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Xie J, Kang J. High-dimensional tests for functional networks of brain anatomic regions. J Multivar Anal. 2017;156:70-88doi: 10.1016/j.jmva.2017.01.011.
Xie, J., & Kang, J. (2017). High-dimensional tests for functional networks of brain anatomic regions. Journal of multivariate analysis, 15670-88. https://doi.org/10.1016/j.jmva.2017.01.011
Xie, Jichun, and Kang, Jian. "High-dimensional tests for functional networks of brain anatomic regions." Journal of multivariate analysis vol. 156 (2017): 70-88. doi: https://doi.org/10.1016/j.jmva.2017.01.011
Xie J, Kang J. High-dimensional tests for functional networks of brain anatomic regions. J Multivar Anal. 2017 Apr;156:70-88. doi: 10.1016/j.jmva.2017.01.011. Epub 2017 Feb 07. PMID: 28413234; PMCID: PMC5391152.
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J Multivar Anal. 2017 Apr;156:70-88. doi: 10.1016/j.jmva.2017.01.011. Epub 2017 Feb 07.

High-dimensional tests for functional networks of brain anatomic regions.

Journal of multivariate analysis

Jichun Xie, Jian Kang

Affiliations

  1. Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27705.
  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109.

PMID: 28413234 PMCID: PMC5391152 DOI: 10.1016/j.jmva.2017.01.011

Abstract

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.

Keywords: Brain network; High dimensionality; Hypothesis testing; Sparsity; fMRI study

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