Morphometricity as a measure of the neuroanatomical signature of a trait

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.

Keywords: brain morphology; neuroimaging; statistical association.

Fig. 1.

Morphometricity estimates of various diseases (on the liability scale) computed using the Gaussian ASM of Eq. 5. Each bar is annotated with study names used to compute these estimates. For Alzheimer’s disease and schizophrenia, we had independent samples used to compute replication estimates (purple bars). Error bars indicate SE of the estimates.

Fig. 2.

Morphometricity estimates of general nonclinical traits computed using the Gaussian ASM of Eq. 5. IQ denotes general intelligence. Each bar is annotated with study names used to compute these estimates. Blue bars correspond to results from the primary analyses, whereas purple bars correspond to independent replication analyses. Error bars indicate SE of the estimates.

Fig. 3.

ROI-based morphometricity estimates of general nonclinical traits (age, intelligence, sex, and education), Alzheimer’s disease (AD), and schizophrenia (SCZ). For AD and SCZ, morphometricity estimates have been transformed to the liability scale. Red circles denote whole-brain morphometricity estimates for each trait. Error bars indicate SE of the estimates.

Fig. 4.

Morphometricity estimates of various measures of cognition computed on data from the HCP and using the Gaussian ASM. Blue and purple bars correspond to primary and secondary analyses, respectively. Error bars indicate SE of the estimates.