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Neurosci."],"abstract":"<jats:p>Background: Schizophrenia (SCZ) is associated with abnormal neural activities and brain connectivity. Electroencephalography (EEG) microstate is a voltage topographical representation of temporary brain network activations. Most research on EEG microstates in SCZ has focused on differences between patients and healthy controls (HC). However, changes in EEG microstates among SCZ patients across various stages of physiological and cognitive development have not been thoroughly assessed. Consequently, we stratified patients with SCZ into four age-specific cohorts (20\u201329 years (brain maturation), 30\u201339 years (stabilization), 40\u201349 years (early aging), and 50\u201359 years (advanced aging)) to evaluate EEG microstate alterations. Additionally, we assessed changes in EEG microstates in first-episode psychosis (FEP) before and after an 8-week treatment period. Methods: We acquired 19-channel resting-state EEG from 140 chronic SCZ patients, aged 20 to 59 years, as well as from 19 FEP and 20 healthy controls. FEP patients underwent an 8-week inpatient follow-up. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the K-Means clustering algorithm was applied to classify the data into 4 microstates. Subsequently, templates of these microstates were used to fit EEG signals from each patient, and the collected microstate parameters were analyzed. Results: Patients with SCZ aged 20 to 29 years demonstrated an increased time coverage of microstate class D compared to other age cohorts. In individuals aged 30\u201339 years, the parameters of microstate class B\u2014specifically time coverage and occurrence\u2014exhibited significant reductions relative to those in the 40\u201349 and 50\u201359 years age groups. Compared to healthy controls, microstates class A parameters were significantly reduced in SCZ patients, while microstates class C parameters were prolonged; after 8 weeks of treatment, microstates class A parameters increased and microstates class C parameters decreased. Conclusions: Alterations in microstate dynamics were observed among SCZ patients across developmental stages, suggesting potential changes in brain activity patterns. Changes in microstates A and C may serve as potential biomarkers for evaluating treatment efficacy, establishing a foundation for personalized therapeutic approaches.<\/jats:p>","DOI":"10.31083\/jin27059","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T12:53:01Z","timestamp":1743166381000},"source":"Crossref","is-referenced-by-count":0,"title":["EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia"],"prefix":"10.31083","volume":"24","author":[{"given":"Shihai","family":"Ling","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643002 Zigong, Sichuan, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, 644000 Yibin, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1743-0539","authenticated-orcid":true,"given":"Lingyan","family":"Du","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643002 Zigong, Sichuan, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, 644000 Yibin, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Tan","sequence":"first","affiliation":[{"name":"Zigong Institute of Brain Science, Zigong Mental Health Center, The Zigong Affiliated Hospital of Southwest Medical University, 643020 Zigong, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guozhi","family":"Tang","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643002 Zigong, Sichuan, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, 644000 Yibin, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Che","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643002 Zigong, Sichuan, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, 644000 Yibin, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirui","family":"Song","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Sichuan University of Science and Engineering, 643002 Zigong, Sichuan, China"},{"name":"Artificial Intelligence Key Laboratory of Sichuan Province, 644000 Yibin, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16242","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Galderisi S, Mucci A, Buchanan RW, Arango C. 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