Multi-tract multi-symptom relationships in pediatric concussion

The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. 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. Using cross-sectional data from 306 previously-concussed children aged 9-10 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. We found multivariate connectivity-symptom correspondences that were stronger than all single-tract single-symptom associations. Expression of multi-tract features was not driven by sociodemographic and injury-related variables. In a replication dataset, the expression of multi-tract features predicted psychiatric diagnoses after accounting for other psychopathology-related variables. These clinically-informative, cross-demographic multi-tract multi-symptom relationships recapitulated well-known findings from the concussion literature and revealed new insights about white matter structure/symptom relationships. These results may pave the way for the development of improved stratification strategies and the development of predictive biomarkers for personalized concussion management approaches.