Center for Studies in Physics and Biology, The Rockefeller University New York, NY, USA.
Department of Anesthesiology, Weill Cornell Medical College New York, NY, USA ; Laboratory for Neurobiology and Behavior, The Rockefeller University New York, NY, USA.
Front Neural Circuits. 2014 Mar 25;8:20. doi: 10.3389/fncir.2014.00020. eCollection 2014.
In this work we analyze electro-corticography (ECoG) recordings in human subjects during induction of anesthesia with propofol. We hypothesize that the decrease in responsiveness that defines the anesthetized state is concomitant with the stabilization of neuronal dynamics. To test this hypothesis, we performed a moving vector autoregressive analysis and quantified stability of neuronal dynamics using eigenmode decomposition of the autoregressive matrices, independently fitted to short sliding temporal windows. Consistent with the hypothesis we show that while the subject is awake, many modes of neuronal activity oscillations are found at the edge of instability. As the subject becomes anesthetized, we observe statistically significant increase in the stability of neuronal dynamics, most prominently observed for high frequency oscillations. Stabilization was not observed in phase randomized surrogates constructed to preserve the spectral signatures of each channel of neuronal activity. Thus, stability analysis offers a novel way of quantifying changes in neuronal activity that characterize loss of consciousness induced by general anesthetics.
在这项工作中,我们分析了人类在异丙酚诱导麻醉过程中的皮层电图(ECoG)记录。我们假设,定义麻醉状态的反应能力下降与神经元动力学的稳定同时发生。为了验证这一假设,我们进行了移动向量自回归分析,并使用自回归矩阵的特征模态分解来量化神经元动力学的稳定性,该分解独立拟合于短的滑动时间窗口。与假设一致,我们表明,当主体清醒时,许多神经元活动振荡模式处于不稳定的边缘。随着主体被麻醉,我们观察到神经元动力学的稳定性显著增加,高频振荡最为明显。在为保留神经元活动每个通道的频谱特征而构建的相位随机化替代物中,未观察到稳定化。因此,稳定性分析为量化特征意识丧失的神经元活动变化提供了一种新方法,这种变化是由全身麻醉诱导的。