基于丙泊酚麻醉的相位模式复杂度同步及脑网络变化
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摘要
麻醉诱导的无意识在宏观尺度表现为大脑区域之间功能连接的中断.然而,在介观尺度,麻醉的脑意识消失作用机制仍不明确.本研究分析了9名接受丙泊酚全身麻醉的癫痫患者(年龄18～54岁)的皮层脑电(electrocorticogram,ECoG)数据,采用了相位模式复杂度(phase-pattern complexity,PPC)的同步估计方法,并基于图论度量了皮层尺度麻醉前后的聚类系数、平均特征路径长度、全局效率、局部效率和中介中心性等几种网络特征参数.结果表明,delta-theta(1～8Hz)频段和gamma2(48～60Hz)频段在意识消失后的PPC值显著降低(分别为P<0.01,P<0.05).皮层尺度脑网络则由节点间的强连接转变为以少数节点为枢纽节点,并成散射状连接其他节点的网络拓扑结构.研究表明,麻醉导致的意识消失与皮层尺度神经活动的相位模式复杂度的降低及对应的拓扑网络的简单化有关.
The neurophysiological mechanisms of anesthetic-induced loss of unconsciousness(LOC) have been extensively investigated at the macro scale. When it comes to the cortical scale, however, it still remains unclear how the cortical network and the information integration mode change during propofol anesthesia. In this study, we employed the phase-pattern complexity(PPC) measure, a synchronization measure of the diversity of temporal patterns in the phase relationships between two time series, to analyze the electrocorticogram(ECoG) changes during propofol anesthesia. Nine epileptic patients who were undergoing intracranial monitoring for surgical treatment were investigated. Network characteristic parameters such as clustering coefficient, average characteristic path length, global efficiency, component efficiency, and betweenness centrality were measured based on the graph theory. Below is a brief summary of our findings. Firstly, the phase-pattern complexity after LOC reduces significantly in the delta-theta(1-8 Hz) and gamma2(48-60 Hz) frequency bands(with a P-value of <0.01 and <0.05, respectively). Secondly, after LOC, the cortical network changes from a strongly connected network of nodes to one where some of the nodes work as hub nodes and are scattered to connect with other nodes. Thirdly, the following conclusions are drawn from the network measure:(1) During propofol anesthesia, the clustering coefficient value increases in the delta-theta and gamma2 frequency bands, which indicates strong aggregation between nodes in the cortical network.(2) The average characteristic path length increases after LOC, which means that the action of propofol impairs the brain's ability of functional integration.(3) The increase in betweenness centrality indicates that propofol simplifies the cortical network. This leads to an increased number of cortical regions that stop communicating with each other, which in turn results in more paths for information transmission and lower transmission efficiency.(4) The decrease in global efficiency and component efficiency reflects that propofol partially interrupts long-distance connections between cortical regions. To sum up, our research indicates that the propofol induced LOC is related to the reduction of the neural activities' phase-pattern complexity in cortical scale and the simplification of the corresponding topological networks. The propofol induced LOC is also underlying correlated with the disruption of direct connection between cortical regions and the increasement of the role of hub nodes in information integration.
The neurophysiological mechanisms of anesthetic-induced loss of unconsciousness(LOC) have been extensively investigated at the macro scale. When it comes to the cortical scale, however, it still remains unclear how the cortical network and the information integration mode change during propofol anesthesia. In this study, we employed the phase-pattern complexity(PPC) measure, a synchronization measure of the diversity of temporal patterns in the phase relationships between two time series, to analyze the electrocorticogram(ECoG) changes during propofol anesthesia. Nine epileptic patients who were undergoing intracranial monitoring for surgical treatment were investigated. Network characteristic parameters such as clustering coefficient, average characteristic path length, global efficiency, component efficiency, and betweenness centrality were measured based on the graph theory. Below is a brief summary of our findings. Firstly, the phase-pattern complexity after LOC reduces significantly in the delta-theta(1-8 Hz) and gamma2(48-60 Hz) frequency bands(with a P-value of <0.01 and <0.05, respectively). Secondly, after LOC, the cortical network changes from a strongly connected network of nodes to one where some of the nodes work as hub nodes and are scattered to connect with other nodes. Thirdly, the following conclusions are drawn from the network measure:(1) During propofol anesthesia, the clustering coefficient value increases in the delta-theta and gamma2 frequency bands, which indicates strong aggregation between nodes in the cortical network.(2) The average characteristic path length increases after LOC, which means that the action of propofol impairs the brain's ability of functional integration.(3) The increase in betweenness centrality indicates that propofol simplifies the cortical network. This leads to an increased number of cortical regions that stop communicating with each other, which in turn results in more paths for information transmission and lower transmission efficiency.(4) The decrease in global efficiency and component efficiency reflects that propofol partially interrupts long-distance connections between cortical regions. To sum up, our research indicates that the propofol induced LOC is related to the reduction of the neural activities' phase-pattern complexity in cortical scale and the simplification of the corresponding topological networks. The propofol induced LOC is also underlying correlated with the disruption of direct connection between cortical regions and the increasement of the role of hub nodes in information integration.
