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Our laboratory has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased throughout the adolescent brain in ASD. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 years through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.<\/jats:p>","DOI":"10.1162\/imag.a.144","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T20:01:17Z","timestamp":1755892877000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Widespread associations between behavioral metrics and brain microstructure in ASD suggest age mediates subtypes of ASD"],"prefix":"10.1162","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8720-8274","authenticated-orcid":false,"given":"Haylee J.","family":"Ressa","sequence":"first","affiliation":[{"name":"Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-2853","authenticated-orcid":false,"given":"Benjamin T.","family":"Newman","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA, United States"},{"name":"Department of Radiology and Medical Imaging, University of Virginia, School of Medicine, Gilmer Hall, Charlottesville, VA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9725-1816","authenticated-orcid":false,"given":"Zachary","family":"Jacokes","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2570-0746","authenticated-orcid":false,"given":"James C.","family":"McPartland","sequence":"additional","affiliation":[{"name":"Yale Child Study Center and the Yale Center for Brain and Mind Health, Yale School of Medicine, Sterling Hall of Medicine, New Haven, CT, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4417-4224","authenticated-orcid":false,"given":"Natalia M.","family":"Kleinhans","sequence":"additional","affiliation":[{"name":"University of Washington Integrated Brain Imaging Center, Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Center, Seattle, WA, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7240-9487","authenticated-orcid":false,"given":"T. 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