14.2.1. Reynolds et al. (2023). Quality control practices in FMRI analysis: Philosophy, methods …

Introduction

Here we present commands used in the following paper:

Abstract: Quality control (QC) is a necessary, but often an under-appreciated, part of FMRI processing. Here we describe procedures for performing QC 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) GTKYD (getting to know your data, esp. its basic acquisition properties), (2) APQUANT (examining quantifiable measures, with thresholds), (3) APQUAL (viewing qualitative images, graphs, and other information in systematic HTML reports) and (4) GUI (checking features interactively with a graphical user interface); and for task data, and (5) STIM (checking stimulus event timing statistics). We describe how these are complementary and reinforce each other to help researchers stay close to their data. We processed and evaluated 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 was placed into one of three categories: Include, exclude or uncertain. The main focus of this paper, however, is the 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.

Study keywords: FMRI, quality control, AFNI, resting state, reproducibility, processing, data visualization, task-based

Main programs: afni_proc.py, ap_run_simple_rest.tcsh, timing_tool.py, 3dinfo, nifti_tool, @SSwarper, recon-all (FS), gen_ss_review_table.py, afni (with InstaCorr)

Github page:
See these authors’ github page for full descriptions and downloads of codes and supplementary text files:
https://github.com/afni/apaper_afniqc_frontiers
APQC HTML outputs:
To download the APQC HTML directories for all the datasets processed in this project, please visit:
https://osf.io/qaesm/files/osfstorage
and click on the team_results_qc_files_ReynoldsEtal2023 directory.

Note: This work was one of several contributed to the following Frontiers Research Topic project, described here:

  • Taylor PA, Etzel JA, Glen D, Reynolds RC (2022). Demonstrating Quality Control (QC) Procedures in fMRI.
    Research Topic homepage

The datasets analyzed within it are publicly available and located here:

  • Taylor PA, Etzel JA, Glen D, Reynolds RC, Moraczewski D, Basavaraj A (2022). FMRI Open QC Project. DOI 10.17605/OSF.IO/QAESM
    https://osf.io/qaesm/

Download scripts

To download, either:

View scripts

Because there are so many scripts for this project, just recommend downloading the full set from the github pages, above. There are helpful README* files there, as well, to describe the contents in details.

Note that these scripts were run on the NIH’s Biowulf HPC, so some scriptiness deals with those specific features (batch/swarm submission, etc.).

We just point to a couple specific examples of the afni_proc.py processing scripts here:

do_21_ap_rest_NL.tcsh

Full processing (through regression modeling) of a resting state FMRI session for a single subject (with blurring, for voxelwise analysis).

https://github.com/afni/apaper_afniqc_frontiers/blob/main/scripts_biowulf_fmri_rest/do_21_ap_rest_NL.tcsh

do_21_ap_task_NL.tcsh

Full processing (through regression modeling) of a task-based FMRI session for a single subject (with blurring, for voxelwise analysis).

https://github.com/afni/apaper_afniqc_frontiers/blob/main/scripts_biowulf_fmri_task/do_21_ap_task_NL.tcsh