Related Experiment Video
Updated: Jul 22, 2026

A Multi-Modal Approach to Assessing Recovery in Youth Athletes Following Concussion
Published on: September 25, 2014
Multi-tract multi-symptom relationships in pediatric concussion
Guido I Guberman1, Sonja Stojanovski2,3, Eman Nishat2,3
1Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Canada.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
Funding:
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.).
Insights
Concussion symptoms and brain damage vary greatly, but new research uses diffusion MRI to link specific symptom patterns to white matter changes. This approach reveals subtypes of concussion, moving beyond simple one-to-one symptom-connectivity links.
Area of Science:
- Neuroimaging
- Neuroscience
- Medical Research
Background:
- Concussion research faces challenges due to varied white matter damage and symptoms.
- Traditional diffusion MRI (dMRI) studies often average out individual differences.
- A novel approach is needed to understand concussion heterogeneity.
Purpose of the Study:
- To leverage concussion heterogeneity by combining dMRI and multivariate statistics.
- To characterize multi-tract, multi-symptom relationships in children with concussion.
- To identify distinct subtypes of symptom-connectivity relationships.
Main Methods:
- Utilized cross-sectional data from 306 children (aged 9-10) from the Adolescent Brain Cognitive Development Study.
- Constructed connectomes weighted by diffusion measures representing microstructural complexity and axonal density.
- Employed pattern-learning algorithms to analyze relationships between connectivity features and 19 symptom measures.
Main Results:
- Identified multi-tract, multi-symptom pairs explaining significant covariance, linking broad and specific symptom categories to white matter networks.
- Found that symptom representation was not consistently tied to tract representation across pairs.
- Observed that sleep problems were implicated across most pairs but related to different connections, and feature expression was independent of sociodemographics and ADHD status.
Conclusions:
- A double-multivariate approach identified clinically relevant, cross-demographic multi-tract, multi-symptom relationships.
- Concussions may be characterized by subtypes of symptom/connectivity relationships, not just one-to-one disturbances.
- Future research should account for multi-tract, multi-symptom heterogeneity to better understand connectivity-symptom relationships.

