If you use this script, please cite:

fmripop

A collection of python scripts and functions to (po)st(p)rocess fmridata, preprocessed with fmriprep. The main file is post_fmriprep.py, which enables users to:

• remove fmri confounds
• filter data
• smooth data
• scrub data
• censor volumes

Many of the function in this file are wrappers of nilearn's functions.

To learn more about the input parameters and their default values type

python post_fmriprep.py --help

USE CASES

Exemplary use cases are detailed below

CASE 0-a: Default values, typical use for resting-state fmri data

Uses default values of non-boolean parameters (ie, confounds, filtering, tr) and outputs data centred around a nonzero mean. The default values of these non-boolean parameters are optimised for resting-state fmri data.

        python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv
                            --detrend
                            --add_orig_mean_img

CASE 0-b: Default values, typical use for resting state fmri data, zero-centred data.

Uses default values of non-boolean parameters (ie, confounds, filtering, tr) and outputs zero-centred data (mean==0).

python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                        --maskpath /path/to/file/file_brainmask.nii.gz
                        --tsvpath /path/to/file/file_confounds.tsv
                        --detrend

CASE 1: Typical use case for task fmri data.

    Does not regress `framwise displacement` -- used for task-fmri data
    Does not filter.
    Uses default value for smoothing the data

    python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv'
                            --detrend
                            --add_orig_mean_img
                            --low_pass None 
                            --high_pass None 
                            --fmw_disp_th None

CASE 2: Calculates scrubbing mask AND removes contaminated volumes

    python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv'
                            --detrend
                            --add_orig_mean_img
                            --calculate_scrubbing_mask
                            --remove_volumes

CASE 3: Calculates scrubbing mask, but DOES NOT remove contaminated volumes

    python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv'
                            --detrend
                            --add_orig_mean_img
                            --calculate_scrubbing_mask

CASE 4: Performs smoothing with a different width along each axis

    python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv'
                            --detrend
                            --add_orig_mean_img
                            --fwhm 1.5 2.5 1.0

CASE 5: Remove confounds other than those in the default list

    python post_fmriprep.py --niipath /path/to/file/file_preproc.nii.gz
                            --maskpath /path/to/file/file_brainmask.nii.gz
                            --tsvpath /path/to/file/file_confounds.tsv
                            --detrend
                            --add_orig_mean_img
                            --confound_list "csf,white_matter"
                            --num_confounds 2