title: Predicting the future relapse of alcohol-dependent patients from structural and functional brain images.
abstract: In alcohol dependence, individual prediction of treatment outcome based on neuroimaging endophenotypes can help to tailor individual therapeutic offers to patients depending on their relapse risk. We built a prediction model for prospective relapse of alcohol-dependent patients that combines structural and functional brain images derived from an experiment in which 46 subjects were exposed to alcohol-related cues. The patient group had been subdivided post hoc regarding relapse behavior defined as a consumption of more than 60 g alcohol for male or more than 40 g alcohol for female patients on one occasion during the 3-month assessment period (16 abstainers and 30 relapsers). Naïve Bayes, support vector machines and learning vector quantization were used to infer prediction models for relapse based on the mean and maximum values of gray matter volume and brain responses on alcohol-related cues within a priori defined regions of interest. Model performance was estimated by leave-one-out cross-validation. Learning vector quantization yielded the model with the highest balanced accuracy (79.4 percent, p < 0.0001; 90 percent sensitivity, 68.8 percent specificity). The most informative individual predictors were functional brain activation features in the right and left ventral tegmental areas and the right ventral striatum, as well as gray matter volume features in left orbitofrontal cortex and right medial prefrontal cortex. In contrast, the best pure clinical model reached only chance-level accuracy (61.3 percent). Our results indicate that an individual prediction of future relapse from imaging measurement outperforms prediction from clinical measurements. The approach may help to target specific interventions at different risk groups.
authors.affiliation: Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, and Bernstein Center for Computational Neuroscience Berlin, Germany.
authors.forename: Sambu
authors.initials: S
authors.lastname: Seo
authors.affiliation: Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, and Bernstein Center for Computational Neuroscience Berlin, Germany.
authors.forename: Johannes
authors.initials: J
authors.lastname: Mohr
authors.affiliation: Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Germany.
authors.forename: Anne
authors.initials: A
authors.lastname: Beck
authors.affiliation: Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Germany.
authors.forename: Torsten
authors.initials: T
authors.lastname: Wüstenberg
authors.affiliation: Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinz
authors.affiliation: Neural Information Processing Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, and Bernstein Center for Computational Neuroscience Berlin, Germany.
authors.forename: Klaus
authors.initials: K
authors.lastname: Obermayer
mesh_terms: D000328:Adult; D000437:Alcoholism / Q000473:pathology* / Q000503:physiopathology; D001927:Brain Diseases / Q000473:pathology* / Q000503:physiopathology; D042241:Early Diagnosis; D005260:Female; D059907:Functional Neuroimaging; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D012008:Recurrence; D012680:Sensitivity and Specificity
keywords: Alcohol dependence; brain endophenotypes; machine learning; magnetic resonance imaging; prediction model; relapse