Sabuncu, MertGe, TianHolmes, AvramSmoller, JordanBuckner, RandyFischl, BruceAlzheimer's Disease Neuroimaging Initiative2019-10-132016Sabuncu, Mert R., Tian Ge, Avram J. Holmes, Jordan W. Smoller, Randy L. Buckner, Bruce Fischl, and the Alzheimer’s Disease Neuroimaging Initiative. 2016. “Morphometricity as a Measure of the Neuroanatomical Signature of a Trait.” Proceedings of the National Academy of Sciences 113 (39): E5749–56. doi:10.1073/pnas.1604378113.0027-84240744-28311091-6490http://nrs.harvard.edu/urn-3:HUL.InstRepos:41542733Complex 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.en-USJournal Article2019-10-1310.1073/pnas.1604378113