Solovey Guillermo, Alonso Leandro M, Yanagawa Toru, Fujii Naotaka, Magnasco Marcelo O, Cecchi Guillermo A, Proekt Alex
Laboratories of Mathematical Physics and Integrative Neuroscience Laboratory, Buenos Aires, Argentina, Instituto de Cálculo, Faculty of Natural Sciences, University of Buenos Aires (C1428EGA), Buenos Aires, Argentina.
Laboratories of Mathematical Physics and.
J Neurosci. 2015 Jul 29;35(30):10866-77. doi: 10.1523/JNEUROSCI.4895-14.2015.
What aspects of neuronal activity distinguish the conscious from the unconscious brain? This has been a subject of intense interest and debate since the early days of neurophysiology. However, as any practicing anesthesiologist can attest, it is currently not possible to reliably distinguish a conscious state from an unconscious one on the basis of brain activity. Here we approach this problem from the perspective of dynamical systems theory. We argue that the brain, as a dynamical system, is self-regulated at the boundary between stable and unstable regimes, allowing it in particular to maintain high susceptibility to stimuli. To test this hypothesis, we performed stability analysis of high-density electrocorticography recordings covering an entire cerebral hemisphere in monkeys during reversible loss of consciousness. We show that, during loss of consciousness, the number of eigenmodes at the edge of instability decreases smoothly, independently of the type of anesthetic and specific features of brain activity. The eigenmodes drift back toward the unstable line during recovery of consciousness. Furthermore, we show that stability is an emergent phenomenon dependent on the correlations among activity in different cortical regions rather than signals taken in isolation. These findings support the conclusion that dynamics at the edge of instability are essential for maintaining consciousness and provide a novel and principled measure that distinguishes between the conscious and the unconscious brain.
What distinguishes brain activity during consciousness from that observed during unconsciousness? Answering this question has proven difficult because neither consciousness nor lack thereof have universal signatures in terms of most specific features of brain activity. For instance, different anesthetics induce different patterns of brain activity. We demonstrate that loss of consciousness is universally and reliably associated with stabilization of cortical dynamics regardless of the specific activity characteristics. To give an analogy, our analysis suggests that loss of consciousness is akin to depressing the damper pedal on the piano, which makes the sounds dissipate quicker regardless of the specific melody being played. This approach may prove useful in detecting consciousness on the basis of brain activity under anesthesia and other settings.
神经元活动的哪些方面能区分有意识的大脑和无意识的大脑？自神经生理学早期以来，这一直是一个备受关注和争论的话题。然而，正如任何一位执业麻醉师都能证明的那样，目前还不可能根据大脑活动可靠地区分有意识状态和无意识状态。在这里，我们从动力系统理论的角度来探讨这个问题。我们认为，大脑作为一个动力系统，在稳定和不稳定状态之间的边界处进行自我调节，这尤其使它能够保持对刺激的高敏感性。为了验证这一假设，我们对猴子在可逆性意识丧失期间覆盖整个大脑半球的高密度皮层脑电图记录进行了稳定性分析。我们发现，在意识丧失期间，不稳定边缘的本征模数量会平稳减少，这与麻醉类型和大脑活动的具体特征无关。在意识恢复过程中，本征模会向不稳定线漂移回来。此外，我们还表明，稳定性是一种涌现现象，它依赖于不同皮层区域活动之间的相关性，而不是孤立的信号。这些发现支持了这样的结论，即不稳定边缘的动力学对于维持意识至关重要，并提供了一种区分有意识和无意识大脑的新颖且有原则的方法。
意识状态下的大脑活动与无意识状态下观察到的大脑活动有何不同？事实证明，回答这个问题很困难，因为就大脑活动的大多数具体特征而言，意识和无意识都没有通用的特征。例如，不同的麻醉剂会诱发不同的大脑活动模式。我们证明，无论具体的活动特征如何，意识丧失都普遍且可靠地与皮层动力学的稳定相关。打个比方，我们的分析表明，意识丧失类似于踩下钢琴的延音踏板，这会使声音更快消散，而不管正在弹奏的具体旋律如何。这种方法可能在基于麻醉和其他情况下的大脑活动检测意识方面证明是有用的。
