PDF(Pubmed)
Abstract：
The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.
摘要：
对意识状态的评估，特别是区分最低意识状态（MCS）和无反应的觉醒状态（UWS），在临床治疗中具有举足轻重的作用。尽管已经提出了许多意识的神经特征，临床意识评估中此类特征的有效性和可靠性仍存在激烈争论.通过对文献的全面回顾,关于不同神经信号的有效性，观察到不一致的发现。值得注意的是,大多数现有研究已经评估了有限数量的受试者（通常低于30）的神经特征，这可能会导致不确定的结论，由于数据偏差小。这项研究提出了一个系统的评估神经特征与大规模的临床静息状态脑电图（EEG）信号包含99UWS，129MCS,36从最低意识状态中出现，在3年内收集了32名健康受试者（共296名）。总共380个基于脑电图的意识检测指标，包括频谱特征，非线性度量，功能连接，和基于图形的度量，进行了总结和评估。为了进一步减轻数据偏差的影响，评估是用自举抽样进行的，以便获得可靠的措施。这项研究的结果表明，α和δ的相对功率可以作为意识的可靠指标。有了MCS组，与UWS组相比,相位滞后指数相关的连通性测量值显著增加,脑区之间的功能连通性增强.特征的组合使得能够开发有意识状态的自动检测器。
