BSNMANI: BAYESIAN SCALAR-ON-NETWORK REGRESSION WITH MANIFOLD LEARNING

• 成果类型: Article
• 署名作者: Li, Yijun; Choi, Ki Sueng; Dunlop, Boadie W.; Craighead, W. Edward; Mayberg, Helen S.; Garmire, Lana; Guo, Ying; Kang, Jian
• 署名单位: University of Michigan System; University of Michigan; Icahn School of Medicine at Mount Sinai; Emory University; University of Michigan System; University of Michigan; Emory University; Rollins School Public Health
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/26-AOAS2140
• 发表日期: 2026-06
• 页码: 1496-1515
• 关键词: Neuroimaging; brain connectivity; Manifold Learning; major depressive disorder

摘要

Brain connectivity analysis is crucial for understanding brain structure and neurological function, shedding light on the mechanisms of mental illness. To study the association between individual brain connectivity networks and the clinical characteristics, we develop BSNMani: a Bayesian scalar-on-network regression model with manifold learning. BSNMani comprises two components: the network manifold learning model for brain connectivity networks, which extracts shared connectivity structures and subject-specific network features, and the joint predictive model for clinical outcomes, which studies the association between clinical phenotypes and subject-specific network features while adjusting for potential confounding covariates. For posterior computation, we develop a novel two-stage hybrid algorithm combining Metropolis-Adjusted Langevin Algorithm (MALA) and Gibbs sampling. Our method is not only able to extract meaningful subnetwork features that reveal shared connectivity patterns but can also reveal their association with clinical phenotypes, further enabling clinical outcome prediction. We demonstrate our method through simulations and through its application to real resting-state fMRI data from a study focusing on Major Depressive Disorder (MDD). Our approach sheds light on the intricate interplay between brain connectivity and clinical features, offering insights that can contribute to our understanding of psychiatric and neurological disorders as well as mental health.