# sMRI-based Brain Age Estimation in MCI using Persistent Homology

arXiv: https://arxiv.org/abs/2511.05520 · HTML: https://mlsanity.com/paper/2511.05520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [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)
- [Analyzing Brain Tumor Connectomics using Graphs and Persistent Homology](https://mlsanity.com/paper/2407.17938/) (2024)
- [Leveraging Persistent Homology for Differential Diagnosis of Mild Cognitive Impairment](https://mlsanity.com/paper/2408.15647/) (2024)
- [Persistent Homology for MCI Classification: A Comparative Analysis between Graph and Vietoris-Rips Filtrations](https://mlsanity.com/paper/2410.22681/) (2024)
- [Towards understanding the nature of direct functional connectivity in visual brain network](https://mlsanity.com/paper/2403.11480/) (2024)

## Abstract (arXiv)

In this study, we propose the use of persistent homology -- specifically Betti curves for brain age prediction and for distinguishing between healthy and pathological aging. The proposed framework is applied to 100 structural MRI scans from the publicly available ADNI dataset. Our results indicate that Betti curve features, particularly those from dimension-1 (connected components) and dimension-2 (1D holes), effectively capture structural brain alterations associated with aging. Furthermore, clinical features are grouped into three categories based on their correlation, or lack thereof, with (i) predicted brain age and (ii) chronological age. The findings demonstrate that this approach successfully differentiates normal from pathological aging and provides a novel framework for understanding how structural brain changes relate to cognitive impairment. The proposed method serves as a foundation for developing potential biomarkers for early detection and monitoring of cognitive decline.