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 …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …

Brain age prediction using machine‐learning techniques has recently attracted growing
attention, as it has the potential to serve as a biomarker for characterizing the typical brain …

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

Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …

Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …

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 …

With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

Problem: Chronological aging in later life is associated with brain degeneration processes
and increased risk for disease such as stroke and dementia. With a worldwide tendency of …

The calculation of so‐called “brain age” from structural MRIs has been an emerging
biomarker in aging research. Data suggests that discrepancies between chronological age …