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J Parkinsons Dis. 2016 May 11;6(3):545-56. doi: 10.3233/JPD-150729.

Automatic Classification on Multi-Modal MRI Data for Diagnosis of the Postural Instability and Gait Difficulty Subtype of Parkinson's Disease.

Author information

Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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.
Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.



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.


Parkinson’s disease; diagnosis; functional neuroimaging; machine learning; support vector machines

[Indexed for MEDLINE]

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