June 2024 Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints
Tong Lu, Yuan Zhang, Peter Kochunov, Elliot Hong, Shuo Chen
Author Affiliations +
Ann. Appl. Stat. 18(2): 1090-1112 (June 2024). DOI: 10.1214/23-AOAS1824


Brain connectome analysis commonly compresses high-resolution brain scans (typically composed of millions of voxels) down to only hundreds of regions of interest (ROIs) by averaging within-ROI signals. This significant dimension reduction improves computational speed and the morphological properties of anatomical structures; however, it comes at the cost of substantial losses in spatial specificity and sensitivity, especially when the signals exhibit high within-ROI heterogeneity. Oftentimes, abnormally expressed functional connectivity (FC) between a pair of ROIs, caused by a brain disease, is primarily driven by only small subsets of voxel pairs within the ROI pair. This article proposes a new network method for the detection of voxel-pair-level neural dysconnectivity with spatial constraints. Specifically, focusing on an ROI pair, our model aims to extract dense subareas that contain aberrant voxel-pair connections while ensuring that the involved voxels are spatially contiguous. In addition, we develop subcommunity-detection algorithms to realize the model, and we justify the consistency of these algorithms. Comprehensive simulation studies demonstrate our method’s effectiveness in reducing the false-positive rate while increasing statistical power, detection replicability, and spatial specificity. We apply our approach to reveal: (i) disrupted voxelwise FC patterns related to nicotine addiction between the basal ganglia, hippocampus, and insular gyrus in 3269 participants using UK Biobank data; (ii) voxelwise schizophrenia-altered FC patterns within the salience and temporal-thalamic network in 330 participants in a schizophrenia study. The detected results align with previous medical findings but include improved localized information.

Funding Statement

The research work of Tong Lu and Shuo Chen was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number 1DP1DA048968-01.
Yuan Zhang’s research was supported by the Division of Mathematical Sciences of the National Science Foundation under Number 2311109.


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Tong Lu. Yuan Zhang. Peter Kochunov. Elliot Hong. Shuo Chen. "Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints." Ann. Appl. Stat. 18 (2) 1090 - 1112, June 2024. https://doi.org/10.1214/23-AOAS1824


Received: 1 January 2023; Revised: 1 August 2023; Published: June 2024
First available in Project Euclid: 5 April 2024

Digital Object Identifier: 10.1214/23-AOAS1824

Keywords: brain connectome , fMRI , spatial contiguity , voxel-pair-level connectivity

Rights: Copyright © 2024 Institute of Mathematical Statistics


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Vol.18 • No. 2 • June 2024
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