parkinsonism' 'motor cortex' 'single unit recording'\n", + " 'pyramidal tract neurons' 'corticostriatal neurons' 'visuomotor task']
processing
antidromic_identification (ProcessingModule)
description: Antidromic stimulation tests for neuron classification. Contains consolidated electrical stimulation and neural response recordings (50ms sweeps at 20kHz) from chronically implanted electrodes used to identify pyramidal tract neurons (PTNs) and corticostriatal neurons (CSNs) via collision tests and frequency-following tests. Data is organized as one response series and one stimulation series per unit/location combination, with the intervals table (AntidromicSweepsIntervals) providing TimeSeriesReference columns to access individual sweeps within the consolidated series.
AntidromicResponseUnit1Peduncle (ElectricalSeries)
starting_time: 940.3059999999998
rate: 20000.0
resolution: -1.0
comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.
description: Consolidated neural responses from M1 to peduncle stimulation for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in volts. CALIBRATION UNCERTAIN: conversion factor estimated from typical recording system specs (10000x gain, +/-5V ADC). Awaiting confirmation from data authors.
conversion: 1.526e-08
offset: 0.0
unit: volts
data
HDF5 dataset
Data typeint16
Shape(62000, 1)
Array size121.09 KiB
Chunk shape(62000, 1)
Compressiongzip
Compression opts4
Uncompressed size (bytes)124000
Compressed size (bytes)59328
Compression ratio2.0900755124056096
starting_time_unit: seconds
electrodes (DynamicTableRegion)
description: Recording electrode in M1
table (ElectrodesTable)
description: metadata about extracellular electrodes
columns
location
Location of the electrode (channel).
group
Reference to the ElectrodeGroup.
chamber_grid_ap_mm
Chamber grid position along the anterior-posterior axis, relative to chamber center (mm, positive = posterior). Recordings span -7 to 0 mm (all anterior to chamber center). NaN for stimulation electrodes.
chamber_grid_ml_mm
Chamber grid position along the medial-lateral axis, relative to chamber center (mm, positive = right). Recordings span -6 to -2 mm (all in left hemisphere). NaN for stimulation electrodes.
chamber_insertion_depth_mm
Electrode insertion depth from chamber reference point (mm, positive = deeper, range: 8.4-27.6mm). NaN for stimulation electrodes.
icms_effect
Movement evoked by intracortical microstimulation (ICMS) at recording site (10 biphasic pulses at 300 Hz, typically at or below 20 uA). Microexcitability at low currents is a hallmark of primary motor cortex. Values: movement description (e.g. 'wrist ext.', 'finger flex'), 'NT' (not tested), 'NR'/'nr' (no response).
icms_threshold_uA
Minimum current (microamps) at which ICMS evoked movement at the recording site. Low thresholds (<30 uA) confirm primary motor cortex (M1) recording location. NaN if not tested or no response.
group_name
Name of the ElectrodeGroup.
x
x coordinate of the channel location in the brain.
y
y coordinate of the channel location in the brain.
z
z coordinate of the channel location in the brain.
imp
Impedance of the channel, in ohms.
filtering
Description of hardware filtering.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationgroupchamber_grid_ap_mmchamber_grid_ml_mmchamber_insertion_depth_mmicms_effecticms_threshold_uAgroup_namexyzimpfiltering
id
0agranular frontal area F1 (or area 4)ElectrodeGroupMicroelectrodeRecording pynwb.ecephys.ElectrodeGroup at 0x128623676456400\\nFields:\\n description: Left primary motor cortex recording within chamber-relative coordinate system. Chamber surgically positioned over left M1. Daily positions relative to chamber center. Functional verification via microstimulation (≤40μA, 10 pulses at 300Hz).\\n device: DeviceMicroelectrodeRecording pynwb.device.Device at 0x128623676449968\\nFields:\\n description: Glass-coated PtIr microelectrode mounted in hydraulic microdrive (MO-95, Narishige Intl., Tokyo). Signal amplified 10^4, bandpass filtered 0.3-10kHz. Sampling: 20kHz for unit discrimination.\\n\\n location: Primary motor cortex (M1), area 4, arm area\\n-6-2.518.3not_testedNaNElectrodeGroupMicroelectrodeRecording-13472.805-30783.554-18067.467NaN0.3-10kHz bandpass for spikes, 1-100Hz for LFP when available
AntidromicStimulationUnit1Peduncle (TimeSeries)
starting_time: 940.3059999999998
rate: 20000.0
resolution: -1.0
comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.
description: Consolidated stimulation currents delivered to peduncle for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in amperes. CALIBRATION UNCERTAIN: conversion factor estimated.
conversion: 1.526e-08
offset: 0.0
unit: amperes
data
HDF5 dataset
Data typeint16
Shape(62000,)
Array size121.09 KiB
Chunk shape(62000,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)124000
Compressed size (bytes)12779
Compression ratio9.703419672900853
starting_time_unit: seconds
StimulationElectrodesTable (DynamicTable)
description: Chronically implanted macroelectrodes for antidromic neuron identification. These electrodes were surgically implanted and present for all recording sessions, though antidromic testing was not performed for every recorded neuron. Used to classify M1 neurons as PTNs (peduncle), CSNs (putamen), thalamocortical neurons (thalamus), or STN-projecting neurons (subthalamic nucleus). Row indices: 0=peduncle, 1-3=putamen (3 electrodes), 4=thalamus, 5=STN.
columns
location
Anatomical location of stimulation site
device_name
Name of the device in nwbfile.devices
notes
Additional electrode-specific notes
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationdevice_namenotes
id
0Cerebral peduncle (pre-pontine)DeviceMicrowirePeduncleStimulationVentral to substantia nigra, arm-responsive pre-pontine region. For PTN identification.
1Putamen (posterolateral)DeviceMicrowirePutamenStimulation1Electrode 1 of 3, posterolateral putamen for M1 CSN projections. Used as representative electrode reference for all StrStim data.
2Putamen (posterolateral)DeviceMicrowirePutamenStimulation2Electrode 2 of 3, posterolateral putamen for M1 CSN projections. Physical electrode documented but not distinguished in source data.
3Putamen (posterolateral)DeviceMicrowirePutamenStimulation3Electrode 3 of 3, posterolateral putamen for M1 CSN projections. Physical electrode documented but not distinguished in source data.

... and 2 more row(s).

behavior (ProcessingModule)
description: Behavioral data
ElbowVelocity (TimeSeries)
resolution: -1.0
comments: no comments
description: Elbow joint velocity during visuomotor task
conversion: 1.0
offset: 0.0
unit: degrees/s
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)4653177
Compression ratio1.1913959000485046
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)4035421
Compression ratio1.3737788448838424
timestamps_unit: seconds
interval: 1
ecephys (ProcessingModule)
description: Processed extracellular electrophysiology data
LFP (ElectricalSeries)
resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. LFP from same electrode as spike recordings. Trials concatenated with inter-trial gaps.
description: Local field potential from M1 microelectrode. Recorded simultaneously with single-unit activity during visuomotor task. Signal processing: 10k gain, 0.1-45 Hz bandpass filtered.
conversion: 1.0
offset: 0.0
unit: volts
data
HDF5 dataset
Data typefloat64
Shape(692972, 1)
Array size5.29 MiB
Chunk shape(692972, 1)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)3594393
Compression ratio1.542339972284611
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)4035421
Compression ratio1.3737788448838424
timestamps_unit: seconds
interval: 1
electrodes (DynamicTableRegion)
description: M1 recording electrode - same electrode used for single-unit recordings
table (ElectrodesTable)
description: metadata about extracellular electrodes
columns
location
Location of the electrode (channel).
group
Reference to the ElectrodeGroup.
chamber_grid_ap_mm
Chamber grid position along the anterior-posterior axis, relative to chamber center (mm, positive = posterior). Recordings span -7 to 0 mm (all anterior to chamber center). NaN for stimulation electrodes.
chamber_grid_ml_mm
Chamber grid position along the medial-lateral axis, relative to chamber center (mm, positive = right). Recordings span -6 to -2 mm (all in left hemisphere). NaN for stimulation electrodes.
chamber_insertion_depth_mm
Electrode insertion depth from chamber reference point (mm, positive = deeper, range: 8.4-27.6mm). NaN for stimulation electrodes.
icms_effect
Movement evoked by intracortical microstimulation (ICMS) at recording site (10 biphasic pulses at 300 Hz, typically at or below 20 uA). Microexcitability at low currents is a hallmark of primary motor cortex. Values: movement description (e.g. 'wrist ext.', 'finger flex'), 'NT' (not tested), 'NR'/'nr' (no response).
icms_threshold_uA
Minimum current (microamps) at which ICMS evoked movement at the recording site. Low thresholds (<30 uA) confirm primary motor cortex (M1) recording location. NaN if not tested or no response.
group_name
Name of the ElectrodeGroup.
x
x coordinate of the channel location in the brain.
y
y coordinate of the channel location in the brain.
z
z coordinate of the channel location in the brain.
imp
Impedance of the channel, in ohms.
filtering
Description of hardware filtering.
table\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
locationgroupchamber_grid_ap_mmchamber_grid_ml_mmchamber_insertion_depth_mmicms_effecticms_threshold_uAgroup_namexyzimpfiltering
id
0agranular frontal area F1 (or area 4)ElectrodeGroupMicroelectrodeRecording pynwb.ecephys.ElectrodeGroup at 0x128623676456400\\nFields:\\n description: Left primary motor cortex recording within chamber-relative coordinate system. Chamber surgically positioned over left M1. Daily positions relative to chamber center. Functional verification via microstimulation (≤40μA, 10 pulses at 300Hz).\\n device: DeviceMicroelectrodeRecording pynwb.device.Device at 0x128623676449968\\nFields:\\n description: Glass-coated PtIr microelectrode mounted in hydraulic microdrive (MO-95, Narishige Intl., Tokyo). Signal amplified 10^4, bandpass filtered 0.3-10kHz. Sampling: 20kHz for unit discrimination.\\n\\n location: Primary motor cortex (M1), area 4, arm area\\n-6-2.518.3not_testedNaNElectrodeGroupMicroelectrodeRecording-13472.805-30783.554-18067.467NaN0.3-10kHz bandpass for spikes, 1-100Hz for LFP when available
electrodes (ElectrodesTable)
description: metadata about extracellular electrodes
columns
location
Location of the electrode (channel).
group
Reference to the ElectrodeGroup.
chamber_grid_ap_mm
Chamber grid position along the anterior-posterior axis, relative to chamber center (mm, positive = posterior). Recordings span -7 to 0 mm (all anterior to chamber center). NaN for stimulation electrodes.
chamber_grid_ml_mm
Chamber grid position along the medial-lateral axis, relative to chamber center (mm, positive = right). Recordings span -6 to -2 mm (all in left hemisphere). NaN for stimulation electrodes.
chamber_insertion_depth_mm
Electrode insertion depth from chamber reference point (mm, positive = deeper, range: 8.4-27.6mm). NaN for stimulation electrodes.
icms_effect
Movement evoked by intracortical microstimulation (ICMS) at recording site (10 biphasic pulses at 300 Hz, typically at or below 20 uA). Microexcitability at low currents is a hallmark of primary motor cortex. Values: movement description (e.g. 'wrist ext.', 'finger flex'), 'NT' (not tested), 'NR'/'nr' (no response).
icms_threshold_uA
Minimum current (microamps) at which ICMS evoked movement at the recording site. Low thresholds (<30 uA) confirm primary motor cortex (M1) recording location. NaN if not tested or no response.
group_name
Name of the ElectrodeGroup.
x
x coordinate of the channel location in the brain.
y
y coordinate of the channel location in the brain.
z
z coordinate of the channel location in the brain.
imp
Impedance of the channel, in ohms.
filtering
Description of hardware filtering.
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locationgroupchamber_grid_ap_mmchamber_grid_ml_mmchamber_insertion_depth_mmicms_effecticms_threshold_uAgroup_namexyzimpfiltering
id
0agranular frontal area F1 (or area 4)ElectrodeGroupMicroelectrodeRecording pynwb.ecephys.ElectrodeGroup at 0x128623676456400\\nFields:\\n description: Left primary motor cortex recording within chamber-relative coordinate system. Chamber surgically positioned over left M1. Daily positions relative to chamber center. Functional verification via microstimulation (≤40μA, 10 pulses at 300Hz).\\n device: DeviceMicroelectrodeRecording pynwb.device.Device at 0x128623676449968\\nFields:\\n description: Glass-coated PtIr microelectrode mounted in hydraulic microdrive (MO-95, Narishige Intl., Tokyo). Signal amplified 10^4, bandpass filtered 0.3-10kHz. Sampling: 20kHz for unit discrimination.\\n\\n location: Primary motor cortex (M1), area 4, arm area\\n-6-2.518.3not_testedNaNElectrodeGroupMicroelectrodeRecording-13472.805-30783.554-18067.467NaN0.3-10kHz bandpass for spikes, 1-100Hz for LFP when available
electrode_groups
ElectrodeGroupMicroelectrodeRecording (ElectrodeGroup)
description: Left primary motor cortex recording within chamber-relative coordinate system. Chamber surgically positioned over left M1. Daily positions relative to chamber center. Functional verification via microstimulation (≤40μA, 10 pulses at 300Hz).
location: Primary motor cortex (M1), area 4, arm area
device (Device)
description: Glass-coated PtIr microelectrode mounted in hydraulic microdrive (MO-95, Narishige Intl., Tokyo). Signal amplified 10^4, bandpass filtered 0.3-10kHz. Sampling: 20kHz for unit discrimination.
devices
DeviceMicroelectrodeRecording (Device)
description: Glass-coated PtIr microelectrode mounted in hydraulic microdrive (MO-95, Narishige Intl., Tokyo). Signal amplified 10^4, bandpass filtered 0.3-10kHz. Sampling: 20kHz for unit discrimination.
DeviceMicrowirePeduncleStimulation (Device)
description: Custom-built PtIr microwire electrode for antidromic stimulation in cerebral peduncle. Chronically implanted for PTN identification.
DeviceMicrowirePutamenStimulation1 (Device)
description: Custom-built PtIr microwire electrode 1 of 3 for antidromic stimulation in posterolateral putamen. Chronically implanted for CSN identification.
DeviceMicrowirePutamenStimulation2 (Device)
description: Custom-built PtIr microwire electrode 2 of 3 for antidromic stimulation in posterolateral putamen. Chronically implanted for CSN identification.
DeviceMicrowirePutamenStimulation3 (Device)
description: Custom-built PtIr microwire electrode 3 of 3 for antidromic stimulation in posterolateral putamen. Chronically implanted for CSN identification.
DeviceMicrowireSTNStimulation (Device)
description: Custom-built PtIr microwire electrode for antidromic stimulation in subthalamic nucleus (STN). Chronically implanted for STN projection identification.
DeviceMicrowireThalamicStimulation (Device)
description: Custom-built PtIr microwire electrode for antidromic stimulation in VL thalamus. Chronically implanted for thalamocortical projection identification.
TorqueMotor (Device)
description: TQ40W torque motor (Aerotech Inc.) used in the manipulandum system for two purposes: (1) providing resistance and position control during the visuomotor step-tracking task, and (2) delivering brief, temporally-unpredictable torque perturbations (0.1 Nm, 50ms impulse) to the elbow joint on randomly-selected trials to evoke proprioceptive stretch reflexes. The monkey's arm was held in a padded manipulandum with the elbow joint aligned with the torque motor axis of rotation.
manufacturer: Aerotech Inc.
intervals
AntidromicSweepsIntervals (TimeIntervals)
description: Intervals table for antidromic stimulation sweeps. Each row represents one sweep with TimeSeriesReference columns linking to specific sample ranges within the consolidated response and stimulation series. Use the 'response' and 'stimulation' columns to extract data for individual sweeps. Filter by 'stimulation_protocol', 'location', or 'unit_name' to select specific sweep types. The 50ms sweeps are centered at t=0 (pulse_time), with 25ms pre-stimulation and 25ms post-stimulation windows.
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
stimulation_onset_time
Time of electrical stimulation pulse delivery (t=0 in the original MATLAB sweep). The 50ms sweep extends from stimulation_onset_time-0.025s to stimulation_onset_time+0.025s. Corresponds to the center point of the sweep where the stimulation current is injected.
unit_name
Unit number (1 or 2) from source filename, matching the unit_name in the units table
location
Anatomical location of stimulation electrode: 'Striatum', 'Peduncle', 'Thalamus', or 'STN'
stimulation_protocol
Antidromic stimulation protocol used: 'Collision' (paired spontaneous and antidromic spikes), 'FrequencyFollowing' (high-frequency train), or 'Orthodromic' (sub-threshold control)
sweep_number
Sweep index number within the series for this unit/location combination (0-based)
response
Reference to neural voltage response segment for this sweep. Contains (idx_start, count, timeseries) tuple pointing to the specific sample range within the consolidated response ElectricalSeries. Access data with: series.data[idx_start:idx_start+count] * series.conversion
stimulation
Reference to stimulation current segment for this sweep. Contains (idx_start, count, timeseries) tuple pointing to the specific sample range within the consolidated stimulation TimeSeries. Access data with: series.data[idx_start:idx_start+count] * series.conversion
stimulation_electrode
Index into StimulationElectrodesTable indicating which electrode delivered the stimulation
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start_timestop_timestimulation_onset_timeunit_namelocationstimulation_protocolsweep_numberresponsestimulationstimulation_electrode
id
0940.306940.35595940.3311PeduncleCollision0(0, 1000, AntidromicResponseUnit1Peduncle pynwb.ecephys.ElectricalSeries at 0x128623718818128\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000, 1), type \"<i2\">\\n description: Consolidated neural responses from M1 to peduncle stimulation for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in volts. CALIBRATION UNCERTAIN: conversion factor estimated from typical recording system specs (10000x gain, +/-5V ADC). Awaiting confirmation from data authors.\\n electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: volts\\n)(0, 1000, AntidromicStimulationUnit1Peduncle pynwb.base.TimeSeries at 0x128623676074864\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000,), type \"<i2\">\\n description: Consolidated stimulation currents delivered to peduncle for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in amperes. CALIBRATION UNCERTAIN: conversion factor estimated.\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: amperes\\n)0
1940.406940.45595940.4311PeduncleCollision1(1000, 1000, AntidromicResponseUnit1Peduncle pynwb.ecephys.ElectricalSeries at 0x128623718818128\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000, 1), type \"<i2\">\\n description: Consolidated neural responses from M1 to peduncle stimulation for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in volts. CALIBRATION UNCERTAIN: conversion factor estimated from typical recording system specs (10000x gain, +/-5V ADC). Awaiting confirmation from data authors.\\n electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: volts\\n)(1000, 1000, AntidromicStimulationUnit1Peduncle pynwb.base.TimeSeries at 0x128623676074864\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000,), type \"<i2\">\\n description: Consolidated stimulation currents delivered to peduncle for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in amperes. CALIBRATION UNCERTAIN: conversion factor estimated.\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: amperes\\n)0
2940.506940.55595940.5311PeduncleCollision2(2000, 1000, AntidromicResponseUnit1Peduncle pynwb.ecephys.ElectricalSeries at 0x128623718818128\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000, 1), type \"<i2\">\\n description: Consolidated neural responses from M1 to peduncle stimulation for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in volts. CALIBRATION UNCERTAIN: conversion factor estimated from typical recording system specs (10000x gain, +/-5V ADC). Awaiting confirmation from data authors.\\n electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: volts\\n)(2000, 1000, AntidromicStimulationUnit1Peduncle pynwb.base.TimeSeries at 0x128623676074864\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000,), type \"<i2\">\\n description: Consolidated stimulation currents delivered to peduncle for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in amperes. CALIBRATION UNCERTAIN: conversion factor estimated.\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: amperes\\n)0
3940.606940.65595940.6311PeduncleCollision3(3000, 1000, AntidromicResponseUnit1Peduncle pynwb.ecephys.ElectricalSeries at 0x128623718818128\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000, 1), type \"<i2\">\\n description: Consolidated neural responses from M1 to peduncle stimulation for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in volts. CALIBRATION UNCERTAIN: conversion factor estimated from typical recording system specs (10000x gain, +/-5V ADC). Awaiting confirmation from data authors.\\n electrodes: electrodes <class 'hdmf.common.table.DynamicTableRegion'>\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: volts\\n)(3000, 1000, AntidromicStimulationUnit1Peduncle pynwb.base.TimeSeries at 0x128623676074864\\nFields:\\n comments: Concatenated sweeps for Unit 1, Peduncle stimulation. Sweep boundaries every 1000 samples.\\n conversion: 1.526e-08\\n data: <HDF5 dataset \"data\": shape (62000,), type \"<i2\">\\n description: Consolidated stimulation currents delivered to peduncle for Unit 1. Contains 62 concatenated 50ms sweeps at 20kHz. Each sweep is 1000 samples. Use the AntidromicSweepsIntervals table to access individual sweeps via TimeSeriesReference. Data in amperes. CALIBRATION UNCERTAIN: conversion factor estimated.\\n offset: 0.0\\n rate: 20000.0\\n resolution: -1.0\\n starting_time: 940.3059999999998\\n starting_time_unit: seconds\\n unit: amperes\\n)0

... and 58 more row(s).

invalid_times (TimeIntervals)
description: time intervals to be removed from analysis
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
tags
user-defined tags
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start_timestop_timetags
id
09.61912.619[artificial_inter_trial_gap, not_recorded]
122.57125.571[artificial_inter_trial_gap, not_recorded]
237.48340.483[artificial_inter_trial_gap, not_recorded]
347.29650.296[artificial_inter_trial_gap, not_recorded]

... and 75 more row(s).

trials (TimeIntervals)
description: experimental trials
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
center_target_appearance_time
Time (in seconds) when center target appeared on screen, cueing the monkey to align cursor with center position and hold
lateral_target_appearance_time
Time (in seconds) when peripheral target appeared (go cue), signaling the monkey to initiate movement toward the flexion or extension target
cursor_departure_time
Time (in seconds) when cursor exited the center target zone following the go cue
reward_time
Time (in seconds) of liquid reward delivery for successful target acquisition and hold
target_amplitude_degrees
Distance from center (home) position to target center in degrees of joint angle (10, 20, or 30). Determines required movement amplitude for target capture. During individual sessions, target amplitude is either always 20 degrees or varies randomly among 10, 20, and 30 degrees.
isolation_monitoring_stim_time
Time (in seconds) of antidromic stimulation pulse delivered during trial to monitor unit isolation. Single pulses were delivered early in some trials (~245 ms after trial start) to activate otherwise silent neurons and verify recording isolation. NaN indicates no stimulation was delivered in that trial
isolation_monitoring_stim_site
Anatomical target of antidromic stimulation for isolation monitoring. Values: 'Str' (striatum), 'Thal' (thalamus), 'Ped' (cerebral peduncle), or empty string if no stimulation was delivered in that trial
movement_type
flexion or extension
torque_perturbation_type
direction of torque perturbation causing muscle stretch (flexion/extension/none)
torque_perturbation_onset_time
Time (in seconds) of unpredictable torque impulse onset (0.1 Nm, 50 ms) applied to manipulandum 1-2 s after center target capture
derived_movement_onset_time
Time (in seconds) of movement onset from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_movement_end_time
Time (in seconds) of movement end from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_peak_velocity
Maximum angular velocity (in degrees/second) of elbow joint during the movement, from kinematic analysis
derived_peak_velocity_time
Time (in seconds) at which peak angular velocity occurred during the movement
derived_movement_amplitude
Total angular displacement (in degrees) of elbow joint from movement onset to end position
derived_end_position
Final elbow joint angle (in degrees) at movement end, relative to center position where 0 is the neutral hold position
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start_timestop_timecenter_target_appearance_timelateral_target_appearance_timecursor_departure_timereward_timetarget_amplitude_degreesisolation_monitoring_stim_timeisolation_monitoring_stim_sitemovement_typetorque_perturbation_typetorque_perturbation_onset_timederived_movement_onset_timederived_movement_end_timederived_peak_velocityderived_peak_velocity_timederived_movement_amplitudederived_end_position
id
00.0009.6194.7267.5267.6558.889NaNNaNflexionnoneNaN7.8168.15053.8806917.9369.56697516.760761
112.61922.57114.98119.82820.41721.176NaNNaNextensionflexion16.67720.15420.66590.53158220.440-27.823461-21.324000
225.57137.48327.96035.34435.92436.759NaNNaNextensionextension29.65635.70936.17853.24170535.969-12.224196-16.109740
340.48347.29642.33145.18045.72946.568NaNNaNflexionnoneNaN45.53446.18463.38704045.71419.07340018.600860

... and 76 more row(s).

