The art and science of using quality control to understand and improve fMRI data
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 dist...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1100544/full |
| _version_ | 1797851345745608704 |
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| author | Joshua B. Teves Javier Gonzalez-Castillo Micah Holness Megan Spurney Peter A. Bandettini Peter A. Bandettini Daniel A. Handwerker |
| author_facet | Joshua B. Teves Javier Gonzalez-Castillo Micah Holness Megan Spurney Peter A. Bandettini Peter A. Bandettini Daniel A. Handwerker |
| author_sort | Joshua B. Teves |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T19:16:25Z |
| format | Article |
| id | doaj.art-060cb4eb949c45d99a8d62b870f8233c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-09T19:16:25Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-060cb4eb949c45d99a8d62b870f8233c2023-04-06T05:02:40ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-04-011710.3389/fnins.2023.11005441100544The art and science of using quality control to understand and improve fMRI dataJoshua B. Teves0Javier Gonzalez-Castillo1Micah Holness2Megan Spurney3Peter A. Bandettini4Peter A. Bandettini5Daniel A. Handwerker6Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesFunctional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesSection on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United StatesDesigning 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1100544/fullfMRIquality controlneuroimagingreproducibilityresting stateGLM |
| spellingShingle | Joshua B. Teves Javier Gonzalez-Castillo Micah Holness Megan Spurney Peter A. Bandettini Peter A. Bandettini Daniel A. Handwerker The art and science of using quality control to understand and improve fMRI data Frontiers in Neuroscience fMRI quality control neuroimaging reproducibility resting state GLM |
| title | The art and science of using quality control to understand and improve fMRI data |
| title_full | The art and science of using quality control to understand and improve fMRI data |
| title_fullStr | The art and science of using quality control to understand and improve fMRI data |
| title_full_unstemmed | The art and science of using quality control to understand and improve fMRI data |
| title_short | The art and science of using quality control to understand and improve fMRI data |
| title_sort | art and science of using quality control to understand and improve fmri data |
| topic | fMRI quality control neuroimaging reproducibility resting state GLM |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1100544/full |
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