%0 Journal Article
%T Electroencephalography microstate alterations reflect potential double-edged cognitive adaptation in Ménière's disease.
%A Li YN
%A Li J
%A Wang PJ
%A Yu DZ
%A Chen ZN
%A Shi ZY
%A Wu YQ
%A Qi WD
%A Lu W
%A Shi HB
%J CNS Neurosci Ther
%V 30
%N 8
%D 2024 Aug
%M 39107944
%F 7.035
%R 10.1111/cns.14896
%X <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.