title: Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy.
abstract: BACKGROUND
  The aim of the current study was to distinguish patients with Parkinson disease (PD) from those with progressive supranuclear palsy (PSP) at the individual level using pattern recognition of magnetic resonance imaging data.
  
  
  METHODS
  We combined diffusion tensor imaging and voxel-based morphometry in a support vector machine algorithm to evaluate 21 patients with PSP and 57 patients with PD.
  
  
  RESULTS
  The automated algorithm correctly distinguished patients who had PD from those who had PSP with 100% accuracy. This accuracy value was obtained when white matter atrophy was considered. Diffusion parameters combined with gray matter atrophy exhibited 90% sensitivity and 96% specificity.
  
  
  CONCLUSIONS
  Our findings demonstrate that automated pattern recognition can help distinguish patients with PSP from those with PD on an individual basis.
authors: Neuroimaging Research Unit, Institute of Neurological Sciences-National Research Council, Catanzaro, Italy.
authors: Andrea
authors: A
authors: Cherubini
authors: Maurizio
authors: M
authors: Morelli
authors: Rita
authors: R
authors: Nisticó
authors: Maria
authors: M
authors: Salsone
authors: Gennarina
authors: G
authors: Arabia
authors: Roberta
authors: R
authors: Vasta
authors: Antonio
authors: A
authors: Augimeri
authors: Maria Eugenia
authors: ME
authors: Caligiuri
authors: Aldo
authors: A
authors: Quattrone
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis*; D060388:Support Vector Machine; D013494:Supranuclear Palsy, Progressive / Q000175:diagnosis*
keywords: computer-aided diagnosis; diffusion tensor imaging; progressive supranuclear palsy; support vector machines