Weipeng Kuang

This study uses EEGViT, a transformer-based model, to classify Alcohol Use Disorder from raw resting-state EEG data, achieving 56% accuracy with sex- and age-stratified results.

This study investigates phase-amplitude coupling (PAC) differences in frontomedial brain regions between individuals with alcohol use disorder (AUD) and controls, identifying significant clusters associated with AUD.

This study estimates brain age in individuals with alcohol use disorder (AUD) and finds accelerated brain aging associated with impaired neurocognition and impulsivity.

The paper examines neural activity in the reward network during win and loss outcomes in a gambling task and links these activations to behavioral and cognitive traits.

This study examines how polygenic risk scores for Alcohol Use Disorder affect microglial responses to ethanol exposure in human iPSC-derived microglia.

The paper predicts brain age in individuals with and at risk for alcohol use disorder using morphological brain features and finds accelerated brain aging in both groups.

This study uses machine learning to predict alcohol-related memory problems in older adults by integrating multi-domain features including brain connectivity, polygenic risk, and lifestyle factors.