RestHoldMove preprocessing

This repository has been made to help searcher to have a quick start on analysing the RestHoldMove dataset available here in openneuro: RestHoldMove dataset.

Introduction

This dataset contains data from externalized DBS patients undergoing simultaneous MEG - STN LFP recordings with (MedOn) and without (MedOn) dopaminergic medication. It has two movement conditions: 1) 5 min of rest followed by static forearm extension (hold) and 2) 5 min of rest followed by self-paced fist-clenching (move). The movement parts contain pauses. Some patients were recorded in resting-state only (rest). The project aimed to understand the neurophysiology of basal ganglia-cortex loops and its modulation by movement and medication. For further information: https://www.nature.com/articles/s41597-024-03768-1

Installation

Use the package manager pip to install every library that are used in the repository.

cd RHM_preprocessing
pip install -r requirements. txt

Usage

Define the root where you downloaded the dataset in the config.py file.

bids_root = '/data/raw/hirsch/RestHoldMove_anon/' #redefine this 

Preprocessing

To perform basic preprocessing and connectivity analysis between MEG-STN in one subject block, run the get_started.py script. (Detailed instructions are provided within the script)

python get_started.py

Technical Validations

The utils folder contains all the scripts used to make the technical validations described in [TBA]. You can run these scripts to perform group analysis for UPDRS, EMG power, MEG-STN coupling. Please note that you would need to run the utils/run_<coherence/emg>.py before starting the utils/run_<coherence/emg>_average.py scripts.

Additionnaly the script utils/technical_validation_utils.py holds function that is used across the whole repository and has not been designed to be run individually.

Figures

python run_updrs.py

python run_emg_average.py

python run_var_average.py

python run_coherence_average.py

source_reconstruction_example.m [matlab required]