The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI: Motion Bias in Analyses of Structural MRI
Open accessAbstract
While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among "good quality" structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion-even at levels which do not manifest in visible motion artifact-can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans. Hum Brain Mapp 37:2385-2397, 2016. © 2016 Wiley Periodicals, Inc.
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“It is increasingly recognized that motion in the scanner can significantly influence measures of cortical thickness and surface area27, 218, as well as DTI parameters (Box 2)219 and resting-state networks (Box 3)220,219.”
From Imaging structural and functional brain development in early childhood (2018) · cites this paper as [218]
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Imaging structural and functional brain development in early childhood
Gilmore, J. H., Knickmeyer, R. C., & Gao, W. (2018) · Nature Reviews Neuroscience · doi:10.1038/nrn.2018.1 · Cited by 1,013
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920
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Alexander-Bloch, A. F., Shou, H., Liu, S., Satterthwaite, T. D., Glahn, D. C., Shinohara, R. T., Vandekar, S. N., & Raznahan, A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.070 · Cited by 947
Self-citationTamnes, C. K., Herting, M. M., Goddings, A.-L., Meuwese, R., Blakemore, S.-J., Dahl, R. E., Güroğlu, B., Raznahan, A., Sowell, E. R., Crone, E. A., et al. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.3302-16.2017 · Cited by 685
Self-citationPredicting brain-age from multimodal imaging data captures cognitive impairment
Liem, F., Varoquaux, G. P., Kynast, J., Beyer, F., Masouleh, S. K., Huntenburg, J. M., Lampe, L., Rahim, M., Abraham, A., Craddock, R. C., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.005 · Cited by 522
Metadata
- Authors
- Alexander-Bloch, A., Clasen, L., Stockman, M., Ronan, L., Lalonde, F., Giedd, J., & Raznahan, A.
- Affiliations
- Developmental Neurogenomics Unit; Child Psychiatry Branch; National Institute of Mental Health; Bethesda Maryland
- National Institutes of Health (US)
- University of Cambridge (GB)
- University of California San Diego (US)
- Corresponding author
- Raznahan, Armin
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2016-03-23
- Volume
- 37
- Issue
- 7
- Pages
- 2385-2397
- ISSN
- 1065-9471 (ISSN-L)
- PMC
- PMC5110234
- OpenAlex
- W2310662295
- Cited by
- 178
- References
- 62
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 18 · 2023: 20 · 2022: 10 · 2021: 21 · 2020: 20 · 2019: 16 · 2018: 22 · 2017: 18 · 2016: 6
- FWCI
- 17.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (095692/Z/11/Z)
- National Institutes of Health (Z99 MH999999)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- structural MRI, brain morphometry, cortical gray matter volume, cortical thickness, mean curvature, automated brain segmentation, motion estimation, pediatric neuroimaging, convergent validity
- MeSH terms
- Adolescent, Adult, Brain, Child, Child, Preschool, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Motion, Organ Size, Pattern Recognition, Automated, Artifacts, Young Adult, Gray Matter
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