Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics.
Awakening
Connectivity
ECD
LHI
QEEG
Small-world
Journal
Neurocritical care
ISSN: 1556-0961
Titre abrégé: Neurocrit Care
Pays: United States
ID NLM: 101156086
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
17
01
2020
accepted:
05
07
2020
pubmed:
25
7
2020
medline:
30
9
2021
entrez:
25
7
2020
Statut:
ppublish
Résumé
Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms. Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group. (1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group. In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.
Sections du résumé
BACKGROUND
Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms.
METHODS
Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group.
RESULTS
(1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group.
CONCLUSION
In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.
Identifiants
pubmed: 32705419
doi: 10.1007/s12028-020-01051-w
pii: 10.1007/s12028-020-01051-w
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
376-388Commentaires et corrections
Type : CommentIn
Références
Torbey MT, Bösel J, Rhoney DH, et al. Evidence-based guidelines for the management of large hemispheric infarction. Neurocrit Care. 2015;22(1):146–64.
pubmed: 25605626
doi: 10.1007/s12028-014-0085-6
Li J, Wang D, Tao W, et al. Early consciousness disorder in acute ischemic stroke: incidence, risk factors and outcome. BMC Neurol. 2016;16(1):140.
pubmed: 27535026
pmcid: 4989496
doi: 10.1186/s12883-016-0666-4
Achard S, Delon-Martin C, Vertes PE, et al. Hubs of brain functional networks are radically reorganized in comatose patients. Proc Natl Acad Sci USA. 2012;109:20608–13.
pubmed: 23185007
doi: 10.1073/pnas.1208933109
Sair HI, Hannawi Y, Li S, et al. Early functional connectome integrity and 1-year recovery in comatose, survivors of cardiac arrest. Radiology. 2018;287(1):247–55.
pubmed: 29043908
doi: 10.1148/radiol.2017162161
Norton L, Hutchison RM, Young GB, et al. Disruptions of functional connectivity in the default mode network of comatose patients. Neurology. 2012;78(3):175–81.
pubmed: 22218274
doi: 10.1212/WNL.0b013e31823fcd61
Fischer DB, Boes AD, Demertzi A, et al. A human brain network derived from coma-causing brainstem lesions. Neurology. 2016;87(23):2427–34.
pubmed: 27815400
pmcid: 5177681
doi: 10.1212/WNL.0000000000003404
Di Perri C, Bastianello S, Bartsch AJ, et al. Limbic hyperconnectivity in the vegetative state. Neurology. 2013;81(16):1417–24.
pubmed: 24049132
doi: 10.1212/WNL.0b013e3182a43b78
Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJF, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain. 2010;133(1):161–71.
pubmed: 20034928
doi: 10.1093/brain/awp313
Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015;88(1):220–35.
pubmed: 26447583
pmcid: 4605134
doi: 10.1016/j.neuron.2015.09.034
Young GB. The EEG in coma. J Clin Neurophysiol. 2000;17(5):473–85.
pubmed: 11085551
doi: 10.1097/00004691-200009000-00006
Liu G, Su Y, Jiang M, et al. Electroencephalography reactivity for prognostication of post-anoxic coma after cardiopulmonary resuscitation: a comparison of quantitative analysis and visual analysis. Neurosci Lett. 2016;626:74–8.
pubmed: 27181515
doi: 10.1016/j.neulet.2016.04.055
Yang Q, Su Y, Hussain M, et al. Poor outcome prediction by burst suppression ratio in adults with post-anoxic coma without hypothermia. Neuro Res. 2014;36(5):453–60.
doi: 10.1179/1743132814Y.0000000346
Jiang M, Su Y, Liu G, et al. Predicting the non-survival outcome of large hemispheric infarction patients via quantitative electroencephalography: superiority to visual electroencephalography and the Glasgow Coma Scale. Neurosci Lett. 2019;706:88–92.
pubmed: 31077739
doi: 10.1016/j.neulet.2019.05.007
Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science. 2013;342(6158):1238411.
pubmed: 24179229
doi: 10.1126/science.1238411
Ller Y, Thomschewski A, Bergmann, et al. Connectivity biomarkers can differentiate patients with different levels of consciousness. Clin Neurophysiol. 2014;125(8):1545–55.
