Dmitruk Emil, Metzner Christoph, Steuber Volker, Kadir Shabnam N
Biocomputation Research Group, Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Germany.
bioRxiv. 2025 Jun 21:2025.06.19.660631. doi: 10.1101/2025.06.19.660631.
We uncover a novel, mesoscale perspective of the differences in the white-matter connectome between healthy controls (HC) and subjects with schizophrenia (SCH) using a method developed from computational algebraic topology: persistent homology (PH) via clique topology. We extract and compare topological motifs found in the structural connectomes of the subjects in the two groups and find significant differences. We compare our results with those obtained from easy-to-interpret null models to build an understanding of the connectivity patterns found in the data, and we explore the overlap of mesoscale structures found in two different datasets, (Center of Biomedical Research Excellence) and (Human Connectome Project). Differences in acquisition usually render experiments recorded on different scanners incomparable, but here we see that there are shared structures. Our method offers a way to establish connectomic fingerprinting that could lead to a neuroimaging-based diagnosis of schizophrenia and other psychiatric and neurological conditions as well as the development of new treatments.
通过团拓扑的持久同调（PH），揭示了健康对照（HC）与精神分裂症患者（SCH）之间白质连接组差异的一种全新的中尺度视角。我们提取并比较了两组受试者结构连接组中发现的拓扑基序，发现了显著差异。我们将我们的结果与从易于解释的空模型获得的结果进行比较，以建立对数据中发现的连接模式的理解，并且我们探索了在两个不同数据集（卓越生物医学研究中心）和（人类连接组计划）中发现的中尺度结构的重叠。采集差异通常会使在不同扫描仪上记录的实验无法比较，但在这里我们看到存在共享结构。我们的方法提供了一种建立连接组指纹识别的方法，这可能会导致基于神经影像学对精神分裂症以及其他精神和神经疾病进行诊断，并推动新治疗方法的开发。
