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Background: Hypomimia is a clinical feature of Parkinson's disease (PD). Based on the embodied simulation theory, the impairment of facial mimicry may worsen facial emotion recognition; however, the empirical results are inconclusive. Objective: We aimed to explore the worsening of emotion recognition by hypomimia. We further explored the relationship between the hypomimia, emotion recognition, and social functioning. Methods: A total of 114 participants were recruited. The patients with PD and normal controls (NCs) were matched for demographic characteristics. All the participants completed the Mini-Mental State Examination and the Chinese Multi-modalities Emotion Recognition Test. In addition to the above tests, the patients were assessed with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale and Parkinson's Disease Social Functioning Scale (PDSFS). Results: Patients with PD with hypomimia had worse recognition of disgust than NCs (p = 0.018). The severity of hypomimia was predictive of the recognition of disgust (β= –0.275, p = 0.028). Facial emotion recognition was predictive of the PDSFS score of PD patients (β= 0.433, p = 0.001). We also found that recognizing disgust could mediate the relationship between hypomimia and the PDSFS score (β= 0.264, p = 0.045). Conclusion: Patients with hypomimia had the worst disgust facial recognition. Hypomimia may affect the social function of PD patients, which is related to recognizing the expression of disgust. Emotion recognition training may improve the social function of patients with PD. [ABSTRACT FROM AUTHOR]

Background: The challenge of differentiating, at an early stage, Parkinson's disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson's disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson's disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson's disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson's disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application. [ABSTRACT FROM AUTHOR]