14.2.8. Teves et al. (2023). The art and science of using quality control to understand and …¶
Introduction¶
Here we present commands used in the following paper:
- Teves JB, Gonzalez-Castillo J, Holness M, Spurney M, Bandettini PA, Handwerker DA (2023). The art and science of using quality control to understand and improve fMRI data. Front Neurosci 17:1100544.
Abstract: Designing and executing a good quality control (QC) process is vital to robust and reproducible science and is often taught through hands on training. As FMRI research trends toward studies with larger sample sizes and highly automated processing pipelines, the people who analyze data are often distinct from those who collect and preprocess the data. While there are good reasons for this trend, it also means that important information about how data were acquired, and their quality, may be missed by those working at later stages of these workflows. Similarly, an abundance of publicly available datasets, where people (not always correctly) assume others already validated data quality, makes it easier for trainees to advance in the field without learning how to identify problematic data. This manuscript is designed as an introduction for researchers who are already familiar with fMRI, but who did not get hands on QC training or who want to think more deeply about QC. This could be someone who has analyzed fMRI data but is planning to personally acquire data for the first time, or someone who regularly uses openly shared data and wants to learn how to better assess data quality. We describe why good QC processes are important, explain key priorities and steps for fMRI QC, and as part of the FMRI Open QC Project, we demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared dataset. A good QC process is context dependent and should address whether data have the potential to answer a scientific question, whether any variation in the data has the potential to skew or hide key results, and whether any problems can potentially be addressed through changes in acquisition or data processing. Automated metrics are essential and can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions.
Study keywords: GLM; fMRI; neuroimaging; noise removal; quality control; reproducibility; resting state
Main programs:
afni_proc.py, timing_tool.py,
abids_tool.py,
@SSwarper, recon-all (FS)
Download scripts¶
... or copy+paste the following in a terminal:
git clone https://github.com/nimh-sfim/SFIM_Frontiers_Neuroimaging_QC_Project.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.
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
View scripts¶
Because there are so many scripts for this project, we recommend downloading the full set from the github pages, above. There are helpful supplemental notes there, as well.
ap_rest.sh¶
Full processing (through regression modeling) of a resting state FMRI session for a single subject (with blurring, for voxelwise analysis).
https://github.com/nimh-sfim/SFIM_Frontiers_Neuroimaging_QC_Project/blob/main/code/ap_rest.sh
ap_task_unifize.sh¶
Full processing (through regression modeling) of a task-based FMRI session for a single subject (with blurring, for voxelwise analysis).
