Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity
Single-trial classification of awareness state during anesthesia by measuring critical dynamics of global brain activity
复制标题
DOI：
10.1038/s41598-019-41345-4
复制
发表时间：
2019-03-20
影响因子：
4.6
通讯作者：
Magnasco, Marcelo O.
中科院分区：
文献类型：
--
作者：
Alonso, Leandro M.;Solovey, Guillermo;Magnasco, Marcelo O.
在日常生活中，在手术室和实验室里，评估觉醒和意识的可操作性方法是通过反应性。许多研究表明，清醒、有意识的状态不是一组神经元的默认行为，而是一种非常特殊的活动状态，必须积极维持和管理，以支持其功能特性。因此，响应性是一个需要积极维护的特征，例如平衡兴奋和抑制的动态平衡机制。在这项工作中，我们开发了一种监控这类维护过程的方法，重点是从动力系统理论中得出的行为的一个特定特征：动态模式的稳定性分析。当这些机制发挥作用时，它们的活动模式处于边际稳定状态，既不是抑制(稳定)，也不是指数增长(不稳定)，而是在两者之间徘徊。相反，我们之前已经证明，在麻醉诱导下，这些模式变得更稳定，因此反应较慢，然后在苏醒时逆转。我们利用这一效应构建了一个单次测试分类器，该分类器检测对象是清醒的还是昏迷的，取得了较高的性能。我们表明，我们的方法可以发展成为术中监测麻醉深度的手段，这对现代临床实践具有重要的应用价值。
In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a means for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.