ArticleFrontiers in neuroscience2023
Quality control in resting-state fMRI: the benefits of visual inspection.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC.Nature protocols · 2026Review
- Quality Assurance Strategies for Brain State Characterization by MEMRI.bioRxiv : the preprint server for biology · 2026Article
- Adverse Childhood Experiences Underlie Race-Related Differences in Neural Reactivity to Stress.Behavioural neurology · 2026Article
- The association between cardiorespiratory fitness and resting-state functional connectivity in adults with Down syndrome.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Stationarity assessment of resting state condition via permutation entropy on EEG recordings.Scientific reports · 2025Article
- Processing, evaluating, and understanding FMRI data with afni_proc.py.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Expression of microRNA induced by postoperative delirium-like behavior is associated with long-term default mode network disruption: Sequencing and a secondary analysis of resting-state fMRI data.CNS neuroscience & therapeutics · 2024Article
- A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth, and interactive QC with afni_proc.py and more.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth and interactive QC with afni_proc.py and more.bioRxiv : the preprint server for biology · 2024Article
- Pediatric Neural Changes to Physical and Emotional Pain After Intensive Interdisciplinary Pain Treatment: A Pilot Study.medRxiv : the preprint server for health sciences · 2023Article
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Authors and funding
10 authors.
Funding
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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.
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