High-dimensional tests for functional networks of brain anatomic regions

JOURNAL OF MULTIVARIATE ANALYSIS

卷:156

Article

Xie, Jichun¹  Kang, Jian² 

[1] Duke Univ, Dept Biostat & Bioinformat, Sch Med, Durham, NC 27705 USA

[2] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA

关键词: High dimensionality;    Hypothesis testing;    Brain network;    Sparsity;    fMRI study;   

DOI  :  10.1016/j.jmva.2017.01.011

来源: Elsevier

【 摘 要 】

Exploring resting-state brain functional connectivity of autism spectrum disorders (ASD) using functional magnetic resonance imaging (fMRI) data has become a popular topic over the past few years. The data in a standard brain template consist of over 170,000 voxel specific points in time for each human subject. Such an ultra-high dimensionality makes the voxel-level functional connectivity analysis (involving four billion voxel pairs) both statistically and computationally inefficient. In this work, we introduce a new framework to identify the functional brain network at the anatomical region level for each individual. We propose two pairwise tests to detect region dependence, and one multiple testing procedure to identify global structures of the network. The limiting null distribution of each test statistic is derived. It is also shown that the tests are rate optimal when the alternative block networks are sparse. The numerical studies show that the proposed tests are valid and powerful. We apply our method to a resting-state fMRI study on autism and identify patient-unique and control-unique hub regions. These findings are biologically meaningful and consistent with the existing literature. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

Free