Morphometricity as a measure of the neuroanatomical signature of a trait
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Sabuncu-2016-Morphometricity as a.pdf
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Author(s)
Ge, Tian
Holmes, Avram J.
Smoller, Jordan W.
Buckner, Randy L.
Alzheimer's Disease Neuroimaging Initiative
Sabuncu, Mert R
Fischl, Bruce
Date Issued
September 2016
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Sabuncu, Mert R.; Ge, Tian; Holmes, Avram J.; Smoller, Jordan W.; Buckner, Randy L. and Fischl, Bruce. “Morphometricity as a Measure of the Neuroanatomical Signature of a Trait.” Proceedings of the National Academy of Sciences 113, no. 39 (September 2016): E5749–E5756. © 2016 National Academy of Sciences
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Final published version
Abstract
Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1604378113