引文
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12 Lee H,Noh G J,Joo P,et al.Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness.Hum Brain Mapp,2017,38:4980-4995
13 Huang W,Bolton T A W,Medaglia J D,et al.A graph signal processing perspective on functional brain imaging.Proc IEEE,2018,106:868-885
14 Zhang R,Ren Y,Liu C,et al.Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy.Clin Neurophysiol,2017,128:1707-1718
15 Widrow B,Glover J R,Mccool J M,et al.Adaptive noise cancelling:Principles and applications.Proc IEEE,2005,63:1692-1716
16 Li X,Cui S,Voss L J.Using permutation entropy to measure the electroencephalographic effects of sevoflurane.Anesthesiology,2008,109:448-456
17 Casula E P,Tarantino V,Basso D,et al.Low-frequency rTMS inhibitory effects in the primary motor cortex:Insights from TMS-evoked potentials.Neuroimage,2014,98:225-232
18 Kreuz T,Mormann F,Andrzejak R G,et al.Measuring synchronization in coupled model systems:A comparison of different approaches.Physica D,2007,225:29-42
19 Mukamel E A,Pirondini E,Babadi B,et al.A transition in brain state during propofol-induced unconsciousness.J Neurosci,2014,34:839-845
20 Tort A B,Komorowski R,Eichenbaum H,et al.Measuring phase-amplitude coupling between neuronal oscillations of different frequencies.J Neurophysiol,2010,104:1195-1210
21 Li X,Ouyang G.Estimating coupling direction between neuronal populations with permutation conditional mutual information.Neuroimage,2010,52:497-507
22 Bullmore E,Sporns O.Complex brain networks:Graph theoretical analysis of structural and functional systems.Nat Rev Neurosci,2009,10:186-198
23 Sporns O,Tononi G,K?tter R.The human connectome:A structural description of the human brain.PLoS Comput Biol,2005,1:245-251
24 Ding X P,Wu S J,Liu J,et al.Functional neural networks of honesty and dishonesty in children:Evidence from graph theory analysis.Sci Rep,2017,7:1-10
25 Friston K J.Functional and effective connectivity in neuroimaging:A synthesis.Brain Connect,2011,1:13-36
26 Murphy M,Bruno M A,Riedner B A,et al.Propofol anesthesia and sleep:A high-density EEG study.Sleep,2011,34:283-291
27 Tang Y,Gao H,Kurths J.Distributed robust synchronization of dynamical networks with stochastic coupling.IEEE T Circuits-I,2017,61:1508-1519
28 Whittington M A,Traub R D,Kopell N,et al.Inhibition-based rhythms:Experimental and mathematical observations on network dynamics.Int J Psychophysiol,2000,38:315-336
29 Maslov S,Sneppen K.Specificity and stability in topology of protein networks.Science,2002,296:910-913
30 Muldoon S F,Bridgeford E W,Bassett D S.Small-world propensity and weighted brain networks.Sci Rep,2016,6:22057
31 Sporns O.Structure and function of complex brain networks.Dialogues Clin Neurosci,2013,15:247-262
32 Goh K I,Oh E,Kahng B,et al.Betweenness centrality correlation in social networks.Phys Rev E Stat Nonlin Soft Matter Phys,2003,67:017101
33 Yoon J,Blumer A,Lee K.An algorithm for modularity analysis of directed and weighted biological networks based on edgebetweenness centrality.Bioinformatics,2006,22:3106-3108
34 Wang H,Hernandez J M,Van M P.Betweenness centrality in a weighted network.Phys Rev E Stat Nonlin Soft Matter Phys,2008,77:046105
35 Percival D,Walden A.Spectral Analysis for Physical Applications.Cambridge:Cambridge University Press,1998
36 Cai L,Dong Q,Niu H.The development of functional network organization in early childhood and early adolescence:A resting-state fnirs study.Dev Cogn Neurosci,2018,30:223-235
37 Wilke C,Worrell G,He B.Graph analysis of epileptogenic networks in human partial epilepsy.Epilepsia,2011,52:84
38 Siegel M,Donner T H,Engel A K.Spectral fingerprints of large-scale neuronal interactions.Nat Rev Neurosci,2012,13:121-134
