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Segovia, Fermin; Gorriz, Juan M.; Ramirez, Javier; Martinez-Murcia, Francisco J.; Levin, Johannes; Schuberth, Madeleine; Brendel, Matthias; Rominger, Axel; Bötzel, Kai; Garraux, Gaetan; Phillips, Christophe (2017): Multivariate Analysis of F-18-DMFP PET Data to Assist the Diagnosis of Parkinsonism. In: Frontiers in Neuroinformatics, Vol. 11, 23


An early and differential diagnosis of parkinsonian syndromes still remains a challenge mainly due to the similarity of their symptoms during the onset of the disease. Recently, F-18-Desmethoxyfallypride (DMFP) has been suggested to increase the diagnostic precision as it is an effective radioligand that allows us to analyze post-synaptic dopamine D2/3 receptors. Nevertheless, the analysis of these data is still poorly covered and its use limited. In order to address this challenge, this paper shows a novel model to automatically distinguish idiopathic parkinsonism from non-idiopathic variants using DMFP data. The proposed method is based on a multiple kernel support vector machine and uses the linear version of this classifier to identify some regions of interest: the olfactory bulb, thalamus, and supplementary motor area. We evaluated the proposed model for both, the binary separation of idiopathic and non-idiopathic parkinsonism and the multigroup separation of parkinsonian variants. These systems achieved accuracy rates higher than 70%, outperforming DaTSCAN neuroimages for this purpose. In addition, a system that combined DaTSCAN and DMFP data was assessed.