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Brain connectome analysis commonly compresses high-resolution brain scans(typically composed of millions of voxels) down to only hundreds of regions ofinterest (ROIs) by averaging within-ROI signals. This huge dimension reductionimproves computational speed and the morphological properties of anatomicalstructures; however, it also comes at the cost of substantial losses in spatialspecificity and sensitivity, especially when the signals exhibit highwithin-ROI heterogeneity. Oftentimes, abnormally expressed functionalconnectivity (FC) between a pair of ROIs caused by a brain disease is primarilydriven by only small subsets of voxel pairs within the ROI pair. This articleproposes a new network method for detection of voxel-pair-level neuraldysconnectivity with spatial constraints. Specifically, focusing on an ROIpair, our model aims to extract dense sub-areas that contain aberrantvoxel-pair connections while ensuring that the involved voxels are spatiallycontiguous. In addition, we develop sub-community-detection algorithms torealize the model, and the consistency of these algorithms is justified.Comprehensive simulation studies demonstrate our method's effectiveness inreducing the false-positive rate while increasing statistical power, detectionreplicability, and spatial specificity. We apply our approach to reveal: (i)voxel-wise schizophrenia-altered FC patterns within the salience andtemporal-thalamic network from 330 participants in a schizophrenia study; (ii)disrupted voxel-wise FC patterns related to nicotine addiction between thebasal ganglia, hippocampus, and insular gyrus from 3269 participants using UKBiobank data. The detected results align with previous medical findings butinclude improved localized information.

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