Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance
Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance
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DOI：
10.1016/j.neuroimage.2023.120436
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发表时间：
2023-11-08
期刊：
影响因子：
5.7
通讯作者：
Struck，Aaron F.
中科院分区：
文献类型：
--
作者：
Chung，Moo K.;Ramos，Camille Garcia;Struck，Aaron F.
持续同源性为从大脑网络中提取隐藏的拓扑信号提供了一个强大的工具。它捕捉了拓扑结构在多个尺度上的演变，称为过滤，从而揭示了在这些尺度上持续存在的拓扑特征。这些特征在持久性图中进行了总结，并使用Wasserstein距离量化了它们的相异性。然而，Wasserstein距离并不遵循已知的分布，这给现有参数统计模型的应用带来了挑战。为了解决这个问题，我们引入了一个统一的拓扑推理框架为中心的Wasserstein距离。我们的方法没有明确的模型和分布假设。推理以完全数据驱动的方式执行。我们将这种方法应用于从两个不同地点收集的颞叶癫痫患者的静息态功能磁共振成像（rs-fMRI）：威斯康星大学麦迪逊分校和威斯康星州医学院。重要的是，我们的拓扑方法是强大的变化，由于性别和图像采集，避免需要考虑这些变量作为滋扰协变量。我们成功地定位了对拓扑差异贡献最大的大脑区域。用于本研究中所有分析的MATLAB软件包可在https://github.com/laplcebeltrami/PH-STAT上获得。
Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.