subject (Subject)
age: P4Y/P15Y
age__reference: birth
description: MPTP-treated parkinsonian female rhesus macaque (Monkey V / Venus) used for motor cortex\n", + "electrophysiology during a step-tracking task performed with the elbow joint.\n", + "
sex: F
species: Macaca mulatta
subject_id: Venus
weight: 4.8 kg
lab_meta_data
hed_schema (HedLabMetaData)
hed_schema_version: 8.4.0
localization (Localization)
anatomical_coordinates_tables
D99v2AtlasCoordinates (AnatomicalCoordinatesTable)
description: Recording electrode coordinates in D99 macaque atlas space (RAS orientation, mm; origin at the anterior commissure). D99 is the author's canonical coordinate frame for this dataset. See https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/nonhuman/macaque_tempatl/atlas_d99v2.html
method: COORDINATE ORIGIN (per-electrode x/y/z). Computed by Dr. Robert Turner (data author) from chamber-relative positions using a geometric transform: constant AP offset (-8.2 mm, chamber center to anterior commissure) and 35-degree coronal-plane rotation coupling ML and Depth to the D99 R and S axes. No individual MRI registration was used; coordinate accuracy is limited by the geometric approximation (no subject-specific warping). REGION ANNOTATION (brain_region_id and voxel_label_id columns). Computed by the conversion pipeline, not provided by the data author. 'voxel_label_id' is the raw D99 atlas voxel label at the coordinate (honest atlas answer). 'brain_region_id' is a curated M1-constrained label: if the raw voxel is F1_(4) (D99 label id 359, primary motor cortex), it is preserved; otherwise the nearest F1_(4) voxel within 5 mm is chosen, with the distance recorded in 'brain_region_distance_mm' and flagged in 'brain_region_lookup_method'. AUTHORITY. The curated label reflects the ICMS-verified motor identity of the recording site (every electrode in this dataset has icms_threshold_uA < 30 uA on the electrodes table, which is cytoarchitectonically specific to primary motor cortex). The curation is a dataset-specific convenience for dandiset 001636 and is not authoritative atlas output; it should not be used as a substitute for the ICMS evidence or extended to datasets with non-motor recordings.
space (D99v2Space)
space_name: D99v2
origin: Anterior commissure (AC). Horizontal plane aligned to the AC-PC line (anterior commissure to posterior commissure).
units: mm
orientation: RAS
columns
x
The x coordinate
y
The y coordinate
z
The z coordinate
localized_entity
A reference to the object being localized (e.g. electrode)
brain_region_id
Curated D99 M1 label (viewer-facing). Always 'F1_(4)' unless the electrode coordinate is > 5 mm from any D99 F1 voxel, in which case 'outside'.
brain_region_lookup_method
How the curated brain_region_id was determined: 'exact' = the raw atlas voxel at this coordinate is already the atlas's native primary motor cortex (M1) label, so the raw voxel answer is used; 'nearest_neighbor' = the raw voxel was non-M1 or unlabeled, so the label was set to the nearest M1 voxel within 5 mm (distance in brain_region_distance_mm); 'no_m1_within_5mm' = no M1 voxel within the 5 mm cap, brain_region_id is 'outside'.
brain_region_distance_mm
Euclidean distance in mm from the electrode coordinate to the nearest atlas-native M1 voxel. 0.0 when brain_region_lookup_method is 'exact'; positive float for 'nearest_neighbor'; NaN for 'no_m1_within_5mm'.
voxel_label_id
Raw atlas voxel label ID at this coordinate (no interpolation). Distinct from brain_region_id, which is curated to the atlas's native M1 label; voxel_label_id is whatever the atlas says at that voxel. Special values: 'unlabeled' when the voxel is inside the template but carries label=0 (sulcal-gap artifact or simply unparcellated tissue); 'outside_volume' when the coordinate falls outside the template's bounding box.
voxel_label
Full-name form of voxel_label_id. Special values: 'unlabeled voxel (label=0)' and 'outside template volume'.
x_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
y_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
z_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
brain_region
The brain region associated with the localization
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xyzlocalized_entitybrain_region_idbrain_region_lookup_methodbrain_region_distance_mmvoxel_label_idvoxel_labelx_rawy_rawz_rawbrain_region
id
0-18.5-2.7516.750F1_(4)nearest_neighbor0.6651F2_(6DR/6DC)agranular frontal area F2 (or 6DR/6DC)-18.563368-2.217.118557agranular frontal area F1 (or area 4)
MEBRAINSAtlasCoordinates (AnatomicalCoordinatesTable)
description: Recording electrode coordinates in MEBRAINS 1.0 (population-based macaque template; Balan et al. 2024) atlas space (RAS orientation, mm; origin at the anterior commissure). Coordinates are derived by nonlinear warping of the author's D99 coordinate into MEBRAINS space. See https://doi.org/10.1162/imag_a_00077
method: COORDINATE ORIGIN (per-electrode x/y/z). The author-provided D99 coordinate (geometric transform by Dr. Robert Turner, no MRI registration) is warped into MEBRAINS space using the RheMAP pre-computed nonlinear composite warp (MEBRAINS_to_D99_CompositeWarp.nii.gz, applied in reverse for point transforms; RAS<->LPS conversion around the ANTs call; https://gin.g-node.org/ChrisKlink/RheMAP). The MEBRAINS coordinate inherits the uncertainty of the source D99 geometric transform and adds the RheMAP warp uncertainty on top. REGION ANNOTATION (brain_region_id and voxel_label_id columns). Computed by the conversion pipeline, not provided by the data author. 'voxel_label_id' is the raw MEBRAINS voxel label at the coordinate (honest atlas answer, numeric string form of the Julich Brain Macaque label ID). 'brain_region_id' is a curated M1-constrained label: if the raw voxel is 4a (301) or 4p (303), it is preserved (both are M1 subdivisions, anterior vs posterior); otherwise the nearest M1 voxel (4a or 4p) within 5 mm is chosen, with the distance in 'brain_region_distance_mm' and the flag in 'brain_region_lookup_method'. MEBRAINS has wider sulcal gaps than D99 due to post-mortem tissue processing, causing many electrodes to land in unlabeled voxels at the central sulcus fundus; the curation absorbs this. AUTHORITY. The curated label reflects the ICMS-verified motor identity of the recording site (see 'icms_threshold_uA' on the electrodes table). MEBRAINS is the only atlas of the three whose native parcellation subdivides M1, so the 4a/4p distinction is the finest-granularity M1 anatomical position this dataset carries. The M1-constrained curation is a dataset-specific convenience for dandiset 001636.
space (MEBRAINSSpace)
space_name: MEBRAINS
origin: Anterior commissure (AC). Horizontal plane approximately aligned to the Horsley-Clarke convention.
units: mm
orientation: RAS
columns
x
The x coordinate
y
The y coordinate
z
The z coordinate
localized_entity
A reference to the object being localized (e.g. electrode)
brain_region_id
Curated MEBRAINS M1 label (viewer-facing, numeric string form). '301' for 4a left (anterior M1), '303' for 4p left (posterior M1). 'outside' only if the electrode coordinate is > 5 mm from any MEBRAINS M1 voxel.
brain_region_lookup_method
How the curated brain_region_id was determined: 'exact' = the raw atlas voxel at this coordinate is already the atlas's native primary motor cortex (M1) label, so the raw voxel answer is used; 'nearest_neighbor' = the raw voxel was non-M1 or unlabeled, so the label was set to the nearest M1 voxel within 5 mm (distance in brain_region_distance_mm); 'no_m1_within_5mm' = no M1 voxel within the 5 mm cap, brain_region_id is 'outside'.
brain_region_distance_mm
Euclidean distance in mm from the electrode coordinate to the nearest atlas-native M1 voxel. 0.0 when brain_region_lookup_method is 'exact'; positive float for 'nearest_neighbor'; NaN for 'no_m1_within_5mm'.
voxel_label_id
Raw atlas voxel label ID at this coordinate (no interpolation). Distinct from brain_region_id, which is curated to the atlas's native M1 label; voxel_label_id is whatever the atlas says at that voxel. Special values: 'unlabeled' when the voxel is inside the template but carries label=0 (sulcal-gap artifact or simply unparcellated tissue); 'outside_volume' when the coordinate falls outside the template's bounding box.
voxel_label
Full-name form of voxel_label_id. Special values: 'unlabeled voxel (label=0)' and 'outside template volume'.
x_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
y_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
z_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
brain_region
The brain region associated with the localization
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xyzlocalized_entitybrain_region_idbrain_region_lookup_methodbrain_region_distance_mmvoxel_label_idvoxel_labelx_rawy_rawz_rawbrain_region
id
0-18.7525-5.53838715.9695450303nearest_neighbor0.2617307F4d left-18.802692-5.07861716.40014p left
NMTv2AtlasCoordinates (AnatomicalCoordinatesTable)
description: Recording electrode coordinates in NMT v2.0-sym (NIMH Macaque Template, symmetric) atlas space (RAS orientation, mm; origin at ear bar zero). Coordinates are derived by nonlinear warping of the author's D99 coordinate into NMT space. See https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/nonhuman/macaque_tempatl/template_nmtv2.html
method: COORDINATE ORIGIN (per-electrode x/y/z). The author-provided D99 coordinate (geometric transform by Dr. Robert Turner, no MRI registration) is warped into NMT v2.0-sym space using the RheMAP pre-computed nonlinear composite warp (NMTv2.0-sym_to_D99_CompositeWarp.nii.gz, applied in reverse for point transforms; RAS<->LPS conversion around the ANTs call; Sirmpilatze & Klink 2020, DOI:10.5281/zenodo.4748589). The NMT coordinate inherits the uncertainty of the source D99 geometric transform and adds the RheMAP warp uncertainty on top. REGION ANNOTATION (brain_region_id and voxel_label_id columns). Computed by the conversion pipeline, not provided by the data author. 'voxel_label_id' is the raw CHARM level-4 voxel label at the coordinate (honest atlas answer). 'brain_region_id' is a curated M1-constrained label: if the raw voxel is M1 (CHARM level-4 index 79, primary motor cortex), it is preserved; otherwise the nearest M1 voxel within 5 mm is chosen, with the distance in 'brain_region_distance_mm' and the flag in 'brain_region_lookup_method'. The CHARM level-4 parcellation does not subdivide M1 (M1 remains a single label at every CHARM level 4-6), so 'brain_region_id' always reads 'M1' on this dataset. Many electrodes land in the central sulcus fundus, where post-mortem tissue shrinkage opens unlabeled gaps in the template that do not exist in vivo; the curation absorbs this artifact. AUTHORITY. The curated label reflects the ICMS-verified motor identity of the recording site (see 'icms_threshold_uA' on the electrodes table). Unconstrained nearest-neighbor (snapping to any labeled region) was tested and rejected: it mislabeled 100% of Leu electrodes as somatosensory cortex, because the S1 bank of the sulcal gap is geometrically closer in the template but functionally wrong for these ICMS-confirmed M1 recordings. The M1-constrained curation is a dataset-specific convenience for dandiset 001636.
space (NMTv2Space)
space_name: NMTv2
origin: Ear bar zero (EBZ): intersection of the midsagittal plane and the interaural line. Horizontal plane aligned to the Horsley-Clarke stereotaxic convention.
units: mm
orientation: RAS
columns
x
The x coordinate
y
The y coordinate
z
The z coordinate
localized_entity
A reference to the object being localized (e.g. electrode)
brain_region_id
Curated CHARM M1 label (viewer-facing). Always 'M1' unless the electrode coordinate is > 5 mm from any CHARM M1 voxel, in which case 'outside'.
brain_region_lookup_method
How the curated brain_region_id was determined: 'exact' = the raw atlas voxel at this coordinate is already the atlas's native primary motor cortex (M1) label, so the raw voxel answer is used; 'nearest_neighbor' = the raw voxel was non-M1 or unlabeled, so the label was set to the nearest M1 voxel within 5 mm (distance in brain_region_distance_mm); 'no_m1_within_5mm' = no M1 voxel within the 5 mm cap, brain_region_id is 'outside'.
brain_region_distance_mm
Euclidean distance in mm from the electrode coordinate to the nearest atlas-native M1 voxel. 0.0 when brain_region_lookup_method is 'exact'; positive float for 'nearest_neighbor'; NaN for 'no_m1_within_5mm'.
voxel_label_id
Raw atlas voxel label ID at this coordinate (no interpolation). Distinct from brain_region_id, which is curated to the atlas's native M1 label; voxel_label_id is whatever the atlas says at that voxel. Special values: 'unlabeled' when the voxel is inside the template but carries label=0 (sulcal-gap artifact or simply unparcellated tissue); 'outside_volume' when the coordinate falls outside the template's bounding box.
voxel_label
Full-name form of voxel_label_id. Special values: 'unlabeled voxel (label=0)' and 'outside template volume'.
x_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
y_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
z_raw
Raw (uncurated) atlas-space coordinate component (mm). For D99 this is the output of the author's chamber-to-D99 geometric transform (no MRI registration). For NMT and MEBRAINS this is the RheMAP warp of that raw D99 point. Distinct from the curated x / y / z columns, which hold the RheMAP warp of the nearest F1_(4) voxel center to the raw D99 coordinate (within a 5 mm cap); the curated values are what the dandi-atlas viewer renders on the map. Equal to the curated value when the raw D99 coord lies exactly on an F1 voxel center.
brain_region
The brain region associated with the localization
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xyzlocalized_entitybrain_region_idbrain_region_lookup_methodbrain_region_distance_mmvoxel_label_idvoxel_labelx_rawy_rawz_rawbrain_region
id
0-18.06746713.47280530.7835540M1nearest_neighbor0.7384PMpremotor_cortex-18.16769813.94363231.249699primary_motor_cortex
spaces
D99v2 (D99v2Space)
space_name: D99v2
origin: Anterior commissure (AC). Horizontal plane aligned to the AC-PC line (anterior commissure to posterior commissure).
units: mm
orientation: RAS
MEBRAINS (MEBRAINSSpace)
space_name: MEBRAINS
origin: Anterior commissure (AC). Horizontal plane approximately aligned to the Horsley-Clarke convention.
units: mm
orientation: RAS
NMTv2 (NMTv2Space)
space_name: NMTv2
origin: Ear bar zero (EBZ): intersection of the midsagittal plane and the interaural line. Horizontal plane aligned to the Horsley-Clarke stereotaxic convention.
units: mm
orientation: RAS
trials (TimeIntervals)
description: experimental trials
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
center_target_appearance_time
Time (in seconds) when center target appeared on screen, cueing the monkey to align cursor with center position and hold
lateral_target_appearance_time
Time (in seconds) when peripheral target appeared (go cue), signaling the monkey to initiate movement toward the flexion or extension target
cursor_departure_time
Time (in seconds) when cursor exited the center target zone following the go cue
reward_time
Time (in seconds) of liquid reward delivery for successful target acquisition and hold
target_amplitude_degrees
Distance from center (home) position to target center in degrees of joint angle (10, 20, or 30). Determines required movement amplitude for target capture. During individual sessions, target amplitude is either always 20 degrees or varies randomly among 10, 20, and 30 degrees.
isolation_monitoring_stim_time
Time (in seconds) of antidromic stimulation pulse delivered during trial to monitor unit isolation. Single pulses were delivered early in some trials (~245 ms after trial start) to activate otherwise silent neurons and verify recording isolation. NaN indicates no stimulation was delivered in that trial
isolation_monitoring_stim_site
Anatomical target of antidromic stimulation for isolation monitoring. Values: 'Str' (striatum), 'Thal' (thalamus), 'Ped' (cerebral peduncle), or empty string if no stimulation was delivered in that trial
movement_type
flexion or extension
torque_perturbation_type
direction of torque perturbation causing muscle stretch (flexion/extension/none)
torque_perturbation_onset_time
Time (in seconds) of unpredictable torque impulse onset (0.1 Nm, 50 ms) applied to manipulandum 1-2 s after center target capture
derived_movement_onset_time
Time (in seconds) of movement onset from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_movement_end_time
Time (in seconds) of movement end from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_peak_velocity
Maximum angular velocity (in degrees/second) of elbow joint during the movement, from kinematic analysis
derived_peak_velocity_time
Time (in seconds) at which peak angular velocity occurred during the movement
derived_movement_amplitude
Total angular displacement (in degrees) of elbow joint from movement onset to end position
derived_end_position
Final elbow joint angle (in degrees) at movement end, relative to center position where 0 is the neutral hold position
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start_timestop_timecenter_target_appearance_timelateral_target_appearance_timecursor_departure_timereward_timetarget_amplitude_degreesisolation_monitoring_stim_timeisolation_monitoring_stim_sitemovement_typetorque_perturbation_typetorque_perturbation_onset_timederived_movement_onset_timederived_movement_end_timederived_peak_velocityderived_peak_velocity_timederived_movement_amplitudederived_end_position
id
00.0009.6194.7267.5267.6558.889NaNNaNflexionnoneNaN7.8168.15053.8806917.9369.56697516.760761
112.61922.57114.98119.82820.41721.176NaNNaNextensionflexion16.67720.15420.66590.53158220.440-27.823461-21.324000
225.57137.48327.96035.34435.92436.759NaNNaNextensionextension29.65635.70936.17853.24170535.969-12.224196-16.109740
340.48347.29642.33145.18045.72946.568NaNNaNflexionnoneNaN45.53446.18463.38704045.71419.07340018.600860

... and 76 more row(s).

invalid_times (TimeIntervals)
description: time intervals to be removed from analysis
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
tags
user-defined tags
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start_timestop_timetags
id
09.61912.619[artificial_inter_trial_gap, not_recorded]
122.57125.571[artificial_inter_trial_gap, not_recorded]
237.48340.483[artificial_inter_trial_gap, not_recorded]
347.29650.296[artificial_inter_trial_gap, not_recorded]

... and 75 more row(s).

units (Units)
description: Autogenerated by NWBFile
waveform_unit: volts
columns
neuron_projection_type
Classification of layer 5 M1 neurons based on axonal projection target, determined through antidromic activation (electrical stimulation at target sites evoking backward-traveling spikes). Values: | 'pyramidal_tract_neuron': Large layer 5b neurons projecting to spinal cord via corticospinal tract, identified by cerebral peduncle stimulation (latency 0.6-2.5 ms) | 'corticostriatal_neuron': Intratelencephalic-type neurons projecting to posterolateral putamen, identified by striatal stimulation (latency 2.0-20 ms) | 'no_response': Antidromic stimulation attempted at one or both sites but neuron did not respond | 'not_tested': Antidromic identification was not performed for this neuron | 'pyramidal_tract_and_corticostriatal_neuron': Dual-projecting neuron responding to both sites | 'pyramidal_tract_and_thalamic_neuron': Neuron projecting to both pyramidal tract and thalamus | 'thalamic_projection_neuron': Neuron projecting to thalamus | 'subthalamic_projection_neuron': Neuron projecting to subthalamic nucleus | 'subthalamic_and_corticostriatal_neuron': Neuron projecting to both STN and striatum | 'subthalamic_and_thalamic_neuron': Neuron projecting to both STN and thalamus | 'pyramidal_tract_and_subthalamic_neuron': Neuron projecting to both pyramidal tract and STN
antidromic_stimulation_sites
Anatomical sites where antidromic stimulation was delivered via chronically implanted electrodes to identify neuron projection targets. Values: | 'cerebral_peduncle': Stimulation of corticospinal tract fibers to identify pyramidal tract neurons (100-400 uA, 0.2 ms biphasic pulses) | 'posterolateral_striatum': Stimulation of putamen to identify corticostriatal neurons (100-600 uA, 0.2 ms biphasic pulses) | 'cerebral_peduncle_and_posterolateral_striatum': Both stimulation sites were tested | 'subthalamic_nucleus': Stimulation of subthalamic nucleus (STN) | 'cerebral_peduncle_and_subthalamic_nucleus': Both peduncle and STN sites were tested | '': Empty string indicates antidromic testing was not attempted for this neuron
antidromic_latency_ms
Latency from stimulation to antidromic spike arrival at the recording electrode (in milliseconds) for the primary stimulation site. Reflects axon conduction velocity: PTNs have shorter latencies (0.6-2.5 ms) due to large, fast-conducting axons; CSNs have longer latencies (2.0-20 ms) due to smaller axons. Classification required fixed latency, high-frequency following (>200 Hz), and collision test confirmation.
antidromic_threshold
Minimum stimulation current (in microamps) required to reliably evoke antidromic activation at the primary stimulation site. Lower thresholds indicate electrode proximity to the axon or larger axon diameter. Typical ranges: 100-400 uA for cerebral peduncle, 100-600 uA for striatum.
antidromic_latency_2_ms
Antidromic response latency (in milliseconds) from secondary stimulation site, for neurons with dual projections (e.g., pyramidal_tract_and_corticostriatal_neuron). NaN if not applicable.
antidromic_threshold_2
Minimum stimulation current (in microamps) for antidromic activation at the secondary stimulation site. NaN if neuron does not have dual projections or secondary site was not tested.
receptive_field_location
Body region comprising this neuron's somatosensory receptive field, determined through qualitative sensorimotor examination. The experimenter manually manipulated the monkey's contralateral arm (passive joint movement, skin contact) while monitoring neural activity on an audio speaker. Values: | 'hand': Receptive field on the hand | 'wrist': Receptive field at the wrist joint | 'elbow': Receptive field at the elbow joint | 'forearm': Receptive field on the forearm | 'shoulder': Receptive field at the shoulder | 'finger': Receptive field on digit(s) | Combinations (e.g., 'hand / wrist'): Neuron responds to multiple body regions | 'no_response': Sensory testing performed but no response detected | 'not_tested': Sensory mapping not performed (limited recording time)
receptive_field_stimulus
Specific sensory stimulus or manipulation that activated this neuron during qualitative receptive field mapping (qualitative observer assessment, not quantified). Values: | 'ext': Joint extension (proprioceptive) | 'flex': Joint flexion (proprioceptive) | 'pron': Forearm pronation (proprioceptive) | 'sup': Forearm supination (proprioceptive) | 'light touch': Light tactile stimulation | 'palm probe': Pressure on palm | 'finger tap': Tapping on digit(s) | 'cut stim': Cutaneous (skin) stimulation | 'active': Firing during voluntary movement | 'active grip': Firing during voluntary grip | 'active hand': Firing during voluntary hand movement | Combinations (e.g., 'finger ext & elbow ext'): Multi-joint responses | 'NR': No response to any stimulus tested | 'NT': Sensory testing not performed | '': Testing data not recorded
unit_name
Unit identifier extracted from source MATLAB filename. When multiple well-isolated single units were recorded simultaneously during the same electrode penetration, each was saved separately. Most sessions contain a single unit. Values: | 1: First (or only) unit from this session, from file '{session}.1.mat' | 2: Second unit from this session, from file '{session}.2.mat'
unit_also_in_session_id
Cross-reference to another NWB session containing recordings from this same neuron, identified by the experimenter based on waveform similarity and recording location during the same electrode penetration. Empty string if this unit was only recorded in one session. Format matches session_id: {subject_id}++{fname}++{Pre|Post}MPTP++Depth{um}um++{YYYYMMDD}. This enables tracking individual neurons across multiple recording sessions or experimental conditions.
is_post_mptp
Boolean indicating parkinsonian state at time of recording. MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) was administered once via unilateral left internal carotid artery injection (0.5 mg/kg), inducing selective dopaminergic cell death in the ipsilateral substantia nigra and producing stable contralateral hemiparkinsonian symptoms. Comparing pre/post recordings reveals how parkinsonism affects cortical motor encoding, particularly in pyramidal tract neurons. Values: | False: Pre-MPTP baseline recording (healthy motor control) | True: Post-MPTP recording after parkinsonian symptoms stabilized (bradykinesia, rigidity, movement initiation deficits)
spike_times
the spike times for each unit in seconds
obs_intervals
the observation intervals for each unit
electrodes
the electrodes that each spike unit came from
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neuron_projection_typeantidromic_stimulation_sitesantidromic_latency_msantidromic_thresholdantidromic_latency_2_msantidromic_threshold_2receptive_field_locationreceptive_field_stimulusunit_nameunit_also_in_session_idis_post_mptpspike_timesobs_intervalselectrodes
id
0pyramidal_tract_neuroncerebral_peduncle1.80550.0NaNNaNno_response1V++v5812++PostMPTP++Depth18300um++20000331True[0.248, 0.524, 0.586, 0.653, 0.952, 1.216, 1.368, 1.431, 1.582, 1.638, 1.683, 1.73, 1.773, 1.829, 2.074, 2.77, 4.469, 4.559, 4.601, 4.79, 5.628, 5.732, 5.868, 5.938, 6.029, 6.096, 6.391, 6.438, 6.482, 6.529, 6.579, 6.634, 6.692, 6.775, 6.843, 6.887, 6.976, 7.036, 7.153, 7.24, 7.287, 7.357, 7.407, 7.472, 8.107, 9.132, 9.179, 9.256, 9.283, 9.316, 9.354, 9.4, 9.454, 9.491, 9.514, 9.534, 9.569, 9.594, 12.697, 12.770999999999999, 13.294, 13.908, 14.062999999999999, 14.145, 14.258, 14.321, 14.4, 14.464, 14.495, 14.54, 14.592, 14.682, 14.775, 14.857, 15.125, 15.562999999999999, 15.911, 15.99, 16.201999999999998, 16.308, 16.439, 16.521, 16.583, 16.669, 17.088, 17.22, 17.317999999999998, 17.38, 17.455, 17.721, 17.851, 17.959, 18.093, 18.293, 18.347, 18.378999999999998, 18.386, 18.409, 18.441, 18.464, ...][[0.0, 9.619], [12.619, 22.570999999999998], [25.570999999999998, 37.483], [40.483, 47.296], [50.29599999999999, 59.41199999999999], [62.41199999999999, 71.92099999999999], [74.92099999999999, 82.00899999999999], [85.00899999999999, 94.09799999999998], [97.09799999999998, 105.58099999999999], [108.58099999999999, 117.61899999999999], [120.61899999999999, 128.992], [131.992, 140.081], [143.081, 150.743], [153.743, 162.84799999999998], [165.84799999999998, 174.35999999999999], [177.35999999999999, 183.93599999999998], [186.93599999999998, 196.134], [199.134, 206.88199999999998], [209.88199999999998, 217.77499999999998], [220.77499999999998, 229.97299999999998], [232.97299999999998, 241.86999999999998], [244.86999999999998, 253.13899999999998], [256.13899999999995, 264.61099999999993], [267.61099999999993, 275.85099999999994], [278.85099999999994, 286.3159999999999], [289.3159999999999, 298.4489999999999], [301.4489999999999, 310.3779999999999], [313.3779999999999, 320.13299999999987], [323.13299999999987, 331.9299999999999], [334.9299999999999, 343.8159999999999], [346.8159999999999, 353.91099999999994], [356.91099999999994, 366.08799999999997], [369.08799999999997, 377.941], [380.941, 389.736], [392.736, 401.062], [404.062, 412.262], [415.262, 422.957], [425.957, 438.36199999999997], [441.36199999999997, 449.36499999999995], [452.36499999999995, 460.29599999999994], [463.29599999999994, 470.36699999999996], [473.36699999999996, 480.32699999999994], [483.32699999999994, 491.9409999999999], [494.9409999999999, 503.4769999999999], [506.4769999999999, 514.8779999999999], [517.8779999999999, 527.7839999999999], [530.7839999999999, 539.1929999999999], [542.1929999999999, 549.5099999999999], [552.5099999999999, 560.5759999999999], [563.5759999999999, 571.8159999999999], [574.8159999999999, 582.1859999999999], [585.1859999999999, 594.603], [597.603, 606.351], [609.351, 616.658], [619.658, 628.812], [631.812, 642.25], [645.25, 653.292], [656.292, 670.683], [673.683, 682.579], [685.579, 692.4169999999999], [695.4169999999999, 705.3449999999999], [708.3449999999999, 719.1399999999999], [722.1399999999999, 730.0409999999998], [733.0409999999998, 742.0029999999998], [745.0029999999998, 753.9309999999998], [756.9309999999998, 765.0589999999999], [768.0589999999999, 776.1509999999998], [779.1509999999998, 787.5959999999999], [790.5959999999999, 798.5819999999999], [801.5819999999999, 810.5159999999998], [813.5159999999998, 822.1919999999999], [825.1919999999999, 832.7279999999998], [835.7279999999998, 843.6679999999999], [846.6679999999999, 858.8649999999999], [861.8649999999999, 869.762], [872.762, 881.2739999999999], [884.2739999999999, 894.2879999999999], [897.2879999999999, 907.3569999999999], [910.3569999999999, 918.6379999999998], [921.6379999999998, 930.3309999999998]][0]
1pyramidal_tract_neuron1.85360.0NaNNaNno_response2True[0.025, 0.078, 0.13, 0.172, 0.212, 0.26, 0.285, 0.318, 0.389, 0.428, 0.491, 0.515, 0.618, 0.638, 0.714, 0.731, 0.78, 0.875, 0.895, 0.94, 0.974, 0.977, 1.07, 1.13, 1.15, 1.182, 1.208, 1.242, 1.274, 1.307, 1.309, 1.344, 1.37, 1.43, 1.489, 1.537, 1.559, 1.612, 1.633, 1.646, 1.684, 1.698, 1.724, 1.75, 1.778, 1.806, 1.822, 1.85, 1.888, 1.91, 1.945, 1.978, 2.003, 2.03, 2.067, 2.101, 2.134, 2.142, 2.176, 2.224, 2.249, 2.291, 2.324, 2.358, 2.387, 2.416, 2.448, 2.469, 2.509, 2.549, 2.6, 2.611, 2.64, 2.676, 2.71, 2.721, 2.754, 2.786, 2.819, 2.859, 2.895, 2.948, 2.975, 3.009, 3.039, 3.048, 3.075, 3.104, 3.139, 3.162, 3.191, 3.195, 3.222, 3.274, 3.362, 3.404, 3.443, 3.468, 3.501, 3.539, ...][[0.0, 9.619], [12.619, 22.570999999999998], [25.570999999999998, 37.483], [40.483, 47.296], [50.29599999999999, 59.41199999999999], [62.41199999999999, 71.92099999999999], [74.92099999999999, 82.00899999999999], [85.00899999999999, 94.09799999999998], [97.09799999999998, 105.58099999999999], [108.58099999999999, 117.61899999999999], [120.61899999999999, 128.992], [131.992, 140.081], [143.081, 150.743], [153.743, 162.84799999999998], [165.84799999999998, 174.35999999999999], [177.35999999999999, 183.93599999999998], [186.93599999999998, 196.134], [199.134, 206.88199999999998], [209.88199999999998, 217.77499999999998], [220.77499999999998, 229.97299999999998], [232.97299999999998, 241.86999999999998], [244.86999999999998, 253.13899999999998], [256.13899999999995, 264.61099999999993], [267.61099999999993, 275.85099999999994], [278.85099999999994, 286.3159999999999], [289.3159999999999, 298.4489999999999], [301.4489999999999, 310.3779999999999], [313.3779999999999, 320.13299999999987], [323.13299999999987, 331.9299999999999], [334.9299999999999, 343.8159999999999], [346.8159999999999, 353.91099999999994], [356.91099999999994, 366.08799999999997], [369.08799999999997, 377.941], [380.941, 389.736], [392.736, 401.062], [404.062, 412.262], [415.262, 422.957], [425.957, 438.36199999999997], [441.36199999999997, 449.36499999999995], [452.36499999999995, 460.29599999999994], [463.29599999999994, 470.36699999999996], [473.36699999999996, 480.32699999999994], [483.32699999999994, 491.9409999999999], [494.9409999999999, 503.4769999999999], [506.4769999999999, 514.8779999999999], [517.8779999999999, 527.7839999999999], [530.7839999999999, 539.1929999999999], [542.1929999999999, 549.5099999999999], [552.5099999999999, 560.5759999999999], [563.5759999999999, 571.8159999999999], [574.8159999999999, 582.1859999999999], [585.1859999999999, 594.603], [597.603, 606.351], [609.351, 616.658], [619.658, 628.812], [631.812, 642.25], [645.25, 653.292], [656.292, 670.683], [673.683, 682.579], [685.579, 692.4169999999999], [695.4169999999999, 705.3449999999999], [708.3449999999999, 719.1399999999999], [722.1399999999999, 730.0409999999998], [733.0409999999998, 742.0029999999998], [745.0029999999998, 753.9309999999998], [756.9309999999998, 765.0589999999999], [768.0589999999999, 776.1509999999998], [779.1509999999998, 787.5959999999999], [790.5959999999999, 798.5819999999999], [801.5819999999999, 810.5159999999998], [813.5159999999998, 822.1919999999999], [825.1919999999999, 832.7279999999998], [835.7279999999998, 843.6679999999999], [846.6679999999999, 858.8649999999999], [861.8649999999999, 869.762], [872.762, 881.2739999999999], [884.2739999999999, 894.2879999999999], [897.2879999999999, 907.3569999999999], [910.3569999999999, 918.6379999999998], [921.6379999999998, 930.3309999999998]][0]
experiment_description: Investigation of motor cortical dysfunction in parkinsonism using single-unit recordings during \n", + "visuomotor motor tasks. MPTP-induced parkinsonian model used to study changes in pyramidal tract \n", + "and corticostriatal neuron activity patterns.\n", + "
session_id: Venus++v5811++PostMPTP++Depth18300um++20000331
lab: Turner Lab
institution: University of Pittsburgh
notes: TEMPORAL DATA STRUCTURE: This dataset was converted from trialized MATLAB files where all timestamps were relative to individual trial starts. Inter-trial intervals were NOT recorded in the source data.\n", + "\n", + "SESSION TIMING: Recording dates are accurate from experimental logs, but precise session start times within each day are not available. session_start_time is set to midnight (Pittsburgh timezone) with 2-hour systematic offsets for multiple sessions recorded on the same date, ordered by file sequence.\n", + "\n", + "ARTIFICIAL GAPS: Trials are separated by fixed 3-second gaps for temporal organization. These gaps are placeholders and should NOT be interpreted as actual behavioral timing.\n", + "\n", + "DATA VALIDITY ANNOTATIONS:\n", + "1. invalid_times table: Lists all inter-trial gaps (tagged 'artificial_inter_trial_gap'). Use this to exclude gap periods from continuous data analysis (LFP, EMG, kinematics).\n", + "2. Units obs_intervals: Each unit has observation intervals specifying when it was recorded. Spikes only occurred during these intervals; gaps represent 'not observable', not 'silent'.\n", + "\n", + "ANALOG vs TRIAL TIMING: Analog data (manipulandum, EMG, LFP) ends approximately 5ms before the trial stop_time because the analog recorder stopped slightly before the behavioral software marked the trial as ended. Some spikes can occur in this gap. Trial boundaries (start_time, stop_time, obs_intervals, invalid_times) use the authoritative Events.end timestamp, not analog data length.
pharmacology: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) administered at 0.5 mg/kg via unilateral\n", + "left internal carotid artery under general anesthesia to induce hemiparkinsonian syndrome.\n", + "Post-MPTP recordings obtained >30 days following MPTP administration (range: 36-117 days).\n", + "Stereological analysis confirmed 67% loss of tyrosine hydroxylase-positive neurons in substantia\n", + "nigra pars compacta of MPTP-treated hemisphere, with contralateral hemisphere showing normal\n", + "dopaminergic neuron counts.\n", + "\n", + "\n", + "Session-specific information: This recording was obtained AFTER MPTP administration (parkinsonian condition).
source_script: Created using NeuroConv v0.9.4
source_script_file_name: /home/heberto/development/work_repos/neuroconv/src/neuroconv/basedatainterface.py
surgery: Aseptic surgery under Isoflurane inhalation anesthesia. Cylindrical stainless steel recording\n", + "chamber implanted with stereotaxic guidance over left primary motor cortex (M1) and posterior\n", + "putamen, oriented 35° to the coronal plane. Custom floating stimulating electrodes (Teflon-coated\n", + "PtIr microwires, 50-μm diameter) implanted in posterior putamen (3 electrodes) and prepontine\n", + "cerebral peduncle (1 electrode) for antidromic identification of corticostriatal neurons and\n", + "pyramidal tract neurons. A macroelectrode was additionally implanted in VL thalamus; orthodromic\n", + "and antidromic responses elicited through this electrode are reported in the recorded data.\n", + "Prophylactic antibiotics and analgesics administered perioperatively.\n", + "
" + ], + "text/plain": [ + "root pynwb.file.NWBFile at 0x128623677799024\n", + "Fields:\n", + " acquisition: {\n", + " EMGDeltoidPosterior ,\n", + " EMGTrapezius ,\n", + " EMGTricepsLateralis ,\n", + " EMGTricepsLongus1 ,\n", + " EMGTricepsLongus2 ,\n", + " ElbowAngle ,\n", + " ElbowTorque \n", + " }\n", + " devices: {\n", + " DeviceMicroelectrodeRecording ,\n", + " DeviceMicrowirePeduncleStimulation ,\n", + " DeviceMicrowirePutamenStimulation1 ,\n", + " DeviceMicrowirePutamenStimulation2 ,\n", + " DeviceMicrowirePutamenStimulation3 ,\n", + " DeviceMicrowireSTNStimulation ,\n", + " DeviceMicrowireThalamicStimulation ,\n", + " TorqueMotor \n", + " }\n", + " electrode_groups: {\n", + " ElectrodeGroupMicroelectrodeRecording \n", + " }\n", + " electrodes: electrodes \n", + " experiment_description: Investigation of motor cortical dysfunction in parkinsonism using single-unit recordings during \n", + "visuomotor motor tasks. MPTP-induced parkinsonian model used to study changes in pyramidal tract \n", + "and corticostriatal neuron activity patterns.\n", + "\n", + " experimenter: ['Turner, Robert S.' 'Pasquereau, Benjamin']\n", + " file_create_date: [datetime.datetime(2026, 4, 23, 14, 9, 49, 441110, tzinfo=tzoffset(None, -21600))]\n", + " identifier: 720f8499-dc14-47be-a640-45f3c83d8a33\n", + " institution: University of Pittsburgh\n", + " intervals: {\n", + " AntidromicSweepsIntervals ,\n", + " invalid_times ,\n", + " trials \n", + " }\n", + " invalid_times: invalid_times \n", + " keywords: \n", + " lab: Turner Lab\n", + " lab_meta_data: {\n", + " hed_schema ,\n", + " localization \n", + " }\n", + " notes: TEMPORAL DATA STRUCTURE: This dataset was converted from trialized MATLAB files where all timestamps were relative to individual trial starts. Inter-trial intervals were NOT recorded in the source data.\n", + "\n", + "SESSION TIMING: Recording dates are accurate from experimental logs, but precise session start times within each day are not available. session_start_time is set to midnight (Pittsburgh timezone) with 2-hour systematic offsets for multiple sessions recorded on the same date, ordered by file sequence.\n", + "\n", + "ARTIFICIAL GAPS: Trials are separated by fixed 3-second gaps for temporal organization. These gaps are placeholders and should NOT be interpreted as actual behavioral timing.\n", + "\n", + "DATA VALIDITY ANNOTATIONS:\n", + "1. invalid_times table: Lists all inter-trial gaps (tagged 'artificial_inter_trial_gap'). Use this to exclude gap periods from continuous data analysis (LFP, EMG, kinematics).\n", + "2. Units obs_intervals: Each unit has observation intervals specifying when it was recorded. Spikes only occurred during these intervals; gaps represent 'not observable', not 'silent'.\n", + "\n", + "ANALOG vs TRIAL TIMING: Analog data (manipulandum, EMG, LFP) ends approximately 5ms before the trial stop_time because the analog recorder stopped slightly before the behavioral software marked the trial as ended. Some spikes can occur in this gap. Trial boundaries (start_time, stop_time, obs_intervals, invalid_times) use the authoritative Events.end timestamp, not analog data length.\n", + " pharmacology: MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) administered at 0.5 mg/kg via unilateral\n", + "left internal carotid artery under general anesthesia to induce hemiparkinsonian syndrome.\n", + "Post-MPTP recordings obtained >30 days following MPTP administration (range: 36-117 days).\n", + "Stereological analysis confirmed 67% loss of tyrosine hydroxylase-positive neurons in substantia\n", + "nigra pars compacta of MPTP-treated hemisphere, with contralateral hemisphere showing normal\n", + "dopaminergic neuron counts.\n", + "\n", + "\n", + "Session-specific information: This recording was obtained AFTER MPTP administration (parkinsonian condition).\n", + " processing: {\n", + " antidromic_identification ,\n", + " behavior ,\n", + " ecephys \n", + " }\n", + " related_publications: ['https://doi.org/10.1093/cercor/bhq217'\n", + " 'https://doi.org/10.3389/fnsys.2013.00098'\n", + " 'https://doi.org/10.1093/brain/awv312']\n", + " session_description: Single-unit recordings from primary motor cortex (M1) of MPTP-treated parkinsonian macaque monkeys\n", + "performing a visuomotor step-tracking task with flexion/extension movements of the elbow (Monkey V)\n", + "or wrist (Monkey L) joint. Data includes spike times from pyramidal tract neurons (PTNs) and\n", + "corticostriatal neurons (CSNs) identified through antidromic stimulation. Task involves center hold,\n", + "target presentation, movement execution, and reward phases with optional torque perturbations on\n", + "random trials.\n", + " MPTP condition: Post. Cell types: PED, PED.\n", + " session_id: Venus++v5811++PostMPTP++Depth18300um++20000331\n", + " session_start_time: 2000-03-31 00:00:00-05:00\n", + " source_script: Created using NeuroConv v0.9.4\n", + " source_script_file_name: /home/heberto/development/work_repos/neuroconv/src/neuroconv/basedatainterface.py\n", + " subject: subject pynwb.file.Subject at 0x128623676453856\n", + "Fields:\n", + " age: P4Y/P15Y\n", + " age__reference: birth\n", + " description: MPTP-treated parkinsonian female rhesus macaque (Monkey V / Venus) used for motor cortex\n", + "electrophysiology during a step-tracking task performed with the elbow joint.\n", + "\n", + " sex: F\n", + " species: Macaca mulatta\n", + " subject_id: Venus\n", + " weight: 4.8 kg\n", + "\n", + " surgery: Aseptic surgery under Isoflurane inhalation anesthesia. Cylindrical stainless steel recording\n", + "chamber implanted with stereotaxic guidance over left primary motor cortex (M1) and posterior\n", + "putamen, oriented 35° to the coronal plane. Custom floating stimulating electrodes (Teflon-coated\n", + "PtIr microwires, 50-μm diameter) implanted in posterior putamen (3 electrodes) and prepontine\n", + "cerebral peduncle (1 electrode) for antidromic identification of corticostriatal neurons and\n", + "pyramidal tract neurons. A macroelectrode was additionally implanted in VL thalamus; orthodromic\n", + "and antidromic responses elicited through this electrode are reported in the recorded data.\n", + "Prophylactic antibiotics and analgesics administered perioperatively.\n", + "\n", + " timestamps_reference_time: 2000-03-31 00:00:00-05:00\n", + " trials: trials \n", + " units: units " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding the Trial Structure\n", + "\n", + "The behavioral task is a visuomotor step-tracking paradigm where monkeys make rapid elbow flexion/extension movements to capture visual targets. Each trial follows a stereotyped sequence of events that we can examine through the trials table." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

