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 Machine learning has been applied to neuroimaging data for estimating brain age
and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters …

Objective Brain tissue changes dynamically during aging. The purpose of this study was to
use synthetic magnetic resonance imaging (syMRI) to evaluate the changes in relaxation …

The gap between predicted brain age using magnetic resonance imaging (MRI) and
chronological age may serve as a biomarker for early-stage neurodegeneration. However …

The aim of this study was to assess the diagnostic validity of a deep learning-based method
estimating brain age based on magnetic resonance imaging (MRI) and to compare it with …

Recent works have extensively investigated the possibility to predict brain aging from T1-
weighted MRI brain scans. The main purposes of these studies are the investigation of …

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 …

Deep neural networks (DNN) have been designed to predict the chronological age of a
healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted …

Application of machine learning (ML) algorithms to structural magnetic resonance imaging
(sMRI) data has yielded behaviorally meaningful estimates of the biological age of the brain …

Predicted brain age could be used to measure individual brain status over development and
degeneration, which could also indicate the potential risk of age-related brain disorders …