{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "execution": {}, "id": "view-in-github" }, "source": [ "\"Open   \"Open" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "# Exploring AJILE12 dataset" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Objective" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "This notebook is designed as a guide for student projects in [Computational Neurosciecne Course](https://compneuro.neuromatch.io/) of [Neuromatch Academy](https://academy.neuromatch.io/)." ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Scientific background\n", "\n", "The AJILE12 dataset is the largest publicly available human neurobehavioral dataset, recorded during passive clinical epilepsy monitoring. It includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata. The dataset was created to understand the neural basis of human movement in naturalistic scenarios and expand neuroscience research beyond constrained laboratory paradigms. It is available on The DANDI Archive in the Neurodata Without Borders (NWB) data standard and can be explored using a browser-based dashboard.\n", "\n", "For scientific background, see the following papers from [Bing Brunton lab](https://www.bingbrunton.com/bing) who graciously has released the **AJILE12 dandiset** on [DANDI](https://dandiarchive.org/dandiset/000055?search=ajile12&pos=1):\n", "\n", "**Behavioral and Neural Variability of Naturalistic Arm Movements**. eNeuro, 2021. https://doi.org/10.1523/ENEURO.0007-21.2021\n", "\n", "**Mining naturalistic human behaviors in long-term video and neural recordings**. Journal of Neuroscience Methods, 2021. https://doi.org/10.1016/j.jneumeth.2021.109199\n", "\n", "## Data\n", "\n", "Annotated Joints in Long-term Electrocorticography (AJILE12) from human participants; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints, including wrist, elbow, and shoulder joints, were sampled at 30 frames per second and estimated from 118 million video frames.\n", "\n", "The following link provides some information on the dataset:\n", "[AJILE Data Readme](https://www.bingbrunton.com/ajile-readme)\n", "\n", "The following paper provides a more through detail on data and acquisition:\n", "\n", "**AJILE12: Long-term naturalistic human intracranial neural recordings and pose**. Scientific Data, 2022. https://doi.org/10.1038/s41597-022-01280-y\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Environment Setup" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "This code is meant to be run in a notebook. It checks if the notebook is running on a Google Colab GPU, and if it is, it clones the Neuromatch-AJILE12 repository from GitHub, changes the current working directory to the Neuromatch-AJILE12k directory, and installs the package in editable mode using pip. This is done to set up the environment for using the Neuromatch-AJILE12 package in a Google Colab notebook. If you wish to run this code on a local machine, you can comment the next cell and simply clone the git link below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [], "source": [ "# @title Install dependencies\n", "!pip install seaborn --quiet\n", "!pip install statsmodels --quiet" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/\n", "[Errno 2] No such file or directory: '/content # change to content directory where AJILE12 will be installed'\n", "/\n" ] } ], "source": [ "# @title Clean install of AJILE12 on Google Colab;\n", "# @markdown This is to prevent overwriting conflicts. You can run this once for each new run instance on colab.\n", "%cd /\n", "%rm -rf /content/Neuromatch-AJILE12/ # removes old version of AJILE12 if it exists\n", "%cd /content # change to content directory where AJILE12 will be installed" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'Neuromatch-AJILE12'...\n", "remote: Enumerating objects: 292, done.\u001b[K\n", "remote: Counting objects: 100% (292/292), done.\u001b[K\n", "remote: Compressing objects: 100% (197/197), done.\u001b[K\n", "remote: Total 292 (delta 130), reused 225 (delta 71), pack-reused 0\u001b[K\n", "Receiving objects: 100% (292/292), 9.15 MiB | 11.18 MiB/s, done.\n", "Resolving deltas: 100% (130/130), done.\n", "/Neuromatch-AJILE12\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.9/131.9 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m197.1/197.1 kB\u001b[0m \u001b[31m19.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.3/10.3 MB\u001b[0m \u001b[31m58.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.0/143.0 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.0/55.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m13.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.2/63.2 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m78.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.8/42.8 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.7/51.7 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.2/112.2 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m206.1/206.1 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m35.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m511.6/511.6 kB\u001b[0m \u001b[31m29.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m59.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m682.3/682.3 kB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m37.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.4/48.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.7/41.7 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.4/58.4 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m485.6/485.6 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m53.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m46.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m260.7/260.7 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.7/11.7 MB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m52.