Combining MRI and cognitive evaluation to classify concussion in university athletes

Monica T Ly; Samantha E Scarneo-Miller; Adam S Lepley; Kelly Coleman; Rebecca Hirschhorn; Susan Yeargin; Douglas J Casa; Chi-Ming Chen

doi:10.1007/s11682-022-00687-w

Combining MRI and cognitive evaluation to classify concussion in university athletes

Brain Imaging Behav. 2022 Oct;16(5):2175-2187. doi: 10.1007/s11682-022-00687-w. Epub 2022 May 31.

Authors

Monica T Ly^{1

2

3}, Samantha E Scarneo-Miller^{4

5}, Adam S Lepley^{4

6}, Kelly Coleman^{4

7}, Rebecca Hirschhorn^{8

9}, Susan Yeargin⁸, Douglas J Casa⁴, Chi-Ming Chen¹⁰

Affiliations

¹ Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA. monicaly@gmail.com.
² Veterans Affairs San Diego Healthcare System, San Diego, CA, USA. monicaly@gmail.com.
³ Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA. monicaly@gmail.com.
⁴ Department of Kinesiology, Korey Stringer Institute, University of Connecticut, Storrs, CT, USA.
⁵ Division of Athletic Training, School of Medicine, West Virginia University, Morgantown, WV, USA.
⁶ School of Kinesiology, Exercise and Sport Science Initiative, University of Michigan, Ann Arbor, MI, USA.
⁷ Department of Health & Movement Sciences, Southern Connecticut State University, New Haven, CT, USA.
⁸ Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
⁹ School of Kinesiology, Louisiana State University, Baton Rouge, LA, USA.
¹⁰ Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA.

PMID: 35639240
DOI: 10.1007/s11682-022-00687-w

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.

Keywords: Classification; Concussion; Diffusion tensor imaging (DTI); Mild traumatic brain injury (mTBI); Resting state functional MRI.

MeSH terms

Athletes
Athletic Injuries* / complications
Brain Concussion* / complications
Cognition
Data Collection
Diffusion Tensor Imaging / methods
Humans
Magnetic Resonance Imaging
Reproducibility of Results
Universities

Grants and funding

#W911QY-14-C-0098/U.S. Navy