Title |
Defining a multimodal signature of remote sports concussions
|
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Published in |
European Journal of Neuroscience, May 2017
|
DOI | 10.1111/ejn.13583 |
Pubmed ID | |
Authors |
Sébastien Tremblay, Yasser Iturria‐Medina, José María Mateos‐Pérez, Alan C. Evans, Louis De Beaumont |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 14% |
United Kingdom | 2 | 9% |
Canada | 2 | 9% |
Spain | 1 | 5% |
Hungary | 1 | 5% |
Switzerland | 1 | 5% |
Kuwait | 1 | 5% |
France | 1 | 5% |
Unknown | 10 | 45% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 14 | 64% |
Scientists | 4 | 18% |
Science communicators (journalists, bloggers, editors) | 3 | 14% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 126 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 22 | 17% |
Student > Master | 15 | 12% |
Student > Ph. D. Student | 14 | 11% |
Student > Doctoral Student | 10 | 8% |
Researcher | 10 | 8% |
Other | 19 | 15% |
Unknown | 36 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 26 | 21% |
Neuroscience | 14 | 11% |
Psychology | 11 | 9% |
Sports and Recreations | 7 | 6% |
Nursing and Health Professions | 5 | 4% |
Other | 22 | 17% |
Unknown | 41 | 33% |