Quality control practices in FMRI analysis: philosophy, methods and examples using AFNI

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Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing quality control on acquired or publicly available FMRI datasets using the widely used AFNI software package. This work is part of the Research Topic, "Demonstrating Quality Control (QC) Procedures in fMRI." We used a sequential, hierarchical approach that contained the following major stages: 1) getting to know your data, esp. its basic acquisition properties (GTKYD, 2) examining quantifiable measures, with thresholds (APQUANT), 3) viewing qualitative images, graphs and other information in systematic HTML reports (APQUAL) and 4) checking features interactively with a graphical user interface (GUI); and for task data, 5) checking stimulus event timing statistics (STIM). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We show how to incorporate these steps within FMRI processing, evaluating the provided, publicly available resting state data collections (7 groups, 139 total subjects) and task-based data collection (1 group, 30 subjects). As specified within the Topic guidelines, each subject's dataset is placed into one of three categories: include, exclude or uncertain. The main focus of this work, however, is presenting a detailed description of QC procedures: how to understand the contents of an FMRI dataset, to check its contents for appropriateness, to verify processing steps, and to examine potential quality issues. Scripts for the processing and analysis are freely available.