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Neurocritical Care

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23.07.2020 | Original Work

Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics

verfasst von: Huijin Huang, Zikang Niu, Gang Liu, Mengdi Jiang, Qingxia Jia, Xiaoli Li, Yingying Su

Erschienen in: Neurocritical Care | Ausgabe 2/2020

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Abstract

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.

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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.PubMedCrossRef

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.PubMedPubMedCentralCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

Fischer DB, Boes AD, Demertzi A, et al. A human brain network derived from coma-causing brainstem lesions. Neurology. 2016;87(23):2427–34.PubMedPubMedCentralCrossRef

Di Perri C, Bastianello S, Bartsch AJ, et al. Limbic hyperconnectivity in the vegetative state. Neurology. 2013;81(16):1417–24.PubMedCrossRef

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.PubMedCrossRef

Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015;88(1):220–35.PubMedPubMedCentralCrossRef

10.

Young GB. The EEG in coma. J Clin Neurophysiol. 2000;17(5):473–85.PubMedCrossRef

11.

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.PubMedCrossRef

12.

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.CrossRef

13.

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.PubMedCrossRef

14.

Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science. 2013;342(6158):1238411.PubMedCrossRef

15.

Ller Y, Thomschewski A, Bergmann, et al. Connectivity biomarkers can differentiate patients with different levels of consciousness. Clin Neurophysiol. 2014;125(8):1545–55.CrossRef

16.

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.PubMedPubMedCentral

17.

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.PubMedCrossRef

18.

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.

19.

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.PubMedCrossRef

20.

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.PubMedCrossRef

21.

Ropper AH, Samuels MA. Adams and victor’s principles of neurology. 9th ed. New York: McGraw-Hill professional; 2009.

22.

Nuwer MR. Quantitative EEG: I. Techniques and problems of frequency analysis and topographic mapping. J Clin Neurophysiol. 1988;5(1):1–43.PubMedCrossRef

23.

Pincus S. Approximate entropy (ApEn) as a complexity measure. Chaos. 1995;5(1):110–7.PubMedCrossRef

24.

Pincus SM. Approximate entropy as a measure of irregularity for psychiatric serial metrics. Bipolar Disord. 2006;8(5 Pt 1):430–40.PubMedCrossRef

25.

Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol. 2005;116(10):2266–301.PubMedCrossRef

26.

Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88(17):174102.PubMedCrossRef

27.

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.PubMedCrossRef

28.

Kaufmann A, Kraft B, Michaleksauberer A, et al. Using permutation entropy to measure the electroencephalographic effects of sevoflurane. Anesthesiology. 2008;109(3):448–56.CrossRef

29.

Shaw JC, Ongley C. The measurement of synchronization. Synchronization of EEG activity in epilepsies; 1972.

30.

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.PubMedCrossRef

31.

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.CrossRef

32.

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.PubMedPubMedCentral

33.

Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 2010;52(3):1059–69.CrossRefPubMed

34.

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.PubMedCrossRef

35.

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.CrossRef

36.

Watts DJ, Strogatz SH. Collective dynamics of small world networks. Nature. 1998;393(6684):440–2.PubMedCrossRef

37.

George A, Richard B, Elizabeth M, et al. Medical aspects of the persistent vegetative state. N Engl J Med. 1994;330(21):1499–508.CrossRef

38.

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.PubMedCrossRef

39.

Young GB, McLachlan RS, Kreeft JH, et al. An electroencephalographic classification for coma. Can J Neurol Sci. 1997;24(4):320–5.PubMedCrossRef

40.

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.PubMedCrossRef

41.

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.PubMedCrossRef

42.

Ward LM. Synchronous neural oscillations and cognitive processes. Trends Cogn Sci. 2003;7(12):553–9.PubMedCrossRef

43.

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.PubMedCrossRef

44.

John ER, Prichep LS. The anesthetic cascade-A theory of how anesthesia suppresses consciousness. Anesthesiology. 2005;102(2):447–71.PubMedCrossRef

45.

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

46.

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.PubMedPubMedCentral

47.

Dehaene S, Changeux JP. Experimental and theoretical approaches to conscious processing. Neuron. 2011;70(2):200–27.PubMedCrossRef

48.

Alkire MT, Hudetz AG, Tononi G. Consciousness and anesthesia. Science. 2008;322(5903):876–80.PubMedPubMedCentralCrossRef

49.

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

50.

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

51.

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.PubMedCrossRef

52.

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.PubMedCrossRef

53.

Malagurski B, Peran P, Sarton B, et al. Topological disintegration of resting state functional connectomes in coma. Neuroimage. 2019;195:354–61.PubMedCrossRef

54.

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.PubMedCrossRef

55.

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.PubMedCrossRef

56.

Rizkallah J, Annen J, Modolo J, et al. Decreased integration of EEG source-space networks in disorders of consciousness. Neuroimage Clin. 2019;23:101841.PubMedPubMedCentralCrossRef

57.

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.PubMedCrossRef

Titel: Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics
verfasst von: Huijin Huang
Zikang Niu
Gang Liu
Mengdi Jiang
Qingxia Jia
Xiaoli Li
Yingying Su
Publikationsdatum: 23.07.2020
Verlag: Springer US
Erschienen in: Neurocritical Care / Ausgabe 2/2020
Print ISSN: 1541-6933
Elektronische ISSN: 1556-0961
DOI: https://doi.org/10.1007/s12028-020-01051-w

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