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.9/135.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.2/81.2 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.5/73.5 MB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 MB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m271.6/271.6 kB\u001b[0m \u001b[31m23.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m83.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m107.5/107.5 kB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.3/4.3 MB\u001b[0m \u001b[31m73.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.7/283.7 kB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for asciitree (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "# @title If running on Google Colab, run this cell once, then restart the runtime and run the rest of the notebook\n", "import os\n", "if \"COLAB_GPU\" in os.environ:\n", " !git clone https://github.com/neurovium/Neuromatch-AJILE12\n", " %cd Neuromatch-AJILE12\n", " %pip install -e . --quiet" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Read/Download data" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Access to data on DANDI\n", "The data is hosted on **[DANDI](https://dandiarchive.org/)**, BRAIN Initiative's archive for publishing and sharing neurophysiology data including electrophysiology, optophysiology, behavioral time-series, and images from immunostaining experiments." ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### NWB format\n", "\n", "**AJILE12** is in **[NWB](https://www.nwb.org/)** format. NWB is a Hierarchical Data Format (HDF) intended for scientific data. HDF is a platform-independent file format that can be used on many different computers, regardless of the operating system that machine is running. To know more about HDF, you can visit [HDFGroup](https://portal.hdfgroup.org/display/support/Documentation).\n", "\n", "For more information about NWB, see the following papers.\n", "\n", "Neurodata Without Borders: Creating a Common Data Format for Neurophysiology. Neuron, 2015. https://doi.org/10.1016/j.neuron.2015.10.025\n", "\n", "The Neurodata Without Borders ecosystem for neurophysiological data science. eLife, 2022. https://doi.org/10.7554/eLife.78362\n", "\n", "[NWB documentation](https://nwb-overview.readthedocs.io/en/latest/) provides further information on the data structure and Python/Matlab APIs to access it.\n" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Setup for read/download" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# Numerical and plotting packages\n", "import seaborn as sns\n", "import numpy as np\n", "import pandas as pd\n", "import statsmodels.api as sm\n", "import natsort\n", "from scipy.signal import sosfiltfilt, butter, hilbert" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# Libraries needed for this notebook to interact with the DANDI API\n", "from pynwb import NWBHDF5IO\n", "from dandi.dandiapi import DandiAPIClient\n", "\n", "# Libraries needed for this notebook to interact with NWB events\n", "from ndx_events import LabeledEvents, AnnotatedEventsTable, Events\n", "\n", "# FSSpec is a library that allows us to read files from the cloud\n", "import fsspec\n", "\n", "# NWB is based on HF5, so we need this library to read NWB files\n", "import h5py" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Access to data on cloud\n", "The data is hosted on [AMAZON AWS](https://aws.amazon.com) in **S3** buckets. The following steps guide you to locate the data based on the **dandiset** information, setup streaming and reading the data from the cloud. Alternatively, you can access the data on **[DANDI](https://dandiarchive.org/dandiset/000055?search=ajile12&pos=1)**. If you choose to directly download from DANDI, you will need a github account. The following code will be sufficient to programatically download/stream data (either for colab notebook or for your own personal machine)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# Subject and session number for loading dataThese parameters can be adjusted to analyze other electrodes, frequency bands, behavior types, participants, sessions, etc.\n", "# Example:\n", "# Select data from participant 1, session 3 only during times that the participant was eating.\n", "# ECoG data will be converted to spectral power in the gamma band (80-100 Hz) for electrode 7, which is located over the motor cortex.\n", "# We will also look at the vertical velocity of the right wrist.\n", "\n", "\n", "sbj, session = 1, 3 # participant 1, session 3\n", "behavior_type = 'Eat' # only analyze data during eating\n", "neural_freq_range = [80, 100] # Frequency band of interest in Hz\n", "ecog_ch_num = 7 # electrode number over motor cortex\n", "keypoint_of_interest = 'R_Wrist' # right wrist movement\n", "pose_direction = 'vertical' # 'vertical' or 'horizontal'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# You can read specific sections within individual data files directly from remote stores such as the DANDI Archive.\n", "# This is especially useful for reading small pieces of data from a large NWB file stored remotely. First, you will need to get the location of the file.\n", "# Now you can get the url of a particular NWB file using the dandiset ID and the path of that file within the dandiset.\n", "with DandiAPIClient() as client:\n", " asset = client.get_dandiset(\"000055\").get_asset_by_path(\n", " \"sub-{0:>02d}/sub-{0:>02d}_ses-{1:.0f}_behavior+ecephys.nwb\".format(sbj, session)\n", " )\n", " s3_path = asset.get_content_url(follow_redirects=1, strip_query=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://dandiarchive.s3.amazonaws.com/blobs/e54/21f/e5421ff3-05f6-4d5e-a884-6d3e57a11951'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# s3_path is the url of the file on the DANDI Archive. You can now use this url to read the file using pynwb.