Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
Ludmer Center for Neuroinformatics and Mental Health, McGill University, Montreal, QC, Canada.
Eur J Neurosci. 2017 Aug;46(4):1956-1967. doi: 10.1111/ejn.13583. Epub 2017 May 16.
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.
运动相关性脑震荡会导致大脑结构和功能的持续异常，这些异常与正常衰老的影响相互作用。虽然尸检研究提出了脑震荡的生物标志物，但目前仍没有明确的体内标志物。在目前的研究中，我们通过全脑弥散连接分析，对有脑震荡病史的退役运动员的脑白质完整性进行了特征描述。接下来，我们将 MRI 扩散标记物与磁共振波谱、MRI 容积、神经行为和遗传标记物相结合，以确定脑震荡的多维体内标志物。使用该标志物训练的用于检测脑震荡的机器学习分类器的检测准确率高达 90%（敏感性：93%，特异性：87%）。这些自动分类器将脑白质完整性确定为脑震荡的特征，并可在进一步验证后，为帮助医疗和法律专家排除出现认知能力下降的迟发性异常的患者的脑震荡病史提供初步的无偏检测工具。
