Morphometricity as a measure of the neuroanatomical signature of a trait

作者：Sabuncu Mert R^*; Ge Tian; Holmes Avram J; Smoller Jordan W; Buckner Randy L; Fischl Bruce

来源：Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(39): E5749-E5756.

DOI：10.1073/pnas.1604378113

• neuroimaging
• brain morphology
• statistical association

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.

• 出版日期2016-9-27
• 单位MIT