PDF(Pubmed)
Abstract：
Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially \"covert\" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.
摘要：
神经影像学研究表明，默认模式网络（DMN）在意识障碍（DoC）中具有重要作用。然而,DMN连接在多大程度上可以区分DoC状态-无反应的觉醒综合征(UWS)和最低意识状态(MCS)-并不那么明显.特别是,目前还不清楚是否有效的DMN连接，通过静息EEG的动态因果模型（DCM）间接测量，可以将UWS与健康对照和被认为有意识的患者（MCS）分开。至关重要的是，这延伸到具有潜在“隐蔽”意识的UWS患者(最低意识明星，MCS*）以自愿性大脑活动与部分保留的额顶代谢结合为索引，如正电子发射断层扫描（PET诊断；与PET诊断完全额顶代谢低下相反）。这里,我们通过使用从11个UWS（6PET-和5PET+）和12个MCS（11PET+和1PET-）的创伤性脑损伤患者获得的EEG数据的DCM来解决这一差距，与11个健康对照。当将UWSPET-与MCS患者和健康对照进行对比时，我们提供了左额顶连接的关键差异的证据。接下来,在保留一个主题交叉验证中，我们测试了DCM模型的分类性能,证明内侧前额叶和左顶叶源之间的连通性能够可靠地将UWSPET-与MCS+患者和对照区分开来.最后,我们说明了这些模型可以推广到一个看不见的数据集：训练来区分UWSPET-与MCS+和控件的模型，将MCS*患者分类为具有高后验概率的有意识受试者(pp>.92)。这些结果确定了严重脑损伤后DMN的特定变化，并强调了基于EEG的有效连接的临床实用性，可用于识别具有潜在隐性意识的患者。
