With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

As our brains age, we tend to experience cognitive decline and are at greater risk of
neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases …

Abstract Machine learning algorithms can be trained to estimate age from brain structural
MRI. The difference between an individual's predicted and chronological age, predicted age …

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy,
aging-related diseases, and mortality. We examined potential advanced brain aging in adult …

Brain-age estimation has gained increased attention in the neuroscientific community owing
to its potential use as a biomarker of brain health. The difference between estimated and …

This study investigated the relationship between education and physical activity and the
difference between a physiological prediction of age and chronological age (CA). Cortical …

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 …

Brain morphology varies across the ageing trajectory and the prediction of a person's age
using brain features can aid the detection of abnormalities in the ageing process. Existing …

Abstract Magnetic Resonance Imaging (MRI) studies have shown that cortical volume
declines with age. Although volume is a multiplicative measure consisting of thickness and …

In recent years, several studies have demonstrated that machine learning and deep learning
systems can be very useful to accurately predict brain age. In this work, we propose a novel …