Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

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

Given the unprecedented phenomenon of population ageing, studies have increasing
captured the heterogeneity within the ageing process. In this context, the concept of …

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

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 …

Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w
MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain …

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been
implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to …

Cerebral small vessel disease (SVD), which is highly age-related, is the most common
neuroimaging finding in community-dwelling elderly individuals. In addition to increasing the …

The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral
small vessel disease are largely unknown. Brain biological age can be estimated by …

Introduction The difference between the chronological and biological brain age, called the
brain age gap (BAG), has been identified as a promising biomarker to detect deviation from …