ArticleFrontiers in aging neuroscience2022
Electroencephalography Microstate Alterations in Otogenic Vertigo: A Potential Disease Marker.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 10 citations in OpenAlex.
- Delta-Band EEG Microstate Dynamics as Promising Candidate Markers of Central Vertigo Severity.Brain sciences · 2026Article
- Evaluating Brain Activity in Patients With Chronic Disorders of Consciousness After Traumatic Brain Injury Using EEG Microstate Analysis During Hyperbaric Oxygen Therapy.CNS neuroscience & therapeutics · 2025Article
- Heterogeneous Brain Dynamics Between Acute Cerebellar and Brainstem Infarction.Cerebellum (London, England) · 2024Article
- Modulation of Brain Activities in Healthy Individuals by Acupuncture at Quchi (LI11).Brain topography · 2024Article
- Dynamics of Cerebral Function in Patients with Acute Cerebellar Infarction.Cerebellum (London, England) · 2024Article
- Effects of acupuncture at limb Acupoints-Guangming (GB37) on UDVA, CS, and EEG microstate in myopia.Frontiers in neuroscience · 2024Article
- Assessing vestibular function using electroencephalogram rhythms evoked during the caloric test.Frontiers in neurology · 2023Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: A 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 Methods: Patients 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. Results: We 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. Conclusion: There 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.
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