{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Su TY"],"funding":["National Institute of Neurological Disorders and Stroke","Rosetrees Trust","Epilepsy Research Institute UK","NINDS NIH HHS","Wellcome Trust"],"pagination":["257-271"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12893262"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["67(1)"],"pubmed_abstract":["<h4>Objective</h4>This study was undertaken to develop a framework for focal cortical dysplasia (FCD) detection using surface-based morphometric (SBM) analysis and machine learning (ML) applied to three-dimensional (3D) magnetic resonance fingerprinting (MRF).<h4>Methods</h4>We included 114 subjects (44 patients with medically intractable focal epilepsy and FCD, 70 healthy controls [HCs]). All subjects underwent high-resolution 3-T MRF scans generating T1 and T2 maps. All patients had clinical T1-weighted (T1w) images; 35 also had 3D fluid-attenuated inversion recovery (FLAIR). A 3D region of interest (ROI) was manually created for each lesion. All maps/images and lesion ROIs were registered to T1w images. Surface-based features were extracted following the Multi-center Epilepsy Lesion Det"],"journal":["Epilepsia"],"pubmed_title":["Surfaced-based detection of focal cortical dysplasia using magnetic resonance fingerprinting and machine learning."],"pmcid":["PMC12893262"],"funding_grant_id":["R01 NS109439","2R01 NS109439"],"pubmed_authors":["Hu S","Su TY","Blumcke I","Alexopoulos AV","Wang ZI","Murakami H","Adler S","Wagstyl K","Choi JY","Sakaie K","Jones SE","Wang X","Ding Z","Ma D","Najm I"],"additional_accession":[]},"is_claimable":false,"name":"Surfaced-based detection of focal cortical dysplasia using magnetic resonance fingerprinting and machine learning.","description":"<h4>Objective</h4>This study was undertaken to develop a framework for focal cortical dysplasia (FCD) detection using surface-based morphometric (SBM) analysis and machine learning (ML) applied to three-dimensional (3D) magnetic resonance fingerprinting (MRF).<h4>Methods</h4>We included 114 subjects (44 patients with medically intractable focal epilepsy and FCD, 70 healthy controls [HCs]). All subjects underwent high-resolution 3-T MRF scans generating T1 and T2 maps. All patients had clinical T1-weighted (T1w) images; 35 also had 3D fluid-attenuated inversion recovery (FLAIR). A 3D region of interest (ROI) was manually created for each lesion. All maps/images and lesion ROIs were registered to T1w images. Surface-based features were extracted following the Multi-center Epilepsy Lesion Det","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-16T09:39:43.591Z","creation":"2026-07-09T10:46:58.114Z"},"accession":"S-EPMC12893262","cross_references":{"pubmed":["41066149"],"doi":["10.1111/epi.18667"]}}