Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity. (20th March 2020)

• Record Type: Journal Article
• Authors: Chen, Yingchuan; Zhu, Guanyu; Liu, Defeng; Liu, Yuye; Yuan, Tianshuo; Zhang, Xin; Jiang, Yin; Du, Tingting; Zhang, Jianguo
• Is Part Of: CNS neuroscience & therapeutics. Volume 26:Number 7(2020)
• Page Start: 711
• Page End: 719
• Publication Date: 2020-03-20
• Subjects: brain morphology -- hypokinetic dysarthria -- machine learning -- Parkinson's disease -- structural magnetic resonance imaging; Neuropharmacology -- Periodicals; Central nervous system -- Diseases -- Effect of drugs on -- Periodicals; 612.8
• DOI: 10.1111/cns.13304
• Languages: English
• ISSNs: 1755-5930

Abstract

Abstract: Background: Up to 90% of patients with Parkinson's disease (PD) eventually develop the speech and voice disorder referred to as hypokinetic dysarthria (HD). However, the brain morphological changes associated with HD have not been investigated. Moreover, no reliable model for predicting the severity of HD based on neuroimaging has yet been developed. Methods: A total of 134 PD patients were included in this study and divided into a training set and a test set. All participants underwent a structural magnetic resonance imaging (MRI) scan and neuropsychological evaluation. Individual cortical thickness, subcortical structure, and white matter volume were extracted, and their association with HD severity was analyzed. After feature selection, a machine‐learning model was established using a support vector machine in the training set. The severity of HD was then predicted in the test set. Results: Atrophy of the right precentral cortex and the right fusiform gyrus was significantly associated with HD. No association was found between HD and volume of white matter or subcortical structures. Favorable and optimal performance of machine learning on HD severity prediction was achieved using feature selection, giving a correlation coefficient ( r ) of .7516 and a coefficient of determination ( R 2 ) of .5649 ( P < .001). Conclusion: The brain morphological changes were associated with HD. Excellent prediction of the severity of HD was achieved using machine learning based on neuroimaging.