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We examined gray matter (GM) thickness patterns of cognitive phenotypes in drug\u2010resistant TLE and assessed potential use for predicting postsurgical cognitive outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      TLE patients undergoing presurgical evaluation were categorized into cognitive phenotypes. Network edge weights and distances were calculated using type III analysis of variance\n                      <jats:italic>F<\/jats:italic>\n                      \u2010statistics from comparisons of GM regions within each TLE cognitive phenotype and age\u2010 and sex\u2010matched healthy participants. In resected patients, logistic regression models (LRMs) based on network analysis results were used for prediction of postsurgical cognitive outcome.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 124 patients (63 females, mean age\u2009\u00b1\u2009standard deviation [SD]\u2009=\u200936.0\u2009\u00b1\u200912.0\u2009years) and 117 healthy controls (63 females, mean age\u2009\u00b1\u2009SD\u2009=\u200936.1\u2009\u00b1\u200912.0\u2009years) were analyzed. In the multidomain CI group (n\u2009=\u200966, 53.2%), 28 GM regions were significantly thinner compared to healthy controls. Focally impaired patients (n\u2009=\u200937, 29.8%) showed 13 regions, whereas minimally impaired patients (n\u2009=\u200921, 16.9%) had 2 significantly thinner GM regions. Regions affected in both multidomain and focally impaired patients included the anterior cingulate cortex, medial prefrontal cortex, medial temporal, and lateral temporal regions. In 69 (35 females, mean age\u2009\u00b1\u2009SD\u2009=\u200933.6\u2009\u00b1\u200918.0\u2009years) patients who underwent surgery, LRMs based on network\u2010identified GM regions predicted postsurgical verbal memory worsening with a receiver operating curve area under the curve of 0.70\u2009\u00b1\u20090.15.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>A differential pattern of GM thickness can be found across different cognitive phenotypes in TLE. Including magnetic resonance imaging with clinical measures associated with cognitive profiles has potential in predicting postsurgical cognitive outcomes in drug\u2010resistant TLE. ANN NEUROL 2024;95:984\u2013997<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ana.26893","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T07:45:52Z","timestamp":1708674352000},"page":"984-997","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cortical Thickness Patterns of Cognitive Impairment Phenotypes in Drug\u2010Resistant Temporal Lobe Epilepsy"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0842-1806","authenticated-orcid":false,"given":"Gadi","family":"Miron","sequence":"first","affiliation":[{"name":"Computational Neurology, Department of Neurology Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin  Berlin Germany"},{"name":"Epilepsy Center Berlin\u2010Brandenburg, Department of Neurology Charit\u00e9\u2013Universit\u00e4tsmedizin Berlin  Berlin Germany"},{"name":"Epilepsy Center Berlin\u2010Brandenburg Institute for 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