A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more

• Published in: Imaging neuroscience (Cambridge, Mass.) Vol. 2; pp. 1 - 39
• Main Authors: Taylor, Paul A., Glen, Daniel R., Chen, Gang, Cox, Robert W., Hanayik, Taylor, Rorden, Chris, Nielson, Dylan M., Rajendra, Justin K., Reynolds, Richard C.
• Format: Journal Article
• Language: English
• Published: 255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 02.08.2024
• Subjects: data visualization; FMRI; processing; quality control; reproducibility; processing; data visualization; quality control; reproducibility; FMRI; artifacts
• ISSN: 2837-6056; 2837-6056
• DOI: 10.1162/imag_a_00246

Quality control (QC) assessment is a vital part of FMRI processing and analysis, and a typically underdiscussed aspect of reproducibility. This includes checking datasets at their very earliest stages (acquisition and conversion) through their processing steps (e.g., alignment and motion correction) to regression modeling (correct stimuli, no collinearity, valid fits, enough degrees of freedom, etc.) for each subject. There are a wide variety of features to verify throughout any single-subject processing pipeline, both quantitatively and qualitatively. We present several FMRI preprocessing QC features available in the AFNI toolbox, many of which are automatically generated by the pipeline-creation tool, . These items include a modular HTML document that covers full single-subject processing from the raw data through statistical modeling, several review scripts in the results directory of processed data, and command line tools for identifying subjects with one or more quantitative properties across a group (such as triaging warnings, making exclusion criteria, or creating informational tables). The HTML itself contains several buttons that efficiently facilitate interactive investigations into the data, when deeper checks are needed beyond the systematic images. The pages are linkable, so that users can evaluate individual items across a group, for increased sensitivity to differences (e.g., in alignment or regression modeling images). Finally, the QC document contains rating buttons for each “QC block,” as well as comment fields for each, to facilitate both saving and sharing the evaluations. This increases the specificity of QC, as well as its shareability, as these files can be shared with others and potentially uploaded into repositories, promoting transparency and open science. We describe the features and applications of these QC tools for FMRI.