Neuroimaging-driven brain age estimation has introduced a robust (reliable and heritable)
biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed …

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of
brain aging, relating to cognitive performance, health outcomes and progression of …

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

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 …

Brain-age can be inferred from structural neuroimaging and compared to chronological age
(brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in …

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 …

Alzheimer's disease (AD), the most common form of dementia, shares many aspects of
abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based …

Neuroimaging‐driven brain age estimation has become popular in measuring brain aging
and identifying neurodegenerations. However, the single estimated brain age (gap) …

The concept of biological age (BA)-although important in clinical practice-is hard to grasp
mainly due to the lack of a clearly defined reference standard. For specific applications …

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 …