Importance Great interest exists in identifying methods to predict neuropsychiatric disease
states and treatment outcomes from high-dimensional data, including neuroimaging and …

During aging, the cellular milieu of the brain exhibits tell-tale signs of compromised
bioenergetics, impaired adaptive neuroplasticity and resilience, aberrant neuronal network …

Biological aging of human organ systems reflects the interplay of age, chronic disease,
lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes …

With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

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 …

As medical imaging enters its information era and presents rapidly increasing needs for big
data analytics, robust pooling and harmonization of imaging data across diverse cohorts …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …

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 …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …