Atlan Lorre S, Lan Ingrid S, Smith Colin, Margulies Susan S
Department of Bioengineering, University of Pennsylvania, 210 S. 33rd St., 240 Skirkanich Hall, Philadelphia, PA 19104-6321, USA.
Academic Department of Neuropathology, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Clin Biomech (Bristol). 2019 Apr;64:14-21. doi: 10.1016/j.clinbiomech.2018.05.013. Epub 2018 Jun 1.
Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species.
Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured.
Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy.
This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive.
创伤性脑损伤是美国每年导致儿童认知和行为缺陷的主要原因。目前的诊断工具，如定量认知和平衡测试，均未经验证可用于识别婴儿、成人和动物的轻度创伤性脑损伤。在这项初步研究中，我们报告了一种新型的定量工具，它有可能快速、可靠地诊断创伤性脑损伤，并能在多个年龄和物种的恢复过程中跟踪大脑状态。
我们使用32个头皮电极，在22只清醒的四周龄仔猪经历两种不同损伤类型（弥漫性和局灶性）或假手术前一天以及术后1天、4天和7天，记录非自愿听觉事件相关电位。从这些记录中，我们生成了事件相关电位功能网络，并评估这些网络中观察到的变化模式是否能区分脑损伤仔猪和未损伤仔猪。
通过五个网络指标评估，仔猪大脑在受伤后表现出显著变化。我们通过对事件相关电位功能网络变化的分析开发的损伤预测算法最终产生了一种预测准确率为82%的工具。
这种新方法是听觉事件相关电位功能网络在创伤性脑损伤预测中的首次应用。由此产生的工具是一种强大、客观且具有预测性的方法，有望检测轻度创伤性脑损伤，特别是因为收集事件相关电位数据是非侵入性且成本低廉的。
