Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and
comorbidities is largely unknown. A recent machine learning method enables us to estimate …

Machine Learning methods are often adopted to infer useful biomarkers for the early
diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing …

We previously developed a novel machine-learning-based brain age model that was
sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility …

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 …

The concept of biological age (BA)-although important in clinical practice-is hard to grasp
mainly due to the lack of a clearly defined reference standard. For specific applications …

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 …

The cosmetic and behavioural aspects of ageing become increasingly apparent with the
passing years. The individual variability in physical ageing can be immediately observed in …

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 …

Age is an important variable to describe the expected brain's anatomy status across the
normal aging trajectory. The deviation from that normative aging trajectory may provide …

Aging is a known non-modifiable risk factor for stroke. Usually, this refers to chronological
rather than biological age. Biological brain age can be estimated based on cortical and …