Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process

• 成果类型: Article
• 署名作者: Wu, Ben; Guo, Ying; Kang, Jian
• 署名单位: Renmin University of China; Emory University; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2123336
• 发表日期: 2024
• 页码: 422-433
• 关键词: INDEPENDENT COMPONENT ANALYSIS; functional architecture; PROCESS MODELS; fmri data; network; brain; dependence; centrality; regression; algorithm

摘要

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the consistency of joint posterior distribution of the latent source intensity functions and the mixing coefficients, and the selection consistency on the number of latent sources. We use extensive simulation studies and an analysis of the resting-state fMRI data in the Autism Brain Imaging Data Exchange (ABIDE) study to demonstrate that BSP-BSS outperforms the existing method for separating latent brain networks and detecting activated brain activation in the latent sources. for this article are available online.