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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

1
Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
2
Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
3
Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
4
Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Abstract

BACKGROUND:

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.

OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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).

CONCLUSIONS:

With these favorable results, our findings suggested the machine learning-based classification as an alternative technique to classifying clinical subtypes in PD.

KEYWORDS:

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

PMID:
27176623
DOI:
10.3233/JPD-150729
[Indexed for MEDLINE]

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