Individual voxel-based subtype prediction can differentiate progressive supranuclear palsy from idiopathic Parkinson syndrome and healthy controls.

Voxel-based morphometry (VBM) shows a differentiated pattern in patients with atypical Parkinson syndrome but so far has had little impact in individual cases. It is desirable to translate VBM findings into clinical practice and individual classification. To this end, we examined whether a support vector machine (SVM) can provide useful accuracies for the differential diagnosis. We acquired a volumetric 3D T1-weighted MRI of 21 patients with idiopathic Parkinson syndrome (IPS), 11 multiple systems atrophy (MSA-P) and 10 progressive supranuclear palsy (PSP), and 22 healthy controls. Images were segmented, normalized, and compared at group level with SPM8 in a classical VBM design. Next, a SVM analysis was performed on an individual basis with leave-one-out cross-validation. VBM showed a strong white matter loss in the mesencephalon of patients with PSP, a putaminal grey matter loss in MSA, and a cerebellar grey matter loss in patients with PSP compared with IPS. The SVM allowed for an individual classification in PSP versus IPS with up to 96.8% accuracy with 90% sensitivity and 100% specificity. In MSA versus IPS, an accuracy of 71.9% was achieved; sensitivity, however, was low with 36.4%. Patients with IPS could not be differentiated from controls. In summary, a voxel-based SVM analysis allows for a reliable classification of individual cases in PSP that can be directly clinically useful. For patients with MSA and IPS, further developments like quantitative MRI are needed.