Abnormal White Matter Connectivity May Point to Long-Term Effects in Athletes

This diagnostic signature, with the help of Artificial Intelligence (AI) could be used to detect long term detect brain trauma.

The new tool was developed by researchers at the Université de Montreal, the Montreal Neurological Institute and Hospital (The Neuro), and the Ludmer Center for NeuroInformatics, all located in Montreal, QC, Canada. The study was published in the May 16, 2017, issue of the European Journal of Neuroscience.

The researchers recruited former university ice hockey and American football players aged 51 to 75 for the study. The researchers selected a cohort of 15 athletes who had suffered from concussion during their sports careers, and 15 athletes without concussion. The researchers performed Magnetic Resonance Spectroscopy (MRS) and Diffusion Weighted Imaging (DWI) scans, and other tests, pooled the data, and used AI software to distinguish a healthy athlete’s brain with those with concussion. The researchers found abnormal connectivity in several brain regions of concussed athletes and were able to accurately detect concussion in 90% of the cases.

First author of the paper, Dr. Sebastien Tremblay, postdoctoral researcher at The Neuro, said, "With 1.6 to 3.8 million concussions per year in the US alone, the prevalence of this injury is alarming. It is unacceptable that no objective tools or techniques yet exist to diagnose them, not to mention the sheer lack of scientifically valid treatment options. With our work, we hope to provide help to the vast population of former athletes who experience neurological issues after retiring from contact sport."

Related Links:
Université de Montreal
Montreal Neurological Institute and Hospital
Ludmer Center for NeuroInformatics