{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T13:09:35Z","timestamp":1778936975105,"version":"3.51.4"},"reference-count":64,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["2022072"],"award-info":[{"award-number":["2022072"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1157145"],"award-info":[{"award-number":["APP1157145"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia Open"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Predicting seizure recurrence following a first unprovoked seizure (FUS) remains a significant clinical challenge, especially when routine clinical magnetic resonance imaging (MRI) and EEG do not reveal abnormalities diagnostic of epilepsy. Here, we incorporate quantitative structural MRI\u2010derived biomarkers into prediction models for seizure recurrence at 12\u2009months and identify brain structural features that are predictive of seizure recurrence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a retrospective, multicenter cohort of 197 adult patients with FUS, comprising 83 with seizure recurrence and 114 with no seizure recurrence at 12\u2009months. All participants had normal or nondiagnostic MRI and EEG findings. Morphometric features were extracted from clinical 3\u2009T\u2009T1\u2010weighted MRI using FreeSurfer. Machine learning algorithms were trained on combined imaging and clinical features using nested cross\u2010validation for model selection. Performance was compared with a logistic regression model based on clinical features only.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The best\u2010performing model, a support vector machine (SVM) trained on a combination of imaging features and clinical factors, achieved an AUC of 0.65 (95% CI: 0.57\u20130.73), significantly better than chance (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.01 when compared with an AUC of 0.5). In contrast, the logistic regression model trained on clinical factors alone yielded an AUC of 0.57 (95% CI: 0.49\u20130.65), not statistically different to chance (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.28). Direct comparison between the SVM and the logistic regression clinical factor\u2010only model was not statistically significant (95% CI for the difference in AUC: \u22120.019 to 0.173,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.11). The most important imaging features for prediction were inter\u2010hemispheric asymmetry of subcortical and cortical gray matter volumes and regional gyral curvatures, particularly in fronto\u2010parietal and limbic regions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>Quantitative structural MRI contributes additional information beyond clinical factors for machine learning models predicting seizure recurrence. Changes to cortical folding and gray matter asymmetries in cortical and subcortical regions show potential as prognostic biomarkers of seizure recurrence risk after a FUS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Plain Language Summary<\/jats:title>\n                    <jats:p>Identifying individuals who will have another seizure after their first unprovoked seizure is difficult when routine brain scans and EEG appear normal. We developed a tool that combines MRI\u2010derived markers with clinical information to predict seizure recurrence. Subtle structural differences in the brain, especially asymmetries between left and right hemispheres and changes to cortical folding, were associated with a higher chance of another seizure within a year. This approach has potential in identifying individuals at risk of seizure recurrence earlier.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/epi4.70236","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T15:21:42Z","timestamp":1771341702000},"page":"538-552","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Structural brain imaging biomarkers for predicting seizure recurrence after a first unprovoked seizure"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8710-0439","authenticated-orcid":false,"given":"Suyi","family":"Ooi","sequence":"first","affiliation":[{"name":"The Florey Institute of Neuroscience and Mental Health Melbourne Brain Centre  Melbourne Victoria Australia"},{"name":"Florey Department of Neuroscience and Mental Health The University of Melbourne  Melbourne Victoria Australia"},{"name":"Department of Neurology Austin 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