Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease. (Articolo in rivista)

Type
Label
  • Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease. (Articolo in rivista) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1002/mds.25869 (literal)
Alternative label
  • Andrea Cherubini 1*, Rita Nisticó 1*, Fabiana Novellino 1, Maria Salsone 2, Salvatore Nigro 1, Giulia Donzuso 1 and Aldo Quattrone 1,2 (2014)
    Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease.
    in Movement disorders
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Andrea Cherubini 1*, Rita Nisticó 1*, Fabiana Novellino 1, Maria Salsone 2, Salvatore Nigro 1, Giulia Donzuso 1 and Aldo Quattrone 1,2 (literal)
Pagina inizio
  • 1216 (literal)
Pagina fine
  • 1219 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 29 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 9 (literal)
Note
  • PubMe (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1. Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology-National Research Council, Catanzaro, Italy 2. Institute of Neurology, Magna Graecia University, Catanzaro, Italy (literal)
Titolo
  • Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease. (literal)
Abstract
  • BACKGROUND: The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET). METHODS: We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth. RESULTS: SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%. CONCLUSIONS: SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level. © 2014 International Parkinson and Movement Disorder Society. (literal)
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