“Morphometricity As a Measure of the Neuroanatomical Signature of a Trait”, 2016-09-09 (; backlinks; similar):
Neuroimaging has largely focused on 2 goals: mapping associations between neuroanatomical features and phenotypes and building individual-level prediction models. This paper presents a complementary analytic strategy called morphometricity that aims to measure the neuroanatomical signatures of different phenotypes.
Inspired by prior work on [genetic] heritability, we define morphometricity as the proportion of phenotypic variation that can be explained by brain morphology (eg. as captured by structural brain MRI). In the dawning era of large-scale datasets comprising traits across a broad phenotypic spectrum, morphometricity will be critical in prioritizing and characterizing behavioral, cognitive, and clinical phenotypes based on their neuroanatomical signatures. Furthermore, the proposed framework will be important in dissecting the functional, morphological, and molecular underpinnings of different traits.
…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 9 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.
[Keywords: neuroimaging, brain morphology, statistical association]