Imagine your brain as a crowded dance floor, with different groups of dancers representing the brain’s microstates. In a study on otogenic vertigo, scientists used electroencephalography (EEG) to analyze the changes in these dance moves and potentially identify markers for the disease. By comparing EEG recordings of patients with recurrent otogenic vertigo to healthy adults, researchers discovered interesting findings. They found that one specific microstate, Microstate D, underwent significant alterations in duration and occurrence, reflecting the brain’s reorganization of visual-vestibular processes and attention distribution. Additionally, they observed changes in other microstates, such as Microstate A and C, pointing to disruptions in the brain’s dynamic network related to vertigo. To make sense of these alterations, researchers used a support vector machine (SVM) classifier that achieved an impressive balanced accuracy of 0.79 in identifying vertigo patients. Understanding these EEG microstate alterations could lead to improved diagnostic tools and personalized treatments for otogenic vertigo. Curious to uncover more about this fascinating dance of the brain? Turn up the music and explore the underlying research!

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.

Read Full Article (External Site)