Author:
Li Yi-Ni,Lu Wen,Li Jie,Li Ming-Xian,Fang Jia,Xu Tao,Yuan Ti-Fei,Qian Di,Shi Hai-Bo,Yin Shan-Kai
Abstract
ObjectivesA huge population, especially the elderly, suffers from otogenic vertigo. However, the multi-modal vestibular network changes, secondary to periphery vestibular dysfunction, have not been fully elucidated. We aim to identify potential microstate electroencephalography (EEG) signatures for otogenic vertigo in this study.Materials and MethodsPatients with recurrent otogenic vertigo and age-matched healthy adults were recruited. We performed 256-channel EEG recording of all participants at resting state. Neuropsychological questionnaires and vestibular function tests were taken as a measurement of patients’ symptoms and severity. We clustered microstates into four classes (A, B, C, and D) and identified their dynamic and syntax alterations of them. These features were further fed into a support vector machine (SVM) classifier to identify microstate signatures for vertigo.ResultsWe compared 40 patients to 45 healthy adults, finding an increase in the duration of Microstate A, and both the occurrence and time coverage of Microstate D. The coverage and occurrence of Microstate C decreased significantly, and the probabilities of non-random transitions between Microstate A and D, as well as Microstate B and C, also changed. To distinguish the patients, the SVM classifier, which is built based on these features, got a balanced accuracy of 0.79 with a sensitivity of 0.78 and a specificity of 0.8.ConclusionThere are several temporal dynamic alterations of EEG microstates in patients with otogenic vertigo, especially in Microstate D, reflecting the underlying process of visual-vestibular reorganization and attention redistribution. This neurophysiological signature of microstates could be used to identify patients with vertigo in the future.
Funder
School of Medicine, Shanghai Jiao Tong University
National Natural Science Foundation of China
Shanghai Municipal Education Commission
Ministry of Science and Technology of the People's Republic of China
Shanghai Jiao Tong University
Subject
Cognitive Neuroscience,Aging
Cited by
6 articles.
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