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In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs2\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Using cross-sectional data from 306 previously concussed children aged 9\u201310 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs3\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs4\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom\/connectivity relationships. The symptom\/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract\/single-symptom analyses. Future studies aiming to better understand connectivity\/symptom relationships should take into account multi-tract multi-symptom heterogeneity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"abs5\">\n                    <jats:title>Funding:<\/jats:title>\n                    <jats:p>Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).<\/jats:p>\n                  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