Exploring Brain Age Calculation Models Available for Alzheimer’s Disease

Abstract

The advantages of structural magnetic resonance imaging (sMRI)-based multidimensional tensor morphological features in brain disease research are the high sensitivity and resolution of sMRI to comprehensively capture the key structural information and quantify the structural deformation. However, its direct application to regression analysis of high-dimensional small-sample data for brain age prediction may cause “dimensional catastrophe”. Therefore, this paper develops a brain age prediction method for high-dimensional small-sample data based on sMRI multidimensional morphological features and constructs brain age gap estimation (BrainAGE) biomarkers to quantify abnormal aging of key subcortical structures by extracting subcortical structural features for brain age prediction, which can then establish statistical analysis models to help diagnose Alzheimer’s disease and monitor health conditions, intervening at the preclinical stage.

Keywords

brain age gap estimation (BrainAGE); Alzheimer’s disease (AD) / structural magnetic resonance imaging (sMRI)