We present a review of the characterization of healthy brain aging using MRI with an
emphasis on morphology, lesions, and quantitative MR parameters. A scope review found …

Background: Neuroimage analysis has made it possible to perform various anatomical
analyses of the brain regions and helps detect different brain conditions/disorders. Recently …

The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …

Abstract Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people. Deviations from healthy brain ageing have been …

Brain age is considered as an important biomarker for detecting aging-related diseases
such as Alzheimer's Disease (AD). Magnetic resonance imaging (MRI) have been widely …

This study investigated the relationship between education and physical activity and the
difference between a physiological prediction of age and chronological age (CA). Cortical …

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a
noninvasive approach to map brain connections, but improving anatomical accuracy has …

Over the past decade, there has been an abundance of research on the difference between
age and age predicted using brain features, which is commonly referred to as the “brain age …

Abstract We present a Deep Learning framework for the prediction of chronological age from
structural magnetic resonance imaging scans. Previous findings associate increased brain …

Brain-predicted age difference scores are calculated by subtracting chronological age from
'brain'age, which is estimated using neuroimaging data. Positive scores reflect accelerated …