Department of Otolaryngology-Head and Neck Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Otolaryngology Head and Neck Surgery, Huashan Hospital Fudan University, Shanghai, China.
CNS Neurosci Ther. 2024 Aug;30(8):e14896. doi: 10.1111/cns.14896.
To explore the microstate characteristics and underlying brain network activity of Ménière's disease (MD) patients based on high-density electroencephalography (EEG), elucidate the association between microstate dynamics and clinical manifestation, and explore the potential of EEG microstate features as future neurobiomarkers for MD.
Thirty-two patients diagnosed with MD and 29 healthy controls (HC) matched for demographic characteristics were included in the study. Dysfunction and subjective symptom severity were assessed by neuropsychological questionnaires, pure tone audiometry, and vestibular function tests. Resting-state EEG recordings were obtained using a 256-channel EEG system, and the electric field topographies were clustered into four dominant microstate classes (A, B, C, and D). The dynamic parameters of each microstate were analyzed and utilized as input for a support vector machine (SVM) classifier to identify significant microstate signatures associated with MD. The clinical significance was further explored through Spearman correlation analysis.
MD patients exhibited an increased presence of microstate class C and a decreased frequency of transitions between microstate class A and B, as well as between class A and D. The transitions from microstate class A to C were also elevated. Further analysis revealed a positive correlation between equilibrium scores and the transitions from microstate class A to C under somatosensory challenging conditions. Conversely, transitions between class A and B were negatively correlated with vertigo symptoms. No significant correlations were detected between these characteristics and auditory test results or emotional scores. Utilizing the microstate features identified via sequential backward selection, the linear SVM classifier achieved a sensitivity of 86.21% and a specificity of 90.61% in distinguishing MD patients from HC.
We identified several EEG microstate characteristics in MD patients that facilitate postural control yet exacerbate subjective symptoms, and effectively discriminate MD from HC. The microstate features may offer a new approach for optimizing cognitive compensation strategies and exploring potential neurobiological markers in MD.
基于高密度脑电图（EEG）探索梅尼埃病（MD）患者的微状态特征及其潜在的脑网络活动，阐明微状态动力学与临床表现之间的关系，并探讨 EEG 微状态特征作为 MD 未来神经生物标志物的潜力。
本研究纳入了 32 名 MD 患者和 29 名匹配人口统计学特征的健康对照者（HC）。神经心理学问卷、纯音测听和前庭功能测试评估了功能障碍和主观症状严重程度。使用 256 通道 EEG 系统获得静息态 EEG 记录，并将电场地形图聚类为四个主要微状态类（A、B、C 和 D）。分析每个微状态的动态参数，并将其用作支持向量机（SVM）分类器的输入，以识别与 MD 相关的显著微状态特征。通过 Spearman 相关分析进一步探讨了临床意义。
MD 患者表现出微状态类 C 的出现增加，微状态类 A 和 B 之间以及微状态类 A 和 D 之间的转换频率降低。微状态类 A 到 C 的转换也升高了。进一步的分析表明，平衡评分与感觉挑战性条件下从微状态类 A 到 C 的转换呈正相关。相反，微状态类 A 和 B 之间的转换与眩晕症状呈负相关。在听觉测试结果或情绪评分方面，未发现这些特征之间存在显著相关性。利用通过顺序后向选择确定的微状态特征，线性 SVM 分类器在区分 MD 患者和 HC 方面达到了 86.21%的敏感性和 90.61%的特异性。
我们在 MD 患者中发现了几种 EEG 微状态特征，这些特征有助于姿势控制但加重了主观症状，并且能够有效区分 MD 患者和 HC。微状态特征可能为优化认知补偿策略和探索 MD 潜在神经生物学标志物提供一种新方法。
