High-dimensional tests for functional networks of brain anatomic regions

• Published in: Journal of multivariate analysis Vol. 156; pp. 70 - 88
• Main Authors: Xie, Jichun, Kang, Jian
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2017
• Subjects: Brain network; fMRI study; High dimensionality; Hypothesis testing; Sparsity; Hypothesis testing; fMRI study; High dimensionality; Sparsity; Brain network
• ISSN: 0047-259X; 1095-7243
• DOI: 10.1016/j.jmva.2017.01.011

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.