The relationship between age and the central nervous system (CNS) in humans has been a
classical issue that has aroused extensive attention. Especially for individuals, it is of far …

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age
based on the brain MRI scan from a person. As a person ages, their brain structure will …

Brain aging is a regional phenomenon, a facet that remains relatively under-explored within
the realm of brain age prediction research using machine learning methods. Voxel-level …

Early detection of age-related diseases will greatly benefit from a model of the underlying
biological aging process. In this paper, we develop a brain-age predictor by using structural …

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 …

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 …

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 …

Brain age estimation from anatomical features has been attracting more attention in recent
years. This interest in brain age estimation is motivated by the importance of biological age …

We read with great interest the study investigating the performance metrics of the “Brain-age
gap” prediction model in two worldrenowned large-scale data sets (de Lange et al., 2022) …

Deep learning models have achieved state-of-the-art results in estimating brain age, which
is an important brain health biomarker, from magnetic resonance (MR) images. However …