Citation

Tremblay S, Iturria-Medina Y, Mateos-Pérez JM, Evans AC, de Beaumont L. Eur. J. Neurosci. 2017; 46(4): 1956-1967.

Affiliation

Department of Surgery, Université de Montréal, Montreal, QC, H3C 3J7, Canada.

Copyright

(Copyright © 2017, Federation of European Neuroscience Societies, Publisher John Wiley and Sons)

DOI

10.1111/ejn.13583

PMID

28512863

Abstract

Sports-related concussions lead to persistent anomalies of the brain structure and function that interact with the effects of normal ageing. Although post-mortem investigations have proposed a bio-signature of remote concussions, there is still no clear in vivo signature. In the current study, we characterized white matter integrity in retired athletes with a history of remote concussions by conducting a full-brain, diffusion-based connectivity analysis. Next, we combined MRI diffusion markers with MR spectroscopic, MRI volumetric, neurobehavioral and genetic markers to identify a multidimensional in vivo signature of remote concussions. Machine learning classifiers trained to detect remote concussions using this signature achieved detection accuracies up to 90% (sensitivity: 93%, specificity: 87%). These automated classifiers identified white matter integrity as the hallmark of remote concussions and could provide, following further validation, a preliminary unbiased detection tool to help medical and legal experts rule out concussion history in patients presenting or complaining about late-life abnormal cognitive decline.

© 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Language: en

Keywords

ageing; concussion; diagnosis; machine learning; neuroimaging