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 …

Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain
undergoes age-related neuroanatomical changes not only regionally but also on the …

The deviation between chronological age and age predicted using brain MRI is a putative
marker of overall brain health. Age prediction based on structural MRI data shows high …

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 …

Discrepancies between the estimated brain age from brain structural MRI and the
chronological age have been associated with a broad spectrum of neurocognitive disorders …

The difference between age predicted using anatomical brain scans and chronological age,
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …

For decades, neuroscientists have been on a quest to search for a biomarker that can help
capture age-related cognitive decline. One well-known candidate is Brain Age, or a …

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 …

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 …

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 …