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es-fMRI connectivity

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All code for the project: Functional connectivity of the human brain investigated using concurrent electrical stimulation and fMRI

OSF link: https://osf.io/pdhfu/

Code

Each projects code

  • ./esfmri_connectivity/projectname/

Where project name is a subset of the anlaysis

In the README of each directory it should be clearly stated which data it is acting on and contain a list or execution order. If a Dockerfile has been used for a specific part of the analysis (e.g. fMRIPrep) this should be stated. If nothing is specified, then the main ./Dockerfile is used.

List of docker commands to replicate project

This is a list of docker commands using the docker container of this repository. This assumes that fMRIPrep and fMRIDenoise have been run. To replicate those steps see ./preprocessing/fmriprep/README.md and ./preprocessing/fmridenoise/README.md.

Next, navigate to this repositories main directory (where this README file is). This is wto be the working directory when rerunning the commaned.

Next, set the bash variable ESFMRI_DATA to point to where the BIDS directory is.

ESFMRI_DATA='path/to/data'

It also assumes the fMRIPrep and fMRIdenoise is saved in:

$ESFMRI_DATA/derivatives/fmriprep-1.5.1/fmriprep/ $ESFMRI_DATA/derivatives/denoise/

This is the output if following the steps above.

Create Smörgåsbord parcellation

The parcellation used is included within the repo. But to replicate all the steps to recreate the parcellation see:

Amygdala - see: ./esfmri_connectivity/parcellation/amygdala/README.md

Cerebellum - see: ./esfmri_connectivity/parcellation/cerebellum/README.md

Subcortical - see: ./esfmri_connectivity/parcellation/cerebellum/README.md

Create Smörgåsbord parcellation - see ./esfmri_connectivity/parcellation/README.md

Preprocessing steps

Find the bad runs with exceesive movement

docker run -u esfmri -v $(pwd):/home/esfmri/ -v $ESFMRI_DATA:/data/ -t esfmri python -m esfmri_connectivity.preprocessing.quality_control.find_bad_runs

Create the good voxel masks

docker run -u esfmri -v $(pwd):/home/esfmri/ -v $ESFMRI_DATA:/data/ -t esfmri python -m esfmri_connectivity.preprocessing.goodvoxel_masks.create_avgvoxdist

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.preprocessing.goodvoxel_masks.plot_gmm

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.preprocessing.goodvoxel_masks.create_mask

Extract time series

docker run -u esfmri -v $(pwd):/home/esfmri/ -v $ESFMRI_DATA:/data/ -t esfmri python -m esfmri_connectivity.preprocessing.extract_timeseries.extract_timeseries

Community detection

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.communitydetection.run_communitydetection

Find stimualtion sites/parcels

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.stimulation_sites.find_stimulation_parcel

Analysis 1

docker run -u esfmri -v $(pwd):/home/esfmri/ -v $ESFMRI_DATA:/data/ -t esfmri python -m esfmri_connectivity.analysis1.calc_fc

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.analysis1.contrast_and_plot

Analysis 2

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.analysis2.calc_pc_stimsite

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.analysis2.trainmodels

docker run -u esfmri -v $(pwd):/home/esfmri/ -t esfmri python -m esfmri_connectivity.analysis2.plot_best_model