{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T19:26:49Z","timestamp":1777577209316,"version":"3.51.4"},"reference-count":49,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["NS109439"],"award-info":[{"award-number":["NS109439"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aim to improve focal cortical dysplasia (FCD) detection by combining high\u2010resolution, three\u2010dimensional (3D) magnetic resonance fingerprinting (MRF) with voxel\u2010based morphometric magnetic resonance imaging (MRI) analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We included 37 patients with pharmacoresistant focal epilepsy and FCD (10 IIa, 15 IIb, 10 mild Malformation of Cortical Development [mMCD], and 2 mMCD with oligodendroglial hyperplasia and epilepsy [MOGHE]). Fifty\u2010nine healthy controls (HCs) were also included. 3D lesion labels were manually created. Whole\u2010brain MRF scans were obtained with 1\u2009mm\n                      <jats:sup>3<\/jats:sup>\n                      isotropic resolution, from which quantitative T1 and T2 maps were reconstructed. Voxel\u2010based MRI postprocessing, implemented with the morphometric analysis program (MAP18), was performed for FCD detection using clinical T1w images, outputting clusters with voxel\u2010wise lesion probabilities. Average MRF T1 and T2 were calculated in each cluster from MAP18 output for gray matter (GM) and white matter (WM) separately. Normalized MRF T1 and T2 were calculated by\n                      <jats:italic>z<\/jats:italic>\n                      \u2010scores using HCs. Clusters that overlapped with the lesion labels were considered true positives (TPs); clusters with no overlap were considered false positives (FPs). Two\u2010sample\n                      <jats:italic>t\u2010tests<\/jats:italic>\n                      \u00a0were performed to compare MRF measures between TP\/FP clusters. A neural network model was trained using MRF values and cluster volume to distinguish TP\/FP clusters. Ten\u2010fold cross\u2010validation was used to evaluate model performance at the cluster level. Leave\u2010one\u2010patient\u2010out cross\u2010validation was used to evaluate performance at the patient level.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MRF metrics were significantly higher in TP than FP clusters, including GM T1, normalized WM T1, and normalized WM T2. The neural network model with normalized MRF measures and cluster volume as input achieved mean area under the curve (AUC) of .83, sensitivity of 82.1%, and specificity of 71.7%. This model showed superior performance over direct thresholding of MAP18 FCD probability map at both the cluster and patient levels, eliminating \u226575% FP clusters in 30% of patients and \u226550% of FP clusters in 91% of patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>This pilot study suggests the efficacy of MRF for reducing FPs in FCD detection, due to its quantitative values reflecting in\u00a0vivo pathological changes. \u00a9 2024 International League Against Epilepsy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/epi.17951","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T08:48:34Z","timestamp":1711010914000},"page":"1631-1643","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Combining\n                    <scp>magnetic resonance<\/scp>\n                    fingerprinting with voxel\u2010based morphometric analysis to reduce false positives for focal cortical dysplasia detection"],"prefix":"10.1111","volume":"65","author":[{"given":"Zheng","family":"Ding","sequence":"first","affiliation":[{"name":"Epilepsy Center, Neurological Institute Cleveland Clinic  Cleveland Ohio USA"},{"name":"Department of Biomedical Engineering Case Western Reserve University  Cleveland Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Case Western Reserve University  Cleveland Ohio USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5054-9163","authenticated-orcid":false,"given":"Ting\u2010Yu","family":"Su","sequence":"additional","affiliation":[{"name":"Epilepsy Center, Neurological Institute Cleveland Clinic  Cleveland Ohio 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