Developed by:
Matteo Lionello @ Social and Affective NEuroscience (SANE), IMT Lucca
This repository contains code for a preprocessing pipeline designed for AFNI datasets. The pipeline offers extensive customization options to suit various preprocessing needs.
$ ~/afni_preprocessing/preprocessing/main.sh --data_folder /data/raw --output_folder /data/derivatives
subject: 2/3, run 1/2
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3dVolreg... 4/12
( ▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇....................................... ) 33%
Clone or download this repository. Modify the script paths in the code (script_path and pckg_path) to point to the locations of your AFNI installation and additional scripts (e.g., anatomical_preprocessing.sh).
The pipeline is executed from the command line using the following syntax inside afni_preprocessing/preprocessing:
./main.sh [OPTIONS]
Options:
--num_cpus: Number of CPUs to use (default: 16)
--fwh: Full width at half maximum for smoothing (default: 4)
--vox_res: Voxel resolution (default: 2)
--data_folder: Path to the data folder
--output_folder: Path to the output folder
--mnitemp: Path to the MNI template
--onlysubj: Space-separated list of subjects to process (optional)
--skipsubj: Space-separated list of subjects to skip (optional)
--compute_anat: Flag to compute anatomical preprocessing (default: 1)
--compute_func: Flag to compute functional preprocessing (default: 1)
--performTShift: Flag to perform temporal shifting (default: 0)
--ciric: Flag for ciric-specific options (default: 1)
--deconv_single_run: Flag for deconvolution in a single run (default: 1)
--tasks_regressors: Flag to include task regressors (default: 1)
--onset_regressor: Flag to include onset regressors (default: 1)
--tr: Temporal resolution (default: 1)
--help: Display help message
The code automatically saves a copy of the script with a timestamp upon execution for reference. Refer to the individual preprocessing scripts (anatomical_preprocessing.sh and functional_preprocessing.sh) for further details on their functionalities.
Please note that this is a general description, and specific details about the preprocessing steps might be found in the code itself or in the additional scripts mentioned.