For resting state analysis, you can use XCP Imaging Pipeline. It’s free and open-sourced. In addition to resting state analysis, XCP Imaging Pipeline have modules like Functional Image Processing (prestats, confound2, regress), Functional Derivatives (task, reho, alff, seed), Anatomical Image Processing (struc), Anatomical Derivatives (jlf, gmd), ASL Image Processing (cbf, basil), Regional quantification (roiquant), Registration (struc, norm), Connectomics and networks (fcon), and Quality assessment (QCFC). You may specify what modules you would like to use in the design file.
It takes the FMRIPREP output as its input and produces the necessary outputs based on the design file. Try to use the latest version of FMRIPREP, since the previous versions of it may have had different file naming convention.
Thus, you need to
- Convert data from DICOMS to a BIDS format, then
- run FMRIPREP on the BIDS data set, and finally
- run xcpEngine.
You can install the xcpengine container using Docker and the xcpengine pyhton wrapper. Therefore, after installing Docker (make sure that you have at least 8GB of memory), run the following phyton code:
pip install xcpengine-container
This wrapper will allow you to run xcpengine without running Docker.
In Singularity, you can install xcpengine by using the following lines.
module load singularity singularity build xcpEngine.simg docker://pennbbl/xcpengine:latest
This will create a Singularity image named xcpengine.simg in your current working directory.
As noted above, you need the FMRIPREP output as input for xcpengine, hence make sure that you have the preprocessed data. In the current version, xcpengine only supports volume data (T1w and MNI152NLin2009cAsym); if you need to use another data form, please check (https://xcpengine.readthedocs.io/overview.html#installation) to learn more about the current version and whether it supports the data form you need to use.
In addition to the preprocessed data, you need to create two files, cohort.csv and a design file.
The cohort file should contain subject metadata (without special shell characters) in id columns and paths to images written by FMRIPREP in the img column. The cohort file might be saved as /home/me/cohort.csv and contain:
In the first line, ‘id0’, which refers to the subject number/name, and ‘img’, which refers to the location of the preprocessed data. If you wish to indicate session numbers, you can add ‘id1’ to the first line, right after ‘id0’ in the first line, and ‘ses-1’ right after ‘sub-01’ in the following lines. Here is an example,
You can imagine the design file (.dsn) as the settings of the xcpengine. It contains,
- A module list; e.g. confound,regress,fcon,reho,alff,net,roiquant,seed,norm,qcfc …
- The set of parameters that configure each module; e.g. alff_hipass=0.01
You can find some sample design files here: https://github.com/PennBBL/xcpEngine/tree/master/designs
xcpengine-docker \ -c /home/me/cohort.csv \ -d /xcpEngine/designs/fc-36p.dsn \ -i /home/me/work \ -o /home/me/xcpOutput \ -r /home/me/fmriprep_outputdir/fmriprep
Here is a sample xcpengine slurm file.
#!/bin/bash #SBATCH --mail-type=ALL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=First.Last@uconn.edu # Your email address #SBATCH --nodes=1 # OpenMP requires a single node #SBATCH --ntasks=1 # Run a single serial task #SBATCH --cpus-per-task=8 #SBATCH --mem=48gb #SBATCH --time=72:00:00 # Time limit hh:mm:ss #SBATCH -e error_%A_%a.log # Standard error #SBATCH -o output_%A_%a.log # Standard output #SBATCH --job-name=fc-36 # Descriptive job name #SBATCH --partition=serial # Use a serial partition 24 cores/7days module load singularity xcpengine-singularity \ --image /scratch/USERNAME/xcpEngine-latest.simg \ -c /scratch/USERNAME/cohort.csv \ -d /scratch/USERNAME/fc-36p.dsn \ -i /scratch/USERNAME/work \ -o /scratch/USERNAME/xcpOutput \ -r /scratch/USERNAME/fmriprep_outputdir/fmriprep
-d: Design file -c: Cohort file -o: Output path -i: Intermediate path -r: Reference directory
Save the script to /scratch/USERNAME as xcpengine.sh, and then you can start running the XCP Imaging Pipeline.