\n", "# Note that this url path may change if the file is updated on the DANDI Archive. ALWAYS use the \"dandiset ID\" and \"path\" to the file within the dandiset to get the url.\n", "s3_path" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Note on streaming\n", "There are two methods for streaming NWB data from the cloud using **[PyNWB streaming](https://pynwb.readthedocs.io/en/stable/tutorials/advanced_io/streaming.html)**. Currently, Colab is natively not compatible with ROS3 (read only S3); though you can use that ROS3 method for a local machine/server. If you wish to use ROS3 (instead of fsspec) on Colab, see the details in the [local readme.txt](./plot_utils/readme.txt) file." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.4.0-alpha because version 1.6.0 is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n", "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.2.5 because version 2.6.0-alpha is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n", "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.1.0 because version 0.3.0 is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n" ] } ], "source": [ "\n", "# You can also read specific sections within individual data files directly from remote stores such as the DANDI Archive.\n", "from fsspec.implementations.cached import CachingFileSystem\n", "\n", "# Note, caching is set once per access. If you want to change the cache location, you will need to restart the kernel.\n", "fs = CachingFileSystem(\n", " fs=fsspec.filesystem(\"http\"),\n", " cache_storage=\"nwb-cache\", # Local folder for the cache\n", ")\n", "\n", "f = fs.open(s3_path, \"rb\")\n", "file = h5py.File(f)\n", "io = NWBHDF5IO(file=file, mode='r', load_namespaces=True)\n", "nwbfile = io.read()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Examine NWB" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Check NWB file and its content." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "root pynwb.file.NWBFile at 0x140485795389664\n", "Fields:\n", " acquisition: {\n", " ECGL ,\n", " ECGR ,\n", " EOGL ,\n", " EOGR ,\n", " ElectricalSeries \n", " }\n", " devices: {\n", " ECG ,\n", " EOG ,\n", " GRID ,\n", " LAT ,\n", " LID ,\n", " LMT ,\n", " LPT ,\n", " LTO \n", " }\n", " electrode_groups: {\n", " ECG ,\n", " EOG ,\n", " GRID ,\n", " LAT ,\n", " LID ,\n", " LMT ,\n", " LPT ,\n", " LTO \n", " }\n", " electrodes: electrodes \n", " epochs: epochs \n", " file_create_date: [datetime.datetime(2021, 6, 9, 5, 44, 48, 194751, tzinfo=tzoffset(None, -14400))]\n", " identifier: 4c571b6c-1028-476f-b0e1-e34aa27b3206\n", " intervals: {\n", " epochs ,\n", " reaches \n", " }\n", " processing: {\n", " behavior \n", " }\n", " session_description: no description\n", " session_id: 3\n", " session_start_time: 2000-01-02 19:00:00-05:00\n", " subject: subject pynwb.file.Subject at 0x140485795395616\n", "Fields:\n", " species: Homo sapiens\n", " subject_id: 01\n", "\n", " timestamps_reference_time: 2000-01-02 19:00:00-05:00" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You can now access the data in the file as you would normally do with NWB files.\n", "nwbfile" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Get information about the electrodes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "electrodes hdmf.common.table.DynamicTable at 0x140485795392208\n", "Fields:\n", " colnames: ['x' 'y' 'z' 'imp' 'location' 'filtering' 'group' 'group_name'\n", " 'standard_deviation' 'kurtosis' 'median_deviation' 'good' 'low_freq_R2'\n", " 'high_freq_R2']\n", " columns: (\n", " x ,\n", " y ,\n", " z ,\n", " imp ,\n", " location ,\n", " filtering ,\n", " group ,\n", " group_name ,\n", " standard_deviation ,\n", " kurtosis ,\n", " median_deviation ,\n", " good ,\n", " low_freq_R2 ,\n", " high_freq_R2 \n", " )\n", " description: metadata about extracellular electrodes\n", " id: id " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Information about the electrodes is stored in the nwbfile.electrodes table.\n", "nwbfile.electrodes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/html": [ "\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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
xyzimplocationfilteringgroupgroup_namestandard_deviationkurtosismedian_deviationgoodlow_freq_R2high_freq_R2
id
7-53.896469-29.05987362.709102NaNunknown250 Hz lowpassGRID pynwb.ecephys.ElectrodeGroup at 0x1404857...GRID45.4225542.70706337.685382True0.1038350.055118
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " x y z imp location filtering \\\n", "id \n", "7 -53.896469 -29.059873 62.709102 NaN unknown 250 Hz lowpass \n", "\n", " group group_name \\\n", "id \n", "7 GRID pynwb.ecephys.ElectrodeGroup at 0x1404857... GRID \n", "\n", " standard_deviation kurtosis median_deviation good low_freq_R2 \\\n", "id \n", "7 45.422554 2.707063 37.685382 True 0.103835 \n", "\n", " high_freq_R2 \n", "id \n", "7 0.055118 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Specific information about the electrode of interest can be accessed using the electrode number.