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Combining MRI and cognitive evaluation to classify concussion in university athletes

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Abstract

Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R2 = 0.32, p < .001), and performed worse on tests of concentration (∆R2 = 0.07, p < .05) and delayed memory (∆R2 = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.

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Data availability

The datasets generated during and/or analyzed during the current study are not publicly available due to the fact that they represent an excerpt of research in progress by the BrainScope Company, Inc. Data may be available upon reasonable request and with permission of BrainScope (info@brainscope.com).

Code availability

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Acknowledgements

Many thanks to Drs. Peter Molfese, Michael Stevens, Fumiko Hoeft, and Deborah Fein for their input and guidance.

Funding

This study was funded in part by a contract to BrainScope Company Inc. from the U.S. Navy (Naval Health Research Center), contract #W911QY-14-C-0098, and in part by a University of Connecticut Institute for the Brain and Cognitive Sciences – Brain Imaging Research Center (IBACS-BIRC) Research Assistantship in Neuroimaging (IBRAiN).

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Author notes

  1. Monica T. Ly

    Present address: Veterans Affairs San Diego Healthcare System, San Diego, CA, USA

  2. Monica T. Ly

    Present address: Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA

  3. Samantha E. Scarneo-Miller

    Present address: Division of Athletic Training, School of Medicine, West Virginia University, Morgantown, WV, USA

  4. Adam S. Lepley

    Present address: School of Kinesiology, Exercise and Sport Science Initiative, University of Michigan, Ann Arbor, MI, USA

  5. Kelly Coleman

    Present address: Department of Health & Movement Sciences, Southern Connecticut State University, New Haven, CT, USA

  6. Rebecca Hirschhorn

    Present address: School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA

Authors and Affiliations

  1. Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA

    Monica T. Ly & Chi-Ming Chen

  2. Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA

    Samantha E. Scarneo-Miller, Adam S. Lepley, Kelly Coleman & Douglas J. Casa

  3. Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

    Rebecca Hirschhorn & Susan Yeargin

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Contributions

Author contributions included conception and study design (SY and DJC), data collection and management (SES-M, ASL, KC, and RH), data analysis (MTL), interpretation of results (MTL, DJC, C-MC), and preparation of the original manuscript (MTL). All authors were involved in manuscript revising, approved the final version of this manuscript, and agree to be accountable for the integrity and accuracy of all aspects of the work.

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Correspondence to Monica T. Ly.

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Ethics approval

All study procedures were approved by each site’s (University of Connecticut and University of South Carolina) respective Institutional Review Board.

Consent to participate

All participants provided written informed consent. Concussed athletes provided informed consent prior to injury.

Consent for publication

All participants acknowledged as part of informed consent that data collected from this study, de-identified, may be used to disseminate research findings such as through publication.

Conflicts of interest

S. Yeargin and D. J. Casa received a grant from BrainScope to conduct data collection, a subset of which was used for this study. S. E. Scarneo-Miller, K. Coleman, and R. Hirschhorn received funding from said grant from BrainScope through research assistantships. S. E. Scarneo-Miller also serves as an expert witness on legal cases related to sudden death in sport. M. T. Ly, A. S. Lepley, and C.-M. Chen have no competing interests to disclose.

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Ly, M.T., Scarneo-Miller, S.E., Lepley, A.S. et al. Combining MRI and cognitive evaluation to classify concussion in university athletes. Brain Imaging and Behavior 16, 2175–2187 (2022). https://doi.org/10.1007/s11682-022-00687-w

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