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 …

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 …

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 …

Epilepsy Surgery for Cognitive Improvement in Epileptic Encephalopathy - PMC Skip to main
content Here's how you know Official websites use .gov A .gov website belongs to an official …

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

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 …

Decoding of the coupling among the brain and heart activations is an important research
area in network physiology. We studied the coupling of brain and heart activations for 48 …

Background Electroencephalography (EEG) is increasingly used for monitoring the depth of
general anaesthesia, but EEG data from general anaesthesia monitoring are rarely reused …

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 …

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy in the oral-maxillofacial
region with a poor prognosis. Oral microbiomes play a potential role in the pathogenesis of …