Measuring differences between an individual's age and biological age with biological
information from the brain have the potential to provide biomarkers of clinically relevant …

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 …

Abstract The Brain Age Gap (BAG), which refers to the difference between chronological age
and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain …

Precise prediction on brain age is urgently needed by many biomedical areas including
mental rehabilitation prognosis as well as various medicine or treatment trials. People …

Deep learning can be used effectively to predict participants' age from brain magnetic
resonance imaging (MRI) data, and a growing body of evidence suggests that the difference …

Studying brain aging improves our understanding in differentiating typical and atypical
aging. Directly utilizing traditional morphological features for brain age estimation did not …

The relationship between age and the central nervous system (CNS) in humans has been a
classical issue that has aroused extensive attention. Especially for individuals, it is of far …

Conceptualizing mental disorders as deviations from normative functioning provides a
statistical perspective for understanding the individual heterogeneity underlying psychiatric …

Brain age prediction from MRI has become a popular imaging biomarker associated with a
wide range of neuropathologies. The datasets used for training, however, are often skewed …

The biological age of the brain differs from its chronological age (CA) and can be used as
biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age …