Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism - Segovia, Fermín; Górriz, Juan M.; Ramírez, Javier; Martínez-Murcia, Francisco J.; Levin, Johannes; Schuberth, Madeleine; Brendel, Matthias; Rominger, Axel; Bötzel, Kai; Garraux, Gaëtan; Phillips, Christophe (Frontiers in Neuroinformatics, 2017)
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, 18F-Desmethoxyfallypride (DMFP) has been suggested to increase the diagnostic precision as it is an effective radioligand that allows us to analyze postsynaptic 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.