Recent studies combining neuroimaging with machine learning methods successfully infer
an individual's brain age, and its discrepancy with the chronological age is used to identify …

Introduction: Brain age prediction using neuroimaging data and machine learning
algorithms holds significant promise for gaining insights into the development of …

Early detection of age-related diseases will greatly benefit from a model of the underlying
biological aging process. In this paper, we develop a brain-age predictor by using structural …

Numerous studies have established that estimated brain age, as derived from statistical
models trained on healthy populations, constitutes a valuable biomarker that is predictive of …

Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently
been used for brain age prediction and have achieved outstanding performance …

Deep learning has emerged as a powerful approach to constructing imaging signatures of
normal brain ageing as well as of various neuropathological processes associated with …

Brain development and aging are dynamic processes that unfold over years on multiple
levels in both healthy and disordered individuals. Recent studies have revealed a disparity …

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging
and AD pathology involve structural changes in the brain, their trajectories of atrophy are not …

Chronological age of healthy people is able to be predicted accurately using deep neural
networks from neuroimaging data, and the predicted brain age could serve as a biomarker …

To study genetic factors associated with brain aging, we first need to quantify brain aging.
Statistical models have been created for estimating the apparent age of the brain, or …