14.2.9. Lepping et al. (2023). Quality control in resting-state fMRI: the benefits of visual inspection

Introduction

Here we present commands used in the following paper:

Abstract:

Background: A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or “scrubbing” volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection.

Results: The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts.

Conclusion: Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis.

Study keywords: artifacts; functional magnetic resonance imaging (fMRI); quality control; reproducibility of results; resting state—fMRI.

Main programs: afni_proc.py, @SSwarper

Download scripts

Github page:
See this GitHub page for full descriptions and downloads of codes and supplementary text files:
https://github.com/rlepping/kumc-hbic/tree/rsfMRI-qc-paper

... or copy+paste the following in a terminal:

git clone https://github.com/rlepping/kumc-hbic/tree/rsfMRI-qc-paper.git

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/

View scripts

Additional notes are available in the GitHub repo above, as well.

1_SSWarper.sh

Process the T1w anatomical volume with @SSwarper, to skullstrip (SS) and estimate nonlinear alignment (warping) to a template.

https://github.com/rlepping/kumc-hbic/blob/rsfMRI-qc-paper/1_SSWarper.sh

2_Preprocess.sh

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

https://github.com/rlepping/kumc-hbic/blob/rsfMRI-qc-paper/2_Preprocess.sh