Bernice Porjesz A

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 performs a multi-ancestral GWAS of DSM-defined nicotine dependence, finding genetic correlations with other substance use disorders and health traits, and identifying a strong general addiction liability factor.

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.

The paper investigates how upregulated GIRK2 affects ethanol-induced changes in human neurons, showing it counteracts excitability and respiration changes.

The paper examines how genetic variants in the cholinergic system affect theta band event-related oscillations, finding age and sex-dependent variations in these associations.

The paper uses polygenic risk scores from GWAS to assess their association with suicidal thoughts and behaviors in a sample with alcohol use disorders, finding significant contributions from multiple PRS.

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.

This paper uses PheWAS to investigate the phenotypic effects of GABRA2 variant rs279858, finding associations with anxiety-related and behavioral traits linked to alcohol use disorders.

This study shows that KCNJ6 noncoding variants increase excitability in human glutamatergic neurons, an effect reversed by ethanol exposure.

The paper integrates GWAS data with multi-omics datasets to identify novel genes for alcohol use disorder and link them to neurodegenerative diseases.

The paper compares genome-wide data from opioid use and dependence cases to identify genetic associations and differences between exposure and dependence.

The paper identifies 129 differentially expressed genes in the PFC of alcoholics and links them to pathways and modules associated with alcohol dependence and consumption.