\n", "nwbfile.electrodes[ecog_ch_num]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "electrodes hdmf.common.table.DynamicTable at 0x140485795392208\n", "Fields:\n", " colnames: ['x' 'y' 'z' 'imp' 'location' 'filtering' 'group' 'group_name'\n", " 'standard_deviation' 'kurtosis' 'median_deviation' 'good' 'low_freq_R2'\n", " 'high_freq_R2']\n", " columns: (\n", " x ,\n", " y ,\n", " z ,\n", " imp ,\n", " location ,\n", " filtering ,\n", " group ,\n", " group_name ,\n", " standard_deviation ,\n", " kurtosis ,\n", " median_deviation ,\n", " good ,\n", " low_freq_R2 ,\n", " high_freq_R2 \n", " )\n", " description: metadata about extracellular electrodes\n", " id: id \n", "\n" ] } ], "source": [ "# assign the cloud path to a variable\n", "from hdmf.common.table import DynamicTable\n", "\n", "# assuming you have already loaded your NWB file into memory\n", "electrodes = nwbfile.electrodes\n", "print(electrodes)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### NWB-WIDGETS" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "It can get cumbersome to manually dissect an NWB file with print statements. There are a few ways to view an NWB graphically instead. A great way to do this in a Jupyter notebook is with **[NWBWidgets](https://github.com/NeurodataWithoutBorders/nwbwidgets)**. Here, you can use NWBWidgets to view a file from a location on your machine. If you don't want to download a file just to view it, you can still use NWBWidgets to view it remotely. Check out [Streaming an NWB File with fsspec](./stream_nwb.ipynb) to learn how to do this. Another way to explore an NWB file, that doesn't require Jupyter, is with [HDFView](https://www.hdfgroup.org/downloads/hdfview/)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1174cd41da5d4e66a3f44a632991611b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HBox(children=(Label(value='session_description:', layout=Layout(max_height='40px', max_width='…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Use the nwbwidgets package to visualize the NWB file, explore the data, and access the metadata.\n", "from nwbwidgets import nwb2widget\n", "nwb2widget(nwbfile)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Information and metadata" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Each subject has multiple experimental sessions. You can check that programatically." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "['sub-01/sub-01_ses-3_behavior+ecephys.nwb',\n", " 'sub-01/sub-01_ses-4_behavior+ecephys.nwb',\n", " 'sub-01/sub-01_ses-5_behavior+ecephys.nwb',\n", " 'sub-01/sub-01_ses-7_behavior+ecephys.nwb',\n", " 'sub-02/sub-02_ses-3_behavior+ecephys.nwb',\n", " 'sub-02/sub-02_ses-4_behavior+ecephys.nwb',\n", " 'sub-02/sub-02_ses-5_behavior+ecephys.nwb',\n", " 'sub-02/sub-02_ses-6_behavior+ecephys.nwb',\n", " 'sub-03/sub-03_ses-3_behavior+ecephys.nwb',\n", " 'sub-03/sub-03_ses-4_behavior+ecephys.nwb',\n", " 'sub-03/sub-03_ses-5_behavior+ecephys.nwb',\n", " 'sub-03/sub-03_ses-6_behavior+ecephys.nwb',\n", " 'sub-04/sub-04_ses-3_behavior+ecephys.nwb',\n", " 'sub-04/sub-04_ses-4_behavior+ecephys.nwb',\n", " 'sub-04/sub-04_ses-5_behavior+ecephys.nwb',\n", " 'sub-04/sub-04_ses-6_behavior+ecephys.nwb',\n", " 'sub-04/sub-04_ses-7_behavior+ecephys.nwb',\n", " 'sub-05/sub-05_ses-3_behavior+ecephys.nwb',\n", " 'sub-05/sub-05_ses-4_behavior+ecephys.nwb',\n", " 'sub-05/sub-05_ses-7_behavior+ecephys.nwb',\n", " 'sub-06/sub-06_ses-3_behavior+ecephys.nwb',\n", " 'sub-06/sub-06_ses-4_behavior+ecephys.nwb',\n", " 'sub-06/sub-06_ses-5_behavior+ecephys.nwb',\n", " 'sub-06/sub-06_ses-6_behavior+ecephys.nwb',\n", " 'sub-06/sub-06_ses-7_behavior+ecephys.nwb',\n", " 'sub-07/sub-07_ses-3_behavior+ecephys.nwb',\n", " 'sub-07/sub-07_ses-4_behavior+ecephys.nwb',\n", " 'sub-07/sub-07_ses-5_behavior+ecephys.nwb',\n", " 'sub-07/sub-07_ses-6_behavior+ecephys.nwb',\n", " 'sub-07/sub-07_ses-7_behavior+ecephys.nwb',\n", " 'sub-08/sub-08_ses-3_behavior+ecephys.nwb',\n", " 'sub-08/sub-08_ses-4_behavior+ecephys.nwb',\n", " 'sub-08/sub-08_ses-5_behavior+ecephys.nwb',\n", " 'sub-08/sub-08_ses-6_behavior+ecephys.nwb',\n", " 'sub-08/sub-08_ses-7_behavior+ecephys.nwb',\n", " 'sub-09/sub-09_ses-3_behavior+ecephys.nwb',\n", " 'sub-09/sub-09_ses-4_behavior+ecephys.nwb',\n", " 'sub-09/sub-09_ses-5_behavior+ecephys.nwb',\n", " 'sub-09/sub-09_ses-6_behavior+ecephys.nwb',\n", " 'sub-09/sub-09_ses-7_behavior+ecephys.nwb',\n", " 'sub-10/sub-10_ses-3_behavior+ecephys.nwb',\n", " 'sub-10/sub-10_ses-4_behavior+ecephys.nwb',\n", " 'sub-10/sub-10_ses-5_behavior+ecephys.nwb',\n", " 'sub-10/sub-10_ses-6_behavior+ecephys.nwb',\n", " 'sub-10/sub-10_ses-7_behavior+ecephys.nwb',\n", " 'sub-11/sub-11_ses-3_behavior+ecephys.nwb',\n", " 'sub-11/sub-11_ses-4_behavior+ecephys.nwb',\n", " 'sub-11/sub-11_ses-5_behavior+ecephys.nwb',\n", " 'sub-11/sub-11_ses-6_behavior+ecephys.nwb',\n", " 'sub-11/sub-11_ses-7_behavior+ecephys.nwb',\n", " 'sub-12/sub-12_ses-3_behavior+ecephys.nwb',\n", " 'sub-12/sub-12_ses-4_behavior+ecephys.nwb',\n", " 'sub-12/sub-12_ses-5_behavior+ecephys.nwb',\n", " 'sub-12/sub-12_ses-6_behavior+ecephys.nwb',\n", " 'sub-12/sub-12_ses-7_behavior+ecephys.nwb']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the path to each subject's session behavior/ecephys files\n", "with DandiAPIClient() as client:\n", " paths = []\n", " for file in client.get_dandiset(\"000055\", \"draft\").get_assets_with_path_prefix(\"\"):\n", " paths.append(file.path)\n", "paths = natsort.natsorted(paths)\n", "# print(paths)\n", "paths" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Data characteristics for each participant\n", "Get the list of hemisphere implanted, and number of recording days for each participant and turn it to a dataframe." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/12 [00:00\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", " \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", " \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", "
ParticipantGenderAge (years)Recording days usedHemisphere implantedSurface electrodes: # good / totalDepth electrodes: # good / total
0P01M444L79 / 866 / 8
1P02M204R69 / 7016 / 16
2P03M334L79 / 800 / 16
3P04F195R67 / 840 / 0
4P05F313R104 / 1060 / 0
5P06M375L70 / 800 / 0
6P07M265R63 / 640 / 0
7P08F335R83 / 920 / 0
8P09M205L96 / 9828 / 28
9P10M345L82 / 8639 / 40
10P11F345L103 / 1060 / 0
11P12M225L88 / 9224 / 32
