Abstract
Morphometricity examines the global statistical association between brain morphology and an observable trait, and is defined as the proportion of the trait variation attributable to brain morphology. In this work, we propose an accurate morphometricity estimator based on the generalized random effects (GRE) model, and perform morphometricity analyses on five cognitive traits in an Alzheimer's study. Our empirical study shows that the proposed GRE model outperforms the widely used LME model on both simulation and real data. In addition, we extend morphometricity estimation from the whole brain to the focal-brain level, and examine and quantify both global and regional neuroanatomical signatures of the cognitive traits. Our global analysis reveals 1) a relatively strong anatomical basis for ADAS13, 2) intermediate ones for MMSE, CDRSB and FAQ, and 3) a relatively weak one for RAVLT.learning. The top associations identified from our regional morphometricity analysis include those between all five cognitive traits and multiple regions such as hippocampus, amygdala, and inferior lateral ventricles. As expected, the identified regional associations are weaker than the global ones. While the whole brain analysis is more powerful in identifying higher morphometricity, the regional analysis could localize the neuroanatomical signatures of the studied cognitive traits and thus provide valuable information in imaging and cognitive biomarker discovery for normal and/or disordered brain research.