LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY

• 成果类型: Article
• 署名作者: Wang, Yikai; Guo, Ying
• 署名单位: Emory University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1670
• 发表日期: 2023
• 页码: 1307-1332
• 关键词: INDEPENDENT COMPONENT ANALYSIS; functional connectivity; variable selection; tensor regression; connectome; networks; patterns; Identifiability; ORGANIZATION; algorithms

摘要

Network-oriented research has been increasingly popular in many sci-entific areas. In neuroscience research, imaging-based network connectiv-ity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major chal-lenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connec-tivity, and the large number of brain connections leading to spurious findings. In this paper we propose a novel blind source separation method with low -rank structure and uniform sparsity (LOCUS) as a fully data-driven decom-position method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LO-CUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative node-rotation algorithm that exploits the block multiconvexity of the objective function to solve the nonconvex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.