Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia. The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants). The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes. These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

中文翻译：

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精神分裂症个体化纹理相似网络

结构协方差网络破坏被认为是精神分裂症的重要病理生理指标。在这里，我们引入了一种新颖的个体化结构协方差网络测量，称为纹理相似性网络（TSN），并假设 TSN 可以可靠地揭示精神分裂症中独特的受试者间异质性和复杂的脱节模式。 TSN 是通过测量每个参与者大脑区域之间 180 个三维体素灰度共生矩阵特征图的协方差来构建的。我们首先测试了 TSN 在描述 2 个纵向重测健康队列中受试者间变异性的有效性和再现性。 TSN 进一步应用于阐明 10 个精神分裂症病例对照数据集（609 名精神分裂症病例与 579 名对照者）以及首发抑郁症数据集（69 名抑郁症患者与 69 名对照参与者）中的受试者间变异性和脱节模式。重测分析表明，受试者间的 TSN 变异性高于受试者内的变异性。此外，TSN 可靠地揭示了慢性和首发精神分裂症中较高的受试者间变异性，但抑郁症中则不然。 TSN 还可重复地检测到精神分裂症患者（但抑郁症患者）的广泛大脑区域中并存的 TSN 强度增加和减少、整体小世界的增加以及中枢网络中的结构低同步和外周网络中的超同步的共存。最后，TSN 揭示的异常主体间变异性和协方差强度模式显示与其他个体化结构协方差网络测量、功能连接和区域体积变化缺失或弱相关性。这些发现支持 TSN 在揭示精神分裂症患者独特的结构异质性和复杂的脱连接性方面的可靠性。