Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference
ObjectivesWe aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD).MethodsWe included 18 people with PD and 18 controls. The participants were asked to perform 12 monosyllabic tests, 8 disyllabic tests, and 6 multisyllabic tests...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1533942/full |
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| author | Meimei Mouse Hongjie Gong Yifeng Liu Fan Xu Xianwei Zou Min Huang Xi Yang |
| author_facet | Meimei Mouse Hongjie Gong Yifeng Liu Fan Xu Xianwei Zou Min Huang Xi Yang |
| author_sort | Meimei Mouse |
| collection | DOAJ |
| description | ObjectivesWe aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD).MethodsWe included 18 people with PD and 18 controls. The participants were asked to perform 12 monosyllabic tests, 8 disyllabic tests, and 6 multisyllabic tests and the whole process were recorded. Then 26 video clips recorded were used to decipher the facial muscle movements and face expression via Noldus FaceReader 7.0 software. 16 suitable variables were selected to construct a Bayesian network model.ResultsThe area under the curve of the unsegmented-syllabic, monosyllabic, dissyllabic, and multisyllabic training models was 0.960, 0.958, and 0.962, respectively, with no significant difference between the models. Based on the Bayesian network models, we found that except for valence in the disyllabic model, all positive facial expressions in the four models are negatively associated with the probability of PD. Moreover, negative facial expressions, including sadness, anger, scared, and disgust in the unsegmented-syllabic, monosyllabic, and multisyllabic models, as well as anger in the disyllabic model, are positively correlated to the probability of PD. Sadness, scare and disgust in disyllabic model are negatively associated with the probability of PD.ConclusionExcept for sad, scared, and disgusted generated by reading disyllables, negative expressions generated by reading other syllables were positively associated with the probability of PD. In addition, scared expressions produced during monosyllabic reading had the greatest effect on the probability of PD, and disgusted expressions produced during multisyllabic reading had the least effect. |
| format | Article |
| id | doaj-art-6c658fb1fc54492f9baa4f32665ff94a |
| institution | DOAJ |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-6c658fb1fc54492f9baa4f32665ff94a2025-08-20T02:49:44ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-03-011610.3389/fneur.2025.15339421533942Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inferenceMeimei Mouse0Hongjie Gong1Yifeng Liu2Fan Xu3Xianwei Zou4Min Huang5Xi Yang6Department of Clinic Medicine, School of Clinical Medicine, Chengdu Medical College, Chengdu, ChinaDepartment of Clinic Medicine, School of Clinical Medicine, Chengdu Medical College, Chengdu, ChinaDepartment of Clinic Medicine, School of Clinical Medicine, Chengdu Medical College, Chengdu, ChinaDepartment of Evidence-Based Medicine and Social Medicine, School of Public Health, Chengdu Medical College, Chengdu, ChinaDepartment of Neurology, First Affiliated Hospital of Chengdu Medical College, Chengdu, ChinaDepartment of Physiology, School of Basic Medical Sciences, Chengdu Medical College, Chengdu, ChinaDepartment of Applied Psychology, School of Psychology, Chengdu Medical College, Chengdu, ChinaObjectivesWe aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson’s disease (PD).MethodsWe included 18 people with PD and 18 controls. The participants were asked to perform 12 monosyllabic tests, 8 disyllabic tests, and 6 multisyllabic tests and the whole process were recorded. Then 26 video clips recorded were used to decipher the facial muscle movements and face expression via Noldus FaceReader 7.0 software. 16 suitable variables were selected to construct a Bayesian network model.ResultsThe area under the curve of the unsegmented-syllabic, monosyllabic, dissyllabic, and multisyllabic training models was 0.960, 0.958, and 0.962, respectively, with no significant difference between the models. Based on the Bayesian network models, we found that except for valence in the disyllabic model, all positive facial expressions in the four models are negatively associated with the probability of PD. Moreover, negative facial expressions, including sadness, anger, scared, and disgust in the unsegmented-syllabic, monosyllabic, and multisyllabic models, as well as anger in the disyllabic model, are positively correlated to the probability of PD. Sadness, scare and disgust in disyllabic model are negatively associated with the probability of PD.ConclusionExcept for sad, scared, and disgusted generated by reading disyllables, negative expressions generated by reading other syllables were positively associated with the probability of PD. In addition, scared expressions produced during monosyllabic reading had the greatest effect on the probability of PD, and disgusted expressions produced during multisyllabic reading had the least effect.https://www.frontiersin.org/articles/10.3389/fneur.2025.1533942/fullParkinson’s diseasefacial expressionsBayesian networktree-augmented networkprediction modelNoldus FaceReader |
| spellingShingle | Meimei Mouse Hongjie Gong Yifeng Liu Fan Xu Xianwei Zou Min Huang Xi Yang Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference Frontiers in Neurology Parkinson’s disease facial expressions Bayesian network tree-augmented network prediction model Noldus FaceReader |
| title | Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference |
| title_full | Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference |
| title_fullStr | Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference |
| title_full_unstemmed | Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference |
| title_short | Changes in facial expressions can distinguish Parkinson’s disease via Bayesian inference |
| title_sort | changes in facial expressions can distinguish parkinson s disease via bayesian inference |
| topic | Parkinson’s disease facial expressions Bayesian network tree-augmented network prediction model Noldus FaceReader |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1533942/full |
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