Note: This processing stream is not included in recent datasets. All datasets moving forward are in BIDS format and do not include processed data (with the exception of a few datasets that include FMRIPREP output).

Older datasets in the OpenFMRI database are processed using a standard processing stream that is implemented on the Lonestar compute cluster at the Texas Advanced Computing Center. The processing steps include the following , with links to the code that implements each operation. To obtain the code mentioned below, see the OpenFMRI GitHub repository.

conversion to compressed NiFTi (.nii.gz) format and organization into the appropriate directory structure

removal of facial information from high-resolution anatomical image (code: deface.py)

motion correction using FSL mcflirt tool (code: run_mcflirt.py)

brain extraction for fMRI data using FSL bet tool (code: run_betfunc.py)

brain extraction for inplane anatomical images using FSL bet tool (code: bet_inplane.py)

convert high-resolution anatomy image into freesurfer format (code: fs_setup.py)

brain extraction and cortical reconstruction for high-resolution anatomical images using freesurfer (code: run_autorecon.py)

copy brain-extracted high-resolution anatomy back into main subject directory (code: run_copy_stripped.py)

Design details are identified from onset files in the model directories as well as key files in the main models directory

Motion parameters are automatically included in the model as nuisance regressors

creation of second-level design files and analysis using FSL feat (code: mk_level2_fsf.py, mk_all_level2_fsf.py, design_level2.stub)

creation of third-level design files and analysis using FSL feat (code: mk_level2_fsf.py, mk_all_level2_fsf.py, design_level3.stub)