FUNCTIONAL RANDOM EFFECTS MODELING OF BRAIN SHAPE AND CONNECTIVITY

• 成果类型: Article
• 署名作者: Lila, Eardi; Aston, John A. D.
• 署名单位: University of Washington; University of Washington Seattle; University of Cambridge
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1572
• 发表日期: 2022
• 页码: 2122-2144
• 关键词: human cerebral-cortex; variance; joint; ORGANIZATION; REGISTRATION; statistics; FRAMEWORK; inference; matrices; LINKAGE

摘要

We present a statistical framework that jointly models brain shape and functional connectivity which are two complex aspects of the brain that have been classically studied independently. We adopt a Riemannian modeling ap-proach to account for the non-Euclidean geometry of the space of shapes and the space of connectivity that constrains trajectories of covariation to be valid statistical estimates. In order to disentangle genetic sources of variabil-ity from those driven by unique environmental factors, we embed a functional random effects model in the Riemannian framework. We apply the proposed model to the Human Connectome Project dataset to explore spontaneous co -variation between brain shape and connectivity in young healthy individuals.