{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:01Z","timestamp":1791474601490,"version":"4.3.4"},"reference-count":46,"publisher":"Walter de Gruyter GmbH","issue":"4-5","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2026-0314_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Autism spectrum disorder (ASD) involves subtle and heterogeneous brain-structural differences that are not usually visible on routine structural magnetic resonance imaging (sMRI). This study investigated a segmentation-guided sMRI feature-classification framework for childhood ASD analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0314_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Pediatric T1-weighted sMRI scans were obtained from the publicly available Autism Brain Imaging Data Exchange II (ABIDE-II) dataset. Preprocessing used the FMRIB Software Library (FSL) Brain Extraction Tool (BET), N4\u00a0bias-field correction, intensity normalization, and Advanced Normalization Tools (ANTs) registration. Gray matter, white matter, and cerebrospinal fluid (CSF) were segmented using\u00a0FSL FAST, and cortical reconstruction\/parcellation was performed using FreeSurfer recon-all. Extracted features included global structural summaries, regional cortical gray-matter volume, cortical surface area, cortical thickness, cortical-thickness variability, mean curvature, and folding index. Each participant was represented by a 414-dimensional numerical feature vector. Features were normalized, filtered, selected using minimum-redundancy maximum-relevance (mRMR) analysis, and classified using support vector machine, random forest, k-nearest neighbors, and multilayer perceptron models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0314_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The multilayer perceptron achieved 69.4\u202f% accuracy, 67.9\u202f% precision, 70.0\u202f% sensitivity, 68.8\u202f% specificity, 68.9\u202f% F1-score, and 72.6\u202f% area under the receiver operating characteristic curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0314_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Extracted quantitative sMRI features supported ASD-versus-typical-control classification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0314","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:55:44Z","timestamp":1784908544000},"page":"335-348","source":"Crossref","is-referenced-by-count":1,"title":["Cortical surface-based descriptors for childhood autism classification using segmentation-guided cortical reconstruction from sMRI"],"prefix":"10.1515","volume":"71","author":[{"given":"Yu","family":"Ke","sequence":"first","affiliation":[{"name":"Department of Preschool Education , Quanzhou Preschool Education College , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingliang","family":"He","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging , The First Affiliated Hospital of Fujian Medical University , Fuzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Quanzhou Normal University , Quanzhou , China"},{"name":"Key Laboratory of Information Functional Material for Fujian Higher Education, Quanzhou Normal University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusi","family":"Chen","sequence":"additional","affiliation":[{"name":"Fujian Provincial Key Laboratory of Data Intensive Computing , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueqian","family":"Ke","sequence":"additional","affiliation":[{"name":"College of Physics and Information Engineering, Quanzhou Normal University , Quanzhou , China"},{"name":"Key Laboratory of Information Functional Material for Fujian Higher Education, Quanzhou Normal University , Quanzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"2026100814512969242_j_bmt-2026-0314_ref_001","doi-asserted-by":"crossref","unstructured":"Lord, C, Elsabbagh, M, Baird, G, Veenstra-VanderWeele, J. 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