AUTHOR=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 TITLE=Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism JOURNAL=Frontiers in Neuroinformatics VOLUME=11 YEAR=2017 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2017.00023 DOI=10.3389/fninf.2017.00023 ISSN=1662-5196 ABSTRACT=

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 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.