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Hum Brain Mapp. 2016 Jul;37(7):2385-97. doi: 10.1002/hbm.23180. Epub 2016 Mar 23.

Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI.

Author information

1
Developmental Neurogenomics Unit, Child Psychiatry Branch, National Institute of Mental Health, Bethesda, Maryland.
2
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
3
Brain Mapping Unit, University of Cambridge, Cambridge, United Kingdom.
4
Department of Psychiatry, UCSD, San Diego, California.

Abstract

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.

KEYWORDS:

bias; cortical curvature; cortical surface area; cortical thickness; functional neuroimaging; magnetic resonance imaging; motion

PMID:
27004471
PMCID:
PMC5110234
DOI:
10.1002/hbm.23180
[Indexed for MEDLINE]
Free PMC Article

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