trials (TimeIntervals)

description: experimental trials
columns
start_time
Start time of epoch, in seconds
stop_time
Stop time of epoch, in seconds
center_target_appearance_time
Time (in seconds) when center target appeared on screen, cueing the monkey to align cursor with center position and hold
lateral_target_appearance_time
Time (in seconds) when peripheral target appeared (go cue), signaling the monkey to initiate movement toward the flexion or extension target
cursor_departure_time
Time (in seconds) when cursor exited the center target zone following the go cue
reward_time
Time (in seconds) of liquid reward delivery for successful target acquisition and hold
target_amplitude_degrees
Distance from center (home) position to target center in degrees of joint angle (10, 20, or 30). Determines required movement amplitude for target capture. During individual sessions, target amplitude is either always 20 degrees or varies randomly among 10, 20, and 30 degrees.
isolation_monitoring_stim_time
Time (in seconds) of antidromic stimulation pulse delivered during trial to monitor unit isolation. Single pulses were delivered early in some trials (~245 ms after trial start) to activate otherwise silent neurons and verify recording isolation. NaN indicates no stimulation was delivered in that trial
isolation_monitoring_stim_site
Anatomical target of antidromic stimulation for isolation monitoring. Values: 'Str' (striatum), 'Thal' (thalamus), 'Ped' (cerebral peduncle), or empty string if no stimulation was delivered in that trial
movement_type
flexion or extension
torque_perturbation_type
direction of torque perturbation causing muscle stretch (flexion/extension/none)
torque_perturbation_onset_time
Time (in seconds) of unpredictable torque impulse onset (0.1 Nm, 50 ms) applied to manipulandum 1-2 s after center target capture
derived_movement_onset_time
Time (in seconds) of movement onset from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_movement_end_time
Time (in seconds) of movement end from post-hoc kinematic analysis; detection algorithm used position, velocity, and duration criteria (exact parameters not documented)
derived_peak_velocity
Maximum angular velocity (in degrees/second) of elbow joint during the movement, from kinematic analysis
derived_peak_velocity_time
Time (in seconds) at which peak angular velocity occurred during the movement
derived_movement_amplitude
Total angular displacement (in degrees) of elbow joint from movement onset to end position
derived_end_position
Final elbow joint angle (in degrees) at movement end, relative to center position where 0 is the neutral hold position
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start_timestop_timecenter_target_appearance_timelateral_target_appearance_timecursor_departure_timereward_timetarget_amplitude_degreesisolation_monitoring_stim_timeisolation_monitoring_stim_sitemovement_typetorque_perturbation_typetorque_perturbation_onset_timederived_movement_onset_timederived_movement_end_timederived_peak_velocityderived_peak_velocity_timederived_movement_amplitudederived_end_position
id
00.0009.6194.7267.5267.6558.889NaNNaNflexionnoneNaN7.8168.15053.8806917.9369.56697516.760761
112.61922.57114.98119.82820.41721.176NaNNaNextensionflexion16.67720.15420.66590.53158220.440-27.823461-21.324000
225.57137.48327.96035.34435.92436.759NaNNaNextensionextension29.65635.70936.17853.24170535.969-12.224196-16.109740
340.48347.29642.33145.18045.72946.568NaNNaNflexionnoneNaN45.53446.18463.38704045.71419.07340018.600860

... and 76 more row(s).

" + ], + "text/plain": [ + "trials pynwb.epoch.TimeIntervals at 0x128623676456448\n", + "Fields:\n", + " colnames: ['start_time' 'stop_time' 'center_target_appearance_time'\n", + " 'lateral_target_appearance_time' 'cursor_departure_time' 'reward_time'\n", + " 'target_amplitude_degrees' 'isolation_monitoring_stim_time'\n", + " 'isolation_monitoring_stim_site' 'movement_type'\n", + " 'torque_perturbation_type' 'torque_perturbation_onset_time'\n", + " 'derived_movement_onset_time' 'derived_movement_end_time'\n", + " 'derived_peak_velocity' 'derived_peak_velocity_time'\n", + " 'derived_movement_amplitude' 'derived_end_position']\n", + " columns: (\n", + " start_time ,\n", + " stop_time ,\n", + " center_target_appearance_time ,\n", + " lateral_target_appearance_time ,\n", + " cursor_departure_time ,\n", + " reward_time ,\n", + " target_amplitude_degrees ,\n", + " isolation_monitoring_stim_time ,\n", + " isolation_monitoring_stim_site ,\n", + " movement_type ,\n", + " torque_perturbation_type ,\n", + " torque_perturbation_onset_time ,\n", + " derived_movement_onset_time ,\n", + " derived_movement_end_time ,\n", + " derived_peak_velocity ,\n", + " derived_peak_velocity_time ,\n", + " derived_movement_amplitude ,\n", + " derived_end_position \n", + " )\n", + " description: experimental trials\n", + " id: id " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.trials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Trials are stored as a table in NWB. We can convert it to a pandas DataFrame to explore the structure of the trial events" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timestop_timecenter_target_appearance_timelateral_target_appearance_timecursor_departure_timereward_timetarget_amplitude_degreesisolation_monitoring_stim_timeisolation_monitoring_stim_sitemovement_typetorque_perturbation_typetorque_perturbation_onset_timederived_movement_onset_timederived_movement_end_timederived_peak_velocityderived_peak_velocity_timederived_movement_amplitudederived_end_position
id
20232.973241.870235.645240.279240.344241.146NaNNaNflexionextension237.342240.518240.88646.100655240.77011.82750020.418560
28323.133331.930325.658330.220330.568331.204NaNNaNflexionflexion327.354330.505330.86185.648640330.63413.73038919.713146
56645.250653.292648.667651.527651.872652.568NaNNaNflexionnoneNaN651.860652.30548.746997652.06711.31728718.277160
59685.579692.417687.862690.618691.067691.688NaNNaNflexionnoneNaN690.930691.376113.989846691.12117.97780020.119760
79921.638930.331924.268928.612928.996929.606NaNNaNflexionextension925.964928.904929.28593.519716929.04915.03264318.924560
\n", + "
" + ], + "text/plain": [ + " start_time stop_time center_target_appearance_time \\\n", + "id \n", + "20 232.973 241.870 235.645 \n", + "28 323.133 331.930 325.658 \n", + "56 645.250 653.292 648.667 \n", + "59 685.579 692.417 687.862 \n", + "79 921.638 930.331 924.268 \n", + "\n", + " lateral_target_appearance_time cursor_departure_time reward_time \\\n", + "id \n", + "20 240.279 240.344 241.146 \n", + "28 330.220 330.568 331.204 \n", + "56 651.527 651.872 652.568 \n", + "59 690.618 691.067 691.688 \n", + "79 928.612 928.996 929.606 \n", + "\n", + " target_amplitude_degrees isolation_monitoring_stim_time \\\n", + "id \n", + "20 NaN NaN \n", + "28 NaN NaN \n", + "56 NaN NaN \n", + "59 NaN NaN \n", + "79 NaN NaN \n", + "\n", + " isolation_monitoring_stim_site movement_type torque_perturbation_type \\\n", + "id \n", + "20 flexion extension \n", + "28 flexion flexion \n", + "56 flexion none \n", + "59 flexion none \n", + "79 flexion extension \n", + "\n", + " torque_perturbation_onset_time derived_movement_onset_time \\\n", + "id \n", + "20 237.342 240.518 \n", + "28 327.354 330.505 \n", + "56 NaN 651.860 \n", + "59 NaN 690.930 \n", + "79 925.964 928.904 \n", + "\n", + " derived_movement_end_time derived_peak_velocity \\\n", + "id \n", + "20 240.886 46.100655 \n", + "28 330.861 85.648640 \n", + "56 652.305 48.746997 \n", + "59 691.376 113.989846 \n", + "79 929.285 93.519716 \n", + "\n", + " derived_peak_velocity_time derived_movement_amplitude \\\n", + "id \n", + "20 240.770 11.827500 \n", + "28 330.634 13.730389 \n", + "56 652.067 11.317287 \n", + "59 691.121 17.977800 \n", + "79 929.049 15.032643 \n", + "\n", + " derived_end_position \n", + "id \n", + "20 20.418560 \n", + "28 19.713146 \n", + "56 18.277160 \n", + "59 20.119760 \n", + "79 18.924560 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trials_df = nwbfile.trials.to_dataframe()\n", + "trials_df.sample(n=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trial Event Sequence\n", + "\n", + "Let's examine the key columns that define the temporal structure of each trial:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timecenter_target_appearance_timelateral_target_appearance_timecursor_departure_timemovement_typereward_time
id
00.0004.7267.5267.655flexion8.889
112.61914.98119.82820.417extension21.176
225.57127.96035.34435.924extension36.759
340.48342.33145.18045.729flexion46.568
450.29652.75157.51257.880flexion58.687
\n", + "
" + ], + "text/plain": [ + " start_time center_target_appearance_time lateral_target_appearance_time \\\n", + "id \n", + "0 0.000 4.726 7.526 \n", + "1 12.619 14.981 19.828 \n", + "2 25.571 27.960 35.344 \n", + "3 40.483 42.331 45.180 \n", + "4 50.296 52.751 57.512 \n", + "\n", + " cursor_departure_time movement_type reward_time \n", + "id \n", + "0 7.655 flexion 8.889 \n", + "1 20.417 extension 21.176 \n", + "2 35.924 extension 36.759 \n", + "3 45.729 flexion 46.568 \n", + "4 57.880 flexion 58.687 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns = [\n", + " \"start_time\",\n", + " \"center_target_appearance_time\",\n", + " \"lateral_target_appearance_time\",\n", + " \"cursor_departure_time\",\n", + " \"movement_type\",\n", + " \"reward_time\",\n", + "]\n", + "trials_df[columns].head(n=5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each trial progresses through distinct phases:\n", + "\n", + "1. **Trial start**: Recording begins with a variable baseline period\n", + "2. **Center target appearance**: A center target appears on screen, cueing the monkey to align the cursor and hold\n", + "3. **Center hold period**: The monkey maintains position for 1-2 seconds (randomized to prevent anticipation)\n", + "4. **Lateral target appearance (go cue)**: A peripheral target appears, signaling the monkey to move\n", + "5. **Cursor departure**: The monkey initiates movement, exiting the center zone\n", + "6. **Movement execution**: Rapid ballistic movement toward the target (flexion or extension)\n", + "7. **Reward**: Liquid reward delivered upon successful target acquisition\n", + "\n", + "The time differences between these events reveal reaction times, movement durations, and other behaviorally relevant measures.\n", + "\n", + "To graphically understand the trial structure, we can look at the following visualization that is using the data in the NWB file:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from trial_structure_plot import plot_trial_structure\n", + "\n", + "plot_trial_structure(trials_df, max_trials=10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The visualization above shows the temporal structure of individual trials as horizontal stacked bars. Each row represents one trial, with colored segments indicating different task phases:\n", + "\n", + "- **Baseline** (gray): Variable waiting period before the task begins\n", + "- **Holding at Center** (teal): The monkey maintains cursor position at the center target, waiting for the go cue\n", + "- **Time to React** (yellow): The interval between go cue (lateral target appearance) and cursor departure from the center zone\n", + "- **Movement** (coral): Active movement execution toward the peripheral target\n", + "- **Holding at Target** (purple): The monkey holds position at the target before reward delivery\n", + "- **Post-Reward** (sky blue): Brief period after reward before trial ends\n", + "\n", + "Trials labeled \"Aborted\" were terminated early (the monkey broke fixation before the go cue). Notice the consistent structure across trials, with the main variability coming from the randomized center hold duration (1-2 seconds) and individual differences in reaction time and movement speed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Derived Kinematic Measures\n", + "\n", + "In addition to trial event times, the trials table includes derived kinematic parameters computed from post-hoc analysis of the elbow angle velocity trace. These measures characterize the movement itself:\n", + "\n", + "| Column | Description | Units |\n", + "|--------|-------------|-------|\n", + "| `derived_movement_onset_time` | Time when movement began (detected from velocity trace) | seconds |\n", + "| `derived_movement_end_time` | Time when movement ended (velocity returned to baseline) | seconds |\n", + "| `derived_peak_velocity` | Maximum angular velocity during the movement | degrees/second |\n", + "| `derived_peak_velocity_time` | Time of peak velocity | seconds |\n", + "| `derived_movement_amplitude` | Total angular displacement (end position - start position) | degrees |\n", + "| `derived_end_position` | Final elbow angle at movement end | degrees |\n", + "\n", + "The \"derived\" prefix indicates these values were computed offline from kinematic analysis, not recorded directly during the experiment. The detection algorithm used position, velocity, and duration criteria (exact parameters not documented in source data).\n", + "\n", + "**Sign conventions:**\n", + "- Positive velocities and amplitudes indicate extension movements\n", + "- Negative velocities and amplitudes indicate flexion movements" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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derived_movement_onset_timederived_movement_end_timederived_peak_velocityderived_peak_velocity_timederived_movement_amplitudederived_end_positionmovement_type
id
07.8168.15053.8806917.9369.56697516.760761flexion
120.15420.66590.53158220.440-27.823461-21.324000extension
235.70936.17853.24170535.969-12.224196-16.109740extension
345.53446.18463.38704045.71419.07340018.600860flexion
457.83258.18373.07174857.97515.20150421.514160flexion
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" + ], + "text/plain": [ + " derived_movement_onset_time derived_movement_end_time \\\n", + "id \n", + "0 7.816 8.150 \n", + "1 20.154 20.665 \n", + "2 35.709 36.178 \n", + "3 45.534 46.184 \n", + "4 57.832 58.183 \n", + "\n", + " derived_peak_velocity derived_peak_velocity_time \\\n", + "id \n", + "0 53.880691 7.936 \n", + "1 90.531582 20.440 \n", + "2 53.241705 35.969 \n", + "3 63.387040 45.714 \n", + "4 73.071748 57.975 \n", + "\n", + " derived_movement_amplitude derived_end_position movement_type \n", + "id \n", + "0 9.566975 16.760761 flexion \n", + "1 -27.823461 -21.324000 extension \n", + "2 -12.224196 -16.109740 extension \n", + "3 19.073400 18.600860 flexion \n", + "4 15.201504 21.514160 flexion " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access derived kinematic measures\n", + "kinematic_columns = [\n", + " 'derived_movement_onset_time',\n", + " 'derived_movement_end_time',\n", + " 'derived_peak_velocity',\n", + " 'derived_peak_velocity_time',\n", + " 'derived_movement_amplitude',\n", + " 'derived_end_position',\n", + " 'movement_type'\n", + "]\n", + "trials_df[kinematic_columns].head(n=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Complete trials with kinematic data: 77 / 80\n", + "\n", + "Movement duration: 459 +/- 99 ms\n", + "Reaction time: 411 +/- 214 ms\n", + "Peak velocity: 79.0 +/- 19.7 deg/s\n" + ] + } + ], + "source": [ + "# Compute commonly used derived measures\n", + "# Note: Some trials may have NaN values if kinematic detection failed\n", + "\n", + "# Movement duration (from movement onset to movement end)\n", + "trials_df['movement_duration'] = (\n", + " trials_df['derived_movement_end_time'] - trials_df['derived_movement_onset_time']\n", + ")\n", + "\n", + "# Reaction time (from go cue to cursor departure)\n", + "trials_df['reaction_time'] = (\n", + " trials_df['cursor_departure_time'] - trials_df['lateral_target_appearance_time']\n", + ")\n", + "\n", + "# Filter to complete trials only\n", + "complete_trials = trials_df.dropna(subset=['derived_movement_end_time'])\n", + "\n", + "print(f\"Complete trials with kinematic data: {len(complete_trials)} / {len(trials_df)}\")\n", + "print(f\"\\nMovement duration: {complete_trials['movement_duration'].mean()*1000:.0f} +/- {complete_trials['movement_duration'].std()*1000:.0f} ms\")\n", + "print(f\"Reaction time: {complete_trials['reaction_time'].mean()*1000:.0f} +/- {complete_trials['reaction_time'].std()*1000:.0f} ms\")\n", + "print(f\"Peak velocity: {complete_trials['derived_peak_velocity'].abs().mean():.1f} +/- {complete_trials['derived_peak_velocity'].abs().std():.1f} deg/s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Torque Perturbation Trials\n", + "\n", + "A subset of trials included brief mechanical perturbations delivered via the torque motor during the center hold period. These perturbations caused rapid muscle stretch, allowing researchers to study proprioceptive responses in M1 neurons (see Pasquereau & Turner, 2013).\n", + "\n", + "**Note:** Not all sessions include perturbation trials. Sessions without perturbations will have `torque_perturbation_type = \"none\"` for all trials and `torque_perturbation_onset_time = NaN`.\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `torque_perturbation_type` | Direction of the perturbation | `\"flexion\"`, `\"extension\"`, or `\"none\"` |\n", + "| `torque_perturbation_onset_time` | Time when perturbation was delivered | seconds (NaN if no perturbation) |\n", + "\n", + "**Perturbation characteristics (when present):**\n", + "- Delivered 150-500 ms after center target appearance (during hold period)\n", + "- Brief torque pulse causing ~5-10 degree displacement\n", + "- Direction (flexion/extension) was independent of the upcoming movement direction\n", + "- Present in approximately 65% of trials in sessions that included perturbations\n", + "\n", + "**Scientific purpose:** These trials enabled analysis of how M1 neurons respond to passive muscle stretch, separate from active movement. The muscle stretch study found that PTNs showed degraded directional selectivity to perturbations in the parkinsonian state." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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torque_perturbation_typetorque_perturbation_onset_timemovement_type
id
0noneNaNflexion
1flexion16.677extension
2extension29.656extension
3noneNaNflexion
4flexion54.447flexion
5extension66.844extension
6noneNaNextension
7flexion89.040flexion
8extension101.724flexion
9noneNaNextension
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" + ], + "text/plain": [ + " torque_perturbation_type torque_perturbation_onset_time movement_type\n", + "id \n", + "0 none NaN flexion\n", + "1 flexion 16.677 extension\n", + "2 extension 29.656 extension\n", + "3 none NaN flexion\n", + "4 flexion 54.447 flexion\n", + "5 extension 66.844 extension\n", + "6 none NaN extension\n", + "7 flexion 89.040 flexion\n", + "8 extension 101.724 flexion\n", + "9 none NaN extension" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access torque perturbation data\n", + "perturbation_columns = ['torque_perturbation_type', 'torque_perturbation_onset_time', 'movement_type']\n", + "trials_df[perturbation_columns].head(n=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This session includes perturbation trials (52 / 80 trials)\n", + "\n", + "Perturbation trial counts:\n", + " none: 28 trials (35.0%)\n", + " flexion: 26 trials (32.5%)\n", + " extension: 26 trials (32.5%)\n", + "\n", + "Perturbation direction vs Movement direction:\n", + "movement_type extension flexion\n", + "torque_perturbation_type \n", + "extension 13 13\n", + "flexion 14 12\n", + "none 13 15\n" + ] + } + ], + "source": [ + "# Analyze perturbation trial distribution\n", + "# Note: Not all sessions include perturbation trials. Sessions without perturbations\n", + "# will have torque_perturbation_type = \"none\" for all trials.\n", + "\n", + "perturbation_counts = trials_df['torque_perturbation_type'].value_counts()\n", + "n_perturbation_trials = len(trials_df[trials_df['torque_perturbation_type'] != 'none'])\n", + "\n", + "# Check if this session has perturbation data\n", + "if n_perturbation_trials == 0:\n", + " print(\"This session does not include torque perturbation trials.\")\n", + " print(\"All trials have torque_perturbation_type = 'none'\")\n", + "else:\n", + " print(f\"This session includes perturbation trials ({n_perturbation_trials} / {len(trials_df)} trials)\\n\")\n", + " print(\"Perturbation trial counts:\")\n", + " for ptype, count in perturbation_counts.items():\n", + " print(f\" {ptype}: {count} trials ({100*count/len(trials_df):.1f}%)\")\n", + " \n", + " # Cross-tabulate perturbation direction vs movement direction\n", + " print(\"\\nPerturbation direction vs Movement direction:\")\n", + " print(trials_df.groupby(['torque_perturbation_type', 'movement_type']).size().unstack(fill_value=0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Isolation Monitoring Stimulation\n", + "\n", + "In some recording sessions, single antidromic stimulation pulses were delivered early in certain trials to activate otherwise silent neurons and monitor unit isolation. This was particularly important for intratelencephalic-type (IT) cortical neurons (including corticostriatal neurons), which have very low spontaneous firing rates.\n", + "\n", + "**Background:** Some neurons rarely spike spontaneously but can be reliably activated by antidromic stimulation from their projection target (striatum, thalamus, or cerebral peduncle). Delivering occasional stimulation pulses during recording confirmed that the unit remained well-isolated throughout the session.\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `isolation_monitoring_stim_time` | Time when stimulation pulse was delivered | seconds (NaN if no stimulation in that trial) |\n", + "| `isolation_monitoring_stim_site` | Anatomical target of the stimulation | `\"Str\"` (striatum), `\"Thal\"` (thalamus), `\"Ped\"` (peduncle), or empty string |\n", + "\n", + "**Characteristics:**\n", + "- Stimulation pulses delivered ~244-245 ms after trial start (early in the baseline period)\n", + "- Present in approximately 43% of sessions (171 out of 400 session files)\n", + "- Most commonly targeting thalamus (117 sessions), followed by striatum (51 sessions)\n", + "- When present, stimulation typically delivered in a subset of trials within the session\n", + "\n", + "**Note:** If you observe spike times that appear suspiciously constant across trials (~245-256 ms after trial start), these likely reflect antidromically-evoked spikes from isolation monitoring, not spontaneous activity." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timeisolation_monitoring_stim_timeisolation_monitoring_stim_site
id
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [start_time, isolation_monitoring_stim_time, isolation_monitoring_stim_site]\n", + "Index: []" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access isolation monitoring stimulation data (only trials with stimulation)\n", + "stim_columns = ['start_time', 'isolation_monitoring_stim_time', 'isolation_monitoring_stim_site']\n", + "stim_trials = trials_df[trials_df['isolation_monitoring_stim_time'].notna()]\n", + "stim_trials[stim_columns].head(n=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This session does not include isolation monitoring stimulation.\n", + "All trials have isolation_monitoring_stim_time = NaN\n" + ] + } + ], + "source": [ + "# Check if this session has isolation monitoring stimulation\n", + "stim_trials = trials_df[trials_df['isolation_monitoring_stim_time'].notna()]\n", + "n_stim_trials = len(stim_trials)\n", + "\n", + "if n_stim_trials == 0:\n", + " print(\"This session does not include isolation monitoring stimulation.\")\n", + " print(\"All trials have isolation_monitoring_stim_time = NaN\")\n", + "else:\n", + " print(f\"This session includes isolation monitoring stimulation ({n_stim_trials} / {len(trials_df)} trials)\\n\")\n", + " \n", + " # Get stimulation site (should be consistent within session)\n", + " stim_site = stim_trials['isolation_monitoring_stim_site'].iloc[0]\n", + " print(f\"Stimulation site: {stim_site}\")\n", + " \n", + " # Calculate timing relative to trial start\n", + " stim_trials = stim_trials.copy()\n", + " stim_trials['stim_time_relative'] = (\n", + " stim_trials['isolation_monitoring_stim_time'] - stim_trials['start_time']\n", + " )\n", + " \n", + " mean_stim_time = stim_trials['stim_time_relative'].mean() * 1000 # Convert to ms\n", + " std_stim_time = stim_trials['stim_time_relative'].std() * 1000\n", + " \n", + " print(f\"Stimulation timing: {mean_stim_time:.1f} +/- {std_stim_time:.1f} ms after trial start\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Accessing Neurons/Units Spikes\n", + "\n", + "The units table contains spike times and metadata for each recorded neuron. A key feature of this dataset is the classification of neurons into pyramidal tract neurons (PTNs) and corticostriatal neurons (CSNs) through antidromic stimulation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + "

