Academic Neurosurgery Unit, St. George's, University of London, London, United Kingdom.
J Neurotrauma. 2018 Dec 15;35(24):2947-2956. doi: 10.1089/neu.2018.5775. Epub 2018 Sep 27.
To guide management of patients with acute spinal cord injuries, we developed intraspinal pressure monitoring from the injury site. Here, we examine the complex fluctuations in the intraspinal pressure signal using network theory. We analyzed 7097 h of intraspinal pressure data from 58 patients with severe cord injuries. Intraspinal pressure signals were split into hourly windows. Each window was mapped into a visibility graph as follows. Vertical bars were drawn at 0.1 Hz representing signal amplitudes. Each bar produced a node, thus totalling 360 nodes per graph. Two nodes were linked with an edge if the straight line through the nodes did not intersect a bar. We computed several topological metrics for each graph including diameter, modularity, eccentricity, and small-worldness. Patients were followed up for 20 months on average. Our data show that the topological structure of intraspinal pressure visibility graphs is highly sensitive to pathological events at the injury site, including cord compression (high intraspinal pressure), ischemia (low spinal cord perfusion pressure), and deranged autoregulation (high spinal pressure reactivity index). These pathological changes correlate with long graph diameter, high modularity, high eccentricity and reduced small-worldness. In a multivariate logistic regression model, age, neurological status on admission, and average node eccentricity were independent predictors of neurological improvement. We conclude that analysis of intraspinal pressure fluctuations after spinal cord injury as graphs, rather than as time series, captures clinically important information. Our novel technique may be applied to other signals recorded from injured central nervous system (CNS); for example, intracranial pressure, tissue metabolite, and oxygen levels.
为了指导急性脊髓损伤患者的治疗，我们从损伤部位开发了脊髓内压监测。在这里，我们使用网络理论来研究脊髓内压信号的复杂波动。我们分析了 58 例严重脊髓损伤患者的 7097 小时脊髓内压数据。将脊髓内压信号分为每小时一个窗口。每个窗口都被映射到一个可见性图中，如下所示。垂直条在 0.1 Hz 处绘制，表示信号幅度。每个条产生一个节点，因此每个图总共 360 个节点。如果通过节点的直线不与条相交，则两个节点用边连接。我们为每个图计算了几个拓扑度量，包括直径、模块性、偏心度和小世界性。患者的平均随访时间为 20 个月。我们的数据表明，脊髓内压可见性图的拓扑结构对损伤部位的病理事件高度敏感，包括脊髓压迫（高脊髓内压）、缺血（低脊髓灌注压）和自动调节障碍（高脊髓压力反应指数）。这些病理变化与长图直径、高模块性、高偏心度和降低的小世界性相关。在多变量逻辑回归模型中，年龄、入院时的神经状态和平均节点偏心度是神经功能改善的独立预测因子。我们得出结论，将脊髓损伤后的脊髓内压波动作为图而不是时间序列进行分析，可以捕捉到临床上重要的信息。我们的新技术可能适用于从受伤的中枢神经系统（CNS）记录的其他信号，例如颅内压、组织代谢物和氧水平。
