# Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment

arXiv: https://arxiv.org/abs/2408.15647 · HTML: https://mlsanity.com/paper/2408.15647/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Ripser: efficient computation of Vietoris-Rips persistence barcodes](https://mlsanity.com/paper/1908.02518/) (2019)
- [TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer](https://mlsanity.com/paper/2105.06049/) (2021)
- [Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia](https://mlsanity.com/paper/1809.08504/) (2018)
- [Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning](https://mlsanity.com/paper/2311.08417/) (2023)
- [Sliding Window Persistence of Quasiperiodic Functions](https://mlsanity.com/paper/2103.04540/) (2021)
- [MCI Detection using fMRI time series embeddings of Recurrence plots](https://mlsanity.com/paper/2311.18265/) (2023)
- [Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI](https://mlsanity.com/paper/2402.02811/) (2024)

## Built on by (most cited first, 3 of 3)

- [Persistent Homology for MCI Classification: A Comparative Analysis between Graph and Vietoris-Rips Filtrations](https://mlsanity.com/paper/2410.22681/) (2024)
- [sMRI-based Brain Age Estimation in MCI using Persistent Homology](https://mlsanity.com/paper/2511.05520/) (2025)
- [DWI Connectome-based Sex Classification in Young using Supervised Variational Autoencoder](https://mlsanity.com/paper/2610.16348/) (2026)

## Abstract (arXiv)

Mild cognitive impairment (MCI) is characterized by subtle changes in cognitive functions, often associated with disruptions in brain connectivity. The present study introduces a novel fine-grained analysis to examine topological alterations in neurodegeneration pertaining to six different brain networks of MCI subjects (Early/Late MCI). To achieve this, fMRI time series from two distinct populations are investigated: (i) the publicly accessible ADNI dataset and (ii) our in-house dataset. The study utilizes sliding window embedding to convert each fMRI time series into a sequence of 3-dimensional vectors, facilitating the assessment of changes in regional brain topology. Distinct persistence diagrams are computed for Betti descriptors of dimension-0, 1, and 2. Wasserstein distance metric is used to quantify differences in topological characteristics. We have examined both (i) ROI-specific inter-subject interactions and (ii) subject-specific inter-ROI interactions. Further, a new deep learning model is proposed for classification, achieving a maximum classification accuracy of 95% for the ADNI dataset and 85% for the in-house dataset. This methodology is further adapted for the differential diagnosis of MCI sub-types, resulting in a peak accuracy of 76.5%, 91.1% and 80% in classifying HC Vs. EMCI, HC Vs. LMCI and EMCI Vs. LMCI, respectively. We showed that the proposed approach surpasses current state-of-the-art techniques designed for classifying MCI and its sub-types using fMRI.