Understanding Brain Aging Across Populations: A Comprehensive Framework for Structural Analysis

TL;DR

This study introduces a comprehensive framework for analyzing and comparing structural brain aging patterns across diverse populations using MRI data and advanced modeling techniques.

Contribution

It presents a novel, global-scale structural analysis framework for brain aging, incorporating multi-population MRI data and diffeomorphic transformation modeling.

Findings

Identified distinct brain deformation patterns across populations.

Provided detailed spatial distribution of age-related brain changes.

Established a baseline for future large-scale aging studies.

Abstract

Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and Caucasian populations were analyzed using a two-pronged approach. Initially, a group analysis was performed involving tissue segmentation through FSL-FAST, examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Subsequently, a continuous model-based analysis was employed, defining aging as a diffeomorphic transformation, which facilitated a detailed intra- and inter-population analysis, and examined both global anatomy and age-dependent distances of each population in comparison to the Indian population. Detailed insights into the spatial distribution of brain deformations over time were obtained, with particular focus on anatomical changes relative to a reference time point. The proposed comprehensive structural comparison framework, applied to a sample dataset encompassing four distinct populations, represents a pioneering effort to compare structural differences related to brain aging on a global scale. Subsequent studies utilizing larger datasets and applying the proposed analysis across groups based on various criteria will further advance our understanding of aging-related changes.