units (Units)

description: Autogenerated by NWBFile
waveform_unit: volts
columns
neuron_projection_type
Classification of layer 5 M1 neurons based on axonal projection target, determined through antidromic activation (electrical stimulation at target sites evoking backward-traveling spikes). Values: | 'pyramidal_tract_neuron': Large layer 5b neurons projecting to spinal cord via corticospinal tract, identified by cerebral peduncle stimulation (latency 0.6-2.5 ms) | 'corticostriatal_neuron': Intratelencephalic-type neurons projecting to posterolateral putamen, identified by striatal stimulation (latency 2.0-20 ms) | 'no_response': Antidromic stimulation attempted at one or both sites but neuron did not respond | 'not_tested': Antidromic identification was not performed for this neuron | 'pyramidal_tract_and_corticostriatal_neuron': Dual-projecting neuron responding to both sites | 'pyramidal_tract_and_thalamic_neuron': Neuron projecting to both pyramidal tract and thalamus | 'thalamic_projection_neuron': Neuron projecting to thalamus | 'subthalamic_projection_neuron': Neuron projecting to subthalamic nucleus | 'subthalamic_and_corticostriatal_neuron': Neuron projecting to both STN and striatum | 'subthalamic_and_thalamic_neuron': Neuron projecting to both STN and thalamus | 'pyramidal_tract_and_subthalamic_neuron': Neuron projecting to both pyramidal tract and STN
antidromic_stimulation_sites
Anatomical sites where antidromic stimulation was delivered via chronically implanted electrodes to identify neuron projection targets. Values: | 'cerebral_peduncle': Stimulation of corticospinal tract fibers to identify pyramidal tract neurons (100-400 uA, 0.2 ms biphasic pulses) | 'posterolateral_striatum': Stimulation of putamen to identify corticostriatal neurons (100-600 uA, 0.2 ms biphasic pulses) | 'cerebral_peduncle_and_posterolateral_striatum': Both stimulation sites were tested | 'subthalamic_nucleus': Stimulation of subthalamic nucleus (STN) | 'cerebral_peduncle_and_subthalamic_nucleus': Both peduncle and STN sites were tested | '': Empty string indicates antidromic testing was not attempted for this neuron
antidromic_latency_ms
Latency from stimulation to antidromic spike arrival at the recording electrode (in milliseconds) for the primary stimulation site. Reflects axon conduction velocity: PTNs have shorter latencies (0.6-2.5 ms) due to large, fast-conducting axons; CSNs have longer latencies (2.0-20 ms) due to smaller axons. Classification required fixed latency, high-frequency following (>200 Hz), and collision test confirmation.
antidromic_threshold
Minimum stimulation current (in microamps) required to reliably evoke antidromic activation at the primary stimulation site. Lower thresholds indicate electrode proximity to the axon or larger axon diameter. Typical ranges: 100-400 uA for cerebral peduncle, 100-600 uA for striatum.
antidromic_latency_2_ms
Antidromic response latency (in milliseconds) from secondary stimulation site, for neurons with dual projections (e.g., pyramidal_tract_and_corticostriatal_neuron). NaN if not applicable.
antidromic_threshold_2
Minimum stimulation current (in microamps) for antidromic activation at the secondary stimulation site. NaN if neuron does not have dual projections or secondary site was not tested.
receptive_field_location
Body region comprising this neuron's somatosensory receptive field, determined through qualitative sensorimotor examination. The experimenter manually manipulated the monkey's contralateral arm (passive joint movement, skin contact) while monitoring neural activity on an audio speaker. Values: | 'hand': Receptive field on the hand | 'wrist': Receptive field at the wrist joint | 'elbow': Receptive field at the elbow joint | 'forearm': Receptive field on the forearm | 'shoulder': Receptive field at the shoulder | 'finger': Receptive field on digit(s) | Combinations (e.g., 'hand / wrist'): Neuron responds to multiple body regions | 'no_response': Sensory testing performed but no response detected | 'not_tested': Sensory mapping not performed (limited recording time)
receptive_field_stimulus
Specific sensory stimulus or manipulation that activated this neuron during qualitative receptive field mapping (qualitative observer assessment, not quantified). Values: | 'ext': Joint extension (proprioceptive) | 'flex': Joint flexion (proprioceptive) | 'pron': Forearm pronation (proprioceptive) | 'sup': Forearm supination (proprioceptive) | 'light touch': Light tactile stimulation | 'palm probe': Pressure on palm | 'finger tap': Tapping on digit(s) | 'cut stim': Cutaneous (skin) stimulation | 'active': Firing during voluntary movement | 'active grip': Firing during voluntary grip | 'active hand': Firing during voluntary hand movement | Combinations (e.g., 'finger ext & elbow ext'): Multi-joint responses | 'NR': No response to any stimulus tested | 'NT': Sensory testing not performed | '': Testing data not recorded
unit_name
Unit identifier extracted from source MATLAB filename. When multiple well-isolated single units were recorded simultaneously during the same electrode penetration, each was saved separately. Most sessions contain a single unit. Values: | 1: First (or only) unit from this session, from file '{session}.1.mat' | 2: Second unit from this session, from file '{session}.2.mat'
unit_also_in_session_id
Cross-reference to another NWB session containing recordings from this same neuron, identified by the experimenter based on waveform similarity and recording location during the same electrode penetration. Empty string if this unit was only recorded in one session. Format matches session_id: {subject_id}++{fname}++{Pre|Post}MPTP++Depth{um}um++{YYYYMMDD}. This enables tracking individual neurons across multiple recording sessions or experimental conditions.
is_post_mptp
Boolean indicating parkinsonian state at time of recording. MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) was administered once via unilateral left internal carotid artery injection (0.5 mg/kg), inducing selective dopaminergic cell death in the ipsilateral substantia nigra and producing stable contralateral hemiparkinsonian symptoms. Comparing pre/post recordings reveals how parkinsonism affects cortical motor encoding, particularly in pyramidal tract neurons. Values: | False: Pre-MPTP baseline recording (healthy motor control) | True: Post-MPTP recording after parkinsonian symptoms stabilized (bradykinesia, rigidity, movement initiation deficits)
spike_times
the spike times for each unit in seconds
obs_intervals
the observation intervals for each unit
electrodes
the electrodes that each spike unit came from
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neuron_projection_typeantidromic_stimulation_sitesantidromic_latency_msantidromic_thresholdantidromic_latency_2_msantidromic_threshold_2receptive_field_locationreceptive_field_stimulusunit_nameunit_also_in_session_idis_post_mptpspike_timesobs_intervalselectrodes
id
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1pyramidal_tract_neuron1.85360.0NaNNaNno_response2True[0.025, 0.078, 0.13, 0.172, 0.212, 0.26, 0.285, 0.318, 0.389, 0.428, 0.491, 0.515, 0.618, 0.638, 0.714, 0.731, 0.78, 0.875, 0.895, 0.94, 0.974, 0.977, 1.07, 1.13, 1.15, 1.182, 1.208, 1.242, 1.274, 1.307, 1.309, 1.344, 1.37, 1.43, 1.489, 1.537, 1.559, 1.612, 1.633, 1.646, 1.684, 1.698, 1.724, 1.75, 1.778, 1.806, 1.822, 1.85, 1.888, 1.91, 1.945, 1.978, 2.003, 2.03, 2.067, 2.101, 2.134, 2.142, 2.176, 2.224, 2.249, 2.291, 2.324, 2.358, 2.387, 2.416, 2.448, 2.469, 2.509, 2.549, 2.6, 2.611, 2.64, 2.676, 2.71, 2.721, 2.754, 2.786, 2.819, 2.859, 2.895, 2.948, 2.975, 3.009, 3.039, 3.048, 3.075, 3.104, 3.139, 3.162, 3.191, 3.195, 3.222, 3.274, 3.362, 3.404, 3.443, 3.468, 3.501, 3.539, ...][[0.0, 9.619], [12.619, 22.570999999999998], [25.570999999999998, 37.483], [40.483, 47.296], [50.29599999999999, 59.41199999999999], [62.41199999999999, 71.92099999999999], [74.92099999999999, 82.00899999999999], [85.00899999999999, 94.09799999999998], [97.09799999999998, 105.58099999999999], [108.58099999999999, 117.61899999999999], [120.61899999999999, 128.992], [131.992, 140.081], [143.081, 150.743], [153.743, 162.84799999999998], [165.84799999999998, 174.35999999999999], [177.35999999999999, 183.93599999999998], [186.93599999999998, 196.134], [199.134, 206.88199999999998], [209.88199999999998, 217.77499999999998], [220.77499999999998, 229.97299999999998], [232.97299999999998, 241.86999999999998], [244.86999999999998, 253.13899999999998], [256.13899999999995, 264.61099999999993], [267.61099999999993, 275.85099999999994], [278.85099999999994, 286.3159999999999], [289.3159999999999, 298.4489999999999], [301.4489999999999, 310.3779999999999], [313.3779999999999, 320.13299999999987], [323.13299999999987, 331.9299999999999], [334.9299999999999, 343.8159999999999], [346.8159999999999, 353.91099999999994], [356.91099999999994, 366.08799999999997], [369.08799999999997, 377.941], [380.941, 389.736], [392.736, 401.062], [404.062, 412.262], [415.262, 422.957], [425.957, 438.36199999999997], [441.36199999999997, 449.36499999999995], [452.36499999999995, 460.29599999999994], [463.29599999999994, 470.36699999999996], [473.36699999999996, 480.32699999999994], [483.32699999999994, 491.9409999999999], [494.9409999999999, 503.4769999999999], [506.4769999999999, 514.8779999999999], [517.8779999999999, 527.7839999999999], [530.7839999999999, 539.1929999999999], [542.1929999999999, 549.5099999999999], [552.5099999999999, 560.5759999999999], [563.5759999999999, 571.8159999999999], [574.8159999999999, 582.1859999999999], [585.1859999999999, 594.603], [597.603, 606.351], [609.351, 616.658], [619.658, 628.812], [631.812, 642.25], [645.25, 653.292], [656.292, 670.683], [673.683, 682.579], [685.579, 692.4169999999999], [695.4169999999999, 705.3449999999999], [708.3449999999999, 719.1399999999999], [722.1399999999999, 730.0409999999998], [733.0409999999998, 742.0029999999998], [745.0029999999998, 753.9309999999998], [756.9309999999998, 765.0589999999999], [768.0589999999999, 776.1509999999998], [779.1509999999998, 787.5959999999999], [790.5959999999999, 798.5819999999999], [801.5819999999999, 810.5159999999998], [813.5159999999998, 822.1919999999999], [825.1919999999999, 832.7279999999998], [835.7279999999998, 843.6679999999999], [846.6679999999999, 858.8649999999999], [861.8649999999999, 869.762], [872.762, 881.2739999999999], [884.2739999999999, 894.2879999999999], [897.2879999999999, 907.3569999999999], [910.3569999999999, 918.6379999999998], [921.6379999999998, 930.3309999999998]][0]
" + ], + "text/plain": [ + "units pynwb.misc.Units at 0x128623676674208\n", + "Fields:\n", + " colnames: ['neuron_projection_type' 'antidromic_stimulation_sites'\n", + " 'antidromic_latency_ms' 'antidromic_threshold' 'antidromic_latency_2_ms'\n", + " 'antidromic_threshold_2' 'receptive_field_location'\n", + " 'receptive_field_stimulus' 'unit_name' 'unit_also_in_session_id'\n", + " 'is_post_mptp' 'spike_times' 'obs_intervals' 'electrodes']\n", + " columns: (\n", + " neuron_projection_type ,\n", + " antidromic_stimulation_sites ,\n", + " antidromic_latency_ms ,\n", + " antidromic_threshold ,\n", + " antidromic_latency_2_ms ,\n", + " antidromic_threshold_2 ,\n", + " receptive_field_location ,\n", + " receptive_field_stimulus ,\n", + " unit_name ,\n", + " unit_also_in_session_id ,\n", + " is_post_mptp ,\n", + " spike_times_index ,\n", + " spike_times ,\n", + " obs_intervals_index ,\n", + " obs_intervals ,\n", + " electrodes_index ,\n", + " electrodes \n", + " )\n", + " description: Autogenerated by NWBFile\n", + " id: id \n", + " waveform_unit: volts" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.units" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like the trials table, the units table is a DynamicTable that can be converted to a pandas DataFrame for easier exploration and analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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neuron_projection_typeantidromic_stimulation_sitesantidromic_latency_msantidromic_thresholdantidromic_latency_2_msantidromic_threshold_2receptive_field_locationreceptive_field_stimulusunit_nameunit_also_in_session_idis_post_mptpspike_timesobs_intervalselectrodes
id
0pyramidal_tract_neuroncerebral_peduncle1.80550.0NaNNaNno_response1V++v5812++PostMPTP++Depth18300um++20000331True[0.248, 0.524, 0.586, 0.653, 0.952, 1.216, 1.3...[[0.0, 9.619], [12.619, 22.570999999999998], [...location \\\n", + "i...
1pyramidal_tract_neuron1.85360.0NaNNaNno_response2True[0.025, 0.078, 0.13, 0.172, 0.212, 0.26, 0.285...[[0.0, 9.619], [12.619, 22.570999999999998], [...location \\\n", + "i...
\n", + "
" + ], + "text/plain": [ + " neuron_projection_type antidromic_stimulation_sites \\\n", + "id \n", + "0 pyramidal_tract_neuron cerebral_peduncle \n", + "1 pyramidal_tract_neuron \n", + "\n", + " antidromic_latency_ms antidromic_threshold antidromic_latency_2_ms \\\n", + "id \n", + "0 1.80 550.0 NaN \n", + "1 1.85 360.0 NaN \n", + "\n", + " antidromic_threshold_2 receptive_field_location receptive_field_stimulus \\\n", + "id \n", + "0 NaN no_response \n", + "1 NaN no_response \n", + "\n", + " unit_name unit_also_in_session_id is_post_mptp \\\n", + "id \n", + "0 1 V++v5812++PostMPTP++Depth18300um++20000331 True \n", + "1 2 True \n", + "\n", + " spike_times \\\n", + "id \n", + "0 [0.248, 0.524, 0.586, 0.653, 0.952, 1.216, 1.3... \n", + "1 [0.025, 0.078, 0.13, 0.172, 0.212, 0.26, 0.285... \n", + "\n", + " obs_intervals \\\n", + "id \n", + "0 [[0.0, 9.619], [12.619, 22.570999999999998], [... \n", + "1 [[0.0, 9.619], [12.619, 22.570999999999998], [... \n", + "\n", + " electrodes \n", + "id \n", + "0 location \\\n", + "i... \n", + "1 location \\\n", + "i... " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "units_df = nwbfile.units.to_dataframe()\n", + "units_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing Spike Times\n", + "\n", + "Each unit's spike times are stored as an array of timestamps (in seconds) relative to session start. You can access them by unit index." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unit 0:\n", + " Total spikes: 6933\n", + " Recording duration: 930.05 s\n", + " Mean firing rate: 7.45 Hz\n", + "\n", + "Unit 1:\n", + " Total spikes: 10451\n", + " Recording duration: 930.08 s\n", + " Mean firing rate: 11.24 Hz\n", + "\n" + ] + } + ], + "source": [ + "# Accessing spike times\n", + "for unit_id in range(len(units_df)):\n", + " spike_times = nwbfile.units['spike_times'][unit_id]\n", + " \n", + " print(f\"Unit {unit_id}:\")\n", + " print(f\" Total spikes: {len(spike_times)}\")\n", + " print(f\" Recording duration: {spike_times[-1] - spike_times[0]:.2f} s\")\n", + " print(f\" Mean firing rate: {len(spike_times) / (spike_times[-1] - spike_times[0]):.2f} Hz\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Simple spike raster plot\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 3))\n", + "\n", + "for unit_id in range(len(units_df)):\n", + " spike_times = nwbfile.units['spike_times'][unit_id]\n", + " ax.scatter(spike_times, np.ones_like(spike_times) * unit_id, \n", + " s=0.5, color='black', alpha=0.6)\n", + "\n", + "ax.set_xlabel('Time (s)')\n", + "ax.set_ylabel('Unit')\n", + "ax.set_yticks(range(len(units_df)))\n", + "ax.set_title('Spike Raster - Full Session')\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Understanding Observation Intervals (Trialized Data)\n", + "\n", + "Looking at the raster above, you might assume spikes were recorded continuously throughout the session. However, this dataset was converted from **trial-based MATLAB files** where each trial's data was stored separately. The trials have been concatenated into a continuous timeline with **3-second artificial gaps** between them.\n", + "\n", + "**Important**: The inter-trial gaps are placeholders - no data was actually recorded during these periods. Spikes appear continuous because the gaps are relatively short compared to the full session duration.\n", + "\n", + "To see this structure clearly, let's zoom in on the first few trials:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notice the ~3-second white gaps between green regions - these are artificial placeholders.\n", + "No spikes occur in the gaps because no data was recorded during inter-trial intervals.\n" + ] + } + ], + "source": [ + "# Zoomed spike raster showing observation intervals (trials) vs gaps\n", + "# Green = data was recorded, White = artificial gap (no data recorded)\n", + "obs_intervals = nwbfile.units.get_unit_obs_intervals(0)\n", + "\n", + "# Zoom to first 5 trials to clearly see the structure\n", + "n_trials_to_show = 5\n", + "xlim_end = obs_intervals[n_trials_to_show - 1, 1] + 5 # Include a bit after last shown trial\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 3))\n", + "\n", + "# Shade observation intervals (when data was actually recorded)\n", + "for i, (start, stop) in enumerate(obs_intervals[:n_trials_to_show]):\n", + " label = 'Recorded (trial)' if i == 0 else '_nolegend_'\n", + " ax.axvspan(start, stop, alpha=0.2, color='green', label=label)\n", + "\n", + "# Plot spikes\n", + "for unit_id in range(len(units_df)):\n", + " spike_times = nwbfile.units['spike_times'][unit_id]\n", + " mask = spike_times < xlim_end\n", + " ax.scatter(spike_times[mask], np.ones(mask.sum()) * unit_id, \n", + " s=2, color='black', alpha=0.8)\n", + "\n", + "ax.set_xlim(0, xlim_end)\n", + "ax.set_xlabel('Time (s)')\n", + "ax.set_ylabel('Unit')\n", + "ax.set_yticks(range(len(units_df)))\n", + "ax.set_title('Spike Raster (zoomed) - Green = recorded trials, White gaps = not recorded')\n", + "ax.legend(loc='upper right')\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f\"Notice the ~3-second white gaps between green regions - these are artificial placeholders.\")\n", + "print(f\"No spikes occur in the gaps because no data was recorded during inter-trial intervals.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each unit has an `obs_intervals` column that specifies exactly when it was being recorded (i.e., during trials only). This is critical for accurate analysis:\n", + "- Firing rates should use observed time, not total session time\n", + "- ISI calculations should not span trial boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unit 0 was observed during 80 trial windows\n", + "\n", + "First 3 observation windows (start, stop in seconds):\n", + " Trial 0: 0.00s - 9.62s (duration: 9.62s)\n", + " Trial 1: 12.62s - 22.57s (duration: 9.95s)\n", + " Trial 2: 25.57s - 37.48s (duration: 11.91s)\n", + "\n", + "Firing rate comparison:\n", + " Correct (using obs_intervals): 10.00 Hz\n", + " Naive (using session duration): 7.45 Hz\n", + " Difference: 25.5% underestimate if gaps not excluded\n" + ] + } + ], + "source": [ + "# Accessing obs_intervals and computing correct firing rates\n", + "obs_intervals = nwbfile.units.get_unit_obs_intervals(0)\n", + "print(f\"Unit 0 was observed during {len(obs_intervals)} trial windows\")\n", + "print(f\"\\nFirst 3 observation windows (start, stop in seconds):\")\n", + "for i, (start, stop) in enumerate(obs_intervals[:3]):\n", + " print(f\" Trial {i}: {start:.2f}s - {stop:.2f}s (duration: {stop-start:.2f}s)\")\n", + "\n", + "# Calculate CORRECT firing rate using only observed time\n", + "spike_times = nwbfile.units.get_unit_spike_times(0)\n", + "total_observed_time = sum(stop - start for start, stop in obs_intervals)\n", + "session_duration = obs_intervals[-1, 1] - obs_intervals[0, 0]\n", + "\n", + "correct_rate = len(spike_times) / total_observed_time\n", + "naive_rate = len(spike_times) / session_duration\n", + "\n", + "print(f\"\\nFiring rate comparison:\")\n", + "print(f\" Correct (using obs_intervals): {correct_rate:.2f} Hz\")\n", + "print(f\" Naive (using session duration): {naive_rate:.2f} Hz\")\n", + "print(f\" Difference: {100*(correct_rate - naive_rate)/correct_rate:.1f}% underestimate if gaps not excluded\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Invalid Times Table (For Continuous Signals)\n", + "\n", + "While `obs_intervals` annotates when each **unit** was observable, the NWB file also includes an `invalid_times` table that marks the inter-trial gaps at the **session level**. This is useful for filtering continuous signals like LFP, EMG, and kinematics.\n", + "\n", + "**Relationship between the two:**\n", + "- `obs_intervals` (per-unit): Specifies when spikes could occur. Use for spike train analysis.\n", + "- `invalid_times` (session-wide): Specifies time periods to exclude. Use for continuous signals.\n", + "\n", + "Both describe the same underlying structure (trialized data with artificial gaps). The gaps listed in `invalid_times` correspond exactly to the periods *between* the `obs_intervals`.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_timestop_timetags
id
09.61912.619[artificial_inter_trial_gap, not_recorded]
122.57125.571[artificial_inter_trial_gap, not_recorded]
237.48340.483[artificial_inter_trial_gap, not_recorded]
347.29650.296[artificial_inter_trial_gap, not_recorded]
459.41262.412[artificial_inter_trial_gap, not_recorded]
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" + ], + "text/plain": [ + " start_time stop_time tags\n", + "id \n", + "0 9.619 12.619 [artificial_inter_trial_gap, not_recorded]\n", + "1 22.571 25.571 [artificial_inter_trial_gap, not_recorded]\n", + "2 37.483 40.483 [artificial_inter_trial_gap, not_recorded]\n", + "3 47.296 50.296 [artificial_inter_trial_gap, not_recorded]\n", + "4 59.412 62.412 [artificial_inter_trial_gap, not_recorded]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access the invalid_times table\n", + "invalid_times_df = nwbfile.invalid_times.to_dataframe()\n", + "invalid_times_df.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Neuron Projection Type (Cell Classification)\n", + "\n", + "Each neuron in this dataset was classified by its axonal projection target using antidromic stimulation. This classification allows you to analyze PTNs and CSNs separately.\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `neuron_projection_type` | Classification based on antidromic response | `\"pyramidal_tract_neuron\"`, `\"corticostriatal_neuron\"`, `\"no_response\"`, `\"not_tested\"` |\n", + "\n", + "**Cell type definitions:**\n", + "\n", + "- **Pyramidal Tract Neurons (PTNs)**: Large layer 5b neurons whose axons descend through the cerebral peduncle to the spinal cord. These are the primary motor output neurons that send commands directly to spinal motor circuits. Identified by antidromic activation from the cerebral peduncle.\n", + "\n", + "- **Corticostriatal Neurons (CSNs)**: Intratelencephalic-type layer 5 neurons projecting to the striatum (putamen). These provide cortical input to basal ganglia circuits involved in action selection. Identified by antidromic activation from the posterolateral putamen.\n", + "\n", + "- **No Response**: Neuron was tested but did not respond antidromically to any stimulation site.\n", + "\n", + "- **Not Tested**: Antidromic testing was not performed for this neuron." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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neuron_projection_typeis_post_mptpunit_name
id
0pyramidal_tract_neuronTrue1
1pyramidal_tract_neuronTrue2
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" + ], + "text/plain": [ + " neuron_projection_type is_post_mptp unit_name\n", + "id \n", + "0 pyramidal_tract_neuron True 1\n", + "1 pyramidal_tract_neuron True 2" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine neuron classification\n", + "classification_columns = ['neuron_projection_type', 'is_post_mptp', 'unit_name']\n", + "units_df[classification_columns]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Antidromic Identification Parameters\n", + "\n", + "For neurons that responded antidromically, the units table includes parameters from the identification protocol:\n", + "\n", + "| Column | Description | Units |\n", + "|--------|-------------|-------|\n", + "| `antidromic_stimulation_sites` | Anatomical site(s) where stimulation evoked antidromic response | `\"cerebral_peduncle\"`, `\"putamen\"`, or both |\n", + "| `antidromic_latency_ms` | Response latency from stimulation to spike | milliseconds |\n", + "| `antidromic_threshold` | Minimum current required to evoke response | microamperes |\n", + "| `antidromic_latency_2_ms` | Secondary site latency (for dual-projection neurons) | milliseconds |\n", + "| `antidromic_threshold_2` | Secondary site threshold | microamperes |\n", + "\n", + "**Latency interpretation:**\n", + "- **PTNs (peduncle stimulation)**: 0.6-2.5 ms latencies indicate large-diameter, fast-conducting axons (~20-50 m/s)\n", + "- **CSNs (putamen stimulation)**: 2-20 ms latencies indicate smaller, slower-conducting axons typical of intratelencephalic neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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neuron_projection_typeantidromic_stimulation_sitesantidromic_latency_msantidromic_threshold
id
0pyramidal_tract_neuroncerebral_peduncle1.80550.0
1pyramidal_tract_neuron1.85360.0
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" + ], + "text/plain": [ + " neuron_projection_type antidromic_stimulation_sites \\\n", + "id \n", + "0 pyramidal_tract_neuron cerebral_peduncle \n", + "1 pyramidal_tract_neuron \n", + "\n", + " antidromic_latency_ms antidromic_threshold \n", + "id \n", + "0 1.80 550.0 \n", + "1 1.85 360.0 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine antidromic identification parameters\n", + "antidromic_columns = [\n", + " 'neuron_projection_type',\n", + " 'antidromic_stimulation_sites',\n", + " 'antidromic_latency_ms',\n", + " 'antidromic_threshold'\n", + "]\n", + "units_df[antidromic_columns]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Receptive Field Properties\n", + "\n", + "During recording sessions, experimenters performed qualitative sensory testing to characterize each neuron's receptive field. This involved manually manipulating the monkey's arm while listening to the neural activity.\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `receptive_field_location` | Body region(s) comprising the receptive field | `\"hand\"`, `\"wrist\"`, `\"elbow\"`, `\"shoulder\"`, combinations, `\"no_response\"`, or `\"not_tested\"` |\n", + "| `receptive_field_stimulus` | Type of manipulation that activated the neuron | `\"ext\"`, `\"flex\"`, `\"pron\"`, `\"light touch\"`, `\"active grip\"`, etc. |\n", + "\n", + "**Common abbreviations in `receptive_field_stimulus`:**\n", + "- `ext` = extension, `flex` = flexion\n", + "- `pron` = pronation, `sup` = supination\n", + "- `cut` = cutaneous (skin stimulation)\n", + "- `NR` = no response, `NT` = not tested\n", + "\n", + "**Interpretation:**\n", + "- Most neurons responded to distal arm regions (hand, wrist, fingers), consistent with recording in arm-related M1\n", + "- Multiple regions separated by ` / ` indicate neurons with broad receptive fields\n", + "- Sensory characterization is independent of but complementary to antidromic cell-type identification" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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receptive_field_locationreceptive_field_stimulusneuron_projection_type
id
0no_responsepyramidal_tract_neuron
1no_responsepyramidal_tract_neuron
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" + ], + "text/plain": [ + " receptive_field_location receptive_field_stimulus neuron_projection_type\n", + "id \n", + "0 no_response pyramidal_tract_neuron\n", + "1 no_response pyramidal_tract_neuron" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine receptive field properties\n", + "rf_columns = ['receptive_field_location', 'receptive_field_stimulus', 'neuron_projection_type']\n", + "units_df[rf_columns]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No units in this session have documented receptive field responses.\n", + "Showing all units:\n" + ] + }, + { + "data": { + "text/html": [ + "
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receptive_field_locationreceptive_field_stimulusneuron_projection_type
id
0no_responsepyramidal_tract_neuron
1no_responsepyramidal_tract_neuron
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" + ], + "text/plain": [ + " receptive_field_location receptive_field_stimulus neuron_projection_type\n", + "id \n", + "0 no_response pyramidal_tract_neuron\n", + "1 no_response pyramidal_tract_neuron" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Examine receptive field properties (units with responses)\n", + "rf_columns = ['receptive_field_location', 'receptive_field_stimulus', 'neuron_projection_type']\n", + "units_with_rf = units_df[~units_df['receptive_field_location'].isin(['not_tested', 'no_response'])]\n", + "\n", + "if len(units_with_rf) > 0:\n", + " print(f\"Units with receptive field responses: {len(units_with_rf)} / {len(units_df)}\")\n", + " display(units_with_rf[rf_columns])\n", + "else:\n", + " print(\"No units in this session have documented receptive field responses.\")\n", + " print(\"Showing all units:\")\n", + " display(units_df[rf_columns])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Additional Unit Metadata\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `is_post_mptp` | Whether recording was obtained after MPTP treatment | `True` or `False` |\n", + "| `unit_also_in_session_id` | Session ID if this unit also appears in another file | session ID string or empty |\n", + "\n", + "**Note on `unit_also_in_session_id`:** Some neurons were recorded across multiple electrode depths within the same penetration. When a unit appears in multiple session files, this column contains the session ID of the related file. This allows linking the same neuron across different recording depths." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Electrodes Table\n", + "\n", + "The electrodes table contains metadata for the recording electrode, including chamber-relative coordinates that allow mapping recordings to anatomical locations in M1.\n", + "\n", + "| Column | Description | Units/Values |\n", + "|--------|-------------|--------------|\n", + "| `chamber_grid_ap_mm` | Anterior-posterior position in chamber grid | mm (positive = posterior, range: -7 to 0) |\n", + "| `chamber_grid_ml_mm` | Medial-lateral position in chamber grid | mm (positive = right, range: -6 to -2) |\n", + "| `chamber_insertion_depth_mm` | Depth from chamber surface | mm (range: 8.4-27.6) |\n", + "\n", + "**Recording electrode:** Glass-coated platinum-iridium microelectrode lowered through the recording chamber into M1. The chamber coordinates allow reconstructing electrode positions relative to the cortical sulcal pattern.\n", + "\n", + "**Coordinate system:** Chamber-relative coordinates specific to each animal's implanted recording chamber. The chamber was positioned over left M1 at 35 degrees to the coronal plane. These are NOT standard stereotaxic atlas coordinates.\n", + "\n", + "**Note:** Stimulation electrodes (for antidromic identification) are represented as separate devices, not in the electrodes table. See `nwbfile.devices` for stimulation electrode descriptions." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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locationgroupchamber_grid_ap_mmchamber_grid_ml_mmchamber_insertion_depth_mmicms_effecticms_threshold_uAgroup_namexyzimpfiltering
id
0agranular frontal area F1 (or area 4)ElectrodeGroupMicroelectrodeRecording pynwb.ec...-6-2.518.3not_testedNaNElectrodeGroupMicroelectrodeRecording-13472.805-30783.554-18067.467NaN0.3-10kHz bandpass for spikes, 1-100Hz for LFP...
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" + ], + "text/plain": [ + " location \\\n", + "id \n", + "0 agranular frontal area F1 (or area 4) \n", + "\n", + " group chamber_grid_ap_mm \\\n", + "id \n", + "0 ElectrodeGroupMicroelectrodeRecording pynwb.ec... -6 \n", + "\n", + " chamber_grid_ml_mm chamber_insertion_depth_mm icms_effect \\\n", + "id \n", + "0 -2.5 18.3 not_tested \n", + "\n", + " icms_threshold_uA group_name x \\\n", + "id \n", + "0 NaN ElectrodeGroupMicroelectrodeRecording -13472.805 \n", + "\n", + " y z imp \\\n", + "id \n", + "0 -30783.554 -18067.467 NaN \n", + "\n", + " filtering \n", + "id \n", + "0 0.3-10kHz bandpass for spikes, 1-100Hz for LFP... " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Access electrodes table\n", + "electrodes_df = nwbfile.electrodes.to_dataframe()\n", + "electrodes_df" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recording electrode location:\n", + " Chamber A/P: -6.0 mm\n", + " Chamber M/L: -2.5 mm\n", + " Depth: 18.30 mm\n" + ] + } + ], + "source": [ + "# Get recording electrode coordinates\n", + "recording_electrode = electrodes_df.iloc[0]\n", + "\n", + "print(\"Recording electrode location:\")\n", + "print(f\" Chamber A/P: {recording_electrode['chamber_grid_ap_mm']:.1f} mm\")\n", + "print(f\" Chamber M/L: {recording_electrode['chamber_grid_ml_mm']:.1f} mm\")\n", + "print(f\" Depth: {recording_electrode['chamber_insertion_depth_mm']:.2f} mm\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analog Signals (Kinematics, EMG, LFP)\n", + "\n", + "The acquisition container holds continuous time series data recorded during the task. These signals are concatenated across trials with the same timestamps as the trials table." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "

