Multi-tract multi-symptom relationships in pediatric concussion
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Curated by eLife
Evaluation Summary:
This manuscript aims to address an important issue in the study of concussion: both the brain damage caused by concussion, as well as the behavioral symptoms that result vary widely across individuals. The study uses novel and interesting methods to relate multi-variate diffusion MRI data with multi-variate symptom-related data. The methods of analysis are sophisticated and well-executed and the results are quite interesting. The methods developed here could have broad impact in their application to the many other neurological diseases that have heterogeneous outcomes.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)
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Abstract
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.).
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Evaluation Summary:
This manuscript aims to address an important issue in the study of concussion: both the brain damage caused by concussion, as well as the behavioral symptoms that result vary widely across individuals. The study uses novel and interesting methods to relate multi-variate diffusion MRI data with multi-variate symptom-related data. The methods of analysis are sophisticated and well-executed and the results are quite interesting. The methods developed here could have broad impact in their application to the many other neurological diseases that have heterogeneous outcomes.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)
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Joint Public Review:
Broad interest:
• This study will be of interest to the large group of neuroscientists interested in delineating the neural and behavioral consequences of heterogeneous neurological diseases
• This manuscript will be of interest to researchers studying complex brain-behavior links
The study tackles the crucial problem of phenotypic complexity head-on:
• This is an important work exploring the complex links between the brain's white matter structure and the symptomatology of concussions in children and adolescents
• Concussion and related brain diseases have great heterogeneity at multiple levels, including variable symptom profiles, different progression patterns, and distinct neurobiological mechanisms. This work acknowledges heterogeneity and the proposed methodology aims to leverage it, rather than averaging it out
Joint Public Review:
Broad interest:
• This study will be of interest to the large group of neuroscientists interested in delineating the neural and behavioral consequences of heterogeneous neurological diseases
• This manuscript will be of interest to researchers studying complex brain-behavior links
The study tackles the crucial problem of phenotypic complexity head-on:
• This is an important work exploring the complex links between the brain's white matter structure and the symptomatology of concussions in children and adolescents
• Concussion and related brain diseases have great heterogeneity at multiple levels, including variable symptom profiles, different progression patterns, and distinct neurobiological mechanisms. This work acknowledges heterogeneity and the proposed methodology aims to leverage it, rather than averaging it out
Rigor:
• The methods are rigorous and original
• The methods used for analysis of dMRI are the state of the art in the field: excellent modeling techniques that overcome some of the known challenges of dMRI, and judicious use of PCA to summarize the multiple dMRI metrics that were derived into concise and interpretable components
• Data processing and analysis are of very high quality
• The main strength of the work is the expertise of the authors in advanced neuroimaging and statistical analysis methodologies. The brain's white matter structure and its connectivity pattern are quantified using a reliable diffusion imaging and procession pipeline. Multiple diffusion metrics are computed and then summarized using PCA. PLSc is used to discover multi-track multi-symptom relationships. Permutation testing and bootstrapping are used to identify significant links and significant weights. It is always a pleasure to read a manuscript that includes a "reliable" analysis pipeline
• Regarding the statistical methodology -- here as well, the methods are sophisticated and seem to be well-executed. The use of a discovery sample and application to a separate replication sample is an excellent approach.
• Another strength is the inclusion of a replication sample
The approach has broad utility:
• As the methods are agnostic to the nature of concussion, the proposed methodology may benefit studies dealing with other brain diseases and disorders as well
• The methodology introduced here is potentially quite valuable and its application to this dataset and to other datasets where there is substantial heterogeneity could improve the inferences made from measurements of large samples of individuals with these disorders.
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