{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:46:10Z","timestamp":1785717970386,"version":"3.56.0"},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain Pathology"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In epilepsy patients lacking magnetic resonance imaging\u2010visible lesions such as focal cortical dysplasia (FCD), surgical resections target regions with abnormal electroencephalographic (EEG) activity. However, histopathological identification of subtle cortical architectural abnormalities in these specimens remains challenging. We investigated whether artificial intelligence (AI)\u2010based morphometric and spatial analysis of NeuN\u2010stained cortical sections could detect neuronal architectural disorganization in epilepsy resections, including regions without definitive histologic dysplasia. Whole slide images were generated from 83 FCD regions and 19 neurologically normal autopsy controls. Regions of interest were annotated in QuPath as FCD, FCD\u2010adjacent, FCD\u2010distant, apparently normal (abnormal EEG without histologic dysplasia), and true normal (autopsy controls). NeuN\u2010positive neurons were detected using QuPath (90% sensitivity, 12% false positive rate). Spatial and morphometric features\u2014including neuronal clustering, distribution inhomogeneity, and nuclear morphology\u2014were extracted and used to train a multinomial, Least Absolute Shrinkage and Selection Operator\u2010regularized regression classifier. The classifier achieved 70.6% accuracy in subregion classification and 88.2% accuracy in overall specimen diagnosis. Notably, regions with abnormal EEG but lacking histologic dysplasia exhibited quantifiable architectural disorganization similar to those seen in areas adjacent to FCD and distinct from control tissue. AI\u2010driven analysis of neuronal morphology and spatial distribution reveals subtle cortical disorganization in epilepsy resections, including in histologically ambiguous regions. Further investigation is warranted to determine if this methodology can enhance the diagnostic yield of neuropathological evaluation and support more precise surgical targeting in epilepsy.<\/jats:p>","DOI":"10.1111\/bpa.70096","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:01:36Z","timestamp":1774537296000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence\u2010based statistical modeling delineates architectural distortion in focal cortical dysplasia"],"prefix":"10.1111","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5892-8907","authenticated-orcid":false,"given":"Andrew","family":"Cannon","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine and Pathology Mayo Clinic  Rochester Minnesota 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