Journal
FRONTIERS IN NEUROINFORMATICS
Volume 11, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2017.00023
Keywords
multivariate analysis; F-18-DMFP PET; Parkinson's disease; multiple kernel learning; support vector machine; multiple system atrophy; progressive supranuclear palsy
Funding
- MINECO/FEDER [TEC2012-34306, TEC2015-64718-R]
- Ministry of Economy, Innovation, Science, and Employment of the Junta de Andalucia [P09-TIC-4530, P11-TIC-7103]
- European Union's Seventh Framework Program
- Marie Sklodowska-Curie actions (COFUND) [291780]
- Ministry of Economy, Innovation, Science, and Employment of the Junta de Andalucia
- Fonds de la Recherche Scientifique - FNRS
- Universities of Liege (Belgium)
- Munich (Germany)
- Granada (Spain)
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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.
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