Background Brain age is a biomarker that predicts chronological age using neuroimaging
features. Deviations of this predicted age from chronological age is considered a sign of age …

The brain-age paradigm is proving increasingly useful for exploring aging-related disease
and can predict important future health outcomes. Most brain-age research uses structural …

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 …

Estimating age based on neuroimaging‐derived data has become a popular approach to
developing markers for brain integrity and health. While a variety of machine‐learning …

Evidence suggests that psychopathology is associated with an advanced brain ageing
process, typically mapped using machine learning models that predict an individual's age …

Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …

Objective During the natural course of multiple sclerosis (MS), the brain is exposed to aging
as well as disease effects. Brain aging can be modeled statistically; the so‐called “brain …

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 …

Brain structural morphology varies over the aging trajectory, and the prediction of a person's
age using brain morphological features can help the detection of an abnormal aging …

Brain-predicted age difference scores are calculated by subtracting chronological age from
'brain'age, which is estimated using neuroimaging data. Positive scores reflect accelerated …