Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …

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 …

The level of prediction error in the brain age estimation frameworks is associated with the
authenticity of statistical inference on the basis of regression models. In this paper, we …

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks.
The impact of regression algorithms on prediction accuracy in the brain age estimation …

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

We propose a new framework for estimating neuroimaging-derived “brain-age” at a local
level within the brain, using deep learning. The local approach, contrary to existing global …

MRI-based brain age prediction has been widely used to characterize normal brain
development, and deviations from the typical developmental trajectory are indications of …

It is now possible to estimate an individual's brain age via brain scans and machine-learning
models. This validated technique has opened up new avenues for addressing clinical …

Sex differences play a vital role in human brain structure and physiology. Previous reports
have proposed evidence hinting at a metabolic advantage in female brains across …

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age
based on the brain MRI scan from a person. As a person ages, their brain structure will …