We propose an approach that uses 3D texture features extracted from fMRI to detect changes in the striatal network induced by alcohol drinking. Scans of eighteen alcohol-preferring rats before and after 30 days of alcohol consumption were analyzed. Data were preprocessed and a group independent component analysis was performed to identify striatal network; in total 36 volumes of interest were studied. Texture analysis was performed using 43 texture features and six predictive models. An AUC of 0.927±0.089 (sensitivity=84.25%, specificity=81.75%) was obtained for the best model (random forests). The proposed method was able to accurately identify subjects with alcohol use disorders.