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Persistent homology for MCI classification: a comparative analysis between graph and Vietoris-Rips filtrations
Debanjali Bhattacharya1,2, Rajneet Kaur2, Ninad Aithal2,3
1Department of Artificial Intelligence, Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Introduction:
Mild cognitive impairment (MCI), often linked to early neurodegeneration, is associated with subtle disruptions in brain connectivity. In this paper, the applicability of persistent homology, a cutting-edge topological data analysis technique is explored for classifying MCI subtypes.
Method:
The study examines brain network topology derived from fMRI time series data. In this regard, we investigate two methods for computing persistent homology: (1) Vietoris-Rips filtration, which leverages point clouds generated from fMRI time series to capture dynamic and global changes in brain connectivity, and (2) graph filtration, which examines connectivity matrices based on static pairwise correlations. The obtained persistent topological features are quantified using Wasserstein distance, which enables a detailed comparison of brain network structures.
Result:
Our findings show that Vietoris-Rips filtration significantly outperforms graph filtration in brain network analysis. Specifically, it achieves a maximum accuracy of 85.7% in the Default Mode Network, for classifying MCI using in-house dataset.
Discussion:
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.
Insights
Persistent homology, a topological data analysis method, effectively classifies subtypes of mild cognitive impairment (MCI). Vietoris-Rips filtration shows superior performance in analyzing brain network connectivity for MCI diagnosis.
Area of Science:
- Neuroscience
- Data Analysis
- Computational Topology
Background:
- Mild cognitive impairment (MCI) is linked to early neurodegeneration and subtle brain connectivity disruptions.
- Accurate classification of MCI subtypes is crucial for timely intervention and treatment.
Purpose of the Study:
- To explore the application of persistent homology for classifying mild cognitive impairment (MCI) subtypes.
- To compare the efficacy of Vietoris-Rips filtration and graph filtration in analyzing brain network topology.
Main Methods:
- Brain network topology was analyzed using functional magnetic resonance imaging (fMRI) time series data.
- Two persistent homology methods were employed: Vietoris-Rips filtration and graph filtration.
- Brain network structures were quantified using Wasserstein distance.
Main Results:
- Vietoris-Rips filtration demonstrated superior performance compared to graph filtration in brain network analysis.
- A maximum accuracy of 85.7% was achieved using Vietoris-Rips filtration for MCI classification within the Default Mode Network.
- The study utilized an in-house dataset for validation.
Conclusions:
- Vietoris-Rips filtration is a powerful tool for capturing complex brain network patterns.
- This topological data analysis technique offers a robust approach for the early diagnosis and precise classification of MCI subtypes.
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