39 Sadaghiani S,Scheeringa R,Lehongre K,et al.Alpha-band phase synchrony is related to activity in the fronto-parietal adaptive control network.J Neurosci,2012,32:14305-14310
40 Neske G T.The slow oscillation in cortical and thalamic networks:Mechanisms and functions.Front Neural Circuit,2016,9:88
41 Koskinen M,Sepp?nen T,Tuukkanen J,et al.Propofol anesthesia induces phase synchronization changes in EEG.Clin Neurophysiol,2001,112:386-392
42 Purdon P L,Pierce E T,Mukamel E A,et al.Electroencephalogram signatures of loss and recovery of consciousness from propofol.Pnas,2013,110:E1142-E1151
43 Pockett S,Holmes M D.Intracranial EEG power spectra and phase synchrony during consciousness and unconsciousness.Conscious Cogn,2009,18:1049-1055
44 Jeong J,Gore J C,Peterson B S.Mutual information analysis of the EEG in patients with Alzheimer’s disease.Clin Neurophysiol,2001,112:827-835
45 Blainmoraes S,Tarnal V,Vanini G,et al.Network efficiency and posterior alpha patterns are markers of recovery from general anesthesia:A high-density electroencephalography study in healthy volunteers.Front Hum Neurosci,2017,11:328
46 Hofstad R V D.Random graphs and complex networks.Preparation,2009,70:188-206
2 Avidan M S,Jacobsohn E,Glick D,et al.Prevention of intraoperative awareness in a high-risk surgical population.N Engl J Med,2011,365:591-600
3 Lee U,Müller M,Noh G J,et al.Dissociable network properties of anesthetic state transitions.Anesthesiology,2011,114:872-881
4 Plankar M,Bre?an S,Jerman I.The principle of coherence in multi-level brain information processing.Prog Biophys Mol Biol,2013,111:8-29
5 Achard S,Salvador R,Whitcher B,et al.A resilient,low-frequency,small-world human brain functional network with highly connected association cortical hubs.J Neurosci,2006,26:63
6 Kim M,Mashour G A,Moraes S B,et al.Functional and topological conditions for explosive synchronization develop in human brain networks with the onset of anesthetic-induced unconsciousness.Front Comput Neurosci,2016,10:1-15
7 Lee H,Mashour G A,Noh G J,et al.Reconfiguration of network hub structure after propofol-induced unconsciousness.Anesthesiology,2013,119:1347-1359
8 Varela F,Lachaux J P,Rodriguez E,et al.The brainweb:Phase synchronization and large-scale integration.Nat Rev Neurosci,2001,2:229-239
9 Friston K J.The labile brain.I.Neuronal transients and nonlinear coupling.Philos T R Soc B,2000,355:215
10 Cheng N,Li Q,Wang S,et al.Permutation mutual information:A novel approach for measuring neuronal phase-amplitude coupling.Brain Topogr,2018,31:1-16
11 Massimini M,Ferrarelli F,Huber R,et al.Breakdown of cortical effective connectivity during sleep.Science,2005,309:2228-2232
12 Lee H,Noh G J,Joo P,et al.Diversity of functional connectivity patterns is reduced in propofol-induced unconsciousness.Hum Brain Mapp,2017,38:4980-4995
13 Huang W,Bolton T A W,Medaglia J D,et al.A graph signal processing perspective on functional brain imaging.Proc IEEE,2018,106:868-885
14 Zhang R,Ren Y,Liu C,et al.Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy.Clin Neurophysiol,2017,128:1707-1718
15 Widrow B,Glover J R,Mccool J M,et al.Adaptive noise cancelling:Principles and applications.Proc IEEE,2005,63:1692-1716
16 Li X,Cui S,Voss L J.Using permutation entropy to measure the electroencephalographic effects of sevoflurane.Anesthesiology,2008,109:448-456
17 Casula E P,Tarantino V,Basso D,et al.Low-frequency rTMS inhibitory effects in the primary motor cortex:Insights from TMS-evoked potentials.Neuroimage,2014,98:225-232
18 Kreuz T,Mormann F,Andrzejak R G,et al.Measuring synchronization in coupled model systems:A comparison of different approaches.Physica D,2007,225:29-42
19 Mukamel E A,Pirondini E,Babadi B,et al.A transition in brain state during propofol-induced unconsciousness.J Neurosci,2014,34:839-845
