sMRI-based Brain Age Estimation in MCI using Persistent Homology

TL;DR

This paper introduces a novel method using persistent homology and Betti curves to estimate brain age and distinguish healthy from pathological aging in MRI scans, aiding early detection of cognitive decline.

Contribution

It applies persistent homology to brain MRI data for age prediction and disease differentiation, offering a new framework for understanding structural brain changes.

Findings

Betti curve features effectively capture age-related brain alterations.

The method differentiates normal from pathological aging.

Provides a foundation for early biomarkers of cognitive decline.

Abstract

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.