Department of Neurology, Mayo Clinic, AZ, USA.
Headache. 2018 Jun;58(6):827-835. doi: 10.1111/head.13269. Epub 2018 Feb 24.
Even when concussions are associated with prolonged physical and cognitive sequelae, concussions are typically "invisible" on diagnostic brain imaging, indicating that the neuropathology associated with concussion lies under the detection threshold of routine imaging. However, data from brain structural and functional research imaging studies using diffusion tensor imaging, resting-state functional magnetic resonance imaging, and brain perfusion imaging indicate that these imaging sequences have a role in identifying concussion-related neuropathology. These advanced imaging techniques provide insights into concussion neuropathology and might be useful for differentiating concussed patients from healthy controls. In this review article, we provide an overview of research findings from brain structural and functional imaging studies of concussion, and discuss the accuracy of classification models developed via machine-learning algorithms for identifying individual patients with concussion based on imaging data.
即使脑震荡与长期的身体和认知后遗症有关，但在诊断性脑成像上，脑震荡通常是“不可见的”，这表明与脑震荡相关的神经病理学位于常规成像的检测阈值以下。然而，使用弥散张量成像、静息态功能磁共振成像和脑灌注成像的脑结构和功能研究影像学数据表明，这些影像学序列在识别与脑震荡相关的神经病理学方面具有作用。这些先进的成像技术提供了对脑震荡神经病理学的深入了解，并且可能有助于区分脑震荡患者和健康对照者。在这篇综述文章中，我们提供了脑震荡的脑结构和功能影像学研究的研究结果概述，并讨论了基于机器学习算法开发的分类模型在根据影像学数据识别个别脑震荡患者方面的准确性。