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", " \n", " " ], "text/plain": [ " Participant Gender Age (years) Recording days used Hemisphere implanted \\\n", "0 P01 M 44 4 L \n", "1 P02 M 20 4 R \n", "2 P03 M 33 4 L \n", "3 P04 F 19 5 R \n", "4 P05 F 31 3 R \n", "5 P06 M 37 5 L \n", "6 P07 M 26 5 R \n", "7 P08 F 33 5 R \n", "8 P09 M 20 5 L \n", "9 P10 M 34 5 L \n", "10 P11 F 34 5 L \n", "11 P12 M 22 5 L \n", "\n", " Surface electrodes: # good / total Depth electrodes: # good / total \n", "0 79 / 86 6 / 8 \n", "1 69 / 70 16 / 16 \n", "2 79 / 80 0 / 16 \n", "3 67 / 84 0 / 0 \n", "4 104 / 106 0 / 0 \n", "5 70 / 80 0 / 0 \n", "6 63 / 64 0 / 0 \n", "7 83 / 92 0 / 0 \n", "8 96 / 98 28 / 28 \n", "9 82 / 86 39 / 40 \n", "10 103 / 106 0 / 0 \n", "11 88 / 92 24 / 32 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from plot_utils import load_data_characteristics\n", "\n", "rec_days, hemi, surf_tot, surf_good, depth_tot, depth_good, _, part, _, _ = load_data_characteristics(fs=fs)\n", "\n", "ages = [\n", " 44, 20, 33, 19, 31, 37, 26, 33, 20, 34, 34, 22\n", "] # not found in data files\n", "gender = [\n", " 'M', 'M', 'M', 'F', 'F', 'M', 'M', 'F', 'M', 'M', 'F', 'M'\n", "] # not found in data files\n", "surf_elecs = [str(val_good)+' / '+str(val_tot) for val_good, val_tot in zip(surf_good, surf_tot)]\n", "depth_elecs = [str(val_good)+' / '+str(val_tot) for val_good, val_tot in zip(depth_good, depth_tot)]\n", "\n", "# Generate a dataframe with the data characteristics\n", "pd.DataFrame(\n", " [part, gender, ages, rec_days, hemi, surf_elecs, depth_elecs],\n", " index=[\n", " 'Participant',\n", " 'Gender',\n", " 'Age (years)',\n", " 'Recording days used',\n", " 'Hemisphere implanted',\n", " 'Surface electrodes: # good / total',\n", " 'Depth electrodes: # good / total'\n", " ]\n", ").T" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Get the duration for coarse behaviors (Sleep/rest, Inactive, Talk, TV, Computer/phone, Eat, Other activity) in each subject" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/12 [00:00\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", " \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", " \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", "
Sleep/restInactiveTalkTVComputer/phoneEatOther activityTotal
P0129.21.915.622.07.82.60.875.7
P0253.22.19.27.72.92.14.879.8
P0311.90.321.81.919.83.50.951.1
P0434.03.128.311.08.22.01.278.9
P0535.42.88.912.15.81.01.262.5
P0637.80.12.85.72.60.30.245.3
P0746.80.35.00.31.90.40.253.6
P0847.80.86.85.31.30.62.261.8
P0987.38.13.80.00.00.61.9100.6
P1067.52.15.80.15.60.01.881.4
P1136.01.46.60.00.10.00.844.9
P1232.40.31.50.10.50.00.635.4
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", " \n", " " ], "text/plain": [ " Sleep/rest Inactive Talk TV Computer/phone Eat Other activity \\\n", "P01 29.2 1.9 15.6 22.0 7.8 2.6 0.8 \n", "P02 53.2 2.1 9.2 7.7 2.9 2.1 4.8 \n", "P03 11.9 0.3 21.8 1.9 19.8 3.5 0.9 \n", "P04 34.0 3.1 28.3 11.0 8.2 2.0 1.2 \n", "P05 35.4 2.8 8.9 12.1 5.8 1.0 1.2 \n", "P06 37.8 0.1 2.8 5.7 2.6 0.3 0.2 \n", "P07 46.8 0.3 5.0 0.3 1.9 0.4 0.2 \n", "P08 47.8 0.8 6.8 5.3 1.3 0.6 2.2 \n", "P09 87.3 8.1 3.8 0.0 0.0 0.6 1.9 \n", "P10 67.5 2.1 5.8 0.1 5.6 0.0 1.8 \n", "P11 36.0 1.4 6.6 0.0 0.1 0.0 0.8 \n", "P12 32.4 0.3 1.5 0.1 0.5 0.0 0.6 \n", "\n", " Total \n", "P01 75.7 \n", "P02 79.8 \n", "P03 51.1 \n", "P04 78.9 \n", "P05 62.5 \n", "P06 45.3 \n", "P07 53.6 \n", "P08 61.8 \n", "P09 100.6 \n", "P10 81.4 \n", "P11 44.9 \n", "P12 35.4 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Count activity and blocklist coarse label durations for each participant\n", "from plot_utils import clabel_table_create\n", "blocklist_labels = False # show blocklist (True) or activity (False) label durations\n", "\n", "if blocklist_labels:\n", " common_acts = [\n", " 'Blocklist (Data break)',\n", " 'Blocklist (Camera move/zoom)',\n", " 'Blocklist (Camera occluded)',\n", " 'Blocklist (Experiment)',\n", " 'Blocklist (Private time)',\n", " 'Blocklist (Tether/bandage)',\n", " 'Blocklist (Hands under blanket)',\n", " 'Blocklist (Clinical procedure)',\n", " ]\n", "else:\n", " common_acts = [\n", " 'Sleep/rest',\n", " 'Inactive',\n", " 'Talk',\n", " 'TV',\n", " 'Computer/phone',\n", " 'Eat',\n", " 'Other activity',\n", " ]\n", "\n", "# Generate table\n", "clabel_table_create(common_acts,fs=fs)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Behavioral labels" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Coarse behavior labelling trace for one recording day.\n", "Note that the figure from the data paper combined the targeted (targeted=True) and untargeted (both first_val=True and first_val=False) behavior labels." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# load the function to plot the coarse labels\n", "from plot_utils import prune_clabels, plot_clabels" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# set parameters for plotting coarse labels\n", "targ_tlims = [13, 17] # targeted window to plot (in hours)\n", "targeted = False # plot targeted window (True) or whole day (False)\n", "targ_label = 'Computer/phone' #behavior_type" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-common' version 1.4.0-alpha because version 1.6.0 is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n", "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'core' version 2.2.5 because version 2.6.0-alpha is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n", "/usr/local/lib/python3.10/dist-packages/hdmf/spec/namespace.py:531: UserWarning: Ignoring cached namespace 'hdmf-experimental' version 0.1.0 because version 0.3.0 is already loaded.\n", " warn(\"Ignoring cached namespace '%s' version %s because version %s is already loaded.