Mouse Meimei, Gong Hongjie, Liu Yifeng, Xu Fan, Zou Xianwei, Huang Min, Yang Xi
Department of Clinic Medicine, School of Clinical Medicine, Chengdu Medical College, Chengdu, China.
Department of Evidence-Based Medicine and Social Medicine, School of Public Health, Chengdu Medical College, Chengdu, China.
Front Neurol. 2025 Mar 27;16:1533942. doi: 10.3389/fneur.2025.1533942. eCollection 2025.
We aimed to clarify the influence of facial expressions on providing early recognition and diagnosis of Parkinson's disease (PD).
We 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.
The 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.
Except 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.
我们旨在阐明面部表情对帕金森病（PD）早期识别和诊断的影响。
我们纳入了18名帕金森病患者和18名对照者。参与者被要求进行12项单音节测试、8项双音节测试和6项多音节测试，整个过程均被记录下来。然后，使用Noldus FaceReader 7.0软件对录制的26个视频片段进行分析，以解读面部肌肉运动和面部表情。选择16个合适的变量构建贝叶斯网络模型。
未分割音节、单音节、双音节和多音节训练模型的曲线下面积分别为0.960、0.958和0.962，各模型之间无显著差异。基于贝叶斯网络模型，我们发现除双音节模型中的效价外，四个模型中的所有积极面部表情均与帕金森病的发生概率呈负相关。此外，未分割音节、单音节和多音节模型中的消极面部表情，包括悲伤、愤怒、恐惧和厌恶，以及双音节模型中的愤怒，均与帕金森病的发生概率呈正相关。双音节模型中的悲伤、恐惧和厌恶与帕金森病的发生概率呈负相关。
除读双音节产生的悲伤、恐惧和厌恶外，读其他音节产生的消极表情与帕金森病的发生概率呈正相关。此外，单音节阅读时产生的恐惧表情对帕金森病发生概率的影响最大；多音节阅读时产生的厌恶表情影响最小。
