Recent advances in molecular and genetic research have identified a diverse range of brain tumor sub-types, shedding light on differences in their molecular mechanisms, heterogeneity, and origins. The present study performs whole-brain connectome analysis using diffusionweighted images. To achieve this, both graph theory and persistent homology - a prominent approach in topological data analysis are employed in order to quantify changes in the structural connectivity of the wholebrain connectome in subjects with brain tumors. Probabilistic tractography is used to map the number of streamlines connecting 84 distinct brain regions, as delineated by the Desikan-Killiany atlas from FreeSurfer. These streamline mappings form the connectome matrix, on which persistent homology based analysis and graph theoretical analysis are executed to evaluate the discriminatory power between tumor sub-types that include meningioma and glioma. A detailed statistical analysis is conducted on persistent homology-derived topological features and graphical features to identify the brain regions where differences between study groups are statistically significant (p < 0.05). For classification purpose, graph-based local features are utilized, achieving a highest accuracy of 88%. In classifying tumor sub-types, an accuracy of 80% is attained. The findings obtained from this study underscore the potential of persistent homology and graph theoretical analysis of the whole-brain connectome in detecting alterations in structural connectivity patterns specific to different types of brain tumors.

翻译：分子与遗传学研究的最新进展已识别出多种脑肿瘤亚型，揭示了它们在分子机制、异质性和起源方面的差异。本研究利用弥散加权图像进行全脑连接组分析。为此，我们同时采用图论和拓扑数据分析中的主流方法——持续同调，以量化脑肿瘤患者全脑连接组结构连接性的变化。研究采用概率性纤维追踪技术，对基于FreeSurfer中Desikan-Killiany图谱划分的84个脑区之间的连接流线数量进行映射。这些流线映射构成连接组矩阵，并在此基础上执行持续同调分析与图论分析，以评估包括脑膜瘤和胶质瘤在内的肿瘤亚型间的区分能力。我们对持续同调导出的拓扑特征与图论特征进行了详细的统计分析，以识别研究组间差异具有统计学意义（p < 0.05）的脑区。在分类任务中，基于图的局部特征实现了最高88%的准确率；在肿瘤亚型分类中，准确率达到80%。本研究结果凸显了全脑连接组的持续同调与图论分析在检测特定类型脑肿瘤结构连接模式改变方面的潜力。