Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Twelve lead electrocardiogram signals capture unique fingerprints about the body's
biological processes and electrical activity of heart muscles. Machine learning and deep …

Currently, significant progress has been made in predicting brain age from structural
Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However …

Brain age in neuroimaging has emerged over the last decade and reflects the estimated age
based on the brain MRI scan from a person. As a person ages, their brain structure will …

Brain-age estimation has gained increased attention in the neuroscientific community owing
to its potential use as a biomarker of brain health. The difference between estimated and …

Purpose Medical imaging can be used to estimate a patient's biological age, which may
provide complementary information to clinicians compared to chronological age. In this …

Simple Summary The consumption of cocaine is linked to a range of detrimental outcomes,
including addiction, cognitive deficits, and an elevated risk of developing psychiatric …

Alzheimer's disease (AD) is the prevalent form of dementia and shares many aspects with
the aging pattern of the abnormal brain. Machine learning models like support vector …

Abnormal brain aging is suggested in epilepsy. Given the brain network dysfunction in
epilepsy, the white matter tracts, which primarily interconnect brain regions, could be of …

Chronic musculoskeletal pain (CMP), a prevalent and heterogeneous condition
characterized by persistent pain in various body parts, is a leading cause of disability …