Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia.
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DOI：
10.1371/journal.pone.0029072
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Seth AK
中科院分区：
文献类型：
--
作者：
Barrett AB;Murphy M;Bruno MA;Noirhomme Q;Boly M;Laureys S;Seth AK
意识水平的变化与分布式大脑区域之间动态整合和分离的变化有关。最近的理论发展强调定向泛函的变化（即，因果）连接性，如在诸如“综合信息”和“因果密度”的量中所反映的。在这里，我们开发和说明了一个严格的方法来评估因果连接脑电图（EEG）信号使用格兰杰因果关系（GC）。我们的方法通过将数据划分为短段并应用排列分析来解决非平稳性和偏差的挑战。我们将该方法应用于从接受丙泊酚诱导麻醉的受试者获得的EEG数据，信号源定位于前扣带皮层和后扣带皮层。我们发现，在大多数受试者在意识丧失期间，双向GC显著增加，特别是在β和γ频率范围内。证实了以前的分析，我们还发现在这些范围内同步增加，重要的是，格兰杰因果关系分析显示出更高的主题间的一致性比同步分析。最后，我们验证了我们的方法使用模拟数据生成的模型，GC值可以解析推导。总之，我们的研究结果推进了脑电数据的格兰杰因果分析方法，并对意识的综合信息和因果密度理论产生了影响。
Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as ‘integrated information’ and ‘causal density’. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.
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DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
DOI：
10.1073/pnas.1011949107
发表时间：
2010-12-07
影响因子：
11.1
作者：
Breshears, Jonathan D.;Roland, Jarod L.;Leuthardt, Eric C.
通讯作者：
Leuthardt, Eric C.
影响因子：
1.9
作者：
Ding, MZ;Bressler, SL;Liang, HL
通讯作者：
Liang, HL
影响因子：
3.7
作者：
GEWEKE, JF
通讯作者：
GEWEKE, JF
影响因子：
4.3
作者：
Barrett AB;Seth AK
通讯作者：
Seth AK