MNE Python Tutorial 2021
Recording now available for download!
Steps to prepare prior to tutorial
Prepare python environment - For 3D rendering and interactive plots, this should be installed on your local computer
!!IF YOU DO NOT HAVE MINICONDA/ANACONDA INSTALLED - have IT install miniconda under your user account!! !!If you already have an mne environment, you can use another name for the environment and adjust accordingly!! conda activate base conda install -n base mamba -c conda-forge -y mamba create -n mne conda-forge::mne main::pip main:jupyter -y
conda activate mne
Download the sample data and scripts.
The data is available from the resources below (Approximately 1Gb compressed and 1.8Gb uncompressed). A final jupyter notebook may be sent over email prior to the tutorial - this will replace the V1 version.
NIMH Users: Download here
scp <USERNAME>@helix.nih.gov:/data/NIMH_scratch/mne_tutorial/mne_tutorial.tar.gz ./ tar -xvf mne_tutorial.tar.gz
Non-NIMH MEG Users: Download from MEG Data server
The data is located on the MEG data server and is accessible by all users with accounts An email will be sent with the path to the data
Running tutorial script - starting Jupyter Notebook
cd mne_tutorial conda activate mne jupyter notebook "MNE Python Tutorial-v1.ipynb"
Prep Non-tutorial Data
MRI Prep
Freesurfer reconstruction
# Run Freesurfer on Dataset (takes 8+ hrs): # Does not run on BRIK/HEAD - convert to .nii first -- e.g (module load afni; 3dAFNItoNIFTI anat+orig. ) # (OPTION1) - Install and process freesurfer locally on computer https://surfer.nmr.mgh.harvard.edu/fswiki/rel7downloads recon-all -all -i <MRI Used w/ MEG> -s <SUBJECT> # (OPTION2) - INSTRUCTIONS FOR BIOWULF - Run line by line on the bash terminal subjid= #Set Freesurfer ID mri= #Set MRI Name - must be a nifti file NOT BRIK/HEAD export SUBJECTS_DIR= #Set output folder, Make sure this directory exists module load freesurfer echo -e '#!/bin/bash\nrecon-all -all -i ' ${mri} -s ${subjid} #VERIFY THIS LOOKS CORRECT echo -e '#!/bin/bash\nrecon-all -all -i ' ${mri} -s ${subjid} | sbatch --mem=3g --time=24:00:00 #SUBMITS JOB TO SBATCH
Watershed Processing for Boundary Element Model
#In bash terminal (5-10minutes)
#Requires freesurfer to be installed
subjid=<SUBJECT>
subjects_dir=<SUBJECTS_DIR>
conda activate mne
python -c "import mne; mne.bem.make_watershed_bem(subject='${subjid}', subjects_dir='${subjects_dir}')"
MEG Prep
# The events of interest must be denoted in the MarkerFile.mrk https://megcore.nih.gov/index.php?title=Pyctf
# The marker file can also be processed with the hv_proc code that is distributed with the data # A limited demonstration on the day of the tutorial will be given to process the data triggers into the MarkerFile.
Copy Data To Tutorial Folder
#Move the subject's freesurfer reconstruction to the mne_tutorial folder. If needed rename the output freesurfer subject folder to the 8 digit meg id found in the meg data
cp -R ${SUBJECTS_DIR}/${subjid} mne_tutorial/SUBJECTS_DIR/
#Copy meg data to top level of mne_tutorial folder
cp -R <MEGDATASET.ds> mne_tutorial/
#Copy dataset used for the coregistration of the MRI
cp {BRIK/HEAD w/ fiducials marked OR Brainsight .txt file } mne_tutorial/
General Guidelines for processing
https://mne.tools/stable/overview/cookbook.html
Additional Code (included w/tutorial data - automatically installed during jupyter notebook processing)
#Manual installation
#conda activate ${CONDA ENV Name}
pip install git+https://github.com/nih-megcore/pyctf-lite
pip install git+https://github.com/nih-megcore/hv_proc
pip install git+https://github.com/nih-megcore/nih_to_mne
pyctf-lite
More info: https://github.com/nih-megcore/pyctf-lite Utility functions for reading in ctf dataset
hv_proc
More info: https://github.com/nih-megcore/pyctf-lite hv_proc: Contains functions to evaluate triggers to generate events - write to dataframe - write to ctf MarkerFile.mrk. MarkerFile.mrk reads into mne as raw.annotations - can be converted to events using events, event_ids = mne.events_from_annotations(raw)
nih_to_mne
More info: https://github.com/nih-megcore/nih_to_mne nih_to_mne: calc_mnetrans.py -h -- Creates MNE transformation matrix bstags.py installed on commandline to use w/orthohull
This section can be ignored
NOTES to reproduce
Backup of tutorial: MegServer ...../mne_tutorial/mne_tutorial.tar.gz Inside backup tarball - environment/pip_freeze.txt and environment/mne_tutorial_env.yml (conda) Also backup of full environment (linux:ubuntu) using conda-pack: envs_BAK/mne_tutorial1_env.tar.gz