Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI. (September 2021) - Pang, HuiZe; Yu, ZiYang; Yu, HongMei; Cao, JiBin; Li, YingMei; Guo, MiaoRan; Cao, ChengHao; Fan, GuoGuang
Abstract: Objective: This study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI). Methods: Ninety-six PD patients, which included thirty-nine postural instability and gait difficulty (PIGD) subtype and fifty-seven tremor-dominant (TD) subtype, were enrolled and allocated to training and validation datasets with a ratio of 7:3. A total of five types of index, consisting of mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and functional connectivity (FC), were extracted. The features were then selected using a two-sample t -test, the least absolute shrinkage and selection operator (LASSO), and Spearman's rank correlation coefficient. Finally, support vector machine (SVM) models based on the separate index and multilevel indices were built, and the performance of models was assessed via the area under the receiver operating characteristic curve (AUC). Feature importance was evaluated using Shapley additive explanation (SHAP) values. Results: The optimal SVM model was obtained based on multilevel rs-fMRI indices, with an AUC of 0.934 in the training dataset and an AUC of 0.917 in the validation dataset. The AUCs of the models based on the separate index were ranged from 0.783 to 0.858 for the training dataset and from 0.713 to 0.792 for the validation dataset. SHAP analysis revealed that functional activity and connectivity in frontal lobe and cerebellum were important features for differentiating PD subtypes. Conclusions: Our findings demonstrated multilevel rs-fMRI indices could provide more comprehensive information on brain functionalteration. Furthermore, the machine learning method based on multilevel rs-fMRI indices might be served as an alternative approach for automatically classifying clinical subtypes in PD at the individual level. Highlights: Classification of different motor subtypes of PD is important, however current approach is subjective. A combination of multilevel rs-fMRI indices yields more comprehensive information on brain functional alteration in PD. The machine learning method based on a combination of multilevel rs-fMRI indices could be useful for PD motor subtyping.