Prediction of chronological age from neuroimaging in the healthy population is an important
issue because the deviations from normal brain age may highlight abnormal trajectories …

Brain age prediction from brain MRI scans not only helps improve brain ageing modelling
generally, but also provides benchmarks for predictive analysis methods. Brain-age delta …

Brain age is the estimate of biological age derived from neuroimaging datasets using
machine learning algorithms. Increasing brain age with respect to chronological age can …

Introduction Electroencephalogram (EEG) provides clinically relevant information for
personalized patient health evaluation and comprehensive assessment of sleep. EEG …

Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early
is crucial for effective treatment and preventing irreversible brain damage. Brain age, a …

Predicting brain age has become one of the most attractive challenges in computational
neuroscience due to the role of the predicted age as an effective biomarker for different brain …

The discrepancy between chronological age and the apparent age of the brain based on
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …

The use of machine learning (ML) algorithms has significantly increased in neuroscience.
However, from the vast extent of possible ML algorithms, which one is the optimal model to …

Beyond these developments in brain aging research, recent advances in ML have exceeded
the expectations of many AI experts, with transformer-based deep learning models leading …

Abstract Brain Age Estimation (BAE) has become a popular challenge in the field of medical
and computer sciences in recent years. In the medical sciences field, the investigation on the …