Quantifying biological aging is critical for understanding why aging is the primary driver of
morbidity and mortality and for assessing novel therapies to counter pathological aging. In …

This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …

China and the world are facing severe population aging and an increasing burden of age-
related diseases. Aging of the brain causes major age-related brain diseases, such as …

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep
neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging …

Importance Dementia is an increasing cause of disability and loss of independence in the
elderly population yet remains largely underdiagnosed. A biomarker for dementia that can …

Abstract Study Objectives To use relatively noisy routinely collected clinical data (brain
magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and …

Electroencephalography (EEG) measures changes in neuronal activity and can reveal
significant changes from infancy to adulthood concomitant with brain maturation, making it a …

Sleep electroencephalogram (EEG) signals likely encode brain health information that may
identify individuals at high risk for age-related brain diseases. Here, we evaluate the …

Background Major depressive disorder (MDD) may be associated with accelerated brain
aging (higher brain age than chronological age). This report evaluated whether brain age is …

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated
aging. We proposed a sleep EEG based brain age prediction model using convolutional …