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.

翻译：脑连接组分析通常将高分辨率脑扫描（通常由数百万个体素组成）通过平均区域内信号压缩至仅有数百个感兴趣区域（ROI）。这种大幅降维虽提升了计算速度及解剖结构的形态特性，但同时也导致空间特异性和敏感性的显著损失，尤其当信号在ROI内呈现高度异质性时。由脑疾病引起的ROI对间的异常功能连接（FC）往往仅由该ROI对内少数体素对子集所主导。本文提出一种新颖的网络方法，用于在空间约束下检测体素对级别的神经连接异常。具体而言，我们的模型聚焦于特定ROI对，旨在提取包含异常体素对连接且确保所涉体素空间邻接的密集子区域。此外，我们开发了子社区检测算法以实现该模型，并验证了算法的一致性。综合仿真研究表明，本方法在降低假阳性率的同时提升了统计功效、检测可重复性及空间特异性。我们应用该方法揭示了：(i) 基于330名精神分裂症研究参与者数据，在突显网络及丘脑-颞叶网络中精神分裂症改变的体素级功能连接模式；(ii) 利用英国生物银行3269名参与者数据，发现尼古丁成瘾相关基底节、海马及脑岛之间的破坏性体素级连接模式。检测结果与既往医学发现一致，但提供了更精细的定位信息。