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Defining a multimodal signature of remote sports concussions

2017; Wiley; Volume: 46; Issue: 4 Linguagem: Inglês

10.1111/ejn.13583

1460-9568

Sébastien Tremblay, Yasser Iturria‐Medina, J.M. Mateos-Pérez, Alan C. Evans, Louis De Beaumont,

Anomaly Detection Techniques and Applications

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.