acquisition

EMGDeltoidPosterior (TimeSeries)
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EMGDeltoidPosterior (TimeSeries)

resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Posterior deltoid - shoulder extensor and external rotator. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)3845682
Compression ratio1.441558610410325
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
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timestamps_unit: seconds
interval: 1
EMGTrapezius (TimeSeries)
\n", + " \n", + " \n", + " \n", + "

EMGTrapezius (TimeSeries)

resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Trapezius - shoulder and scapula stabilizer. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
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Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
Compression opts4
Uncompressed size (bytes)5543776
Compressed size (bytes)3746068
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timestamps
HDF5 dataset
Data typefloat64
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Chunk shape(692972,)
Compressiongzip
Compression opts4
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Compressed size (bytes)4035421
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timestamps_unit: seconds
interval: 1
EMGTricepsLateralis (TimeSeries)
\n", + " \n", + " \n", + " \n", + "

EMGTricepsLateralis (TimeSeries)

resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps lateralis (lateral head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Uncompressed size (bytes)5543776
Compressed size (bytes)3493671
Compression ratio1.586805397531708
timestamps
HDF5 dataset
Data typefloat64
Shape(692972,)
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Chunk shape(692972,)
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timestamps_unit: seconds
interval: 1
EMGTricepsLongus1 (TimeSeries)
\n", + " \n", + " \n", + " \n", + "

EMGTricepsLongus1 (TimeSeries)

resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
Data typefloat64
Shape(692972,)
Array size5.29 MiB
Chunk shape(692972,)
Compressiongzip
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Compressed size (bytes)3431351
Compression ratio1.6156248661241592
timestamps
HDF5 dataset
Data typefloat64
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timestamps_unit: seconds
interval: 1
EMGTricepsLongus2 (TimeSeries)
\n", + " \n", + " \n", + " \n", + "

EMGTricepsLongus2 (TimeSeries)

resolution: -1.0
comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.
description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.
conversion: 1.0
offset: 0.0
unit: a.u.
data
HDF5 dataset
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Shape(692972,)
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Chunk shape(692972,)
Compressiongzip
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Compressed size (bytes)3323797
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timestamps
HDF5 dataset
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timestamps_unit: seconds
interval: 1
ElbowAngle (SpatialSeries)
\n", + " \n", + " \n", + " \n", + "

ElbowAngle (SpatialSeries)

resolution: -1.0
comments: no comments
description: Raw manipulandum angle from sensor during visuomotor flexion/extension task
conversion: 1.0
offset: 0.0
unit: degrees
data
HDF5 dataset
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timestamps
HDF5 dataset
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timestamps_unit: seconds
interval: 1
reference_frame: Anatomical flexion/extension axis, 0 degrees = neutral position, positive = extension
ElbowTorque (TimeSeries)
\n", + " \n", + " \n", + " \n", + "

ElbowTorque (TimeSeries)

