Predicting the future relapse of alcohol-dependent patients from structural and functional brain images
| Autor: | Klaus Obermayer, Anne Beck, Andreas Heinz, Johannes Mohr, Sambu Seo, Torsten Wüstenberg |
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| Rok vydání: | 2015 |
| Předmět: |
Pharmacology
Learning vector quantization medicine.medical_specialty Alcohol dependence Ventral striatum Medicine (miscellaneous) Psychiatry and Mental health Naive Bayes classifier Physical medicine and rehabilitation medicine.anatomical_structure Neuroimaging Endophenotype medicine Orbitofrontal cortex Prefrontal cortex Psychology Neuroscience |
| Zdroj: | Addiction Biology. 20:1042-1055 |
| ISSN: | 1355-6215 |
| Popis: | 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). Naive 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 |
| Databáze: | OpenAIRE |
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