Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations

This study highlights the superior ability of Vietoris-Rips filtration to capture intricate brain network patterns, offering a robust tool for early diagnosis and precise classification of MCI subtypes.

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Publication Details

Journal
Frontiers in Neuroscience
Published
2025-02-26
DOI
https://doi.org/10.3389/fnins.2025.1518984
Citations
9
Primary Topic
Topological and Geometric Data Analysis
Type
article
Field-Weighted Citation Impact
13.54
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article

Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations

Neelam Sinha, Debanjali Bhattacharya, Ninad Aithal, R. Kaur et al.
9 citations
Frontiers in Neuroscience
Topological and Geometric Data Analysis
13.54
article

Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations

Neelam Sinha, Debanjali Bhattacharya, Ninad Aithal, R. Kaur, Thomas Gregor Issac
article en
9 citations

Abstract

This study highlights the superior ability of Vietoris-Rips filtration to capture intricate brain network patterns, offering a robust tool for early diagnosis and precise classification of MCI subtypes.

Frontiers in NeuroscienceVol. 19
Indian Institute of Science Bangalore (IN), Amrita Vishwa Vidyapeetham (IN)
Openalex Percentile: Top 1%
Topological and Geometric Data Analysis
13.54
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