Background Biological aging is revealed by physical measures, eg, DNA probes or brain
scans. In contrast, individual differences in mental function are explained by psychological …

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle,
environment and disease. We developed a deep neural network model trained on brain MRI …

From a biological perspective, humans differ in the speed they age, and this may manifest in
both mental and physical health disparities. The discrepancy between an individual's …

Brain imaging can be used to study how individuals' brains are aging, compared against
population norms. This can inform on aspects of brain health; for example, smoking and …

Deep learning has emerged as a powerful approach to constructing imaging signatures of
normal brain ageing as well as of various neuropathological processes associated with …

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 …

Brain age refers to age predicted by brain features. Brain age has previously been
associated with various health and disease outcomes and suggested as a potential …

Predictive modeling using neuroimaging data has the potential to improve our
understanding of the neurobiology underlying psychiatric disorders and putatively …

Background With a rapidly expanding gap between the need for and availability of mental
health care, artificial intelligence (AI) presents a promising, scalable solution to mental …

Brain age is becoming a widely applied imaging-based biomarker of neural aging and
potential proxy for brain integrity and health. We estimated multimodal and modality-specific …