CONTINUOUS AND ATLAS-FREE ANALYSIS OF BRAIN STRUCTURAL CONNECTIVITY

被引:0
|
作者
Consagra, William [1 ]
Cole, Martin [2 ]
Qiu, Xing [2 ]
Zhang, Zhengwu [3 ]
机构
[1] Harvard Med Sch, Psychiat Neuroimaging Lab, Boston, MA 02215 USA
[2] Univ Rochester, Med Ctr, Dept Biostat & Computat Biol, Rochester, NY USA
[3] Univ North Carolina Chapel Hill, Dept Stat & Operat Res, Chapel Hill, NC USA
来源
ANNALS OF APPLIED STATISTICS | 2024年 / 18卷 / 03期
关键词
Point process; functional data analysis; structural connectivity; neuroimaging; high dimensional; FUNCTIONAL DATA-ANALYSIS; DIFFUSION TENSOR; NETWORKS; CHOICE; MRI; PARCELLATION; PRINCIPAL;
D O I
10.1214/23-AOAS1858
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
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.
引用
收藏
页码:1815 / 1839
页数:25
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