\"\n" ] } ], "source": [ "# Load the data and coarse labels for the targeted window\n", "with DandiAPIClient() as client:\n", " asset = client.get_dandiset(\"000055\", \"draft\").get_asset_by_path(\n", " \"sub-01/sub-01_ses-4_behavior+ecephys.nwb\"\n", " )\n", " s3_path = asset.get_content_url(follow_redirects=1, strip_query=True)\n", "f = fs.open(s3_path, \"rb\")\n", "file = h5py.File(f)\n", "with NWBHDF5IO(file=file, mode='r', load_namespaces=True) as io:\n", "# with NWBHDF5IO(s3_path, mode='r', load_namespaces=True, driver='ros3') as io: #if you want to use ROS3 to stream data, use this line instead and comment the three lines above\n", " nwb = io.read()\n", " clabels_orig = nwb.intervals['epochs'].to_dataframe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# Select coarse labels based on user parameters\n", "label_col_d = {\n", " 'Other activity': 0,\n", " 'Computer/phone': 1,\n", " 'Eat': 2,\n", " 'TV': 3,\n", " 'Talk': 4\n", "}\n", "\n", "clabels, uni_labs = prune_clabels(clabels_orig, targeted,\n", " targ_tlims, None,\n", " targ_label)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot coarse labels over time\n", "fig = plot_clabels(clabels, uni_labs, targeted, None, targ_tlims, targlab_colind=label_col_d[targ_label])" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Spectral power in select frequency band for different grid locations." ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Get the information on grids/subjects" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "P01_Postcentral.npy P05_Postcentral.npy P09_Postcentral.npy\n", "P01_Precentral.npy P05_Precentral.npy P09_Precentral.npy\n", "P01_Temporal_Inf.npy P05_Temporal_Inf.npy P09_Temporal_Inf.npy\n", "P01_Temporal_Mid.npy P05_Temporal_Mid.npy P09_Temporal_Mid.npy\n", "P02_Postcentral.npy P06_Postcentral.npy P10_Postcentral.npy\n", "P02_Precentral.npy P06_Precentral.npy P10_Precentral.npy\n", "P02_Temporal_Inf.npy P06_Temporal_Inf.npy P10_Temporal_Inf.npy\n", "P02_Temporal_Mid.npy P06_Temporal_Mid.npy P10_Temporal_Mid.npy\n", "P03_Postcentral.npy P07_Postcentral.npy P11_Postcentral.npy\n", "P03_Precentral.npy P07_Precentral.npy P11_Precentral.npy\n", "P03_Temporal_Inf.npy P07_Temporal_Inf.npy P11_Temporal_Inf.npy\n", "P03_Temporal_Mid.npy P07_Temporal_Mid.npy P11_Temporal_Mid.npy\n", "P04_Postcentral.npy P08_Postcentral.npy P12_Postcentral.npy\n", "P04_Precentral.npy P08_Precentral.npy P12_Precentral.npy\n", "P04_Temporal_Inf.npy P08_Temporal_Inf.npy P12_Temporal_Inf.npy\n", "P04_Temporal_Mid.npy P08_Temporal_Mid.npy P12_Temporal_Mid.npy\n" ] } ], "source": [ "# check if pickle file exists\n", "!ls data/\n", "# you should get a lost of files in the data folder:\n", "# P01_Postcentral.npy P05_Postcentral.npy P09_Postcentral.npy\n", "# P01_Precentral.npy P05_Precentral.npy P09_Precentral.npy\n", "# P01_Temporal_Inf.npy P05_Temporal_Inf.npy P09_Temporal_Inf.npy\n", "# P01_Temporal_Mid.npy P05_Temporal_Mid.npy P09_Temporal_Mid.npy\n", "# P02_Postcentral.npy P06_Postcentral.npy P10_Postcentral.npy\n", "# P02_Precentral.npy P06_Precentral.npy P10_Precentral.npy\n", "# P02_Temporal_Inf.npy P06_Temporal_Inf.npy P10_Temporal_Inf.npy\n", "# P02_Temporal_Mid.npy P06_Temporal_Mid.npy P10_Temporal_Mid.npy\n", "# P03_Postcentral.npy P07_Postcentral.npy P11_Postcentral.npy\n", "# P03_Precentral.npy P07_Precentral.npy P11_Precentral.npy\n", "# P03_Temporal_Inf.npy P07_Temporal_Inf.npy P11_Temporal_Inf.npy\n", "# P03_Temporal_Mid.npy P07_Temporal_Mid.npy P11_Temporal_Mid.npy\n", "# P04_Postcentral.npy P08_Postcentral.npy P12_Postcentral.npy\n", "# P04_Precentral.npy P08_Precentral.npy P12_Precentral.npy\n", "# P04_Temporal_Inf.npy P08_Temporal_Inf.npy P12_Temporal_Inf.npy\n", "# P04_Temporal_Mid.npy P08_Temporal_Mid.npy P12_Temporal_Mid.npy" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "If the pickle file does not exist, then run the following cell to create it (You may need to do this if you are running the notebook locally). If you are running the notebook on colab, \"data\" folder will be automatically installed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "# import requests\n", "# from bs4 import BeautifulSoup\n", "\n", "# url = 'https://github.com/neurovium/Neuromatch-AJILE12/tree/master/data'\n", "# html = requests.get(url).content\n", "# soup = BeautifulSoup(html, 'html.parser')\n", "# files = [a['href'] for a in soup.select('a.js-navigation-open') if a['href'].endswith('.npy')]\n", "\n", "# !mkdir -p data\n", "# for file in files:\n", "# filename = file.split('/')[-1]\n", "# raw_url = f'https://raw.githubusercontent.com{file.replace(\"/blob\", \"\")}'\n", "# !wget -O data/{filename} {raw_url}" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Plot ECoG electrode locations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", " 0%| | 0/12 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load data characteristics including the number of good and total ECoG electrodes,\n", "# # hemisphere implanted, and number of recording days for each participant.\n", "from plot_utils import (\n", " load_data_characteristics,\n", " plot_ecog_descript,\n", ")\n", "\n", "dat_chact = load_data_characteristics(fs=fs) # call argument to \"fs\" is specific to fsspec. \"fs\" was created as a cache earlier in the notebook.\n", "n_elecs_good, n_elecs_tot = dat_chact[-2], dat_chact[-1]\n", "part_ids = dat_chact[-3]\n", "\n", "fig = plot_ecog_descript(n_elecs_tot, n_elecs_good, part_ids, nrows=2,fs=fs)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Project power analyses on different ECOG grids" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Neuromatch-AJILE12/plot_utils/pow.py:94: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:94: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:94: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:94: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar='sd'` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:167: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:167: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:167: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n", "\n", " sns.lineplot(\n", "/Neuromatch-AJILE12/plot_utils/pow.py:167: FutureWarning: \n", "\n", "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n", "\n", " sns.lineplot(\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import modules\n", "from plot_utils import plot_ecog_pow\n", "\n", "# Define variables\n", "rois_plt = [\n", " 'Precentral',\n", " 'Postcentral',\n", " 'Temporal_Mid',\n", " 'Temporal_Inf'\n", "]\n", "sbplt_titles = [\n", " 'Precentral\\nGyrus',\n", " 'Postcentral\\nGyrus',\n", " 'Middle Temporal\\nGyrus',\n", " 'Inferior Temporal\\nGyrus'\n", "]\n", "freq_range = [3, 125]\n", "lp = 'data/'\n", "\n", "# Plot power spectra\n", "plot_ecog_pow(\n", " lp,\n", " rois_plt,\n", " freq_range,\n", " sbplt_titles,\n", " part_id='P01',\n", ")" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "## Neural activity and movement behavior relationship" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Identify the start and stop times when the behavioral label of interest occurs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/html": [ "\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", " \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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
