Sivan Kinreich

The paper derives lifespan trajectories of resting state bipolar EEG power from a large sample, finding age-related decreases followed by divergent patterns and sex-specific inter-frequency correlations.

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.

The paper analyzes resting state EEG power trajectories across age and sex in a large sample, finding age-related decreases and sex-specific reversals in certain frequency bands.

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 uses a multimodal AI approach to identify distinct Alzheimer's subtypes in men and women using MRI, genetic, and clinical data from ADNI.

The paper analyzes EEG coherence development in adolescents and young adults using tensorial analysis of covariance matrices, revealing non-linear patterns and sex differences in neural connectivity.

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.