Wang Jisung, Noh Gyu-Jeong, Choi Byung-Moon, Ku Seung-Woo, Joo Pangyu, Jung Woo-Sung, Kim Seunghwan, Lee Heonsoo
Department of Physics, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, South Korea.
Department of Clinical Pharmacology and Therapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea; Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, South Korea.
Neurosci Lett. 2017 Jul 13;653:320-325. doi: 10.1016/j.neulet.2017.05.045. Epub 2017 May 29.
Ketamine and propofol have distinctively different molecular mechanisms of action and neurophysiological features, although both induce loss of consciousness. Therefore, identifying a common feature of ketamine- and propofol-induced unconsciousness would provide insight into the underlying mechanism of losing consciousness. In this study we search for a common feature by applying the concept of type-II complexity, and argue that neural complexity is essential for a brain to maintain consciousness. To test this hypothesis, we show that complexity is suppressed during loss of consciousness induced by ketamine or propofol. We analyzed the randomness (type-I complexity) and complexity (type-II complexity) of electroencephalogram (EEG) signals before and after bolus injection of ketamine or propofol. For the analysis, we use Mean Information Gain (MIG) and Fluctuation Complexity (FC), which are information-theory-based measures that quantify disorder and complexity of dynamics respectively. Both ketamine and propofol reduced the complexity of the EEG signal, but ketamine increased the randomness of the signal and propofol decreased it. The finding supports our claim and suggests EEG complexity as a candidate for a consciousness indicator.
氯胺酮和丙泊酚虽然都能导致意识丧失,但其分子作用机制和神经生理特征却截然不同。因此,确定氯胺酮和丙泊酚诱导意识丧失的共同特征,将有助于深入了解意识丧失的潜在机制。在本研究中,我们通过应用II型复杂性的概念来寻找共同特征,并认为神经复杂性对于大脑维持意识至关重要。为了验证这一假设,我们发现氯胺酮或丙泊酚诱导意识丧失期间复杂性受到抑制。我们分析了静脉注射氯胺酮或丙泊酚前后脑电图(EEG)信号的随机性(I型复杂性)和复杂性(II型复杂性)。为了进行分析,我们使用了平均信息增益(MIG)和波动复杂性(FC),这两种基于信息论的测量方法分别量化了动力学的无序性和复杂性。氯胺酮和丙泊酚均降低了EEG信号的复杂性,但氯胺酮增加了信号的随机性,而丙泊酚则降低了信号的随机性。这一发现支持了我们的观点,并表明EEG复杂性可作为意识指标的一个候选因素。