Kim Minkyung, Mashour George A, Moraes Stefanie-Blain, Vanini Giancarlo, Tarnal Vijay, Janke Ellen, Hudetz Anthony G, Lee Uncheol
Department of Anesthesiology, University of Michigan Medical SchoolAnn Arbor, MI, USA; Center for Consciousness Science, University of Michigan Medical SchoolAnn Arbor, MI, USA; Department of Physics, Pohang University of Science and TechnologyPohang, South Korea.
Department of Anesthesiology, University of Michigan Medical SchoolAnn Arbor, MI, USA; Center for Consciousness Science, University of Michigan Medical SchoolAnn Arbor, MI, USA; Neuroscience Graduate Program, University of Michigan Medical SchoolAnn Arbor, MI, USA.
Front Comput Neurosci. 2016 Jan 21;10:1. doi: 10.3389/fncom.2016.00001. eCollection 2016.
Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.
睡眠、麻醉和昏迷具有一些神经特征，但恢复情况却截然不同。为了在网络层面理解意识丧失的可逆机制，我们研究了清醒和麻醉状态下逐渐转变和突然转变的条件。我们假设，人类大脑网络中爆发性同步（ES）的条件在刚刚超过意识丧失阈值的麻醉大脑中会存在。为了验证这一假设，我们从7名健康受试者在清醒、无意识和恢复状态下的多通道脑电图（EEG）记录构建了功能性脑网络。我们分析了在一般的非生物网络中促进ES的四个变量：（1）节点度与频率之间的相关性，（2）非同类性（即高度连接的节点与连接较少的节点相连的趋势，反之亦然），（3）耦合节点的频率差异，以及（4）局部和全局网络属性之间的不等式关系，这被称为抑制规则。我们观察到在无意识状态下满足了ES的四个网络条件。人类大脑中ES的条件表明了从轻度麻醉状态快速恢复的潜在机制。这项研究首次证明，以前仅在一般网络中显示的ES的网络条件存在于通过实验得出的功能性脑网络中。对深度麻醉、睡眠和昏迷的进一步研究可以深入了解这些无意识状态恢复情况变化的潜在原因。
