Naro Antonino, Maggio Maria Grazia, Leo Antonino, Calabrò Rocco Salvatore
IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy, Via Palermo, SS 113, Contrada Casazza, 98124 Messina, Italy.
Int J Neural Syst. 2021 Feb;31(2):2050052. doi: 10.1142/S0129065720500525. Epub 2020 Oct 9.
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.
量化全脑功能网络组织不同特征的特定拓扑网络指标的恶化可被视为意识障碍的一个标志。此类拓扑指标反映了多个脑结构的功能相互作用,这些相互作用支持构成意识基础的不同感觉运动信息的整合。然而,基于传统单层图论分析(GTA)的方法并不总能可靠地区分意识障碍(DoC)患者。通过对频率特异性和区域特异性网络进行多重和多层网络分析,我们研究了17例无反应觉醒综合征(UWS)患者和15例最低意识状态(MCS)患者静息态脑电图期间的功能连接性。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),可作为MCS和UWS患者之间的判别指标。在考虑每个单独的频率特异性网络时,这些数据并不明显。多重/多层网络指标和单个频率特异性网络指标的独特属性进一步表明,与一次分析一个频率特异性网络指标相比,整合网络具有价值。这些区域的枢纽脆弱性与行为反应性呈正相关,从而加强了基于临床的鉴别诊断。因此,在扩展基于传统GTA的分析以用于DoC患者的鉴别诊断时,采用多重和多层网络分析可能是有益的。多重分析在群体水平上区分患者,而多层分析提供补充信息以个别区分DoC患者。尽管需要进一步研究来证实我们的初步发现,但这些结果有助于解决DoC的鉴别诊断问题,并可能有助于指导针对患者的个性化管理。