Image response regression via deep neural networks

• 成果类型: Article
• 署名作者: Zhang, Daiwei; Li, Lexin; Sripada, Chandra; Kang, Jian
• 署名单位: University of Pennsylvania; University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkad073
• 发表日期: 2024
• 页码: 1589-1614
• 关键词: fluid intelligence; working-memory; frontal-lobe; ORGANIZATION; CONVERGENCE; efficient; dimensionality; approximation; regions; MODEL

摘要

Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method's advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.