Michael Marxen

This study uses machine learning to predict alcohol consumption from resting-state fMRI connectivity, finding weak but reproducible signals with limited predictive power.

The paper examines associations between impaired control symptoms in AUD and behavioral, neural, and drinking behavior measures, finding specific links to inhibitory control deficits.

This study investigates Pavlovian-to-instrumental transfer (PIT) interference in individuals with alcohol use disorder (AUD) and smoking status, finding greater PIT interference and reduced prefrontal activation in AUD participants.

The paper examines alcohol-specific and general Pavlovian-to-instrumental transfer (PIT) in individuals with alcohol use disorder (AUD) and controls, finding stronger alcohol-specific PIT in AUD.

This study evaluates the test-retest reliability of dynamic functional connectivity parameters using a two-state model in resting-state fMRI data across different scan lengths and atlases.

The paper examines functional brain network organization in alcohol-dependent patients to identify predictors of relapse during a 12-month follow-up.

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

This paper compares fMRI and behavioral models in predicting inter-temporal choices, finding that behavioral models outperform fMRI-based classifiers.