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A precise and sex\u2010specific brain age prediction model is desirable for investigating the systematic deviation and individual heterogeneity of disorders associated with atypical brain development, such as autism spectrum disorders. In this study, we used partial least squares regression and the stacking algorithm to establish a sex\u2010specific brain age prediction model based on T1\u2010weighted structural magnetic resonance imaging and resting\u2010state functional magnetic resonance imaging. The model showed good generalization and high robustness on four independent datasets with different ethnic information and age ranges. A predictor weights analysis showed the differences and similarities in changes in structure and function during brain development. At the group level, the brain age gap estimation for autistic patients was significantly smaller than that for healthy controls in both the ABIDE dataset and the healthy brain network dataset, which suggested that autistic patients as a whole exhibited the characteristics of delayed development. However, within the ABIDE dataset, the premature development group had significantly higher Autism Diagnostic Observation Schedule (ADOS) scores than those of the delayed development group, implying that individuals with premature development had greater severity. Using these findings, we built an accurate typical brain development trajectory and developed a method of atypical trajectory analysis that considers sex differences and individual heterogeneity. This strategy may provide valuable clues for understanding the relationship between brain development and mental disorders.<\/jats:p>","DOI":"10.1002\/hbm.25660","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T13:02:31Z","timestamp":1631624551000},"page":"5943-5955","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Predicting brain age during typical and atypical development based on structural and functional neuroimaging"],"prefix":"10.1002","volume":"42","author":[{"given":"Qi","family":"Wang","sequence":"first","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Hu","sequence":"additional","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingzhong","family":"Fan","sequence":"additional","affiliation":[{"name":"Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences Beijing China"},{"name":"School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China"},{"name":"CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation, Chinese Academy of Sciences Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2029-5187","authenticated-orcid":false,"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China"},{"name":"Chinese Institute for Brain Research Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198505013.001.0001","volume-title":"The amygdala: A functional analysis","author":"Aggleton J. 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