AUTHOR=Teves Joshua B. , Gonzalez-Castillo Javier , Holness Micah , Spurney Megan , Bandettini Peter A. , Handwerker Daniel A. 

TITLE=The art and science of using quality control to understand and improve fMRI data

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1100544

DOI=10.3389/fnins.2023.1100544

ISSN=1662-453X

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 towards 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 quality control (QC) processes are important, explain key priorities and steps for fMRI QC, and demonstrate some of these steps by using AFNI software and AFNI’s QC reports on an openly shared example dataset.
A good quality control (QC) process is context dependent and should address whether data has 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 can often highlight a possible problem, but human interpretation at every stage of a study is vital for understanding causes and potential solutions. We emphasize that a good QC process must include automation to bring useful metrics or potential problems to the forefront of researchers’ attention, but that human inspection and judgement remains the most critical element of QC.