Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition

• 成果类型: Article
• 署名作者: Wang, Shiying; Constable, Todd; Zhang, Heping; Zhao, Yize
• 署名单位: Yale University; Yale University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2311363
• 发表日期: 2024
• 页码: 851-863
• 关键词: cell-type; alzheimers-disease; gene-expression; selection; networks

摘要

Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by analyzing the data from a recent landmark brain development and child health study. We propose a nonparametric, Bayesian supervised heterogeneity analysis to uncover neurodevelopmental subtypes with distinct effect mechanisms. We impose stochastic block structures to identify network-based functional phenotypes and develop a variational expectation-maximization algorithm to facilitate an efficient posterior computation. Through integrating resting-state and task-related functional connectomes, we dissect heterogeneous effect mechanisms on children's fluid intelligence from the functional network phenotypes, including Fronto-parietal Network and Default Mode Network, under different cognitive states. Based on extensive simulations, we further confirm the superior performance of our method on uncovering brain-to-behavior relationships. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.