start_timestop_timelabelsdiff
034176.40000034297.766667Eat, Talk121.366667
144378.26666744497.500000Eat, Talk119.233333
244497.53333344615.700000Eat, Talk118.166667
344615.93333344736.066667Eat, Talk120.133333
444736.20000044976.433333Eat, Talk240.233333
544976.50000045095.933333Eat, Talk119.433333
645096.06666745216.766667Eat, Talk120.700000
745216.80000045336.100000Eat, Talk119.300000
864655.53333364776.133333Eat, Talk120.600000
964776.16666764895.733333Eat, Talk119.566667
1064895.83333365023.033333Eat, Talk127.200000
1165375.53333365501.966667Eat, TV126.433333
1270177.00000070297.566667Eat, TV120.566667
1370297.63333370418.000000Eat, TV120.366667
1470418.03333370537.633333Eat, TV119.600000
1570537.70000070655.333333Eat, TV117.633333
1670655.46666770774.933333Eat, TV119.466667
1770775.03333370897.666667Eat, TV122.633333
1870897.76666771015.100000Eat, TV117.333333
1971015.13333371136.366667Eat, TV121.233333
2071377.23333371495.700000Eat, TV118.466667
2171495.80000071615.500000Eat, TV119.700000
2271615.56666771735.033333Eat, TV119.466667
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " start_time stop_time labels diff\n", "0 34176.400000 34297.766667 Eat, Talk 121.366667\n", "1 44378.266667 44497.500000 Eat, Talk 119.233333\n", "2 44497.533333 44615.700000 Eat, Talk 118.166667\n", "3 44615.933333 44736.066667 Eat, Talk 120.133333\n", "4 44736.200000 44976.433333 Eat, Talk 240.233333\n", "5 44976.500000 45095.933333 Eat, Talk 119.433333\n", "6 45096.066667 45216.766667 Eat, Talk 120.700000\n", "7 45216.800000 45336.100000 Eat, Talk 119.300000\n", "8 64655.533333 64776.133333 Eat, Talk 120.600000\n", "9 64776.166667 64895.733333 Eat, Talk 119.566667\n", "10 64895.833333 65023.033333 Eat, Talk 127.200000\n", "11 65375.533333 65501.966667 Eat, TV 126.433333\n", "12 70177.000000 70297.566667 Eat, TV 120.566667\n", "13 70297.633333 70418.000000 Eat, TV 120.366667\n", "14 70418.033333 70537.633333 Eat, TV 119.600000\n", "15 70537.700000 70655.333333 Eat, TV 117.633333\n", "16 70655.466667 70774.933333 Eat, TV 119.466667\n", "17 70775.033333 70897.666667 Eat, TV 122.633333\n", "18 70897.766667 71015.100000 Eat, TV 117.333333\n", "19 71015.133333 71136.366667 Eat, TV 121.233333\n", "20 71377.233333 71495.700000 Eat, TV 118.466667\n", "21 71495.800000 71615.500000 Eat, TV 119.700000\n", "22 71615.566667 71735.033333 Eat, TV 119.466667" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get coarse labels from NWB file\n", "min_len = 100 # (sec) only keep times when the given label appears for longer than this amount of time at once\n", "\n", "coarse_labels = nwbfile.intervals['epochs'].to_dataframe()\n", "coarse_labels = coarse_labels[coarse_labels['labels'].str.contains(behavior_type)]\n", "coarse_labels['diff'] = coarse_labels['stop_time'] - coarse_labels['start_time']\n", "coarse_labels = coarse_labels[coarse_labels['diff'] > min_len]\n", "coarse_labels.reset_index(inplace=True, drop=True)\n", "\n", "# Print the coarse labels as a table\n", "coarse_labels" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Load the corresponding ECoG data for each behavioral label chunk and convert to spectral power via the Hilbert transform." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "filter_order = 4 # order of butterworth filter used to bandpass filter the ECoG data\n", "\n", "neural_data = nwbfile.acquisition['ElectricalSeries'].data\n", "sampling_rate = nwbfile.acquisition['ElectricalSeries'].rate # (Hz) ECoG sampling rate\n", "neural_power = []\n", "for i in range(coarse_labels.shape[0]):\n", " # Identify the start/end indices for each continuous chunk of the given behavioral label\n", " start_t = int(coarse_labels.loc[i, 'start_time']*sampling_rate)\n", " end_t = int(coarse_labels.loc[i, 'stop_time']*sampling_rate)\n", "\n", " # Load data snippet\n", " neur_data_curr = neural_data[start_t:end_t, ecog_ch_num]\n", "\n", " # Bandpass filter\n", " sos = butter(filter_order, neural_freq_range, btype='bandpass', output='sos', fs=sampling_rate)\n", " neur_data_filtered = sosfiltfilt(sos, neur_data_curr)\n", "\n", " # Apply Hilbert transform and convert to decibels\n", " neur_pow = np.abs(hilbert(neur_data_filtered))\n", " neur_pow = 10*np.log(neur_pow)\n", "\n", " # Take the difference between neighboring timepoints\n", " neural_power.append(np.diff(neur_pow))" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "Load the corresponding pose data for each behavioral label chunk and convert to vertical velocity." