School of Computing, University of Kent, United Kingdom.
Coma Science Group, GIGA Consciousness, University and University Hospital of Liège, Liège, Belgium.
Neuroimage. 2021 May 1;231:117841. doi: 10.1016/j.neuroimage.2021.117841. Epub 2021 Feb 9.
In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs - the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) - to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 min of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior 'hot zone', frontoparietal connections, and between-RSN connections. We estimate - for the first time - a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.
近年来,人们提出了特定的皮质网络对于维持意识至关重要,包括后热点和额顶静息态网络(RSN)。在这里,我们通过计算评估了三个 RSN(默认模式网络(DMN)、突显网络(SAL)和中央执行网络(CEN))对意识及其在异丙酚麻醉期间丧失的相对贡献。具体来说,我们使用 10 分钟高密度 EEG 记录(N=10,4 名男性)的动态因果建模(DCM),这些记录是在行为反应性、无意识和麻醉后恢复期间获得的,以描述额区、后热点、额顶连接以及 RSN 之间的有效连接内的差异。我们首次估计了静息 EEG 的大型 DCM 模型(LAR),将三个 RSN 结合到一个丰富的互连带中。与热点理论一致,我们的研究结果表明,顶叶皮层的 RSN 之间的连接减少。在 DMN 本身中,在前扣带节点处,额顶和顶叶的前馈连接减少最多。在 SAL 和 CEN 中,意识丧失会导致双向连接略有增加。使用新颖的 DCM 留一交叉验证,我们表明,意识状态的最一致的样本外预测来自一组关键的额顶连接。这一发现也适用于麻醉后恢复期间收集的未见数据。我们的研究结果为后热点在解释意识丧失方面的重要性提供了新的计算证据,同时也强调了额顶连接在支撑意识反应性方面的独特作用,因此,建议在与解释意识知觉和无意识之间的对比最相关的机制之间存在分离,以及那些维持意识的机制。