PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目的：基于高密度脑电图（EEG）探讨梅尼埃病（MD）患者的微状态特征和潜在的脑网络活动，阐明微状态动力学与临床表现之间的关联，并探索脑电图微状态特征作为MD未来神经生物标志物的潜力。
方法：研究中纳入了32例诊断为MD的患者和29例人口统计学特征相匹配的健康对照（HC）。通过神经心理学问卷评估功能障碍和主观症状严重程度，纯音测听法,和前庭功能测试.使用256通道EEG系统获得静息状态EEG记录，电场形貌分为四个主要的微态类别(A，B,C,andD).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相关的重要微状态特征。通过Spearman相关分析进一步探讨其临床意义。
结果：MD患者表现出微状态C类的存在增加，微状态A类和B类之间的转变频率降低，以及A级和D级之间。从A级到C级的微状态过渡也升高了。进一步的分析显示，在体感挑战性条件下，平衡分数与从微状态A类到C类的转变之间存在正相关。相反，A级和B级之间的过渡与眩晕症状呈负相关。在这些特征与听觉测试结果或情绪评分之间未检测到显着相关性。利用通过顺序反向选择识别的微态特征，线性SVM分类器在区分MD患者和HC患者方面的敏感性为86.21%,特异性为90.61%.
结论：我们发现了MD患者的一些脑电图微状态特征，这些特征有助于姿势控制，但却加剧了主观症状，并有效区分MD和HC。微状态特征可能为优化认知补偿策略和探索MD中潜在的神经生物学标志物提供新的方法。
方法：研究中纳入了32例诊断为MD的患者和29例人口统计学特征相匹配的健康对照（HC）。通过神经心理学问卷评估功能障碍和主观症状严重程度，纯音测听法,和前庭功能测试.使用256通道EEG系统获得静息状态EEG记录，电场形貌分为四个主要的微态类别(A，B,C,andD).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相关的重要微状态特征。通过Spearman相关分析进一步探讨其临床意义。
结果：MD患者表现出微状态C类的存在增加，微状态A类和B类之间的转变频率降低，以及A级和D级之间。从A级到C级的微状态过渡也升高了。进一步的分析显示，在体感挑战性条件下，平衡分数与从微状态A类到C类的转变之间存在正相关。相反，A级和B级之间的过渡与眩晕症状呈负相关。在这些特征与听觉测试结果或情绪评分之间未检测到显着相关性。利用通过顺序反向选择识别的微态特征，线性SVM分类器在区分MD患者和HC患者方面的敏感性为86.21%,特异性为90.61%.
结论：我们发现了MD患者的一些脑电图微状态特征，这些特征有助于姿势控制，但却加剧了主观症状，并有效区分MD和HC。微状态特征可能为优化认知补偿策略和探索MD中潜在的神经生物学标志物提供新的方法。
