ArticleCNS neuroscience & therapeutics2020
Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.
Article in CNS neuroscience & therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.Movement disorders : official journal of the Movement Disorder Society · 2024Pooled it
- Striatal network dysfunction underlying speech impairment in early Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Acoustic assessment in mandarin-speaking Parkinson's disease patients and disease progression monitoring and brain impairment within the speech subsystem.NPJ Parkinson's disease · 2024Review
- Neural basis of speech and grammar symptoms in non-fluent variant primary progressive aphasia spectrum.Brain : a journal of neurology · 2024Article
- A neurocomputational view of the effects of Parkinson's disease on speech production.Frontiers in human neuroscience · 2024Article
- Mapping subcortical brain lesions, behavioral and acoustic analysis for early assessment of subacute stroke patients with dysarthria.Frontiers in neuroscience · 2024Article
- Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson's disease.Annals of translational medicine · 2022Article
- Characterization of Mild and Moderate Dysarthria in Parkinson's Disease: Behavioral Measures and Neural Correlates.Frontiers in aging neuroscience · 2022Article
- Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.CNS neuroscience & therapeutics · 2021Article
- Brain morphological changes in hypokinetic dysarthria of Parkinson's disease and use of machine learning to predict severity.CNS neuroscience & therapeutics · 2020Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUp 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.
methodsA 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.
resultsAtrophy 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
conclusionThe brain morphological changes were associated with HD. Excellent prediction of the severity of HD was achieved using machine learning based on neuroimaging.
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