Persistent Homology for MCI Classification: A Comparative Analysis between Graph and Vietoris-Rips Filtrations

• URL: http://arxiv.org/abs/2410.22681v1
• Date: Wed, 30 Oct 2024 04:24:40 GMT
• Title: Persistent Homology for MCI Classification: A Comparative Analysis between Graph and Vietoris-Rips Filtrations
• Authors: Debanjali Bhattacharya, Rajneet Kaur, Ninad Aithal, Neelam Sinha, Thomas Gregor Issac,
• Abstract summary: The present study offers a detailed analysis of topological changes associated with Mild cognitive impairment (MCI) This analysis utilizes fMRI time series data from two distinct populations: the publicly available ADNI dataset and the in-house TLSA dataset. For Vietoris-Rips filtration, inter-ROI MCI Wasserstein distance matrices between persistent diagrams are used for classification, while graph filtration relies on the top ten most persistent homology features.
• Score: 2.306862732864727
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• Abstract: 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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