Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy


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  • Full financial disclosures and author roles may be found in the online version of this article.



The aim of the current study was to distinguish patients with Parkinson disease (PD) from those with progressive supranuclear palsy (PSP) at the individual level using pattern recognition of magnetic resonance imaging data.


We combined diffusion tensor imaging and voxel-based morphometry in a support vector machine algorithm to evaluate 21 patients with PSP and 57 patients with PD.


The automated algorithm correctly distinguished patients who had PD from those who had PSP with 100% accuracy. This accuracy value was obtained when white matter atrophy was considered. Diffusion parameters combined with gray matter atrophy exhibited 90% sensitivity and 96% specificity.


Our findings demonstrate that automated pattern recognition can help distinguish patients with PSP from those with PD on an individual basis. © 2013 International Parkinson and Movement Disorder Society