title: Automatic Classification on Multi-Modal MRI Data for Diagnosis of the Postural Instability and Gait Difficulty Subtype of Parkinson's Disease.
abstract: Patients with the postural instability and gait difficulty subtype (PIGD) of Parkinson's disease (PD) are a refractory challenge in clinical practice. Despite previous attempts that have been made at studying subtype-specific brain alterations across PD population, conclusive neuroimaging biomarkers on patients with the PIGD subtype are still lacking. Machine learning-based classifications are a promising tool for differential diagnosis that effectively integrate complex and multivariate data.
  
  
  
  Our present study aimed to introduce the machine learning-based automatic classification for the first time to distinguish patients with the PIGD subtype from those with the non-PIGD subtype of PD at the individual level.
  
  
  
  Fifty-two PD patients and forty-five normal controls (NCs) were recruited and underwent multi-modal MRI scans including a set of resting-state functional, 3D T1-weighted and diffusion tensor imaging sequences. By comparing the PD patients with the NCs, features that were not conducive to the subtype-specific classification were ruled out from massive brain features. We applied a support vector machine classifier with the recursive feature elimination method to multi-modal MRI data for selecting features with the best discriminating power, and evaluated the proposed classifier with the leave-one-out cross-validation.
  
  
  
  Using this classifier, we obtained satisfactory diagnostic rates (accuracy = 92.31%, specificity = 96.97%, sensitivity = 84.21% and AUCmax  = 0.9585). The diagnostic agreement evaluated by the Kappa test showed an almost perfect agreement with the existing clinical categorization (Kappa value = 0.83).
  
  
  
  With these favorable results, our findings suggested the machine learning-based classification as an alternative technique to classifying clinical subtypes in PD.
authors.affiliation: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Quanquan
authors.initials: Q
authors.lastname: Gu
authors.affiliation: Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.|Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
authors.forename: Huan
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Min
authors.initials: M
authors.lastname: Xuan
authors.affiliation: Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Luo
authors.affiliation: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Peiyu
authors.initials: P
authors.lastname: Huang
authors.affiliation: Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.|Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
authors.forename: Shunren
authors.initials: S
authors.lastname: Xia
authors.affiliation: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Minming
authors.initials: M
authors.lastname: Zhang
mesh_terms: D000368:Aged; D005260:Female; D020233:Gait Disorders, Neurologic / Q000145:classification / Q000000981:diagnostic imaging* / Q000209:etiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000145:classification / Q000150:complications / Q000000981:diagnostic imaging*; D004856:Postural Balance / Q000502:physiology*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Parkinson’s disease; diagnosis; functional neuroimaging; machine learning; support vector machines