This repository contains the scripts and software to run the simulations and real data analysis published in:
Andrew J. Quinn, Lauren Atkinson, Chetan Gohil, Oliver Kohl, Jemma Pitt, Catharina Zich, Anna C. Nobre & Mark W. Woolrich (2022) The GLM-Spectrum: Multilevel power spectrum analysis with covariate and confound modelling
First, clone this repository into a directory on your computer:
git clone https://github.com/OHBA-analysis/Quinn2022_GLMSpectrum.git
then create a conda environment and install the dependencies
conda env create -f glmspectrum_env.yml
conda activate glm-spectrum
Next, you need to configure the lemon_raw and lemon_output directories in glm_config.yml. lemon_raw specifies a directory where the raw data will be downloaded to (or where the raw data already exists) and lemon_output specifies a directory where the generatedoutputs from this analysis will be stored.
After specifying these paths, glm_config.yml should look something like this:
lemon_raw: /path/to/my/raw/data_folder
lemon_output: /path/to/my/output_folder
lemon_raw_url: https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/EEG_MPILMBB_LEMON/EEG_Raw_BIDS_ID/
lemon_behav_url: https://ftp.gwdg.de/pub/misc/MPI-Leipzig_Mind-Brain-Body-LEMON/Behavioural_Data_MPILMBB_LEMON/
From here you can run the analysis, plotting and supplemental scripts in order. Outputs will be saved into your lemon_output directory.
A full list of requirements is specified in the requirements.txt file and the glmspectrum_env.yml anaconda environment.
The EEG data analysis depends on MNE-Python and OSL. The GLM-Spectrum used in this paper is implemented in the SAILS toolbox as sails.stft.glm_periodogram. Another implementation is available in osl-dynamics as osl_dynamics.analysis.regression_spectra. The GLM analysis and statistics further depend on glmtools
This paper uses the open-data availiable from the mind-body-brain dataset.
Babayan, A., Erbey, M., Kumral, D. et al. A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults. Sci Data 6, 180308 (2019). https://doi.org/10.1038/sdata.2018.308