Age-associated diseases rise as life expectancy increases. The brain presents age-related
structural changes across life, with different extends between subjects and groups. During …

The clinical applications of brain age prediction have expanded, particularly in anticipating
the onset and prognosis of various neurodegenerative diseases. In the current study, we …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Brain age is an imaging-based biomarker with excellent feasibility for characterizing
individual brain health and may serve as a single quantitative index for clinical and domain …

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 …

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 …

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's
disease (AD), a representative neurodegenerative disease, has been linked to accelerated …

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 …

Brain age prediction has been shown to be clinically relevant, with the errors in the
prediction associated with various psychiatric and neurological conditions. While the …

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