doi: 10.1016/j.clinph.2013.12.095
Lehembre R, MarieAurélie B, Vanhaudenhuyse A, et al. Resting-state EEG study of comatose patients: a connectivity and frequency analysis to find differences between vegetative and minimally conscious states. Funct Neurol. 2012;27(1):41–7.
pubmed: 22687166
pmcid: 3812750
Cavinato M, Genna C, Manganotti P, et al. Coherence and consciousness: study of fronto-parietal gamma synchrony in patients with disorders of consciousness. Brain Topogr. 2015;28(4):570–9.
pubmed: 25070585
doi: 10.1007/s10548-014-0383-5
Sarà M, Pistoia F. Complexity loss in physiological time series of patients in a vegetative state. Nonlinear Dyn Psychol Life Sci. 2010;14(1):1–13.
Sarà M, Pistoia F, Pasqualetti P, et al. Functional isolation within the cerebral cortex in the vegetative state. Neurorehabil Neural Repair. 2011;25(1):35–42.
pubmed: 20952634
doi: 10.1177/1545968310378508
Thul A, Lechinger J, Donis J, et al. EEG entropy measures indicate decrease of cortical information processing in disorders of consciousness. Clin Neurophysiol. 2016;127(2):1419–27.
pubmed: 26480834
doi: 10.1016/j.clinph.2015.07.039
Ropper AH, Samuels MA. Adams and victor’s principles of neurology. 9th ed. New York: McGraw-Hill professional; 2009.
Nuwer MR. Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping. J Clin Neurophysiol. 1988;5(1):1–43.
pubmed: 3074969
doi: 10.1097/00004691-198801000-00001
Pincus S. Approximate entropy (ApEn) as a complexity measure. Chaos. 1995;5(1):110–7.
pubmed: 12780163
doi: 10.1063/1.166092
Pincus SM. Approximate entropy as a measure of irregularity for psychiatric serial metrics. Bipolar Disord. 2006;8(5 Pt 1):430–40.
pubmed: 17042881
doi: 10.1111/j.1399-5618.2006.00375.x
Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol. 2005;116(10):2266–301.
pubmed: 16115797
doi: 10.1016/j.clinph.2005.06.011
Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88(17):174102.
pubmed: 12005759
doi: 10.1103/PhysRevLett.88.174102
Ferlazzo E, Mammone N, Cianci V, et al. Permutation entropy of scalp EEG: a tool to investigate epilepsies. Clin Neurophysiol. 2014;125(1):13–20.
pubmed: 23859939
doi: 10.1016/j.clinph.2013.06.023
Kaufmann A, Kraft B, Michaleksauberer A, et al. Using permutation entropy to measure the electroencephalographic effects of sevoflurane. Anesthesiology. 2008;109(3):448–56.
doi: 10.1097/ALN.0b013e318182a93b
Shaw JC, Ongley C. The measurement of synchronization. Synchronization of EEG activity in epilepsies; 1972.
Ito J, Nikolaev AR, Leeuwen CV. Spatial and temporal structure of phase synchronization of spontaneous alpha EEG activity. Biol Cybern. 2005;92(1):54–60.
pubmed: 15650899
doi: 10.1007/s00422-004-0533-z
Le M, Quyen V, Foucher J, et al. Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. J Neurosci Methods. 2001;111(2):83–98.
doi: 10.1016/S0165-0270(01)00372-7
Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: a critique for a brave new small-world. Netw Neurosci. 2018;3:1–58.
pubmed: 30793071
pmcid: 6326733
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.
doi: 10.1016/j.neuroimage.2009.10.003
pubmed: 19819337
Stam CJ, de Haan W, Daffertshofer A, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer”s disease. Brain. 2008;132(1):213–24.
pubmed: 18952674
doi: 10.1093/brain/awn262
Onnela JP, Saramaki J, Kertesz J, Kaski K. Intensity and coherence of motifs in weighted complex networks. Phys Rev E Stat Nonlinear Soft Matter Phys. 2005;71(6 Pt 2):065103.
doi: 10.1103/PhysRevE.71.065103
Watts DJ, Strogatz SH. Collective dynamics of small world networks. Nature. 1998;393(6684):440–2.
pubmed: 9623998
doi: 10.1038/30918
George A, Richard B, Elizabeth M, et al. Medical aspects of the persistent vegetative state. N Engl J Med. 1994;330(21):1499–508.
