{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:56:53Z","timestamp":1791896213070,"version":"4.3.4"},"reference-count":42,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["MH109773"],"award-info":[{"award-number":["MH109773"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["MH117943"],"award-info":[{"award-number":["MH117943"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Autism Research"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:label\/><jats:p>Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2\u2009years of age based on abnormal behaviors. Existing neuroimaging\u2010based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI\u2010based studies include subjects older than 5\u2009years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24\u2009months of age. Specifically, by leveraging an infant\u2010dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols\/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state\u2010of\u2010the\u2010art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject\u2010specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early\u2010stage status prediction of ASD by sMRI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Lay Summary<\/jats:title><jats:p>The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24\u2009months that uses infant structural magnetic resonance imaging to identify neural features.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aur.2626","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T00:14:51Z","timestamp":1634429691000},"page":"2512-2523","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1729-2700","authenticated-orcid":false,"given":"Kun","family":"Gao","sequence":"first","affiliation":[{"name":"Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[{"name":"Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijie","family":"Niu","sequence":"additional","affiliation":[{"name":"Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"},{"name":"School of Information Science and Engineering University of Jinan  Jinan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2165-0080","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"Developing Brain Computing Lab, Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10803-009-0755-y"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnpbp.2015.06.014"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/aur.2595"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"Chopra S. Hadsell R. &LeCun Y.(2005).Learning a similarity metric discriminatively with application to face verification. Paper presented at the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).https:\/\/doi.org\/10.1109\/CVPR.2005.202","DOI":"10.1109\/CVPR.2005.202"},{"key":"e_1_2_8_6_1","volume-title":"Educating children with autism","author":"Committee on Educational Interventions for Children with Autism","year":"2001"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/jpm10040275"},{"key":"e_1_2_8_8_1","article-title":"Why early diagnosis of autism in children is a good thing","author":"Dillenburger K.","year":"2014","journal-title":"The Conversation"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.11.024"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.08.047"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-012-9151-4"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature21369"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.03.049"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2913372"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2002.1132"},{"key":"e_1_2_8_17_1","article-title":"Early brain imaging can predict autism: Application of machine learning to a clinical imaging archive","author":"Katuwal G. J.","year":"2018","journal-title":"bioRxiv"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1097\/YCO.0000000000000575"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"Li G. Liu M. Sun Q. Shen D.&Wang L.(2018). Early diagnosis of autism disease by multi\u2010channel CNNs. Paper presented at the International Workshop on Machine Learning in Medical Imaging.https:\/\/doi.org\/10.1007\/978-3-030-00919-9_35","DOI":"10.1007\/978-3-030-00919-9_35"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"Lian C. Liu M. Wang L.&Shen D.(2019). End\u2010to\u2010end dementia status prediction from brain MRI using multi\u2010task weakly\u2010supervised attention network. Paper presented at the International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.https:\/\/doi.org\/10.1007\/978-3-030-32251-9_18","DOI":"10.1007\/978-3-030-32251-9_18"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32606-7_8"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.ss6904a1"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.01.054"},{"key":"e_1_2_8_24_1","doi-asserted-by":"crossref","unstructured":"Mostapha M. Casanova M. F. Gimel'farb G. &El\u2010Baz A.(2015).Towards non\u2010invasive image\u2010based early diagnosis of autism. Paper presented at the International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.https:\/\/doi.org\/10.1007\/978-3-319-24571-3_20","DOI":"10.1007\/978-3-319-24571-3_20"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13229-015-0024-1"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaac.2019.11.022"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2019.0624"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10803-015-2366-0"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2013.00015"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2019.00070"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(18)30294-3"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awt166"},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018746"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnsys.2014.00092"},{"key":"e_1_2_8_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3055428"},{"key":"e_1_2_8_36_1","doi-asserted-by":"crossref","unstructured":"Wang L. Li G. Shi F. Cao X. Lian C. Nie D. Liu M. Zhang H. Li G. Wu Z. Lin W.&Shen D.(2018). Volume\u2010based analysis of 6\u2010month\u2010old infant brain MRI for autism biomarker identification and early diagnosis. Paper presented at the International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.https:\/\/doi.org\/10.1007\/978-3-030-00931-1_47","DOI":"10.1007\/978-3-030-00931-1_47"},{"key":"e_1_2_8_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901712"},{"key":"e_1_2_8_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/aur.1711"},{"key":"e_1_2_8_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2020.03.049"},{"key":"e_1_2_8_40_1","unstructured":"Yale\u2010Medicine. (2021).Functional MRI of the Brain.https:\/\/ym.care\/mbq"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13229-020-00353-2"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.07.004"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2582386"}],"container-title":["Autism Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aur.2626","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/aur.2626","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/aur.2626","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T23:32:34Z","timestamp":1693006354000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/aur.2626"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":42,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.1002\/aur.2626"],"URL":"https:\/\/doi.org\/10.1002\/aur.2626","archive":["Portico"],"relation":{},"ISSN":["1939-3792","1939-3806"],"issn-type":[{"value":"1939-3792","type":"print"},{"value":"1939-3806","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,13]]},"assertion":[{"value":"2021-06-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-24","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}