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "keypoints = list(nwbfile.processing['behavior'].data_interfaces['Position'].spatial_series.keys())\n", "assert keypoint_of_interest in keypoints\n", "assert pose_direction in ['vertical', 'horizontal']\n", "keypoint_series = nwbfile.processing['behavior'].data_interfaces['Position'].spatial_series[keypoint_of_interest]\n", "sampling_rate_keypoint = keypoint_series.rate # Hz\n", "keypoint_velocity = []\n", "for i in range(coarse_labels.shape[0]):\n", " start_t = int(coarse_labels.loc[i, 'start_time']*sampling_rate_keypoint)\n", " end_t = int(coarse_labels.loc[i, 'stop_time']*sampling_rate_keypoint)\n", "\n", " # Load pose data snippet\n", " pose_data_curr = keypoint_series.data[start_t:end_t, :]\n", " pose_mag_curr = pose_data_curr[:, 1 if pose_direction == 'vertical' else 0]\n", "\n", " # Convert to velocity (delta X / delta t)\n", " velocity_curr = np.diff(pose_mag_curr)/(1/sampling_rate_keypoint)\n", " keypoint_velocity.append(velocity_curr)" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Align and combine neural and pose data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [], "source": [ "assert len(neural_power) == len(keypoint_velocity)\n", "measures_all = []\n", "for i in range(len(neural_power)):\n", " # Neural power for the given chunk\n", " neur_curr = neural_power[i]\n", " l_neur = len(neur_curr)\n", "\n", " # Pose velocity for the given chunk\n", " accel_curr = keypoint_velocity[i]\n", " l_accel = len(accel_curr)\n", "\n", " # Downsample neural data to match pose data\n", " inds_split = np.array_split(np.arange(l_neur), l_accel)\n", " for j, inds in enumerate(inds_split):\n", " measures_all.append([neur_curr[inds].mean(), accel_curr[j]])\n", "\n", "# Combine neural/pose data into a dataframe\n", "df_measures_all = pd.DataFrame(np.asarray(measures_all), columns=['Neural power (dB)', 'Keypoint velocity (pixels/sec)'])\n", "\n", "# Remove any NaN's\n", "df_measures_all.dropna(inplace=True)\n", "\n", "# Remove instances with velocity close to 0\n", "df_measures_all = df_measures_all[(df_measures_all['Keypoint velocity (pixels/sec)'] > 100) |\\\n", " (df_measures_all['Keypoint velocity (pixels/sec)'] < -100)]" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Print correlation between neural power and keypoint velocity\n", "We find a small, positive correlation between neural power in the gamma band and right wrist vertical velocity." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/html": [ "\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", "
Neural power (dB)Keypoint velocity (pixels/sec)
Neural power (dB)1.0000000.025379
Keypoint velocity (pixels/sec)0.0253791.000000
\n", "
\n", " \n", " \n", " \n", "\n", " \n", "
\n", "
\n", " " ], "text/plain": [ " Neural power (dB) \\\n", "Neural power (dB) 1.000000 \n", "Keypoint velocity (pixels/sec) 0.025379 \n", "\n", " Keypoint velocity (pixels/sec) \n", "Neural power (dB) 0.025379 \n", "Keypoint velocity (pixels/sec) 1.000000 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_measures_all.corr(method='pearson')" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Perform robust linear regression to quantify any linear relationships\n", "Regression identifies a small, but significant (p<0.05) positive relationship between neural power in the gamma band and right wrist vertical velocity. This result makes sense because moving one's wrist upward takes more effort (fighting against gravity) than moving one's arm downward and thus may require slightly more cortical control." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/html": [ "\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", "
Robust linear Model Regression Results
Dep. Variable: Neural power (dB) No. Observations: 6447
Model: RLM Df Residuals: 6445
Method: IRLS Df Model: 1
Norm: HuberT
Scale Est.: mad
Cov Type: H1
Date: Mon, 03 Jul 2023
Time: 08:21:31
No. Iterations: 16
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
const -0.0075 0.006 -1.251 0.211 -0.019 0.004
Keypoint velocity (pixels/sec) 1.635e-05 6.62e-06 2.470 0.014 3.37e-06 2.93e-05


If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore ." ], "text/plain": [ "\n", "\"\"\"\n", " Robust linear Model Regression Results \n", "==============================================================================\n", "Dep. Variable: Neural power (dB) No. Observations: 6447\n", "Model: RLM Df Residuals: 6445\n", "Method: IRLS Df Model: 1\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Mon, 03 Jul 2023 \n", "Time: 08:21:31 \n", "No. Iterations: 16 \n", "==================================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "--------------------------------------------------------------------------------------------------\n", "const -0.0075 0.006 -1.251 0.211 -0.019 0.004\n", "Keypoint velocity (pixels/sec) 1.635e-05 6.62e-06 2.470 0.014 3.37e-06 2.93e-05\n", "==================================================================================================\n", "\n", "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n", "\"\"\"" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = df_measures_all['Keypoint velocity (pixels/sec)']\n", "Y = df_measures_all['Neural power (dB)']\n", "\n", "X = sm.add_constant(X)\n", "rlm_model = sm.RLM(Y, X, M=sm.robust.norms.HuberT())\n", "rlm_results = rlm_model.fit()\n", "rlm_results.summary()" ] }, { "cell_type": "markdown", "metadata": { "execution": {} }, "source": [ "### Plot data with linear fit\n", "Most of the data appears clustered near 0 velocity. The positive relationship between neural power and right wrist vertical velocity is only barely visible. Additional steps that may help better understand the relationship between neural spectral power and wrist velocity include: removing pose data with abnormally high standard deviation due to noisy tracking, subtracting spectral power in nearby periods with minimal movement from ECoG spectral power, and manually reviewing pose trajectories to remove noisy tracking periods." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(data=df_measures_all, x='Keypoint velocity (pixels/sec)', y='Neural power (dB)', robust=True, ci=None)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "exploreAJILE12", "provenance": [], "toc_visible": true }, "kernel": { "display_name": "Python 3", "language": "python", "name": "python3" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }