  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 huge dimension reduction
improves computational speed and the morphological properties of anatomical
structures; however, it also 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 detection of voxel-pair-level neural
dysconnectivity with spatial constraints. Specifically, focusing on an ROI
pair, our model aims to extract dense sub-areas that contain aberrant
voxel-pair connections while ensuring that the involved voxels are spatially
contiguous. In addition, we develop sub-community-detection algorithms to
realize the model, and the consistency of these algorithms is justified.
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)
voxel-wise schizophrenia-altered FC patterns within the salience and
temporal-thalamic network from 330 participants in a schizophrenia study; (ii)
disrupted voxel-wise FC patterns related to nicotine addiction between the
basal ganglia, hippocampus, and insular gyrus from 3269 participants using UK
Biobank data. The detected results align with previous medical findings but
include improved localized information.
