CONTINUOUS AND ATLAS-FREE ANALYSIS OF BRAIN STRUCTURAL CONNECTIVITY

• 成果类型: Article
• 署名作者: Consagra, William; Cole, Martin; Qiu, Xing; Zhang, Zhengwu
• 署名单位: Harvard University; Harvard Medical School; University of Rochester; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1858
• 发表日期: 2024
• 页码: 1815-1839
• 关键词: functional data-analysis; diffusion tensor; networks; CHOICE; mri; parcellation; principal

摘要

Brain structural networks are often represented as discrete adjacency matrices with elements summarizing the connectivity between pairs of regions of interest (ROIs). These ROIs are typically determined a priori using a brain atlas. The choice of atlas is often arbitrary and can lead to a loss of important connectivity information at the sub-ROI level. This work introduces an atlasfree framework that overcomes these issues by modeling brain connectivity using smooth random functions. In particular, we assume that the observed pattern of white matter fiber tract endpoints is driven by a latent random function defined over a product manifold domain. To facilitate statistical analysis of these high-dimensional functional data objects, we develop a novel algorithm to construct a data-driven reduced-rank function space that offers a desirable trade-off between computational complexity and flexibility. Using real data from the Human Connectome Project, we show that our method outperforms state-of-the-art approaches that use the traditional atlas-based structural connectivity representation on a variety of connectivity analysis tasks. We further demonstrate how our method can be used to detect localized regions and connectivity patterns associated with group differences.