Currently there is an important delay between the onset of Parkinson's disease and its diagnosis. The detection of changes in physical properties of brain structures may help to detect the disease earlier. In this work, we propose to take advantage of the informative features provided by quantitative MRI to construct statistical models representing healthy brain tissues. This allows us to detect atypical values for these features in the brain of Parkinsonian patients. We introduce mixture models to capture the non-standard shape of the data multivariate distribution. Promising preliminary results demonstrate the potential of our approach in discriminating patients from controls and revealing the subcortical structures the most impacted by the disease.