The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.

中文翻译：

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多重和多层网络脑电图分析：慢性意识障碍患者鉴别诊断的新策略

量化全脑功能网络组织的不同特征的特定拓扑网络措施的恶化可以被认为是意识障碍的标志。这种拓扑测量反映了多个大脑结构的功能相互作用，支持整合不同的感觉运动信息对向意识。然而，传统的、单层的、基于图论分析 (GTA) 的方法不能总是可靠地区分有意识障碍 (DoC) 的患者。使用频率特定和区域特定网络的多路复用和多层网络分析，我们调查了 17 名无反应觉醒综合征 (UWS) 和 15 名最小意识状态 (MCS) 患者在静息状态脑电图中的功能连接。多重和多层网络指标表明功能网络的恶化和异质性，特别是额顶叶（FP），作为 MCS 和 UWS 患者之间的区别。在考虑每个特定频率的网络时，这些数据并不明显。多路复用/多层网络度量和单个频率特定网络度量的独特属性进一步表明了集成网络的价值，而不是一次分析一个频率特定网络度量。这些区域的枢纽脆弱性与行为反应呈正相关，从而加强了基于临床的鉴别诊断。所以，在扩展基于 GTA 的传统分析对 DoC 患者进行鉴别诊断时，采用多重和多层网络分析可能是有益的。多重分析在组水平上区分患者，而多层分析提供补充信息来区分单独的 DoC 患者。尽管需要进一步的研究来证实我们的初步研究结果，但这些结果有助于解决 DoC 鉴别诊断问题，并可能有助于指导患者量身定制的管理。