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Brain network topology in alcohol dependence (FOR1617)

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Brain network topology in alcohol dependence (FOR1617)

Alcohol dependence is a debilitating disease associated with high relapse rates even after long periods of abstinence. In this project, we analyze resting-state functional magnetic resonance imaging data from a cohort of recently detoxified alcohol-dependent patients who were followed-up for 12 months to study neurobiological substrates associated with relapse risk in alcohol dependence. Specifically, we employ graph theoretic approaches to characterize the resting-state network topology in prospective relapsers and abstainers and implement these connectome-based features within a machine learning framework to predict alcohol relapse at the single subject level. Thus, our study could contribute to the development of novel targeted interventions that might promote longer lasting abstinence and reduce health burdens associated with alcohol dependence. 

Members: Justin Böhmer 
Head: Dr. Johann Kruschwitz, Prof. Dr. Dr. Henrik Walter 
Funding: FOR1617 (German Research Foundation, project number: 186318919) 

contact: Justin Böhmer (