Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States; Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, United States.
Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
Neuroimage. 2019 Mar;188:228-238. doi: 10.1016/j.neuroimage.2018.12.011. Epub 2018 Dec 6.
Recent modeling and empirical studies support the hypothesis that large-scale brain networks function near a critical state. Similar functional connectivity patterns derived from resting state empirical data and brain network models at criticality provide further support. However, despite the strong implication of a relationship, there has been no principled explanation of how criticality shapes the characteristic functional connectivity in large-scale brain networks. Here, we hypothesized that the network science concept of partial phase locking is the underlying mechanism of optimal functional connectivity in the resting state. We further hypothesized that the characteristic connectivity of the critical state provides a theoretical boundary to quantify how far pharmacologically or pathologically perturbed brain connectivity deviates from its critical state, which could enable the differentiation of various states of consciousness with a theory-based metric. To test the hypothesis, we used a neuroanatomically informed brain network model with the resulting source signals projected to electroencephalogram (EEG)-like sensor signals with a forward model. Phase lag entropy (PLE), a measure of phase relation diversity, was estimated and the topography of PLE was analyzed. To measure the distance from criticality, the PLE topography at a critical state was compared with those of the EEG data from baseline consciousness, isoflurane anesthesia, ketamine anesthesia, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. We demonstrate that the partial phase locking at criticality shapes the functional connectivity and asymmetric anterior-posterior PLE topography, with low (high) PLE for high (low) degree nodes. The topographical similarity and the strength of PLE differentiates various pharmacologic and pathologic states of consciousness. Moreover, this model-based EEG network analysis provides a novel metric to quantify how far a pharmacologically or pathologically perturbed brain network is away from critical state, rather than merely determining whether it is in a critical or non-critical state.
最近的建模和实证研究支持了这样一种假设，即大规模脑网络在接近临界状态下运作。从静息状态实证数据和临界网络模型中得出的类似功能连接模式提供了进一步的支持。然而，尽管这种关系具有很强的暗示，但还没有一个原则性的解释来说明临界状态如何塑造大规模脑网络的特征功能连接。在这里，我们假设网络科学中部分相位锁定的概念是静息状态下最佳功能连接的潜在机制。我们进一步假设，临界状态的特征连接为量化药物或病理扰动的脑连接偏离其临界状态的程度提供了一个理论边界，这可以使基于理论的度量来区分各种意识状态。为了检验这一假设，我们使用了一个神经解剖学启发的大脑网络模型，其结果源信号被投射到具有正向模型的脑电图（EEG）样传感器信号。相位滞后熵（PLE）是衡量相位关系多样性的一个指标，我们估计了 PLE 并分析了其地形。为了测量与临界状态的距离，我们将临界状态下的 PLE 地形与基线意识、异氟烷麻醉、氯胺酮麻醉、植物状态/无反应性觉醒综合征和最小意识状态的 EEG 数据进行了比较。我们证明了临界状态下的部分相位锁定塑造了功能连接和不对称的前后 PLE 地形，高（低）度节点的 PLE 低（高）。PLE 的地形相似性和强度可以区分各种药理学和病理学意识状态。此外，这种基于模型的 EEG 网络分析提供了一种新的度量标准，用于量化药物或病理扰动的脑网络与临界状态的偏离程度，而不仅仅是确定它是否处于临界或非临界状态。
