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  1. 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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  2. 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


    • 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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