The afni_proc.py output also contains a summary of useful quantitative features. This can be put into a simple AFNI command to apply drop/exclusion criteria for subjects automatically. In this way, one can integrate both qualitative and quantitative QC efficiently.

#neuroimaging #fmri #qc #afni

Why else is visualization of data helpful? Consider afni_proc.py's QC HTML images of EPI-anatomical alignment (latter are overlay edges). This makes it easy to spot localized signal loss/dropout, distortion, and more. Want to study the subcortex? Better check signal there!

#qc #neuroimaging #afni #fmri

As part of the FMRI Open QC project, we discuss combining visualization and quantitative criteria in AFNI, particularly with afni_proc.py:
Quality control practices in FMRI analysis: Philosophy, methods and examples using AFNI (Reynolds et al., 2023):
https://www.frontiersin.org/articles/10.3389/fnins.2022.1073800/full

#fmri #neuroimaging #QC #afni