resolution: -1.0
comments: no comments
description: Commands sent to torque controller during visuomotor task
conversion: 1.0
offset: 0.0
unit: arbitrary
data
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timestamps_unit: seconds
interval: 1
" + ], + "text/plain": [ + "{'EMGDeltoidPosterior': EMGDeltoidPosterior pynwb.base.TimeSeries at 0x128623677799936\n", + " Fields:\n", + " comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.\n", + " conversion: 1.0\n", + " data: \n", + " description: Preprocessed EMG from Posterior deltoid - shoulder extensor and external rotator. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: a.u.,\n", + " 'EMGTrapezius': EMGTrapezius pynwb.base.TimeSeries at 0x128623676068672\n", + " Fields:\n", + " comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.\n", + " conversion: 1.0\n", + " data: \n", + " description: Preprocessed EMG from Trapezius - shoulder and scapula stabilizer. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: a.u.,\n", + " 'EMGTricepsLateralis': EMGTricepsLateralis pynwb.base.TimeSeries at 0x128623676069488\n", + " Fields:\n", + " comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.\n", + " conversion: 1.0\n", + " data: \n", + " description: Preprocessed EMG from Triceps lateralis (lateral head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: a.u.,\n", + " 'EMGTricepsLongus1': EMGTricepsLongus1 pynwb.base.TimeSeries at 0x128623676070352\n", + " Fields:\n", + " comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.\n", + " conversion: 1.0\n", + " data: \n", + " description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: a.u.,\n", + " 'EMGTricepsLongus2': EMGTricepsLongus2 pynwb.base.TimeSeries at 0x128623676071216\n", + " Fields:\n", + " comments: Data are uncalibrated A/D converter values (centered ~2048, consistent with 12-bit digitization). No voltage calibration factor is available. Values represent relative muscle activation magnitude.\n", + " conversion: 1.0\n", + " data: \n", + " description: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: a.u.,\n", + " 'ElbowAngle': ElbowAngle pynwb.behavior.SpatialSeries at 0x128623676072128\n", + " Fields:\n", + " comments: no comments\n", + " conversion: 1.0\n", + " data: \n", + " description: Raw manipulandum angle from sensor during visuomotor flexion/extension task\n", + " interval: 1\n", + " offset: 0.0\n", + " reference_frame: Anatomical flexion/extension axis, 0 degrees = neutral position, positive = extension\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: degrees,\n", + " 'ElbowTorque': ElbowTorque pynwb.base.TimeSeries at 0x128623676072272\n", + " Fields:\n", + " comments: no comments\n", + " conversion: 1.0\n", + " data: \n", + " description: Commands sent to torque controller during visuomotor task\n", + " interval: 1\n", + " offset: 0.0\n", + " resolution: -1.0\n", + " timestamps: \n", + " timestamps_unit: seconds\n", + " unit: arbitrary}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nwbfile.acquisition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manipulandum Signals (Kinematics)\n", + "\n", + "The monkey controlled a cursor by rotating a manipulandum with their elbow. Three kinematic signals are recorded:\n", + "\n", + "| Signal | Location | Description | Units |\n", + "|--------|----------|-------------|-------|\n", + "| `ElbowAngle` | `acquisition` | Elbow joint angle | degrees |\n", + "| `ElbowVelocity` | `processing['behavior']` | Angular velocity (derivative of position) | degrees/second |\n", + "| `ElbowTorque` | `acquisition` | Torque command sent to the motor | arbitrary units |\n", + "\n", + "**Sign conventions:**\n", + "- Positive values indicate extension direction\n", + "- Negative values indicate flexion direction" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elbow Angle: 692972 samples\n", + "Elbow Velocity: 692972 samples\n", + "Duration: 930.0 s\n" + ] + } + ], + "source": [ + "# Access kinematic signals\n", + "elbow_angle = nwbfile.acquisition['ElbowAngle']\n", + "elbow_velocity = nwbfile.processing['behavior']['ElbowVelocity']\n", + "\n", + "print(f\"Elbow Angle: {elbow_angle.data.shape[0]} samples\")\n", + "print(f\"Elbow Velocity: {elbow_velocity.data.shape[0]} samples\")\n", + "\n", + "# Get data and timestamps\n", + "angle_data = elbow_angle.data[:]\n", + "velocity_data = elbow_velocity.data[:]\n", + "timestamps = elbow_angle.timestamps[:]\n", + "\n", + "print(f\"Duration: {timestamps[-1] - timestamps[0]:.1f} s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot kinematic signals for a single trial\n", + "trial_idx = 5\n", + "trial = trials_df.iloc[trial_idx]\n", + "t_start, t_stop = trial['start_time'], trial['stop_time']\n", + "\n", + "# Extract trial segment using timestamps\n", + "mask = (timestamps >= t_start) & (timestamps <= t_stop)\n", + "trial_time = timestamps[mask] - t_start # Relative to trial start\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n", + "\n", + "axes[0].plot(trial_time, angle_data[mask], 'b-', linewidth=1)\n", + "axes[0].set_ylabel('Angle (deg)')\n", + "axes[0].set_title(f'Trial {trial_idx} - {trial[\"movement_type\"].capitalize()} Movement')\n", + "axes[0].spines['top'].set_visible(False)\n", + "axes[0].spines['right'].set_visible(False)\n", + "\n", + "axes[1].plot(trial_time, velocity_data[mask], 'r-', linewidth=1)\n", + "axes[1].set_ylabel('Velocity (deg/s)')\n", + "axes[1].set_xlabel('Time (s)')\n", + "axes[1].axhline(0, color='gray', linestyle='--', alpha=0.5)\n", + "axes[1].spines['top'].set_visible(False)\n", + "axes[1].spines['right'].set_visible(False)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EMG Signals\n", + "\n", + "Electromyography (EMG) recordings from arm muscles are available in some sessions. Each muscle has its own TimeSeries named `EMG{MuscleName}`.\n", + "\n", + "**Note:** Not all sessions include EMG data. Check `nwbfile.acquisition` for available signals." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EMG signals available (5 muscles):\n", + " DeltoidPosterior: Preprocessed EMG from Posterior deltoid - shoulder extensor and external rotator. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " Trapezius: Preprocessed EMG from Trapezius - shoulder and scapula stabilizer. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " TricepsLateralis: Preprocessed EMG from Triceps lateralis (lateral head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " TricepsLongus1: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n", + " TricepsLongus2: Preprocessed EMG from Triceps longus (long head) - elbow extensor. Recording: chronically-implanted Teflon-insulated multistranded stainless steel wire electrodes. Signal processing: differentially amplified (gain=10,000), bandpass filtered (20 Hz - 5 kHz), full-wave rectified, sample-hold integrated (10 ms integration interval), then digitized.\n" + ] + } + ], + "source": [ + "# Find EMG signals in acquisition\n", + "emg_signals = {name: ts for name, ts in nwbfile.acquisition.items() if name.startswith('EMG')}\n", + "\n", + "if emg_signals:\n", + " print(f\"EMG signals available ({len(emg_signals)} muscles):\")\n", + " for name, ts in emg_signals.items():\n", + " muscle_name = name.replace('EMG', '')\n", + " print(f\" {muscle_name}: {ts.description}\")\n", + "else:\n", + " print(\"No EMG signals in this session.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot EMG signals for a single trial (if available)\n", + "if emg_signals:\n", + " trial_idx = 5\n", + " trial = trials_df.iloc[trial_idx]\n", + " t_start, t_stop = trial['start_time'], trial['stop_time']\n", + " \n", + " # Get first EMG signal for plotting\n", + " emg_name = list(emg_signals.keys())[0]\n", + " emg_ts = emg_signals[emg_name]\n", + " emg_data = emg_ts.data[:]\n", + " emg_timestamps = emg_ts.timestamps[:]\n", + " \n", + " # Extract trial segment\n", + " mask = (emg_timestamps >= t_start) & (emg_timestamps <= t_stop)\n", + " \n", + " fig, ax = plt.subplots(figsize=(10, 3))\n", + " ax.plot(emg_timestamps[mask] - t_start, emg_data[mask], 'g-', linewidth=0.5)\n", + " ax.set_xlabel('Time (s)')\n", + " ax.set_ylabel('EMG (a.u.)')\n", + " ax.set_title(f'Trial {trial_idx} - {emg_name}')\n", + " ax.spines['top'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LFP Signal\n", + "\n", + "Local field potential (LFP) recorded from the same electrode as the single units. Bandpass filtered 1-100 Hz.\n", + "\n", + "**Note:** Not all sessions include LFP data." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LFP signal available: 692972 samples\n", + "Units: volts\n", + "Description: Local field potential from M1 microelectrode. Recorded simultaneously with single-unit activity during visuomotor task. Signal processing: 10k gain, 0.1-45 Hz bandpass filtered.\n" + ] + }, + { + "data": { + "image/png": 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IqA3IvpUNWYpMK+fKvZPb6Cw3AFh7W0OWrJ3nJN1j0E2kY2XSMphYm2jtfEqFEpaulijOKtbaOYmIiIioefa/vB/fen+LE5+caPG5rmy+gl6zezXaztrTGtIkaYufj1oHg26iVqCoUmB96PoWn6eiuAJGpkYAAJFIBKWCCTSIiIiI9EksEWP89+OReDyxxcnNMq9kwmOgR6PtrL2sIU1m0N1eMOgmagWdJnZCRmQGSnJLWnSevJg82HWyAwBY+1hzLw8RERGRHkiTpKgqqxImQPq92A9OPZyQfTO7ZSdWAiIDUaPNrDytIEvi8vL2gkE3kQ4plUpACQyYPwDDPxiO7Bst+yDOjc6FfRd7AIBLLxdkRGZoo5tEREREWtXRy1n92O9HHFh4ADd33IStvy0AoMvkLrj+6/Vmn7OytBKGJprlAZKYS1CQUIDSvNJmPx+1HgbdRDpUWVwJiYUq+6R9Z3vk3slt0flkqTJYe6rqNrr1cUN6RHqL+0hERESkTdd/vY5tE7fpuxs6o1Qq4drbFeXScuyYvgMuvVwAAN7DvZF2IQ3HPjyG8FXhTTpnyrkUrOiyQphc0UTYR2E49Kb2ytKS7jDoJtKh4uximDmaAQBs/WyRH9ey5eAl2SXC+Ry7ObZ45pyIiIhI26J2RaEkp6TDznYXxBfAxs8GU7dOxfOXnkfQQ0EAVPl2HLs54tw35xB7ILZJ50wNT4UsWQb7zpoH3QFjA1CcUYwyaRkSjidwBWQbpr06RkRUS3GWetBdEFfQsvNlF8PMQXU+sZEYEAFlBWUwsdFednQiIiKi5qooqkBVWRV8wnyQEZkB1xBXfXdJ6+KPxMMnzAcAar2+7o92h4mNCdLC0yCvkEMsEWt0zqL0IkxaOwldHujSpL6EvhqK5f7LYWpnCufuzuh8f2f0erJXk85BuseZbiIdKskugbmTOQDA2NIY5YXlLTpfaW6pEHQDQJf7uyBmf0yLzklERP9dmdcyO+xsJOlH4slE+I7yReDUwBbtb27LYg/GwneEb533ufV1w/DFw+E5xBO3993W+JxF6UUIfDCwyWVmfcJ88PDvD8O9nzuSzyTjz9l/orK0sknnIN1j0E2kQ8XZxTB3NFc71pIyX4oqhWqG+/95D/NGyrmUZp+PiIj+u5RKJdb0WIOcqBx9d4U6kMyrmXDp5QL3Ae5Ij0jvUIM6SqUShWmFUMqVapMgden9bG9E7YzS+NyleaUwtTdtVr98R/hi6tapeObsMxj23jAuM2+DGHQT6dDde7ABwDHIEVk3srR2fhtfmxbvEyciov8maZIUdgF2uLPvjr67Qh2INEkKGx8biEQiWLpZoiy/TN9d0pprW6/ha/ev4Tuq7lnuu5nZmzUps7hSoYRI1HipsIbY+NjAI9QDaRfTWnQe0j4G3UQ6dO9Mt+dgTySfTtba+Q3EBi2aOSciov+ujMsZ6D+/PxKPJ+q7K9SBFKYWwtLNEgBg7W2NgsQC/XZIi7JuZCF4ZjB6PtFTo/aa1NvWNre+bki/yOo2bQ2DbiIdunem2zO0+UF3fSOgZg5mKM4ubnYfiYjovyn5bDJ8hvvA0MQQZQUdZzaS9EspV8LAUBViWLhYoDiz41yjlGSXYNy344RysI2RWEpQLms8n0+ZtAwSS83O2RhzR3NeF7ZBDLqJdKg0rxSmtjX7c8ydzFEmLUPEjxFNPleZtAzG1sa1jjsFO7F0WDuiVCg71P42Imq/sq9nw7GbI/zH+SP2YNPKGxFpQl8BYLmsHGVS7Q8kleSUNGnftZWHFWSpskbbRW6IhGOQY0u6psbMwQxFmUVaOx+1HINuIl1S1l5a9PDvD+P2Hs2zWVYrza07wYZrb1ckHE9obg+plZ3+8jQ+NfkUp744pe+uENF/WGVJJcTGYhiIDeA1xAvJZ7W39Yn+uyqKKmBkbiTcNncyR3FW6wfdxz8+jl/v/7VZj5VXyuu9TylXwkCsefhk6W4JWUrDQfeN327gzt93NF6yrgnfUb6IPxyvtfNRyzHoJmplhsaGUCqUWBm0skklHUpyS2BmXztTpvdwb6SeS9VmF0mHsq5lYeaemUg5w6zzRKQ/GVcy4NpHVV/YvpM98u7k6blH1BEUJBTAxsdGuG3maKaXoLs4s7jB4Lk+8ko5PpF8AkWVou4GTdyibeVhhcLUwnrvL80rxcU1FzFzz0xYe1k37eQN8BjggbQIJlNrSxh0E+lSPR/Obn3dUJxZjPRLmie6KEwthIWrRa3jBmIDiMQiLlnWocK0QqRf1k5SkrKCMviP8WcCPCLSq7yYPNgF2AGoWZHFzyVqqexb2XDo6iDcNncyR0l2Sav3ozSvFKZ2TS+/lXQyCQCQezu37gZN/BOxcm94eXnqhVQETg2EobFh007cCLsAO+THsLpNW8Kgm0hHKksqYWRqVOd9YR+EYerWqU0q6ZATlQPHwLr3+5jYmGiUqIOaJ3x1OH6Z/EuLz6NUKoUvbLFEjKryqhafk4ioOfJj82Hnbyfcdu7hzDJD1GI5UTlwCKwJuk3tTFGaq3nZLG1qTvmtqN1R6De3X50Z1ytLK2Fo2rTg2MrDqsHl5ZlXM+HU3amp3WyUgaEBFPJ6ZutJLxh0E+lIcXaxWubye7n2ccWZpWdw7MNjGp0vJyoH9l3s67xPkz1D1HyFaYWwdLVs0mPkFfJaeyRLc0th5qB6T1j7WKMgoUBbXSQi0tipL04h80qmMNMNAMEzgnF9+3U99oo6gtyoXDh0qQm69VHatKKoQpVdXIQmrQJUKpXIicqB331+dV5TFWUUwdzZvI5H1s/CxQLFGfUvr8+IzIBLT5cmnVNTVp5WyL1Tz4w9tToG3UQ6cm+5sHuZO5rD0NQQcQfjNDpfaW5pnXu6AcDSzRKFafXvGaKWKc4shq2/bZNWEySfScZPg35C9s2azPK5t3OFgRO7ADvkxaj2UBamFSL7FjPQE5HuySvkOPzWYUTtjoKJjYlw3Km7E3KjeYFOLVMmLVN7X+lDQWIBrL2tYWJj0qRSeGUFZbBwtqh3drowrRBW7lZN6ouBoUG9e8sVcgVKckp09vvq8VgPXNl8RSfnpqZj0E2kI8VZxTB3bHhEdM6VObX2HK0MWlnrS6KxkVpLV0sUpbM0hC4593BG5rVMjdunXkjFuO/G4fKGy8Kx3Du5sO+sCrq9h3nj1o5bUCqU+PWBX7Hr8V1a7zMR0b3SItIQ+loohr8/XO24SCRq1nJcorZGmiiFjbeNKnN6E2qEFyQUwNrHuv6gO7UQlu5NW/UGqHIm1LXUO/ZgLHxH+jb5fJryDPVExuUMnZ2fmoZBN5GONLa8HAAk5hK1vb2KKgVybuUg7t+a2e/jHx9H8ulkWLjVTqJWzcLVAoXpnOnWhYriCkjMJaqg+6rmQXfaxTR0f7Q78mNrEpnk3q4Jup26OaGiqAJpEWlwH+AOGx8bZF3PQsp5ZjUnIt25+ftNBE8PRtgHYbXvZMxNLVAuK4exlXGt40ZmRqgormi1fkiTpLD2toa5s3mTalXfHayXZNVO/pYfn9+sDOPW3taQJklrHb+95za6PdKtyefTVHWCxJLcEibbbQOaFXQnJSXh5MmTOHDgAC5duoTyciZwIrpXSXYJzJ0a3/tj5WkFWbJqRLUgsQAB4wKQeDJRuP/UklM4/uFxtT1S97J0tURRBme6dSH3di7sOtvBuaczMq9oHnRXFFXAzMEM8oqaZWV5d/LU9lD6jPBBxA8RcO7hDNfertgxfQf+nP2n2mOIiLSlqrwK2Tez4dbXrc77DY0NUVXGBI/UPHmxebD1t6113MzJrFUymCuVSijkChQkFsDG2wYWzhZNnum28bGBSFR3RZiMSxlwDXFtcr/sO9vXmQ1dliJTK6+mC7Z+tvjS4UtE7Y7S6fNQ4zQOuhMSEvDmm2/C29sbvr6+GD58OMaPH4++ffvC2toa9913H37//XcoFMyURwSoZrobW14OqEZAq7Nk5sXkIWB8ALKvZ0OpUKKytBK+I30R928c3Pu713sOC1cLLi/XkZyoHDh0cVAtN0vWLFndxuEbhSVjdy/XrCqtgpFZTUZ7j1APXF5/Gc49nOE+wB3ZN7PhPsAdn9t8rt0XQUQEID8uv8FMyWaOZijObv2aytQxFMQXwNa3dtBt7miu8/dVxI8ROPbBMWwetRnSRCmsvZo+0129Fxyoey92RXFFnTP5jakv6FYqlMJstK5UD7A1pUQt6YZGQff8+fPRs2dPxMfH45NPPsHNmzchlUpRUVGBjIwM/P333xgyZAgWL16MHj16IDw8XNf9JmrzGkukVs3G2wbSRNWyo7yYPNh1Us2qZt/KRta1LLj1dcOoJaPgOdiz3nNIzCWoLK5Euawc/8z/R2uvgYDcaFXyM5FIVGvp5anPTyF8lfrnnUKugJG5EQa9NggAYGhiiMrSyjpHzZ2CVRe/zj2c4TvCFy9cewH3r78fviN9W20pWPKZZBx590irPBcR6dfdtbnrYubYOjOS1DFVzxTfy9zJHMVZtYPuI+8dQcTaCK0899llZ3HioxNQKpSoKFQFxxbOFnU+b31kSTJh+biNr41ahZGWfCffG3SX5pUi82omxBJxs8+pqR6P9cBrWa8xSWIboFHQbW5ujri4OPz22294/PHH0aVLF1haWsLQ0BBOTk4YOXIk3n//fdy6dQtfffUVkpOTGz8pUQdXmltaK0laXRwCHZB1PQtAzayqR6gH0sLTkH4pHa69XTHkrSEwEDf856pUKHF1y1Vc+P4CSvP1UxOzI8q7kyfsw753X1rq+VRc23ZNrX31/1k1C1cLFGUUoSi9CBau6vvyxUZiLJYvhpGZEUQGIjgFO8FAbKDaT5bTOhe+cf/G4eSnJ7Fp5KZWeT4i0p+8mDzYd6q79CTQOjOS1HEVJNTMFN/NzNGszuD31h+3kHFFO4m+7DvZ49WMV2FsaSwEyObOTUukJq+Qw9DYUDhf3p084b7izGJYuNSfW6ch1l7WkCXJhIolxz44hjU918CuU/0DYNoiMhDB3NEclSWVOn8uaphGQfeSJUtgb1//h/Tdxo0bh6lTp7aoU0QdgVKhbDRQBgCHLg7CCGRBvOoLyynYCVk3spB+OR0uIZrVbxywYABOfnoSIz8bicQTiWr33fzjJrY/uL3pL4JQLiuHibWqnIeNT82qBED1BW3rZwtZas2y87h/4+A32k+4beGiWvpf3wxTXUvLrDyt6ky6ogs5t3IwcOFAyFJkLDtH1ME1NtPd1CClLVo3cB0DjFZWVlAGhVyBrOtZ9c5037uCQiFXwMbbBoWpWvreEQEWzhYwtjYWKsCYOWi+ckOpVKrNZlu6WaolqG3sb6chBmID5ETl4PtO3yP5bDKKM4vReVJnOAY6Nut8ze2DoopbgPWJ2cuJtKyytLJJH2wiAxEMDA2EiwQDsYGqhvOdPMiSZbDy0KwmpP99/liYuhDB04Oxfcp2nP7yNL7z+w7Xt1/Hjuk7GFBpgY2PjbD/vqK4AkZmRujyQBfc+fuO0CbpRBI8B9VsBajOLN+UL+ymLolrifLCcoxdNhaDXh+ElHPMnE7UUpfWX8Lu2bv13Y06FaYUNvidYuFi0aQ9sG1NQUIBUs+nCqvHqHV8YfsFDi86jG6PdKtzssHUzhQluerBb8TaCHSZ0gVKecu3Uskr5cLz2vjYwMxBtbVPbCTW+HosLyYPtn41+9Hv3WpRX5I4TZk5mqHTxE749f5fMfbbsZjx1wz0mt2r2edrKlt/W+TF5jXekHRGa0H322+/jaefflpbpyNqt3ZM34EDCw9otJ+7mlN3J1z/9bqQ4EZsJEZVaRXERuIm10219bPF69mv48qmK7DxscGxxcfwv0v/g10nO5Tmcdl5S9j41OzxyricAedezrDvbI+CeNWxsoIymNiYqO3Tqq6hnnsnV+OlZK21r/LuUX2PAR5IvZCq8+ckdesGrBMGcqhjyI3ORcKxBH13oxalUgl5pbzBxE0WLhbtshJG+uV0lBeW449H/0Doa6HIvpnd6GPSLqZx5q8Zzn17Dqc+PyXcLs1XbaU78+WZeoNIY0tjVBSplwyL3R+LkKdDtNKnsoIymNipVqQNe28Ypm5t+orb5DPJ8Aj1EG7fuw+9JTPdAPD0qacxYeUEBIwLgKWrZZOv7VrKIdBBo78L0h2tBd2pqalISEjQ1umomcoLy2tlW9SlqrIq3Np5q9Werz2oLKnElU1XYN9Fsy0ZAODWzw0XVlyAx4CaD/z69kZpwszBDC9cewFhH4TBra8bnHs4w7mns9b2Tv1XlBeq1xy18bERAuzYQ7HwG+UHG++aQDw/Pr9WYF09050WntZg2be7mTvWnXRG26RJUmHWyzHIEWnhaVAq2nctz+yb2fjrub/03Q2NlOSWIP1yOmIPxuq7KyhML8T3nb5HRmQGTn1xqvEHUL3yY/Nh52+nNqjVFgZW4o/EwzGo4eWsFs4WKM5oX8vLFVUKrO29FgcWHkDgtED0fLwncqJzUFlaiWVuy+p8THlhOX7s9yPiDse1cm/bv9t7byPpVJJwuyC+AL2f742nTz8NQxPDOh8jsZSgolA96FYqlBAbiQFRy5KUAarEZNU5dIxMjSAxl9Q8zz3LxuuTcjZFbZWauaP6kvh7S342h423DR78+cEWnaO5HIMckXMrRy/PTSpaC7o3bdqEI0eYAVfftozdgk8knyD1Qiqybmi+vKoktwSyFFmT6wPHH4nHb9N+Uxst/nfRv826aJNXyHHsw2NIu5jWruuEGpkawWeED7pO6arxY9z6uiHjcga8hngJx3o93Qv+Y/yb3Q+RSATvYd7CiK9nqCdi/olp9vn+iwrTCtWSn9kF2AnLFlPOpMCtnxuMrYxRLisHoKq5ee/STUs3S5z67BR8RvhonKm0tcr2XP/lOrrc3wWAqjyK3xg/3Np1CynnUlAmLWuX+yITjiXg8rrL+u6GRm7uuInRX4xG0omkxhvfRZszkYknE/Gd73fY//J+FKYV4vzy84jaFYWz35xFRmSG1gdh/gs16KvKq+AQ5FCzAkZahjU91uh92fa5b84h7IOwBttILCXCfti2rjSvFBlXMvCV81cI+zAMl9ddht9oP9h1Um3PSruYhqL0IpRJa7+ef176B0PeHoKUsynYPnU7UsO1v8qnNL+01XJztIaSnBKErwqHpaul2udCflw+HLo6qAWs9zK2NK4VdFdXAzG2quO+Jro76K7ruSuLG/4uy4vJE8qMCY+7a284AFVGdMumlwtrK5y6OSHrWhaW+y9v8H3J1R+6wz3dHUhZQRms3K0wL3oefnvoN6wOXq3xYy98fwHfeH6DpfZLhWPRe6Lx90t/13vRpVQqcenHS+g0sRPy4/OF49JEKaJ2RgEArv1yTeML95RzKTj+wXFsGLYB17dfR2Vp+7vgBwCIgBm7Z2g8qwkAVu5WuO/L+4R9SAAw5M0h6Dyps9a65TnYE9k3soXEIBuGbuhQFwS6UJRRBHPnmlrrYokYZvZmyL6VDQtXi1p71wpTa++XNHc0x2MHHsPQt4dq/Lz3jrBrgzRJii/svlA7lnAsAQHjAoTbvZ7shd8f+h3rB63H7id34+BrB7Xah9aQcjYFHgM92uSMfUVxhfBZqVQqcWvHLQx4aQCKs4s1nukpLyzHMtdlyIjUzqqVi6sv4qmTTyHu3zgM/2A4IjdE4qmTTyHxeCJ+CPkByWc0r0aiVCpRVVYFhbzui7birGJ8YvxJh7moUyqUOPjaQbX/u8qSShgaG8K1tyvSItIAqL5fuzzQBVc2X1G1Ka1EyvkUVJXXDC6X5JaoVUbQtuLsYkgsJJBYSBpsV10asb7/w7bkxKcn8EOvHzBz70wMXzwcUzZPgXMPZxiZGqGqrApJp5LgE+YjlGr685k/kXk1EynnUmBkboRh7w5D7MFYVJVV4exXZ7Xat7yYPPz6wK/Y89werZ5XX3KicvCl45eI2R+D+3+6H8aWxigvVA0258Wq74Wui4GhQb3vKRMbkxZXXGko6DZzMmtwwKtcVo7vO32PypJKtSXfd/+7TFrWrPrcbYmJjQkKEgqQH5dfb/6WxJOJ+NLxS4SvZulnXWhy0P3RRx81+EP6E7U7Cv5j/WHf2R7FWcXwGeEjfCje694L0ozLGXjkj0fgPcxb+PC7uPoixBKxcOGg9nilErEHY2HlZYUu93dRW7JSLiuHjY8NSvNLsfPRnUg4nqBR/1POpWDyj5Px2P7H8OfsP7Fz1k4UJBagIKEA6ZfSNfwt6FdFUYXasqamqK7rrCsikQg9Hu+B2AOxUMgVSL+Ujqtbr+r0Odu74sxiWDirlwjxHOyJiB8i4NzTWTgmMhBBqVBCliKDpbtlrfP4j/Fv0v4tU3tTlOZqd/999q1slOWXIet6FpRKJcqkqv3nd+/vtHCxwLRfpiF4ejBkyTJkRGZAXilHflx+nStnzn9/vk1dnMcdjkP6pXS49HaBNFk1oFRVXoW/X/pbzz1TOffNOfw06CcAqgEda29rGBgawK6THXKiGl/2p1QqsWHIBoz8dCQiN0Zq5XdfLi2HlYcV3sx7E8HTgzHk7SEQG4kxcdVEPLjlQcQdjkPmtcxGz5N+OR1fOX+FjWEba9Wur5ZyPgWOQY64ueNmi/vdFmTdyMLZZWdxe+9t4djWCVvRaVInBIwLwMVVF5F9MxsJxxIw6YdJyLySie1Tt+Nbr2+xfuB6RP8VDQD4e97f+MbzGxx5R3urBZUKJWL216xsivghQuOkTfZd1GsKt0XSZCmyrmVhUdEieIaqZlh7Pt5T+JwtyS5B1K4o9HiiB/JjVQNdCUcSsG7gOqwPXY8es3rAyNQIKWdT0HVKV1SVVakNgmji9JencXrp6Vp7lQHVpMWwd4fBwMigTQ4ANsWtXbewdcJWPPr3o5iyaQrERmI4Bjsi7aLq2jDrahacuzs3chZ1iiqFMGhtYmuCsvyWra5oKOi2cLZoMCN/angqQp4NwX1f3Vdvm4xIVQ6X9s5/rD8GvjKw3q2GGZEZCHokCGeXnVXL3E7a0eSge9euXWo/v/32G7744gssW7YMu3fv1kEXSVPpl9OFJc3vlL4DryFeQk1AeaUcu2fvVs1CVCmwuvtqYfnqiU9OQKlQInBqILyGegnLZw3EBvAa4lXnjMpS+6U48vYRhL4SCscgx1rJGbo80AWRGyJh4WqBzKuqC7acqBxcWn+pzr5XllQi+s9oBD0cBO9h3nin9B0UZRThO5/vsG3SNvz1TM0eTXmlHBFrI+r9Pdx9IRr3b1ydyxlzb+fi9t7bWl8+K0utO+hqK3xH+iL+cDzyY/PR66leSDyeqNcBjZgDMbi97zY2hm1s8Z4uXSjKVJ/pBgCPUA9cWHEBriE1tbirl4PXtby8OQzE2r9Qy4/NR/DMYKzuvho3f7+pShoz0KNWu+AZwXDu5Yys61kIejgIh98+jPBV4dg8crNaO3mlHPvn7xcuaNuCiB8i8OTRJ+HQ1UEIYmMPxiJ8RThyonIQvSe6wccXZxU3e2mtvFKOpfZL630f39p5C2kX04QSgNJEKWx8bQAAgVMDceuPxnNj5N3Jg3eYN4a8NQRXNl3BpXV1f55qSqlQqg26WHtZY9SnowCotkUEPRSEk5+cxJoea5B9KxtKhbLO309VeRUOLjwIh64OKMooqncbS+qFVExcPRFXf9b/YF/c4Tgkn9V8Fr8uWdezMH7FeJz/9jwA1fYSW19bhDwVAktXSzgEOuD88vMYsmgIjEyN8MCGB2DubA7X3q6wcLFA6nnVkub8uHzMvTVXKB3ZUuGrwrEyaCW2jt+KiuIKKOQKJBxLgP9YzbYrufdzR+r5VK1scdk2cZtO9k1/6/UtHIMc6x3kvu+r+/DATw/AoasD8mLzoKhSwLmHM166/RJez35dWAo9+K3B6DShE+y7qtdk1kTSySRcWHEBP/b7Edm3aq6Bzn13DnEH4+Ax0AO2frbIj2v4M1KpVGLTiE1q+/5Tw1PbTNKrq5uvYtSSUeg0vhNMbVWBbe9ne+PkpydRWVqJ4uximNiYNOmcd+dLMbU11elMt7mzeYMz3annU9HryV5w7+de+87/32+efSO7yQMLbVHYB2EY+enIej9rpIlShL4SivuW3oeo3VFq9ynkCiGmoOZpctB9+fJltZ/r168jPT0do0aNwiuvvKKLPpKGxn83XlieLBKJYN9ZNVqtVCixddxW5MXkIWZ/DHZM3wFLd0tc/fkq5BVyJBxLwOilowGosmhnXcuCQq6ASCyCS08XJJ9KxvrQ9cJSZIVcASsPK1h7W8PGx0YtI2JJbglM7UwRODUQJz45ge6PdkfWVVUQf/GHizj67tE6L9rOLz+P4JnBQj1kQxNDjP1mLKbvno6q0ipYuqkC2crSSvzY90cc/+i42iz+v4v+FRK6fW79uTBQcPDVg7U/OKoUWNFlBX6Z/EutetYtJU1S3xPU1pg7maNcVo79L++Hx0APDF88XG2WpjWV5JTg+IfH8cukX5B5JbNZJV62jN2i0yRURRlFsHBRn+l2CnaCUq6Ea5+aoNvKwwqyFBlKc+v/4teVMmkZDr99uNFyX3mxeRj+/nC8WfAmjrxzBLse24VOEzrV2bb3s70x58ocDFwwENk3spEflw/Hbo5qAaUsWQYDQ4M29SVcWVIJcydztaD79t7bCHk2BAdeOYBDrx/CL5N/waaRm3ByyUmsH7Re7fH7XtyHXyb/Iix/LpOWIeZADDYM2yC0KcktEQYS75Z6IRWleaVqddzvdm3bNUzdMlVVwkauUH1WeKo+K7yHegsBGKD6jIo/Gg9FlULtd555LRMuvVwgMhDhf5f/V2c/6pN1I6vWQE5BYsPJGg2NDREwLgDDFg/DqqBVOPnZSXxh+wV+e+g3tX6lnE2Bz0gfjF8+HhNXTYS5ozl2zNhRa8Az62oWPEI9IJaIhVnFb72/Rfrl5g38KZXKOpe/l+SW4Nx35xp87Plvz+Pou0cbfY78uHxc3VJ7kGD3k7txZdMVeA/zho2fDaRJUsQejIXvaF+hje9IX0T8ECEEeGIjMSatnoTx34/HE0eeQF5Mnuo709YUNt42MDQ1rLXEvKq8qkkDkjH7Y5BwNAG2frYY9MYg/P7Q7/jrmb+atNrGa6gX9r+8H9/5fqfx89anIKEA17Zda/F5ANVKibKCMuTF5qHPnD4Y9+24ett6D/WGU7AT7ALskB+Tj4KEAtj42sDKw0ptG9foJaNh5WEFx0BHtcBZE0qFEq8kvYLRX4zGz6N/RklOCZYHLMfF1RcR928cjK2M4RLiIlyP1Jccc9OITXAIcsDuJ3ajvLAc4avCsW3CNhx89SAK0wtx/dfrTZ6F14bK0kpc3XIVVeVVCJ4erHafpasl7DrZ4cg7R9DrqV5NPndFYQUklqoBE63MdDfw3dvQTPeFFRdw5J0jcO3tWuf91Tlbsm5kNZqEsL2o3n5Rl+prWL/7/JB4TP36OOlUEr7v9H1rdLHD0sqebisrK3z44Yd47733mvS4JUuWoF+/frC0tISTkxOmTJmC6Gj1mYiysjLMnTsX9vb2sLCwwLRp05CZqX6hkZSUhIkTJ8LMzAxOTk54/fXXUVWl/oY6duwYevfuDWNjYwQEBGDjxo3Neq3tSXXQnX0rG869nDFm2Rj8++a/MHcxx/Rd03HknSPY89we+I7yhWOg6sPEubszMq9moii9CBauFrDxtcGVzVfg0NUBvz7wK/6Y+QfOLjuLgHEBmL5zOgDAzN4MubdzkXQqCV86fAnnns4wNDGExwAPBIwPQO7tXGREZiA3KhdD3h5S50VS+qX0Wh/cHgM80PWBrpgfOx9iYzGqyqoQ/Vc0fEb6IPTVUCSfSYZSqUT65XTc2XsH0X9GCwMDyWeSUVFUAQtXC+yYvkNthiv3Ti5CXw3F02ee1mqJpOxb2dgydgusPFs+06lLD+94GGkX0+AT5gPX3q7IvKL5hbs2rQ9dD5cQFzx24DFMWjsJSSeblkyqXFaOkpySOi+I69PU2fT8GFUW4rsZiA3wRu4bwgARoArEz31zTjVYpYMyIBVFFTj05qE677u27RrkFXL8PbfhJdQFcQWw9bWFibUJ5lyZgzdy34B957oz7JvZm8GhqwNEIhEMjQ1RVlAGu052KEytWW5WkFgA35G+rRJ011djPmJthLBa5e7litVBt1KpREF8AYIeCkLM/hjM2D0D3Wd1h7WXNY68fURtpYu8Qo7K4kr43adKJpd8JhmbwjYhfEU4DI0NUZRZBKVCiR/7/YjNozfXWnJ9/dfr6De3n3D8yHtHhJU9FUUVqCyphMRCAisvK8iSZZAmS4XPCgND1cqG6vfn2a/PYvPIzdg3dx/2vbBPeI7MK5nCbIu1lzVkSTK1Pvw97+86l4LLK+RYHby61sBW1vUsOHZr+EJy5p6ZGPHhCDx74Vkcfe8oJq2dBHm5XJgpUSqVSDmXAu9h3nDp5YJOEzphyqYpUMqV+MT4E2E2ec//9qCiuAJiIzHsOtsh93YuKooqYGJrgh/7/tiswTNZigw/Df6p1vHk08k4sOAAlEolFHIF5BXyWsGsUqHUKLFh7KFY7Hp8l9qxpNNJKEgoQOyBWDgGOcIz1BNJp5NwZfMVBE4NFNr5jvRFv3n9YGRqpPZ4+872cAx0hDRJiu8DvofnEFVQ7t7fXViyW23j8I0ar2goLyzH1vFbEbM/BrP+noV+L/SDoYkhUs6moOeTPTU6B6DKMWLlaQW/UX71Ju1Lu5iG4qxilOTUzj1R/T4ul5XDKdgJ+TH5yL6Vjc9tPm9REsBfJv2CP5/+E8c/PK6Wi6IhZg5mKMkpQe7t3Ho/7wDVZ0ZudG69K4wUcoXaCruijCKYO6lWQXW5vwt6zu6JyI2RcO3tiscOPIa3i98GALj0ckH65XTIK+X4yvmrWtc/iioFTKxNMHHlRASMD8Dlny4j4ocIjFs+DtIkKb71/hZ/zPwDyaebvyqjOKsY8kp5ncvgGxJ/JB67Ht8FA8O6Q4VOEzrh3DfnmpV7plym3ZnuktwSmNnXXaa1oZnu1POpePLYkzAyM6rzfnMnVX4VWZKsTU+oNJWRmRHKC8vx+8O/C4lgAaCqtApGZkbCnv27r5nSLqbB2tta7fs4LSKtTa5SbKu0lkhNKpVCKm1aUqbjx49j7ty5OHfuHA4dOoTKykqMGTMGxcU1I1KvvPIK9uzZg99//x3Hjx9HWloapk6tqb8nl8sxceJEVFRU4MyZM9i0aRM2btyIxYsXC23i4+MxceJEjBgxApGRkViwYAGeffZZHDhwoOUvvA2z62SH7OvZyLyaCdcQV7j1cUPu7VyEvR8GibkEb+a/ibh/44TsxQBg5VlzQWjtaQ2RSDWjMnndZDx16ikoqhT4981/4d5ffRnOtG3TsGHoBkxcMxEDXx4IAJj1zyz4jfKD5xBPbAzbCGNrYzgGOgp7xVIvpCLptCrQqiypbHAvtENXB+RE5yA3OhfdZ3aH91Bv7HluD46+dxRre6+FjY8Nbu26hW+9v8X45eORfikdaRFp8Bnhg4VpC3FrR83SzaxrWXDq7gSnYCdk39De8q3YA7GAEnDr46a1c+qC2EiM17Nfh5WHFQxNDOvNJqyQK9QS5LVE5MZItX2e5YXlcO3tiokrJ8J/jD/87/NX/f4AofxWY3bO2olxy8ehOKu40b1H1TOLn5p8ih3Td2i8p7SiuKLOxEP3jqh3ntgZlm6W9ZZLaQ6xcc1sYNzhOISvCEf8kfha7TKvZGLggoGwdLdscI+vvFIuBBn1XWDUpTqDq0tPF7WtJtIkKXxG+jS6dLKl5BVyfO3+da33YkVxBQ6+ehBbJ2yFUqlEflw+bPxsAKhWHuTdzkPG5Qy4hLjAZ7gPZh+fDYeuDgieEYwpG6fg3Yp34drHFYXphUKCL/9x/ug3tx+id0fj5GcnMWHVBMzcMxPBM4MR808Msq5noeuDXTHph0lIOVuzskBeKUfenTwEPRwk5LdIPZeKa1uvQalQYonlEviOVM2A2gXYIS8mD7JkmTDTDQDW3taQJkpRklOCxOOJGLhwIGIPxCLnlmrwICMyA1G7o4RcAiIDUa2LneTTyTi86LBaorLCtEIs918OnxE+tVb2pIWn1b2ksg5ufd3w4JYH0ee5PujxeA8kn0lG5tVMfGz0MdLC0+DWV/1zb8DLA9DtkW7CYJo0UYpZ/8wCAOGzNy0iDcEzg/HC9RfU9h9rKi1cFaDeu3oq40oGnLo7oTC1EEfeOYI/Zv6BJRZLhItFeaUcIrEIxlbGdWa2vlvmlUz4hPkg8aTqd6dUKvHPvH/w4JYH8dKdl2AgNoBTdyec+/ocutzfRS3ANrUzxYTvJ9R77mnbpmHi6ono9nA3AKp8EXcHZftf2Q+lXNlowHXqi1OQV8qRcjYFo5eOxgvXXgCgKnE4fdd0zIueVys3RWNevP4iAiYE1LkEv6KoAj/2+xFfOX+lVq9ZUaXArV23sK7/OtzedxtpEWlw7eOKSWsn4eDCgyiXlquVmtLEzR03kR+XD6VCCfcB7vAe7g1pohR+o/w0erxIJEJOdA72PL8HTsFO9baz72KPCysuYHnA8jrvj/knBnv/t1cYqIvaHQWfET7C/a4hrohYG4H+L/WHjbeN8Fnr1M0Jpz47hb+e+Qv95vbD7id3ozSvJsAsSCyAtY/qc6D/vP64tPYSfEf7ovvM7nj070fxZv6bmPHXDOREN5zzoUxahos/XMTB1w/i3LfqqzzWDVyH89+dxxLLJU1KjJhyLgXTd0/HhJV1v4d9R/gieGZwkzJ6CwMyheXan+m2r3um29TOVO13rva4/FJ4D/Ou97zVtbrv3YrT3nkN9UL8kXjc3HFT+JtUKpVqg052nezUBtVzb+ei75y+iD+qug5RKpT4se+Pja6yoxpNDrqXL1+u9vPdd9/hrbfewvTp0zF+/PgmnWv//v2YPXs2unXrhp49e2Ljxo1ISkpCRIRqNFEqlWL9+vX4+uuvMXLkSPTp0wcbNmzAmTNncO6c6kPl4MGDuHnzJrZs2YJevXph/Pjx+Pjjj7Fy5UpUVKhG9dasWQNfX18sW7YMgYGBmDdvHh566CF88803TX357YqprSmMzI1wZukZuPVzg4GhARYkLRBGZw1NDLEwdSGcutV8EYlEIogMRCiILxBmYVx6ucBAbACJuQQP//4wHjvwWK1RZls/WwxcOBC9ZveqNSo67ptx8BjgAY9QD2H2vSSnBOErw3Hz95sajZK59nFFyrkU5Ebnwq6THVz7uGLaL9OQcCwBz55/FuNXjMf8mPlYrFiMkKdDUJRehJRzqizGlq6WaqPx6ZfS4dLLRVXCookjv/UpTC9E6oVUvJn/ZvsbDRWpPjzllXLc/OOmkGE3Zn8MlvstR/at7BZlG866kYXwVeG4vfe2EERmXc+CY3DNDJuJjQnMXcxx5587+M73O7WR17tF/RmFXY/vwpF3j8DMwQxeg70w5K0hOPreUZTLyiFNktaZdfPG9hv41vtb+I7yxY3fbtQ5q5YTnaP2OpUKpcaz1kZmRrhv6X3C6g9tMHM0EzKYZ9/MxoNbHsSVTVdqtZMmSWHpbqkK2urJRl9RVNHsAYHBbw7GlI1T4NKrJug++PpBHHn7CHzCfOpdTq0t4avD4THQo1bAmH4pHUMWDYFbXzdkXs1E8tlkYYmgSCSCY7AjTn9xGl3u7wJDE8NaF1ZiIzFcQ1yReDwRd/6+gymbpmDgywNhZq/any8SiYQETT4jfPDnU3/i5/t+htcQL7j3d1crMZQekQ6PgR5wDHRE+qV0VJZUQiwRwyPUAxE/RmDQG4OERIl3B9137/93CVHNiCUcS0DgtEB0mtAJEgsJ3Pq5IeafGJz+4jRGLRmlqmv7/wzEBsJ7VqlQwsrTCs49nNUGZ2IPxqLfvH64f939iNwYqZbDISMyA849NNunKBKJ0GNWDwCA+wB3/PXMX1jTcw26TumK2EOxtQZNvYZ4YfK6yUiPSEdhmiqrf3XfnYKdsPOxndg8ajM8BnrAoYtDg/uZlUolvu/8fa1a19m3suE32g95seqrLbKuZSHkmRDEH4lH9s1s1WBFD2ccee8IcqJykHVdNfDqNdQLSSeTcG3bNdzee7vOWU5pohTTfp2GQ68fgkKuQEF8ATxCPWDtaS3U7HXq5oS0i2kInhFc6/ENqR4Eql7u7NbXDWkX0oTXnHImBVM2TRFmKv+Y+Yfa4ETunVz88/I/OPzWYSSdTELU7igEjAuAjY9Nk/pRH9+RvsKA9d3f03H/xmHou0MR9FAQitJrZhATjifgt6m/QZYiQ9TuKJxZegb+Y/3h0MUB0mQp+s/vj5Tzml2gFyQU4MrPV7DvhX24uOaiaiLA2xoDXx6I2cdnN5qF/W4vXn8R/eb2g/uA+geYTG1N4dLLBfad7bF79m61+ypLKnF40WH0fbEvDrx6ABmRGbi54ya6z+wutHHp5YK8O3m1Bt3FEjFejn8ZV3++isBpgQj7MEx95d3tXNh3Us3ASywkyL6ZLeTasPG2gcRcovr7+P/Jiiubr9QamFYqlVhqtxQHFhzAhe8v4OCrB7Fh6Abc3HET6waug4WLBY68cwTD3huG/S/v1+h3plSo3n+dJ3ZWGxy8m8RCgmnbpml0PkB1zSkvVw3y3z3TLbGQtDi/TmVxZb2Dyaa2pigvqPuaAkCD3/N2AXa4uuUqrLza9grGpvIJ8xFK7FVPfp1ddhYmtjUr+Nz7uwurQavKq1CUVoQej/UQvl9yonPgOdgTd/6+U+/zVJZWaq3SRkfQ5KD7m2++UftZvnw5jh07hieffBI//PBDizpTPVNuZ6f6IouIiEBlZSVGjx4ttOnatSu8vLxw9qyqvMPZs2fRvXt3ODvXXDiMHTsWMpkMN27cENrcfY7qNtXnuFd5eTlkMpnaT3s19uuxqCqvEpZV1ffheTfnns64s+9OvW39x/jX+YU3dtlYGBrXfWH/6L5HMWD+AFh5WCE/Lh9fOn6JuMNxyLuTh7L8skb3wQaMDUDM3zEoKyiDqa0pRCIRvAZ74elTT8O9vztsvG1g7mQufHgqlUpcXH1RmH0xNDEUAuzsG9nCQIOBoYFWytd87fY1bu642eRkIm2BW1833Np5C9F/RePMl2dw5qszSDqVhH9e+gf95/fHqqBV2Dd3H85+o/p7ufP3nToz0kuTpAhfFY6YAzHIvJaJk5+dhLxCjsiNkRizbAygBD41+RRxh+MQ928cXHq6qD2+88TOOPHRCfiP8cfpL0/XeQF89L2jCBgfgJOfnkSnSar9yD5hPihMLcT2B7fjW+9v8e8b/9YaTEk4noCHf38YM3bPwBt5b0CWrP43LU2SYmXXlbi1q2ZFRFFmEcxd1JOotSZzR3MhkZE0SQr3fu61lnKWScsgsZCo5XCoy+WfLqste20Kx0BHOPdwhl0nO+TH5qtmXS9nwO8+P7j1cdNp+aeSnBLE/BODKZunqO15vrL5CjYO2wjPQZ7wCPVAekQ6rm6+qvYag6YF4cZvN+pMFlfNJcQFf8z8A38+9Sf8x9Qkmbp71hwAbH1tsahwEbyGesFvlB+s3K1wZ98dXFhxAYCq9JpPmA/MHM2Qej4V26duh4mtCbo/2h375uxTG6R07u6MpJNJqCqrUhsIce/vjoRjCUJA6DfKDy9cewHD3x+O00tPo0xahs4T1ZdxWnpYCvuhC9MKYelmiW6PdBNmIgAg+WwygqcHw9bPFtZe1jj56UkAqkDCwNBA49rxd7P2ssbDvz+MxYrFGPvNWEzdOrXOdsaWxijJLUHC8QQhgRygClBm/T0LA+YPgNdgL4gMRDAyN6pziWl5YTkOv30YlcWVaoNl8ko5cm7mIGBCAPLj8lFRXIHPLD4TltH2erIXrv96HfJyOfrP748JKycg6WQS9i/Yj79f/BtdH+gKv9F+iPs3Drf33MaOGTtqLSNXVKlym1g4W6DL/V2wxHIJ9r24D56D1esRG5kZ4fWc14XcI81lZGoEiFSvuSi9CE49nOAY5AgLVwus6LICWdezcON31XVN8tlk7Ji+A4Bqy9Du2bshr5BrNdlTdTB4e99tfGTwETYOVyW8vL3vNno/2xsP//6wMEBaml+KY4uP4YkjT+DxQ4/j2tZr6Dy5s/A5//TppzHmqzHIuJxRa6A94ViCainr/3/m739lP/5961/sfmI3vId5I/5wPA69dqjB5eENEUvEGLpoaL3XJ9Ue2/8Y7l93P0rzStVm+OIOx6Hnkz3R9399EbEmAjsf2ylMZFSz9bPFfV/eV2fgZ+Njg6fPPA2f4T7wGuKltnIh704e7DrVbGGac2WO2upDQLUKpnoryT/z/8HlDZcBqFZ0KOQKFKYWInhGMGb9MwvzY+bj5fiX4RjsiN8f/h1mDmYY9dkoOHR1QN85feEY5Ch8r1Tn9Klr+0LSqSR4DvGsd2l5c0gsJUIunrtrXhuZGmklqW19wbOxdd0rWsqkZWrbxOriM8IHaRfTMHSR5iU/2wOnbk64vfc2uj/WHVG7opB0OgkX11xE72d7C23sO9kLiVLXD1yP1PBUWHlYoShNNdB2e+9tDHh5AFLPpaIoowibR22u9TyxB2LxQ0jLYsOOpMlTH/HxtZc3aoNCocCCBQswePBgBAerRoszMjIgkUhgY2Oj1tbZ2RkZGRlCm7sD7ur7q+9rqI1MJkNpaSlMTdUDviVLluDDDz/U2mvTJxMbE8y9ObdJj3Hu6Yzz353HiE9GaK0fwge3SLVcL+SZEPSb2w/H3j+G3Du5jY7MSywkMLYy1jhx0MRVE1GaVyrMvnSa1AkXVlzAoNcHQalUCv2xDbBFXkweHLpqXlP7XvJKOTwHe2LwG4ObfQ59Cn01FL9M/gXWntZ4ZMcjuPzTZfz51J946NeH4NrHFWEfhGFtn7XCMuaj7x2FrZ8t7DvZq11khq8OV+0PVwL2Xe0hS5bh6OKjyI3OhWeoJ3o83gO2/rb4efTPAIBX019V64fXEC+knEvBizdfxOFFh3HozUPo+kBXeA3xAqCqM+vS0wXdH+0O1z6uanutzZ3MkXElA+O/Hw+JpQRH3z+KscvGCvfLkmQIeigIAGAqMVXt8fr/JGmpF1KxbsA6TFo7CXue2wNLV0t4DfGCNEkKG28bXf3aG3X3THdRmirHgsRCddFSfbGScDRBWOJYHXQHjK291zHhWEK9gZHG/bFX7Y1MPp0Mj1APjPx4pOoOUe0s2NqSdT0LfqP9VMmQ7lrGfmffHYS+GgqPgR4oSCzAv2/8C7vOdmqzrV5DvTDnypxatdTv5hnqiZcTXoZSrlRLmDd88fBan0kSCwke2fGIcHvUklG4uPoi+s/rj+QzyRjw8gCIRCK8HP8yllguwZBFQ+DUzQnBM4LhPbRmlt3ay1qVvOyeX5dLTxfk3c6DWCLGoNdVs+IikQjGlsbCVpB7ufV1w7r+6zBx9URYulvC1s8Wjt0ckXNTtRS1rKAMeXfyhNcyfed0/DL5FwCqahKN7eeuj0gkEv6erD2tGxzM7T+vP7Y/uB0v3nxR7fH+Y/zVBjqCHg5S7Yt/oR8A1eqObZO2IeyDMFz4/gIe3fcoNoVtgsRcgu6Pdsf+Bftxbds19H2xLxJPJCL5TDLM7M3wY98f4RLiAhMbE8z6exYU8pq9/i/dfgnrBq5D3zl94d7fHUqlEjlRORAZiLBItgjbJm7DugHr8ND2h2DjY4PEE4lw66cauA2eGYzc6Fxc2XwFk36YVOt11reftKk6TeiE36b9Bq+hXkLA+sD6B5Afp0oGdunHS7jz9x1EboiE/1h/jF4yGkqlEhE/RGDs12MbOXvTBYwPwIFXDmDI20OQdDIJubdzUZhSqPbZqFSoEtr5jvaF7wjVNoqFqQvVBqGrgxv/Mf6I3BiJkKdCAKi2/mwasQkAMGHVBLj1cUPyqWRUFFfAf6w/HLs54qHtD+Fjo4+blbCrqaw8rBD0cBDSItJgF2CH6D3RiPs3Dv3n9od9Z3u8r3wf3/l9h55PqO+RFxmIGiz7Wb1qxtrLGtk3s1FZWgkjUyPk3s5F58k1g2l1rTwxNDYUVom593fH5fWXIUuSIXJjJCaumYjsm9nwH+sPnzAf4TGTVk+ClbsVBr02CIYmhphzZQ4AwCHIATm3cmDuaI5tk7Yh7lAc3Pq5qQVbgGoWs77kYs1VnZTM3FGV0LV6ssXITDtBd30MxAZQymsP4t874FEXM3szPHfhOV11TW9EBiJMWDkBncZ3QlVpFTYM2YDpu6ervYeqV2UBqmoWT518CoBqECjxZCJu/XELs4/NRvaNbBx64xDij8Srfd4Cqi0KTsFOyI/Ph61vw7Xc/wu0N4TVQnPnzsX169fx66+/6rsrWLRokbBHXSqVIjm5ZWVF2huXni6oKKrQ2TJp+y726PFYD7iGuMJjoAc2jdikNgtSn5BnQjQueWLjY6P2hdHziZ5IOJaA7JvZahkoq/eKt0RxZjGcujvVGp1uLyTmEngP98atnbdg5WGFrg92hddQ1RJaA7EBTG1N8XLcy3Do4oAf+/2I4EeDkROVg6/dv1bLIJ8fk49HdjwCQxNDZF3LwkPbH1Jlwq9SwMDQAN0f7Y7x34/HizdexPvK92tlBTe1M8UjOx+BY6Ajpm6ZirhDcTj+0XEAqlm5g68ehPdwVfDi0MVBbQQ+YHwAAqcFov+8/ugxqwcK4guETLS/PfQb7Luqz5D4hPkIs2Y3fr+BZy88iz7P9cGcK3OExGz6zkRv7mguZLtVKpQwEBvAuZez2lKt+KPxwt7Ge2e644/Eozi7GEqlElVlVbWSOTVVdVAdezBW7b1u4Wqhs3qeWTdUib5EIhEsXCxQmFYIpUKJ8sJyjPlqDAxNDGHnb4fov6JrzQKLRKJGl06LDESw8baBrZ/6xUDPJ3o2uM8PAHo92QuWrpbIvJYJE2sTtd+v7yhfIbv9tF+m1ZotGvXZKEzdUnsQJGB8AKL/iq61VHvSD5MwYUXtfZU9n+iJ+bHzEbM/Bjsf3Qm7ADtVdvT/z3oe8WMEQl8NVXuMUqlK2JZ9K1tIoKlLXad0xXuV7zX6XJ3Gd0LC0QTsfnI3jrx7BBuGbYD3UG8c//A4njnzDHyG++ClmJeEfAwF8QV4t+Jd2AXYIelEEpJOJal+TysnqO1BvXfQ5enTTwv1qkUiEUQikTBoZO1tjYLEAlxccxEAcOO3G+j+qGoJsa2vLaZsmoJ3St/R6WBcl/u7IO5QHI4tPibs3wdUM6m+I31x//r7EbE2AhVFFRi9ZLTwOh4/+LiwXFebOk3shIL4AoR9EIaQp0MQ92+cWhlFSw9LyFJlyLyaCb/RNfusq1ek3av/S/0RtaumokheTB4GLBiAl+68hL9f/Btbxm1Bnzl9MPfmXMzYPQPD3x8OA0MDPHHkCSEvgq45dFElYsy4koFf7/8VN7bfUAvOXo57uUV/O32e74N9L+zDqS9O4fae2xp/zxRlFMHKwwoz/pwBeaUcY5aNwb45+6CoUtR5/THs3WG1BuvuztIuNhLj+Yjn4dTNCWe+OqO2AiE/Lr/W52JLVQfdgPqebl0H3fXJvpXdogmX9q7fi/1g7mSOvnP64rGDj9UasK/eC18mLYOxtbGwwtW5pzPCV4Rj4IKBMDQxRMgzIbj681UEPRxUK8dL1vUsDHtvGNb2WdviZHkdgdaC7lWrVuGjjz5q1mPnzZuHvXv34ujRo/DwqFkK6OLigoqKChQUFKi1z8zMhIuLi9Dm3mzm1bcba2NlZVVrlhsAjI2NYWVlpfbzX2LXyQ7Tfp3W4AxRS8w+NlsYTRv02iA88NMD6DK58YDVJ8wH9y29r1nPaSA2gLGlMeKPxKstN21sL6EmCtMLYenadmtza6L3M70xdZsqCHDu7owHfnqgVpuQZ1WrEwa9OggeoR7oPKkzMi7XBICVpao9VQ6BDsi+kQ0DsQHKCtSXb4lEogbLbgQ+qFoeLLGQYE7kHEgsJCjJKUHsoVhE/xVd78BG8IxgDH9vOADVqor+8/oj+s9oyCvlqCqtwrhv1EvLBE4NxO09t5F8NhlXN18VtiFYe1kLGbr1HXRX1/6+m1sfN6RH1OzJLYgrEPaV3p3NWqlU4ucxP+PqlqtIPZ+qtYz6SqUSWdez1Jaw2gXY6axWd9b1LGErSMC4AMQciMGdv++oBdMGhgbo9VQvtaRGrcW+qz32z99fK7CdsXtGnSsOGtNtejfc91XtzziJuaTOmW4DsQFs/WwxY/cMVBRVCIGBU3fV0sG08DT4DPdRe0x1YqDc6FzYd2nect2m0mSJqsRCgsriSkiTpDj56UnMPj4b9311H8oKyoQZeTt/OyiqFLi69SqMzIwgNhLD3Mkc7gPcceKjE/Ae5g2vIV4NJuW893ut//z+6DunLwBgzLIxeDnuZSSeSERRZhHy42pXL9BmssS6mDuZ433l+5j2yzR4DKi9NcLIzAjFWcVN2tPcEqa2pniv8j2IjcRwCnbC5fWX1fZGV1cKyLqWpdHSdiNT1f9bdeBX/Ti7ADs8uOVBvJL8Cno/o5p1NTQxFP6/fEf46vx3X82+iz3iD8dj//z9mL57OrpN76bVqhQ9n+iJoowiXF53Gc49nTW61jJzMMPtfbfhEuICtz5umLplKkIXhuLZ889i4sqJGm9tcwxyRPbNbFWALQJce7ti+AfDkRaepjZoWxBXoP2g29IYFYWqrV/lspoVW0ZmRqgqbX5JtKryqsa3ydTx35ceocrvQ4D/ff71/n3l3MqBQ2DN4IRjoCNu/HYDXkNVqxCtPa0xP3Y+/Mf4q1XIyI/Ph6mdKbo90g3D3huG679ex9YJW3X7Qto4rUVVf/zxR5PLcCmVSsybNw+7du3CkSNH4OurPorZp08fGBkZ4fDhw8Kx6OhoJCUlITRUdZETGhqKa9euISur5j/60KFDsLKyQlBQkNDm7nNUt6k+B6kTiUS1ajLqilgiRvCMYK3uG6qPcy9nXFp7Se2CwdrbGtLkliWCqi6v1p5ZeVg1OvDhPdRbWBI4ee1k9J/fXyhvc/f+616ze2Hyj5MBqPZDVZfDaY7OkzvjS8cvkXQyCU/8+4SQBLAxnoM8cfmny1jdfXWt/ZeAasS9orgCt/fexsy9M4ULKpGoJiO0NEmq1/Jv5o6qUiXySrnw9+Hax1WtpJBSWbOs++6kWtF/RmPo20Nx7utzWB+6Xm0GqiWMTI1QVlCm9uVs529XK5GVtsiSZcL/gd9oP8QfjkfkhkgMXDBQrd0DPz3QYKClK849nJFyLkVrF24WzhYY9Gr9S1Qb8pbsLSE7c9iHYbiw/AIqimpn33cJcUH6pXTkxeQJAzZtRfWF8xt5b8CpmxPMHc0x+/hstaBkxMcjcHH1RQTPVH1HiUQihH0QhoVpC5uUlb9ap/Gd0HVKVwCqwQ0jMyOM+mwUfpv6m15nwYJnBNcbSAROCxRyWrQmh0AHZFzOUBsMcAxyROaVTJRLyzUO/MI+DMM/L/0DQLUvuXpGv8esHnr5O76XibUJsm9mY/TS0ej6QFeM/65pSYI14TvSFz0e74GZf83UqH3gtEAceOVArdwc91aSaYyNrw0K4gpQnFUsfJ/aeNug+6zuiD9cs320orhC6/8Xd890VxRWCCszDE0NWzTT3VDm8voo5ApkXc9qtYHH9srY0hgp51PUJktcQlzgNdQLVu4110e2frZwCnZSC7ovfH8BA+YPAKDaCnXi4xNIOJag8f/174/8jpj9MR0qO7rWIp3Dhw8jLi6uSY+ZO3cutmzZgm3btsHS0hIZGRnIyMhAaalqCYK1tTWeeeYZLFy4EEePHkVERASeeuophIaGYuBA1UXXmDFjEBQUhMcffxxXrlzBgQMH8O6772Lu3LkwNlb9Qc+ZMwdxcXF44403EBUVhVWrVuG3337DK6+8oq2XT+2AW183ZN/MVvugMLU1bXGpio4w090crr1dhUzI0iRVZllAtcy5um7nyE9Gov/c/s1+jpCnQjB+xXic/fpsg+Ve7mVoYoiXbr+EgPEBtfbeVbP2tkbUzqha2Wark7oUphaqvVdaW/VMd3FmsbCc08zeDKW5pZBXyhF7KLbWclK7znZIOZ+Ca9uuIXRhKIzMjTDn6hyhJFFLmTrULr1SXepK28pl5TAwNBAGRMwczCBLkUFeKa+1NUFfgqYFYV70PH13A4Dq4qj6d2VobAhLN8s6kxF6DfbCpbWXUJJT0mgSodYW8kwI+szpA1Pbmgvoe//uXXq64OlTTyNoWpDacW1+BvuE+SAvJg+B05qXfFDXBr06CL2e7NXqzysxl8DW3xZO3Wv+T3zCfBC1O6rOmcT6OAU7wb6zPS6uuYhLay816bO9tbye/XqdKw20ZfAbgzF88XCN23eZ3AWLZIta/J1kIDaAUqFE6vlUtUE3nzAf3PrjFipLdbfMW215uaxmeXl1n5qrJKdEqABQH7GRWK086s0dN9Hl/i5aXcHQETn1cMLpz0+r/S2YWJvgqRNP1W4b7ITL6y6jMK0Qubdz1UpJOvdwRlF6EQYuGCgk/2xMwtEEbB2/Fb8+oP9tx9rSOmt16rF69WoAQFhYmNrxDRs2YPbs2QBU2dINDAwwbdo0lJeXY+zYsVi1apXQViwWY+/evXjhhRcQGhoKc3NzPPnkk2pL3X19fbFv3z688sor+O677+Dh4YF169Zh7FjtJx2htstjgAeGLBqidkxiKam3PJWmijOLtZ5wpD0wszdDYVohMiIzcPyj4wh6OKjxBzVD1we64uyys81aWnjvsvK7ufRyQeLxxFoJwOy72iP7Vjbk5fJWW85YFzMHM5TmlKoyad+V1MvSwxI3f7+JnbN2Yth7w9Qe0++Ffjiz7AwqiythYmOCeVHaDQgNDA1qJUOxcLZAUWZRPY9ovotrLiLkmRC1Y6V5pW0qd4JYIm6zJQLHLBtT54yCa29X+I3xw5kvz+ihVw2rHqxrCxamLdTZFqv2bH7MfLXbYiMxAqcGCjXQNeV3nx9+m/obXs95vdGs4vrQkYOxwW8NxsZhG/HovkeFY8ZWxug2vRsurbsE+872jVaUaQ6JpUSoHFIuK1cb9NOkdGx9pMmNr0oztjFGWUGZMLt/8/ebuH/d/c1+zv+KgQsGwjHQUa28ZX0kFhKM+GQE4o/G48LyCxi1ZJRwn4m1CV6KeQn5sflIC0+D12CvBs+lkCvgPsAd8grVdZi8Qt6sShttTbM/6W7evImkpCShFna1++/X/E2syR+ZiYkJVq5ciZUrV9bbxtvbG3///XeD5wkLC8Ply5c17ht1PCY2Jhj12Si1Y9r4Yi3NK9XJF1R7cP+6+/H7I7/DJ8xHrWapNll5WOHluJe1ft6Qp0PQ8/Has+DO3VXJylpyEaAN1Qmx0i+lqw3quPd3x6V1lxD2YRh6PN5D7TF2nexwdfPVWsG4toz+fHStvxlzJ3OUZJXU84jmq84IfrcnDj/RLsvy6UNDMz99/9dXSBBGdWPArbnQhU3fquc3yg8jPx2ptazvpDnvod7o+2LfWuUUezzWA59bfw5FlQLD39d8Fl5TasvLiypgZN6y5J7VpIk1K+3qY2pritL8UiHoriqr4neJBoxMjYTtN5pwDXFFxI8RsPKwqpX40M7fDqa2priy6Uqj5ynJKYGFiwXuX3c/Dr5+EHmxea2S+FPXmhx0x8XF4cEHH8S1a9fU9j9WX4jJ5fKGHk7U4ZTll6kth/wvsfWzhdhI3C4v4MVGYoiNao+cBowLwE9DfhJq1OpbxuUMdJ9V8/t17+eOvc/vxYM/P1hrqaFIJMLwD4YLCem0ra49fmKJGPJK7X7uK5VKKCoVtWbAzB31Vze9IxEZiNrc0nL6bzG2MsbQtztW7eP2ZOLKibWOGZkZ4Y3cN1CUWaSTiQRjK2O1iid3D+C2ZAIk9mAsJq6u/XruZmJrImwllFfI6/zup5azC7DDzd9vot+L/eq839TOFGUFjW/pLM6syTkw6tNRMDDqGIOgTX4VL7/8Mnx9fZGVlQUzMzPcuHEDJ06cQN++fXHs2DEddJGobasup/Bf9czZZzrU8npjK2PM3DMTo78Yre+uAFDVJ7872KzO5Hx3jfS7DXlzCOw7t40Bg4trLuLIu0ea9JiygjKs6rYKNn42uukUERHVydjKGPad7HWyAsHE2qTFOXTulROdA1N700ZzOpja1gR7hemFsHT/7+XhaQ2GJoYoSi9SS1h8L5G48QGW4qyaXDZiibjDbPdo8kz32bNnceTIETg4OMDAwAAGBgYYMmQIlixZgvnz53MJN7VLSqWy2X/U1TWUqePQZR3eppBX1t7HJDYS473K99r8l1BVeRWu/ny1yTMm8UfjUZZf1qySW0RE1DaZOZihJKfurUjN3c6VdT0LXkMa3h8MqLYXVteJvjtzO2nfC9deUCsxdi8jM6NGs+MXZRbBwrltJEzVpiZHCnK5HJaWqhEiBwcHpKWpytd4e3sjOjpau70jagXGlsaoKKpovCFRK6uvHF1rlNhrCom5pNbfUMLRBAROC2zyxVTqhVTM2j+rTSXVIiKiljEyN0JlcaVW86Xkx+VrVE/87uXl5bLy//TqRF1zCnZqcCLKwsUCRRmq5KsXf7iIgsSCWm066sBIk6/cgoODceWKahP8gAEDsHTpUpw+fRofffQR/Py0UwuWqDVVZ7UkamvGrxiP0FeanqSotZk7m9fKYJ5+KR3uA9xhIDbQeM93cXYx7uy9A6duba+EEBERNV/16qyq0ioYmWoniVpBfEGtihp1uXt5ebmsvFa5TWo9Fi4WKEpXXS+c+OgENoVtQvyReLU2d5dK7UiaHHS/++67UCgUAICPPvoI8fHxGDp0KP7++28sX75c6x0k0jUTGxMG3dQmeQ/1hkPX+pdptRUWLha1Sgblx+XDzt8Odp3tkHcnT6PzHFhwAP3n929zM/lERKQdxdnFMHOqvWe8OTPgRelF9eY3uZuJbc3y8orCChhbMujWFwvXmplu196umLxucq0Slpzp/n9jx47F1KlTAQABAQGIiopCTk4OsrKyMHLkSK13kEjXWhJ0V5VVtckao0StybWPK1IvpKodqx6pdunlgkvrLyE1PBW3991u8DxlBWXo81wfXXaViIj0qCi99n7d6hKZTaWoUmg0SGtic8/ycs50642lqyUK0wuFsnF+o/zgGOyIqD+jhDaluaUdspygVqYT7Ozs2nxSH6L6tCToLs0vhYktS+/Qf5vnIE+knEmpdVwkEiHooSCUy8rx0+CfcPS9owCAfS/uQ358vlrbMmkZJJb1J1YhIqL2zdjaGLl3cmHhoh50G5oYoqqsSmfPa2pryqC7jbBwVS0vL0gsEOqrj/xkJMJXhgttlEolRAYdL67UKOieM2cOUlJqX1DVZfv27di6dWuLOkXUmloSdJfllzHopv88ibkEVWVVOPPVGRQkFqCqvEqoq2lobIjRn49G8IxgWLhYIHJjJOIOxSHxeKLweKVSifCV4fAc7Kmvl0BERDpm5miGrGtZtYJusYm4yUF3ZUklDE00W2koloiF3CIMuvXL0tUSRelFkKXIYOVhBUB1nWDhbFFvdvuOQqOg29HREd26dcOECROwevVqhIeHIzU1Fbm5uYiJicFff/2FN954A15eXvjmm2/QvXt3XfebSGtMrFsQdBeUwdS2aSWRiDoiG18bHHr9EKL/ikbG5QzYBdgJ95nZm+HBzQ/C3NEcpz4/hQe3PIiChALh/qzrWUg8kYjgGcF66DkREbUGKw8rpIWnaWWm+8rmK/AO89a4ffWK3PLCcq6q0iNTe1MUZaoH3QDg0tsF6ZfTtZrdvq3RaIjo448/xrx587Bu3TqsWrUKN2/eVLvf0tISo0ePxtq1azFu3DiddJRIV1o00y0tY+kJIgCD3xiMwGmB+PvFv7E/aj8e3vFwrTaB0wLhN8YPdv52QtC9f8F+RG6IxMy9M2Hu2PESpxARkYqtry2STifVKoXZnKD79p7bmLlnpsbtq4O5ClkFZ7r1SCQSwdTOFPH/xmPgwoHCcbc+bkg+mwz3/u4d9v9H4wxQzs7OeOedd/DOO+8gPz8fSUlJKC0thYODA/z9/bmnm9qtFgXdBWUwseHyciJrL2tYe1nDoasDHjvwGKy9rGu16XJ/FwCqi5/SPFUm2eyb2RiyaAg8B3FpORFRR2bjYwNFpQKWruoZxw1NDCEv16y0pECEZu37LS8sh8SCM936NOKjEVjuvxzjvx8vHHPp5YLwVeGqzOUdsFwY0ISg+262trawtW28Lh5Re9CSoLtcWl5ncEH0XzV91/RG21QP0spSZbByt8KQt4boultERKRn9p3t0XlS51rBclNnupuTaEssEaOqvApKhRIGYpal1CdbP1vMjZoLM4eaDOXGVsYol5ajOLO4Vnb7joLvOvrPM7ExQVkeZ7qJWpUSiPknBv7j/PXdEyIiagUSC0mdS8KbGnRXllRCYt602WpbP1vkx+VzZW4b4dDFodYxSw9LpEWkdcga3QCDbiK1rJZNxT3dRM1jYGSA23tvI2BsgL67QkREetTUoLtcVg6JVdOCbvvO9siJyunQibraO48BHri145ZQSqyjYdBN1AKc6SZqHnNnc2Rdz+LfDxHRf1xTg+6KwqYnQ3Mf4I6Us5qVPyb9cO7pjKRTSbD17ZhbmJu1p5uoozEQG0BRpYCBYcPjUNUjpELpCWk5TKwZNBA1VchTIeg8sbO+u0FERHrWnJnupgbdTt2ckHk1E4bGDH3aKqdgJwBQKyXWkTTpnXfu3Dns2bMHFRUVGDVqFMuDUYdh4WqBooyiRv/Q/3npHxhbG2PUp6MAABVFFTAyN2qNLhJ1KO793fXdBSIiagOaFXRbNi3oFhmIUFlcydVVbZiRqRHekr3V6ARYe6Vx0L1jxw5Mnz4dpqamMDIywtdff40vvvgCr732mi77R9QqLN0tVZmUGwm682Ly1D8MlGBSDiIiIqJmMjQxRLm0XOP2zZnpBgDHbo6QJkmb/DhqPU0dTGlPNB5KWLJkCZ577jlIpVLk5+fjk08+wWeffabLvhG1GisPKxSmFjbYpnr5uYGhAarKVSOyTMhBRERE1HyGxrpfXg4AY74ag6lbpzb5cUTaoHHQHR0djddeew1isRgA8Oqrr6KwsBBZWVk66xxRa7Fyt4IsRQYAkCZJ6wym0y+nwyXEBW593ZAekY7SvFKY2pq2dleJiIiIOozW2NMNqEqW8bqN9EXjoLukpARWVjVLbyUSCUxMTFBUVKSTjhG1purl5QDwrfe3SDyRWKtN/JF4+I70hdcQLySdSkJOVA7su9q3dleJiIiIOoxmlQyzbFrJMCJ9a1IitXXr1sHCwkK4XVVVhY0bN8LBoabA+fz587XXO6JWUr28vDSvFN7DvHHk7SN45I9HYOFS835PPZ+KAfMHAErgwvcXYOZgBsdARz32moiIiKh9a3LQXdi8mW4ifdI46Pby8sKPP/6odszFxQU///yzcFskEjHopnbJ2MoY5dJyJJ1OQrfp3WDhYoHoPdHo81wfAKq921VlVTAyVWUqryqvQvatbIQ8FaLPbhMRERG1a62RvZxI3zQOuhMSEnTYDSL9qs5AnnA0Ab2f6w0DsQHCV4cL90uTpLD2tq5pbyBCbnQu7ALsWr2vRERERB1FU4PuisIKznRTu9MxC6ERNUNFcQVSzqXAoasDbHxsIE2sKSuReiEV7v1q6gpbeVghPy4fYolYH10lIiIi6hCaHHQXVUBiwT3d1L5oHHR7eXkhNzdXuL1ixQrIZDKddIpIH8I+CMO478ZBJBJBLBFDUakQ7ks8ngjPQZ7CbXMncyiqFHWdhoiIiIg01NSgu7qEK1F7ovE7NiUlBXK5XLj99ttvIycnRyedItIH72HearPZgGovd8r5FOTH5cOha03CwAHzB2Dmnpmt3UUiIiKiDqWpQTdRe9Sk7OV3q6uOMVFHYuFqgaL0IkT/GY2Rn45Uu8/UzhSmdqz1SERERNQSBkYGkFfIG2/4/6rz8BC1J1ybQVQPW39b5MXmIetaFpy7O+u7O0REREQdTlODaE78UXvU7DrdddXoBlinmzoO+8722DhsI1x7u3LvEBERERERNUuz63TfW6MbYJ1u6li6TumK17NfR7msXN9dISIiIvrPUyqVXF5O7RLrdBPVQyQSwczBDGYOZvruChEREdF/XmVxJYzMjfTdDaIm09qa2ZSUFDz//PPaOh0REREREZGgvLCcNbqpXdJa0J2bm4v169dr63RERERERESCyhLOdFP7xOxQRERERETU5lUWV0Jizpluan8YdBMRERERkf5oWAWsoriCM93ULjHoJiIiIiKiNo8z3dReaZy9fOrUqQ3eX1BQ0NK+EBERERHRf4yBoQEUVQoYGDY8H8iZbmqvNA66ra2tG73/iSeeaHGHiIiIiIjov8PQxBBV5VWQGDY8i11ZXAkjMwbd1P5oHHRv2LBBl/0gIiIiIqL/IEMTQ1SVVTW6dLyypBLmTuat1Csi7eGebiIiIiIi0huxiRhVZVWNtuPycmqvGHQTEREREZHeVM90N4aJ1Ki90mvQfeLECUyePBlubm4QiUTYvXu32v2zZ8+GSCRS+xk3bpxam7y8PMyaNQtWVlawsbHBM888g6KiIrU2V69exdChQ2FiYgJPT08sXbpU1y+NiIiIiIg0oGnQzZluaq/0GnQXFxejZ8+eWLlyZb1txo0bh/T0dOHnl19+Ubt/1qxZuHHjBg4dOoS9e/fixIkTeP7554X7ZTIZxowZA29vb0RERODLL7/EBx98gLVr1+rsdRERERERkWaMTI1QWVLZaDsmUqP2SuNEarowfvx4jB8/vsE2xsbGcHFxqfO+W7duYf/+/QgPD0ffvn0BAN9//z0mTJiAr776Cm5ubti6dSsqKirw008/QSKRoFu3boiMjMTXX3+tFpwTEREREVHrM7YyRkVhRaPtKku4vJzapza/p/vYsWNwcnJCly5d8MILLyA3N1e47+zZs7CxsRECbgAYPXo0DAwMcP78eaHNsGHDIJHU/IGOHTsW0dHRyM/Pr/M5y8vLIZPJ1H6IiIiIiEj7jK2MUS4rb7RdZXEll5dTu9Smg+5x48Zh8+bNOHz4ML744gscP34c48ePh1wuBwBkZGTAyclJ7TGGhoaws7NDRkaG0MbZ2VmtTfXt6jb3WrJkCaytrYUfT09Pbb80IiIiIiJCE4LuEi4vp/ZJr8vLGzNjxgzh3927d0ePHj3g7++PY8eOYdSoUTp73kWLFmHhwoXCbZlMxsCbiIiIiEgHjK2MUZhe2Gg7pUIJA3GbnjMkqlO7etf6+fnBwcEBMTExAAAXFxdkZWWptamqqkJeXp6wD9zFxQWZmZlqbapv17dX3NjYGFZWVmo/RERERESkfZrOdCuVylboDZH2taugOyUlBbm5uXB1dQUAhIaGoqCgABEREUKbI0eOQKFQYMCAAUKbEydOoLKyJiPioUOH0KVLF9ja2rbuCyAiIiIiIjXG1poF3UTtlV6D7qKiIkRGRiIyMhIAEB8fj8jISCQlJaGoqAivv/46zp07h4SEBBw+fBgPPPAAAgICMHbsWABAYGAgxo0bh+eeew4XLlzA6dOnMW/ePMyYMQNubm4AgEcffRQSiQTPPPMMbty4ge3bt+O7775TWz5ORERERET6oelMN1F7pdeg++LFiwgJCUFISAgAYOHChQgJCcHixYshFotx9epV3H///ejcuTOeeeYZ9OnTBydPnoSxsbFwjq1bt6Jr164YNWoUJkyYgCFDhqjV4La2tsbBgwcRHx+PPn364NVXX8XixYtZLoyIiIiIqA0wtjJGhazxkmEikagVekOkfXpNpBYWFtbg3owDBw40eg47Ozts27atwTY9evTAyZMnm9w/IiIiIiLSLYm5BBVFjQfd3NNN7VW72tNNREREREQdi8hA1GhAzYCb2jMG3UREREREpF+NxNTyCjnEEnHr9IVIyxh0ExERERGRfjWyXbuyuBISc0nr9IVIyxh0ExERERGRXhmIDaCoUtR7f2VJJYzMjVqxR0Taw6CbiIiIiIj0yszRDMXZxfXeX1FcwaCb2i0G3UREREREpFcWLhYoyiiq934uL6f2jEE3ERERERHpVWNBd0VxBYzMONNN7RODbiIiIiIi0itNZrq5vJzaKwbdRERERESkV+bO5ijObHhPN5eXU3vFoJuIiIiIiPTKwtkCRZmc6aaOiUE3ERERERHpVWMz3ZUlldzTTe0Wg24iIiIiItIriYUEFUUV9d7P5eXUnjHoJiIiIiIivRKJRA3ez+Xl1J4x6CYiIiIiojaNM93UnjHoJiIiIiIivTMQG0BRpajzvorCCkgsGXRT+8Sgm4iIiIiI9M7M0QzF2XUnUysrKIOJjUkr94hIOxh0ExERERGR3jWUwbyiqAISC850U/vEoJuIiIiIiPSusVrdjSVbI2qrGHQTEREREZHeNVarm6i9YtBNRERERER619hMN1F7xaCbiIiIiIj0jjPd1FEx6CYiIiIiIr2zcLaoM+iWV8phYMiwhdovvnuJiIiIiEjvjK2NUVZQVut4WX4ZTGxZLozaLwbdRERERESkdyKRCEqlstbxwvRCWLpZ6qFHRNrBoJuIiIiIiNqswrRCWLoy6Kb2i0E3ERERERG1CSIDERRyhdqxwlTOdFP7xqCbiIiIiIjaBDMHM5TklAi3lUolov+KhksvFz32iqhlGHQTEREREVGbYOaoHnSnXkiFQ1cH2PjY6K9TRC3EoJuIiIiIiNoEE2sTlMvKhdt5d/Lg2ttVjz0iajkG3URERERE1CYYWxujXFoTdOfH58PG10Z/HSLSAgbdRERERETUJhhbGavNdBfEF8DW11aPPSJqOQbdRERERETUJphYm6BMWibcLs4qhpmjmR57RNRyDLqJiIiIiKhNuHemGwBEIpGeekOkHQy6iYiIiIioTbh7T3eZtAzGVsZ67hFRyzHoJiIiIiKiNuHume7c27mw72Kv5x4RtRyDbiIiIiIiahNMrE2Eme7c6Fw4dHHQc4+IWo5BNxERERERtQlqM913cmHXyU7PPSJqOQbdRERERETUJoglYsgr5QAAWYoM1p7Weu4RUcsx6CYiIiIiojanNKcUZg4sF0btH4NuIiIiIiJqc5RKJUQGLBdG7R+DbiIiIiIianuU+u4AkXYw6CYiIiIiojalsrQShiaG+u4GkVYw6CYiIiIiojbDwNAA0iQpLN0t9d0VIq3Qa9B94sQJTJ48GW5ubhCJRNi9e7fa/UqlEosXL4arqytMTU0xevRo3LlzR61NXl4eZs2aBSsrK9jY2OCZZ55BUVGRWpurV69i6NChMDExgaenJ5YuXarrl0ZERERERM1gbGWM7BvZsPKw0ndXiLRCr0F3cXExevbsiZUrV9Z5/9KlS7F8+XKsWbMG58+fh7m5OcaOHYuysjKhzaxZs3Djxg0cOnQIe/fuxYkTJ/D8888L98tkMowZMwbe3t6IiIjAl19+iQ8++ABr167V+esjIiIiIqKmsfKwQuKJRFh7s1wYdQwipVLZJlIUiEQi7Nq1C1OmTAGgmuV2c3PDq6++itdeew0AIJVK4ezsjI0bN2LGjBm4desWgoKCEB4ejr59+wIA9u/fjwkTJiAlJQVubm5YvXo13nnnHWRkZEAikQAA3nrrLezevRtRUVEa9U0mk8Ha2hpSqRRWVhxxIyIiIiLSlVs7b+GvZ/7C7OOz4dzDWd/dIWqxNrunOz4+HhkZGRg9erRwzNraGgMGDMDZs2cBAGfPnoWNjY0QcAPA6NGjYWBggPPnzwtthg0bJgTcADB27FhER0cjPz+/zucuLy+HTCZT+yEiIiIiIt3zH+sPryFecAp20ndXiLSizQbdGRkZAABnZ/XRLWdnZ+G+jIwMODmp/zEaGhrCzs5OrU1d57j7Oe61ZMkSWFtbCz+enp4tf0FERERERNQoibkEM/fMZI1u6jDabNCtT4sWLYJUKhV+kpOT9d0lIiIiIiIiaofabNDt4uICAMjMzFQ7npmZKdzn4uKCrKwstfurqqqQl5en1qauc9z9HPcyNjaGlZWV2g8RERERERFRU7XZoNvX1xcuLi44fPiwcEwmk+H8+fMIDQ0FAISGhqKgoAARERFCmyNHjkChUGDAgAFCmxMnTqCyslJoc+jQIXTp0gW2trat9GqIiIiIiIjov0ivQXdRUREiIyMRGRkJQJU8LTIyEklJSRCJRFiwYAE++eQT/PXXX7h27RqeeOIJuLm5CRnOAwMDMW7cODz33HO4cOECTp8+jXnz5mHGjBlwc3MDADz66KOQSCR45plncOPGDWzfvh3fffcdFi5cqKdXTURERERERP8Vei0ZduzYMYwYMaLW8SeffBIbN26EUqnE+++/j7Vr16KgoABDhgzBqlWr0LlzZ6FtXl4e5s2bhz179sDAwADTpk3D8uXLYWFhIbS5evUq5s6di/DwcDg4OOCll17Cm2++qXE/WTKMiIiIiIiImqPN1Oluyxh0ExERERERUXO02T3dRERERERERO2dob470B5ULwaQyWR67gkRERERERG1JZaWlhCJ6q8rz6BbA4WFhQAAT09PPfeEiIiIiIiI2pLGtiFzT7cGFAoF0tLSGh3B0DeZTAZPT08kJydz7zm1C3zPUnvE9y21R3zfUnvD9yy1J5zp1gIDAwN4eHjouxsas7Ky4ocTtSt8z1J7xPcttUd831J7w/csdQRMpEZERERERESkIwy6iYiIiIiIiHSEQXcHYmxsjPfffx/Gxsb67gqRRviepfaI71tqj/i+pfaG71nqSJhIjYiIiIiIiEhHONNNREREREREpCMMuomIiIiIiIh0hEE3ERERERERkY4w6O4gVq5cCR8fH5iYmGDAgAG4cOGCvrtEVK8lS5agX79+sLS0hJOTE6ZMmYLo6Gh9d4tIY59//jlEIhEWLFig764QNSg1NRWPPfYY7O3tYWpqiu7du+PixYv67hZRveRyOd577z34+vrC1NQU/v7++Pjjj8E0VNSeMejuALZv346FCxfi/fffx6VLl9CzZ0+MHTsWWVlZ+u4aUZ2OHz+OuXPn4ty5czh06BAqKysxZswYFBcX67trRI0KDw/HDz/8gB49eui7K0QNys/Px+DBg2FkZIR//vkHN2/exLJly2Bra6vvrhHV64svvsDq1auxYsUK3Lp1C1988QWWLl2K77//Xt9dI2o2Zi/vAAYMGIB+/fphxYoVAACFQgFPT0+89NJLeOutt/TcO6LGZWdnw8nJCcePH8ewYcP03R2iehUVFaF3795YtWoVPvnkE/Tq1QvffvutvrtFVKe33noLp0+fxsmTJ/XdFSKNTZo0Cc7Ozli/fr1wbNq0aTA1NcWWLVv02DOi5uNMdztXUVGBiIgIjB49WjhmYGCA0aNH4+zZs3rsGZHmpFIpAMDOzk7PPSFq2Ny5czFx4kS1z1yituqvv/5C37598fDDD8PJyQkhISH48ccf9d0togYNGjQIhw8fxu3btwEAV65cwalTpzB+/Hg994yo+Qz13QFqmZycHMjlcjg7O6sdd3Z2RlRUlJ56RaQ5hUKBBQsWYPDgwQgODtZ3d4jq9euvv+LSpUsIDw/Xd1eINBIXF4fVq1dj4cKFePvttxEeHo758+dDIpHgySef1Hf3iOr01ltvQSaToWvXrhCLxZDL5fj0008xa9YsfXeNqNkYdBORXs2dOxfXr1/HqVOn9N0VonolJyfj5ZdfxqFDh2BiYqLv7hBpRKFQoG/fvvjss88AACEhIbh+/TrWrFnDoJvarN9++w1bt27Ftm3b0K1bN0RGRmLBggVwc3Pj+5baLQbd7ZyDgwPEYjEyMzPVjmdmZsLFxUVPvSLSzLx587B3716cOHECHh4e+u4OUb0iIiKQlZWF3r17C8fkcjlOnDiBFStWoLy8HGKxWI89JKrN1dUVQUFBascCAwPxxx9/6KlHRI17/fXX8dZbb2HGjBkAgO7duyMxMRFLlixh0E3tFvd0t3MSiQR9+vTB4cOHhWMKhQKHDx9GaGioHntGVD+lUol58+Zh165dOHLkCHx9ffXdJaIGjRo1CteuXUNkZKTw07dvX8yaNQuRkZEMuKlNGjx4cK1yjLdv34a3t7eeekTUuJKSEhgYqIcoYrEYCoVCTz0iajnOdHcACxcuxJNPPom+ffuif//++Pbbb1FcXIynnnpK310jqtPcuXOxbds2/Pnnn7C0tERGRgYAwNraGqampnruHVFtlpaWtXIOmJubw97enrkIqM165ZVXMGjQIHz22Wd45JFHcOHCBaxduxZr167Vd9eI6jV58mR8+umn8PLyQrdu3XD58mV8/fXXePrpp/XdNaJmY8mwDmLFihX48ssvkZGRgV69emH58uUYMGCAvrtFVCeRSFTn8Q0bNmD27Nmt2xmiZgoLC2PJMGrz9u7di0WLFuHOnTvw9fXFwoUL8dxzz+m7W0T1KiwsxHvvvYddu3YhKysLbm5umDlzJhYvXgyJRKLv7hE1C4NuIiIiIiIiIh3hnm4iIiIiIiIiHWHQTURERERERKQjDLqJiIiIiIiIdIRBNxEREREREZGOMOgmIiIiIiIi0hEG3UREREREREQ6wqCbiIiIiIiISEcYdBMRERERERHpCINuIiIiIiIiIh1h0E1ERPQfMHv2bEyZMkVvz//444/js88+06jtjBkzsGzZMh33iIiIqHWIlEqlUt+dICIiouYTiUQN3v/+++/jlVdegVKphI2NTet06i5XrlzByJEjkZiYCAsLi0bbX79+HcOGDUN8fDysra1boYdERES6w6CbiIioncvIyBD+vX37dixevBjR0dHCMQsLC42CXV159tlnYWhoiDVr1mj8mH79+mH27NmYO3euDntGRESke1xeTkRE1M65uLgIP9bW1hCJRGrHLCwsai0vDwsLw0svvYQFCxbA1tYWzs7O+PHHH1FcXIynnnoKlpaWCAgIwD///KP2XNevX8f48eNhYWEBZ2dnPP7448jJyam3b3K5HDt27MDkyZPVjq9atQqdOnWCiYkJnJ2d8dBDD6ndP3nyZPz6668t/+UQERHpGYNuIiKi/6hNmzbBwcEBFy5cwEsvvYQXXngBDz/8MAYNGoRLly5hzJgxePzxx1FSUgIAKCgowMiRIxESEoKLFy9i//79yMzMxCOPPFLvc1y9ehVSqRR9+/YVjl28eBHz58/HRx99hOjoaOzfvx/Dhg1Te1z//v1x4cIFlJeX6+bFExERtRIG3URERP9RPXv2xLvvvotOnTph0aJFMDExgYODA5577jl06tQJixcvRm5uLq5evQoAWLFiBUJCQvDZZ5+ha9euCAkJwU8//YSjR4/i9u3bdT5HYmIixGIxnJychGNJSUkwNzfHpEmT4O3tjZCQEMyfP1/tcW5ubqioqFBbOk9ERNQeMegmIiL6j+rRo4fwb7FYDHt7e3Tv3l045uzsDADIysoCoEqIdvToUWGPuIWFBbp27QoAiI2NrfM5SktLYWxsrJbs7b777oO3tzf8/Pzw+OOPY+vWrcJsejVTU1MAqHWciIiovWHQTURE9B9lZGSkdlskEqkdqw6UFQoFAKCoqAiTJ09GZGSk2s+dO3dqLQ+v5uDggJKSElRUVAjHLC0tcenSJfzyyy9wdXXF4sWL0bNnTxQUFAht8vLyAACOjo5aea1ERET6wqCbiIiINNK7d2/cuHEDPj4+CAgIUPsxNzev8zG9evUCANy8eVPtuKGhIUaPHo2lS5fi6tWrSEhIwJEjR4T7r1+/Dg8PDzg4OOjs9RAREbUGBt1ERESkkblz5yIvLw8zZ85EeHg4YmNjceDAATz11FOQy+V1PsbR0RG9e/fGqVOnhGN79+7F8uXLERkZicTERGzevBkKhQJdunQR2pw8eRJjxozR+WsiIiLSNQbdREREpBE3NzecPn0acrkcY8aMQffu3bFgwQLY2NjAwKD+S4pnn30WW7duFW7b2Nhg586dGDlyJAIDA7FmzRr88ssv6NatGwCgrKwMu3fvxnPPPafz10RERKRrIqVSqdR3J4iIiKjjKi0tRZcuXbB9+3aEhoY22n716tXYtWsXDh482Aq9IyIi0i3OdBMREZFOmZqaYvPmzcjJydGovZGREb7//nsd94qIiKh1cKabiIiIiIiISEc4001ERERERESkIwy6iYiIiIiIiHSEQTcRERERERGRjjDoJiIiIiIiItIRBt1EREREREREOsKgm4iIiIiIiEhHGHQTERERERER6QiDbiIiIiIiIiIdYdBNREREREREpCP/B26irBVrLKW0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Check for LFP signal in processing module\n", + "if 'ecephys' in nwbfile.processing and 'LFP' in nwbfile.processing['ecephys'].data_interfaces:\n", + " lfp_ts = nwbfile.processing['ecephys']['LFP']\n", + " print(f\"LFP signal available: {lfp_ts.data.shape[0]} samples\")\n", + " print(f\"Units: {lfp_ts.unit}\")\n", + " print(f\"Description: {lfp_ts.description}\")\n", + " \n", + " # Plot LFP for a single trial\n", + " # Note: Data are uncalibrated A/D converter values (centered ~2048), not volts\n", + " trial_idx = 5\n", + " trial = trials_df.iloc[trial_idx]\n", + " t_start, t_stop = trial['start_time'], trial['stop_time']\n", + " \n", + " lfp_data = lfp_ts.data[:]\n", + " lfp_timestamps = lfp_ts.timestamps[:]\n", + " mask = (lfp_timestamps >= t_start) & (lfp_timestamps <= t_stop)\n", + " \n", + " fig, ax = plt.subplots(figsize=(10, 3))\n", + " ax.plot(lfp_timestamps[mask] - t_start, lfp_data[mask], 'purple', linewidth=0.5)\n", + " ax.set_xlabel('Time (s)')\n", + " ax.set_ylabel('LFP (a.u.)')\n", + " ax.set_title(f'Trial {trial_idx} - LFP')\n", + " ax.spines['top'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"No LFP signal in this session.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Antidromic Stimulation Raw Waveforms\n", + "\n", + "In addition to the classification results in the units table, the raw antidromic stimulation data is available in the `AntidromicSweepsIntervals` table. Each row represents one 50ms sweep containing both the neural response and stimulation current waveforms.\n", + "\n", + "| Column | Description | Values |\n", + "|--------|-------------|--------|\n", + "| `start_time` | Sweep start time (25ms before stimulation) | seconds |\n", + "| `stop_time` | Sweep end time (25ms after stimulation) | seconds |\n", + "| `stimulation_onset_time` | Time of stimulation pulse delivery (t=0 in sweep) | seconds |\n", + "| `unit_name` | Unit number from source filename | `\"1\"` or `\"2\"` |\n", + "| `location` | Stimulation site | `\"Striatum\"`, `\"Peduncle\"`, `\"Thalamus\"` |\n", + "| `stimulation_protocol` | Test type performed | `\"Collision\"`, `\"FrequencyFollowing\"`, `\"Orthodromic\"` |\n", + "| `sweep_number` | Sweep index within the stimulation series | integer |\n", + "| `response` | Reference to neural voltage ElectricalSeries | object reference |\n", + "| `stimulation` | Reference to stimulation current TimeSeries | object reference |\n", + "\n", + "**Protocol types explained:**\n", + "- **Collision**: Tests whether a spontaneous spike traveling down the axon blocks the antidromic spike - the gold-standard verification for antidromic activation\n", + "- **FrequencyFollowing**: Tests whether the neuron can follow high-frequency stimulation (>200 Hz) with consistent latency - verifies direct axonal activation vs synaptic\n", + "- **Orthodromic**: Control stimulation that activates the neuron through synaptic pathways (sub-threshold for antidromic)\n", + "\n", + "**Note:** Not all sessions include antidromic stimulation data. Check if the intervals table exists before accessing." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Antidromic sweeps available: 62 sweeps\n", + "\n", + "Sweeps by stimulation location:\n", + "location\n", + "Peduncle 62\n", + "Name: count, dtype: int64\n", + "\n", + "Sweeps by protocol type:\n", + "stimulation_protocol\n", + "Collision 49\n", + "FrequencyFollowing 13\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# Check if antidromic sweeps data exists and explore the table\n", + "if \"AntidromicSweepsIntervals\" in nwbfile.intervals:\n", + " sweeps_df = nwbfile.intervals[\"AntidromicSweepsIntervals\"].to_dataframe()\n", + " print(f\"Antidromic sweeps available: {len(sweeps_df)} sweeps\")\n", + " print(f\"\\nSweeps by stimulation location:\")\n", + " print(sweeps_df['location'].value_counts())\n", + " print(f\"\\nSweeps by protocol type:\")\n", + " print(sweeps_df['stimulation_protocol'].value_counts())\n", + "else:\n", + " print(\"No antidromic sweeps data in this session.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Preview the sweeps table structure\n", + "if \"AntidromicSweepsIntervals\" in nwbfile.intervals:\n", + " display_columns = ['unit_name', 'location', 'stimulation_protocol', 'sweep_number', \n", + " 'stimulation_onset_time']\n", + " sweeps_df[display_columns].head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing Raw Antidromic Waveforms\n", + "\n", + "Each sweep contains two linked time series:\n", + "- **response**: Neural voltage recording from the M1 electrode (in volts)\n", + "- **stimulation**: Stimulation current delivered to the target site (in amperes)\n", + "\n", + "The sweeps are 50ms long (1000 samples at 20kHz), centered on the stimulation pulse. To extract waveforms aligned to stimulation onset:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sweep metadata:\n", + " Stimulation site: Peduncle\n", + " Protocol: Collision\n", + " Sampling rate: 20000.0 Hz\n" + ] + } + ], + "source": [ + "# Extract and plot a single antidromic sweep\n", + "if \"AntidromicSweepsIntervals\" in nwbfile.intervals:\n", + " # Get the first sweep\n", + " sweep = sweeps_df.iloc[0]\n", + " \n", + " # Access the response and stimulation data through TimeSeriesReference\n", + " # Each reference is a tuple: (idx_start, count, timeseries)\n", + " response_ref = sweep['response']\n", + " index_start, count, response_series = response_ref\n", + " response_data = response_series.data[index_start:index_start + count].flatten() * response_series.conversion * 1e6 # uV\n", + " \n", + " stim_ref = sweep['stimulation']\n", + " index_start_s, count_s, stim_series = stim_ref\n", + " stim_data = stim_series.data[index_start_s:index_start_s + count_s].flatten() * stim_series.conversion * 1e6 # uA\n", + " \n", + " # Create time axis relative to stimulation onset\n", + " # Data starts 25ms before stimulation (t=0 is at sample 500 for 20kHz)\n", + " sampling_rate = response_series.rate\n", + " time_ms = (np.arange(count) / sampling_rate - 0.025) * 1000 # Time in ms relative to stim\n", + " \n", + " # Plot\n", + " fig, axes = plt.subplots(2, 1, figsize=(10, 5), sharex=True)\n", + " \n", + " axes[0].plot(time_ms, response_data, 'b-', linewidth=0.8)\n", + " axes[0].axvline(0, color='red', linestyle='--', alpha=0.7, label='Stimulation')\n", + " axes[0].set_ylabel('Neural Response (uV)')\n", + " axes[0].set_title(f\"Antidromic Sweep - Unit {sweep['unit_name']}, {sweep['location']}, {sweep['stimulation_protocol']}\")\n", + " axes[0].legend(loc='upper right')\n", + " axes[0].spines['top'].set_visible(False)\n", + " axes[0].spines['right'].set_visible(False)\n", + " \n", + " axes[1].plot(time_ms, stim_data, 'r-', linewidth=0.8)\n", + " axes[1].axvline(0, color='red', linestyle='--', alpha=0.7)\n", + " axes[1].set_ylabel('Stim Current (uA)')\n", + " axes[1].set_xlabel('Time relative to stimulation (ms)')\n", + " axes[1].spines['top'].set_visible(False)\n", + " axes[1].spines['right'].set_visible(False)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " print(f\"\\nSweep metadata:\")\n", + " print(f\" Stimulation site: {sweep['location']}\")\n", + " print(f\" Protocol: {sweep['stimulation_protocol']}\")\n", + " print(f\" Sampling rate: {sampling_rate} Hz\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Overlay multiple collision test sweeps to visualize response consistency\n", + "if \"AntidromicSweepsIntervals\" in nwbfile.intervals:\n", + " # Filter to collision test sweeps only\n", + " collision_sweeps = sweeps_df[sweeps_df['stimulation_protocol'] == 'Collision']\n", + " \n", + " if len(collision_sweeps) > 0:\n", + " fig, ax = plt.subplots(figsize=(10, 4))\n", + " \n", + " # Plot first 20 sweeps (or all if fewer)\n", + " n_to_plot = min(20, len(collision_sweeps))\n", + " \n", + " for i in range(n_to_plot):\n", + " sweep = collision_sweeps.iloc[i]\n", + " \n", + " # Access data through TimeSeriesReference\n", + " response_ref = sweep['response']\n", + " index_start, count, response_series = response_ref\n", + " response_data = response_series.data[index_start:index_start + count].flatten() * response_series.conversion * 1e6\n", + " \n", + " sampling_rate = response_series.rate\n", + " time_ms = (np.arange(count) / sampling_rate - 0.025) * 1000\n", + " \n", + " ax.plot(time_ms, response_data, 'b-', linewidth=0.5, alpha=0.5)\n", + " \n", + " ax.axvline(0, color='red', linestyle='--', linewidth=2, label='Stimulation')\n", + " ax.set_xlabel('Time relative to stimulation (ms)')\n", + " ax.set_ylabel('Neural Response (uV)')\n", + " ax.set_title(f'Collision Test Sweeps Overlay (n={n_to_plot})')\n", + " ax.legend(loc='upper right')\n", + " ax.set_xlim(-5, 15) # Focus on the response window\n", + " ax.spines['top'].set_visible(False)\n", + " ax.spines['right'].set_visible(False)\n", + " \n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Get the antidromic latency from the units table for comparison\n", + " unit_name = collision_sweeps.iloc[0]['unit_name']\n", + " unit_row = units_df[units_df['unit_name'] == unit_name]\n", + " if len(unit_row) > 0:\n", + " latency = unit_row['antidromic_latency_ms'].values[0]\n", + " if latency == latency: # Check for NaN (NaN != NaN is True)\n", + " print(f\"Expected antidromic response at {latency:.2f} ms (from units table)\")\n", + " else:\n", + " print(\"No collision test sweeps found in this session.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stimulation Electrodes Table\n", + "\n", + "The `StimulationElectrodesTable` in the `antidromic_identification` processing module documents the chronically implanted electrodes used for antidromic testing:\n", + "\n", + "| Index | Location | Purpose |\n", + "|-------|----------|---------|\n", + "| 0 | Cerebral peduncle | PTN identification (pyramidal tract) |\n", + "| 1-3 | Posterolateral putamen | CSN identification (3 electrodes) |\n", + "| 4 | Ventrolateral thalamus | Thalamocortical projection identification |\n", + "\n", + "These are distinct from the recording microelectrode in the electrodes table - they are macroelectrodes surgically implanted at fixed locations for stimulation, not recording." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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locationdevice_namenotes
id
0Cerebral peduncle (pre-pontine)DeviceMicrowirePeduncleStimulationVentral to substantia nigra, arm-responsive pr...
1Putamen (posterolateral)DeviceMicrowirePutamenStimulation1Electrode 1 of 3, posterolateral putamen for M...
2Putamen (posterolateral)DeviceMicrowirePutamenStimulation2Electrode 2 of 3, posterolateral putamen for M...
3Putamen (posterolateral)DeviceMicrowirePutamenStimulation3Electrode 3 of 3, posterolateral putamen for M...
4Ventrolateral thalamusDeviceMicrowireThalamicStimulationVL thalamus for thalamocortical projection ide...
5Subthalamic nucleus (STN)DeviceMicrowireSTNStimulationSubthalamic nucleus for STN projection identif...
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" + ], + "text/plain": [ + " location device_name \\\n", + "id \n", + "0 Cerebral peduncle (pre-pontine) DeviceMicrowirePeduncleStimulation \n", + "1 Putamen (posterolateral) DeviceMicrowirePutamenStimulation1 \n", + "2 Putamen (posterolateral) DeviceMicrowirePutamenStimulation2 \n", + "3 Putamen (posterolateral) DeviceMicrowirePutamenStimulation3 \n", + "4 Ventrolateral thalamus DeviceMicrowireThalamicStimulation \n", + "5 Subthalamic nucleus (STN) DeviceMicrowireSTNStimulation \n", + "\n", + " notes \n", + "id \n", + "0 Ventral to substantia nigra, arm-responsive pr... \n", + "1 Electrode 1 of 3, posterolateral putamen for M... \n", + "2 Electrode 2 of 3, posterolateral putamen for M... \n", + "3 Electrode 3 of 3, posterolateral putamen for M... \n", + "4 VL thalamus for thalamocortical projection ide... \n", + "5 Subthalamic nucleus for STN projection identif... " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Access the stimulation electrodes table\n", + "if \"antidromic_identification\" in nwbfile.processing:\n", + " antidromic_module = nwbfile.processing[\"antidromic_identification\"]\n", + " if \"StimulationElectrodesTable\" in antidromic_module.data_interfaces:\n", + " stim_electrodes = antidromic_module[\"StimulationElectrodesTable\"].to_dataframe()\n", + " display(stim_electrodes)\n", + " else:\n", + " print(\"StimulationElectrodesTable not found in this session.\")\n", + "else:\n", + " print(\"No antidromic_identification processing module in this session.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "turner-lab-to-nwb (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}