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This study aimed to characterize microstate\u2010based network dynamics in TLE and evaluate their diagnostic value for seizure lateralization and structural etiology using machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Resting\u2010state electroencephalography (EEG) recordings from 150 patients with unilateral TLE (71 right, 79 left) and 65 healthy controls (HCs) were analyzed. EEG signals were segmented into canonical microstates (A, B, C, D), and microstate\u2010specific spatial, and temporal dynamic functional connectivity (dFC) variability metrics were extracted using phase lag index analysis. After two\u2010step feature selection, the appropriate number of features were derived and input into Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers to distinguish: TLE vs HCs, left vs right TLE, and magnetic resonance imaging (MRI)\u2010negative (MRI\u2010neg) vs hippocampal sclerosis (HS) TLE subtypes (TLE\u2010HS). Model performance was evaluated on independent hold\u2010out validation set using receiver operating characteristic analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with HCs, patients with TLE exhibited increased duration and occurrence of microstate D and reduced expression microstate B, reflecting maladaptive attentional overactivation and visual suppression. Spatial variability was globally decreased, most prominently in left TLE. SVM achieved excellent performance for TLE detection (area under the curve [AUC]\u2009=\u2009.98) and lateralization (AUC\u2009=\u2009.97), whereas classification between MRI\u2010neg TLE and TLE\u2010HS was limited (AUC\u2009=\u2009.58).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>EEG microstate\u2010derived dFC metrics provide reliable, non\u2010invasive biomarkers for identifying and lateralizing TLE using short duration resting\u2010state EEG recordings. This framework advances understanding of TLE heterogeneity and supports the development of individualized electrophysiological tools for precision diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/epi.70121","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:18:26Z","timestamp":1773001106000},"page":"2518-2532","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>EEG<\/scp>\n                    microstate\u2010derived dynamic network biomarkers for lateralization and structural etiology in temporal lobe epilepsy"],"prefix":"10.1002","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7666-9558","authenticated-orcid":false,"given":"Kailing","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Neurology West China Hospital, Sichuan University  Chengdu 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