Simulation-informed Modelling and Personalized Evaluation (SIMPLE) in Ageing Populations

Multimodal magnetic resonance imaging (MRI), detecting brain structural and functional changes, has emerged as a powerful and promising technique to study individual's brain, as T1-weighted scans can detect morphometric features, diffusion tensor imaging (DTI) scans can quantify structural connectivity, and functional MRI can capture the features of functional connectivity. Notably, with the advances in quantitative methods, computational models of brain age and brain-predicted age difference (brain-PAD) detecting the ageing effects on individual's brain features are becoming increasingly popular in clinical studies, which might revolutionize the diagnostic and prognostic phonotypes of age-related brain diseases globally.

The score of brain-PAD has threefold explanations: a. negative score representing decelerated brain ageing (brain age\<chronological age); b. positive score representing accelerated brain ageing (brain age\>chronological age); c. score equal to zero, representing normal brain ageing (brain age=chronological age). The score of brain-PAD indicates the brain ageing pattern with the interaction of lifestyle and cognitive status at individual level. For example, based on structural MRI scans, a younger brain age or a negative score of brain-PAD was found to be associated with better cognition, which indicates the potential utilities of brain age matrices in predicting individual's cognitive maintenance and healthy longevity