Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints

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.