用拓扑分析方法区分轻度认知障碍亚型，效果优于传统图模型。
Persistent Homology for MCI Classification: A Comparative Analysis between Graph and Vietoris-Rips Filtrations
- 对比维托里斯-里普斯与图滤波两种拓扑分析方法。
- 维托里斯-里普斯法准确率达85.7%，显著高于图法的71.4%。
- 适合神经退行性疾病早期诊断研究者参考。
轻度认知障碍（MCI）常与早期神经退行性病变相关，表现为轻微认知下降及脑连接紊乱。本研究针对两种亚型——早发性与晚发性MCI，利用来自公开数据集ADNI（西方队列）和自建TLSA数据集（印度城市队列）的fMRI时间序列数据，采用持久同调（Persistent Homology）方法，结合维托里斯-里普斯（Vietoris-Rips）与图滤波两种过滤技术进行分类。维托里斯-里普斯法基于不同区域间持久图的沃尔什斯特距离矩阵进行分类，而图滤波法则采用前十个最持久的同调特征。对比结果显示，维托里斯-里普斯法显著优于图滤波法，能更精准捕捉脑连接中的细微拓扑变化。该方法在区分年龄、性别匹配的健康对照与MCI患者时，最高准确率达85.7%；而图滤波法最高仅达71.4%。这一优势凸显了维托里斯-里普斯法在检测神经退行性病变相关复杂拓扑特征方面的敏感性。研究结果表明，结合沃尔什斯特距离的持久同调分析，是早期诊断与精确分类认知障碍的有力工具，为理解MCI中脑连接变化提供了新视角。
原文摘要 · Abstract (English)
Mild cognitive impairment (MCI), often linked to early neurodegeneration, is characterized by subtle cognitive declines and disruptions in brain connectivity. The present study offers a detailed analysis of topological changes associated with MCI, focusing on two subtypes: Early MCI and Late MCI. This analysis utilizes fMRI time series data from two distinct populations: the publicly available ADNI dataset (Western cohort) and the in-house TLSA dataset (Indian Urban cohort). Persistent Homology, a topological data analysis method, is employed with two distinct filtration techniques - Vietoris-Rips and graph filtration-for classifying MCI subtypes. For Vietoris-Rips filtration, inter-ROI Wasserstein distance matrices between persistent diagrams are used for classification, while graph filtration relies on the top ten most persistent homology features. Comparative analysis shows that the Vietoris-Rips filtration significantly outperforms graph filtration, capturing subtle variations in brain connectivity with greater accuracy. The Vietoris-Rips filtration method achieved the highest classification accuracy of 85.7\% for distinguishing between age and gender matched healthy controls and MCI, whereas graph filtration reached a maximum accuracy of 71.4\% for the same task. This superior performance highlights the sensitivity of Vietoris-Rips filtration in detecting intricate topological features associated with neurodegeneration. The findings underscore the potential of persistent homology, particularly when combined with the Wasserstein distance, as a powerful tool for early diagnosis and precise classification of cognitive impairments, offering valuable insights into brain connectivity changes in MCI.
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