doi: 10.1056/NEJM199405263302107
Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med. 2019;380(26):2497–505.
pubmed: 31242361
doi: 10.1056/NEJMoa1812757
Young GB, McLachlan RS, Kreeft JH, et al. An electroencephalographic classification for coma. Can J Neurol Sci. 1997;24(4):320–5.
pubmed: 9398979
doi: 10.1017/S0317167100032996
Matousek M, Takeuchi E, Starmark JE, Stalhammar D. Quantitative EEG analysis as a supplement to the clinical coma scale RLS85. Acta Anaesthesiol Scand. 1996;40(7):824–31.
pubmed: 8874570
doi: 10.1111/j.1399-6576.1996.tb04540.x
Lechinger J, Bothe K, Pichler G, et al. CRS-R score in disorders of consciousness is strongly related to spectral EEG at rest. J Neurol. 2013;260(9):2348–56.
pubmed: 23765089
doi: 10.1007/s00415-013-6982-3
Ward LM. Synchronous neural oscillations and cognitive processes. Trends Cogn Sci. 2003;7(12):553–9.
pubmed: 14643372
doi: 10.1016/j.tics.2003.10.012
Piarulli A, Bergamasco M, Thibaut A, et al. EEG ultradian rhythmicity differences in disorders of consciousness during wakefulness. J Neurol. 2016;263(9):1746–60.
pubmed: 27294259
doi: 10.1007/s00415-016-8196-y
John ER, Prichep LS. The anesthetic cascade-A theory of how anesthesia suppresses consciousness. Anesthesiology. 2005;102(2):447–71.
pubmed: 15681963
doi: 10.1097/00000542-200502000-00030
Lin M, Chan H, Fang S. Linear and nonlinear EEG indexes in relation to the severity of coma. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4580–3.
pubmed: 17281259
Gosseries O, Schnakers C, Ledoux D, et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct Neurol. 2011;26(1):25–30.
pubmed: 21693085
pmcid: 3814509
Dehaene S, Changeux JP. Experimental and theoretical approaches to conscious processing. Neuron. 2011;70(2):200–27.
pubmed: 21521609
doi: 10.1016/j.neuron.2011.03.018
Alkire MT, Hudetz AG, Tononi G. Consciousness and anesthesia. Science. 2008;322(5903):876–80.
pubmed: 18988836
pmcid: 2743249
doi: 10.1126/science.1149213
Grindel’ OM. Optimal level of EEG coherence and its importance in evaluating the functional state of the human brain. Zh Vyssh Nerv Deiat Im I P Pavlova. 1980;30(1):62–70.
pubmed: 7386017
Grindel’ OM. Intercentral correlations in the cerebral cortex according to the EEG coherence index after restoration of consciousness and speech following prolonged coma. Zh Vyssh Nerv Deiat Im I P Pavlova. 1985;35(1):60–7.
pubmed: 3984511
Fernández-Espejo D, Soddu A, Cruse D, et al. A role for the default mode network in the bases of disorders of consciousness. Ann Neurol. 2012;72(3):335–43.
pubmed: 23034909
doi: 10.1002/ana.23635
Thibaut A, Bruno M, Chatelle C, et al. Metabolic activity in external and internal awareness networks in severely brain-damaged patients. J Rehabil Med. 2012;44(6):487–94.
pubmed: 22366927
doi: 10.2340/16501977-0940
Malagurski B, Peran P, Sarton B, et al. Topological disintegration of resting state functional connectomes in coma. Neuroimage. 2019;195:354–61.
pubmed: 30862533
doi: 10.1016/j.neuroimage.2019.03.012
Crone JS, Soddu A, Holler Y, et al. Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. NeuroImage Clin. 2014;4:240–8.
pubmed: 24455474
doi: 10.1016/j.nicl.2013.12.005
Chennu S, Annen J, Wannez S, et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain. 2017;140(8):2120–32.
pubmed: 28666351
doi: 10.1093/brain/awx163
Rizkallah J, Annen J, Modolo J, et al. Decreased integration of EEG source-space networks in disorders of consciousness. Neuroimage Clin. 2019;23:101841.
pubmed: 31063944
pmcid: 6503216
doi: 10.1016/j.nicl.2019.101841
Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol. 2007;118(11):2317–31.
pubmed: 17900977
doi: 10.1016/j.clinph.2007.08.010