The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …

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 …

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 …

Machine learning (ML) has transformed neuroimaging research by enabling accurate
predictions and feature extraction from large datasets. In this study, we investigate the …

The temporal characteristics of adolescent neurodevelopment are shaped by a complex
interplay of genetic, biological, and environmental factors. Using a large longitudinal dataset …

Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental
question for understanding the ageing brain. In an extensive comparison of brain age …

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to
assess the biological age of the human brain. The difference between a person's …

Brain age prediction has been shown to be clinically relevant, with the errors in the
prediction associated with various psychiatric and neurological conditions. While the …