Höller Yvonne, Thomschewski Aljoscha, Bergmann Jürgen, Kronbichler Martin, Crone Julia S, Schmid Elisabeth V, Butz Kevin, Höller Peter, Nardone Raffaele, Trinka Eugen
Department of Neurology, Christian-Doppler-Klinik, Paracelsus Medical University, Salzburg, Austria; Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria.
Department of Neurology, Christian-Doppler-Klinik, Paracelsus Medical University, Salzburg, Austria; Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria.
Clin Neurophysiol. 2014 Aug;125(8):1545-55. doi: 10.1016/j.clinph.2013.12.095. Epub 2013 Dec 18.
In the present study, we searched for resting-EEG biomarkers that distinguish different levels of consciousness on a single subject level with an accuracy that is significantly above chance.
We assessed 44 biomarkers extracted from the resting EEG with respect to their discriminative value between groups of minimally conscious (MCS, N=22) patients, vegetative state patients (VS, N=27), and - for a proof of concept - healthy participants (N=23). We applied classification with support vector machines.
Partial coherence, directed transfer function, and generalized partial directed coherence yielded accuracies that were significantly above chance for the group distinction of MCS vs. VS (.88, .80, and .78, respectively), as well as healthy participants vs. MCS (.96, .87, and .93, respectively) and VS (.98, .84, and .96, respectively) patients.
The concept of connectivity is crucial for determining the level of consciousness, supporting the view that assessing brain networks in the resting state is the golden way to examine brain functions such as consciousness.
The present results directly show that it is possible to distinguish patients with different levels of consciousness on the basis of resting-state EEG.
在本研究中，我们寻找静息脑电图生物标志物，以在个体水平上区分不同意识水平，其准确率显著高于随机水平。
我们评估了从静息脑电图中提取的44种生物标志物在最低意识状态（MCS，n = 22）患者组、植物状态患者（VS，n = 27）以及——作为概念验证——健康参与者（n = 23）之间的判别价值。我们应用支持向量机进行分类。
部分相干性、定向传递函数和广义部分定向相干性在区分MCS与VS组（分别为0.88、0.80和0.78）、健康参与者与MCS组（分别为0.96、0.87和0.93）以及健康参与者与VS组（分别为0.98、0.84和0.96）患者时，准确率显著高于随机水平。
连通性概念对于确定意识水平至关重要，支持了这样一种观点，即评估静息状态下的脑网络是检查诸如意识等脑功能的黄金方法。
本研究结果直接表明，基于静息态脑电图区分不同意识水平的患者是可行的。
