This project contains a dataset used to demonstrate quality control (QC) tools for FMRI processing in AFNI.
More details about the tools are provided in:
> [**A Set of FMRI Quality Control Tools in AFNI: Systematic,
> in-depth and interactive QC with afni_proc.py and more**][1]
>
> by Paul A. Taylor, Daniel R. Glen, Gang Chen, Robert W. Cox, Taylor
> Hanayik, Chris Rorden, Dylan M. Nielson, Justin K. Rajendra, Richard
> C. Reynolds.
>
> Imaging Neuroscience (2024) 2: 1–39.
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### Demo data
This subject's data is actually the same subject used in the standard AFNI Bootcamp courses (AFNI_data6/FT_analysis/FT).
To get the raw data for this demo, go to "Files" here and click to download data_proc_example.tgz. Then to unpack it, copy+paste this in a terminal:
tar -xf data_proc_example.tgz
Under "Files", there is also a package of some QC outputs from the sswarper2 and afni_proc.py processing. These are contained in qc_ssw_ap.tgz. This can be downloaded and unpacked with:
tar -xf qc_ssw_ap.tgz
The README.txt file explains the contains of each subdirectory.
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### Demo scripts
A directory of the scripts used to process this task-based FMRI data are provided on GitHub: https://github.com/afni/apaper_apqc_etal.
To process the data here, copy the "scripts" from the above GitHub link and place it in the same directory as the "sub-002" data directory. The scripts can then be executed in order:
# run sswarper2 (skullstripping+nonlinear template alignment)
tcsh do_13_ssw.tcsh
# run afni_proc.py (full task-based FMRI processing)
tcsh do_20_ap.tcsh
[1]: https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00246/123633/A-Set-of-FMRI-Quality-Control-Tools-in-AFNI