20 Tort A B,Komorowski R,Eichenbaum H,et al.Measuring phase-amplitude coupling between neuronal oscillations of different frequencies.J Neurophysiol,2010,104:1195-1210
21 Li X,Ouyang G.Estimating coupling direction between neuronal populations with permutation conditional mutual information.Neuroimage,2010,52:497-507
22 Bullmore E,Sporns O.Complex brain networks:Graph theoretical analysis of structural and functional systems.Nat Rev Neurosci,2009,10:186-198
23 Sporns O,Tononi G,K?tter R.The human connectome:A structural description of the human brain.PLoS Comput Biol,2005,1:245-251
24 Ding X P,Wu S J,Liu J,et al.Functional neural networks of honesty and dishonesty in children:Evidence from graph theory analysis.Sci Rep,2017,7:1-10
25 Friston K J.Functional and effective connectivity in neuroimaging:A synthesis.Brain Connect,2011,1:13-36
26 Murphy M,Bruno M A,Riedner B A,et al.Propofol anesthesia and sleep:A high-density EEG study.Sleep,2011,34:283-291
27 Tang Y,Gao H,Kurths J.Distributed robust synchronization of dynamical networks with stochastic coupling.IEEE T Circuits-I,2017,61:1508-1519
28 Whittington M A,Traub R D,Kopell N,et al.Inhibition-based rhythms:Experimental and mathematical observations on network dynamics.Int J Psychophysiol,2000,38:315-336
29 Maslov S,Sneppen K.Specificity and stability in topology of protein networks.Science,2002,296:910-913
30 Muldoon S F,Bridgeford E W,Bassett D S.Small-world propensity and weighted brain networks.Sci Rep,2016,6:22057
31 Sporns O.Structure and function of complex brain networks.Dialogues Clin Neurosci,2013,15:247-262
32 Goh K I,Oh E,Kahng B,et al.Betweenness centrality correlation in social networks.Phys Rev E Stat Nonlin Soft Matter Phys,2003,67:017101
33 Yoon J,Blumer A,Lee K.An algorithm for modularity analysis of directed and weighted biological networks based on edgebetweenness centrality.Bioinformatics,2006,22:3106-3108
34 Wang H,Hernandez J M,Van M P.Betweenness centrality in a weighted network.Phys Rev E Stat Nonlin Soft Matter Phys,2008,77:046105
35 Percival D,Walden A.Spectral Analysis for Physical Applications.Cambridge:Cambridge University Press,1998
36 Cai L,Dong Q,Niu H.The development of functional network organization in early childhood and early adolescence:A resting-state fnirs study.Dev Cogn Neurosci,2018,30:223-235
37 Wilke C,Worrell G,He B.Graph analysis of epileptogenic networks in human partial epilepsy.Epilepsia,2011,52:84
38 Siegel M,Donner T H,Engel A K.Spectral fingerprints of large-scale neuronal interactions.Nat Rev Neurosci,2012,13:121-134
39 Sadaghiani S,Scheeringa R,Lehongre K,et al.Alpha-band phase synchrony is related to activity in the fronto-parietal adaptive control network.J Neurosci,2012,32:14305-14310
40 Neske G T.The slow oscillation in cortical and thalamic networks:Mechanisms and functions.Front Neural Circuit,2016,9:88
41 Koskinen M,Sepp?nen T,Tuukkanen J,et al.Propofol anesthesia induces phase synchronization changes in EEG.Clin Neurophysiol,2001,112:386-392
42 Purdon P L,Pierce E T,Mukamel E A,et al.Electroencephalogram signatures of loss and recovery of consciousness from propofol.Pnas,2013,110:E1142-E1151
43 Pockett S,Holmes M D.Intracranial EEG power spectra and phase synchrony during consciousness and unconsciousness.Conscious Cogn,2009,18:1049-1055
44 Jeong J,Gore J C,Peterson B S.Mutual information analysis of the EEG in patients with Alzheimer’s disease.Clin Neurophysiol,2001,112:827-835
45 Blainmoraes S,Tarnal V,Vanini G,et al.Network efficiency and posterior alpha patterns are markers of recovery from general anesthesia:A high-density electroencephalography study in healthy volunteers.Front Hum Neurosci,2017,11:328
46 Hofstad R V D.Random graphs and complex networks.Preparation,2009,70:188-206