期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 113, 期 39, 页码 E5749-E5756出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1604378113
关键词
neuroimaging; brain morphology; statistical association
资金
- National Institute for Biomedical Imaging and Bioengineering [P41EB015896, R01EB006758, R21EB018907, R01EB019956]
- National Institute on Aging [5R01AG008122, R01AG016495]
- National Institute for Neurological Disorders and Stroke [R01NS0525851, R21NS072652, R01NS070963, R01NS083534, 5U01NS086625]
- Shared Instrumentation [1S10RR023401, 1S10RR019307, 1S10RR023043]
- NIH Blueprint for Neuroscience Research Grant [5U01-MH093765]
- multiinstitutional Human Connectome Project
- Center for Brain Science Neuroinformatics Research Group
- NIH [R01NS070963, R01NS083534, R41AG052246, 1K25EB013649-01, 1R21AG050122-01A1, K01MH099232, K24MH094614, R01MH101486]
- MGH ECOR Tosteson Postdoctoral Fellowship Award
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 non-clinical 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.
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