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

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 AUC(max) = 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.