Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
05 Aug 2024
05 Aug 2024
Historique:
received:
13
02
2024
accepted:
22
07
2024
medline:
6
8
2024
pubmed:
6
8
2024
entrez:
5
8
2024
Statut:
epublish
Résumé
Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects' PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.
Identifiants
pubmed: 39103539
doi: 10.1038/s42003-024-06613-8
pii: 10.1038/s42003-024-06613-8
doi:
Substances chimiques
Ketamine
690G0D6V8H
Propofol
YI7VU623SF
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
946Informations de copyright
© 2024. The Author(s).
Références
Bak, P., Tang, C. & Wiesenfeld, K. Self-organized criticality: An explanation of the 1/f noise. Phys. Rev. Lett. 59, 381–384 (1987).
pubmed: 10035754
doi: 10.1103/PhysRevLett.59.381
O’Byrne, J. & Jerbi, K. How critical is brain criticality? Trends in Neurosci. https://doi.org/10.1016/j.tins.2022.08.007 (2022).
Carhart-Harris, R. L. The entropic brain - revisited. Neuropharmacology 142, 167–178 (2018).
pubmed: 29548884
doi: 10.1016/j.neuropharm.2018.03.010
Zimmern, V. Why brain criticality is clinically relevant: a scoping review. Front. Neural Circ. 14, (2020).
Solovey, G. et al. Loss of consciousness is associated with stabilization of cortical activity. J. Neurosci. 35, 10866–10877 (2015).
pubmed: 26224868
pmcid: 4518057
doi: 10.1523/JNEUROSCI.4895-14.2015
Kim, H. & Lee, U. Criticality as a determinant of integrated information Φ in human brain networks. Entropy 21, 981 (2019).
pmcid: 7514311
doi: 10.3390/e21100981
Toker, D. et al. Consciousness is supported by near-critical slow cortical electrodynamics. PNAS 119, e2024455119 (2022).
Walter, N. & Hinterberger, T. Self-organized criticality as a framework for consciousness: A review study. Front. Psychol. 13, 911620 (2022).
Tononi, G. & Edelman, G. M. Consciousness and complexity. Science 282, 1846–1851 (1998).
pubmed: 9836628
doi: 10.1126/science.282.5395.1846
Tononi, G. An information integration theory of consciousness. BMC Neurosci. 5, 42 (2004).
pubmed: 15522121
pmcid: 543470
doi: 10.1186/1471-2202-5-42
Oizumi, M., Albantakis, L. & Tononi, G. From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0. PLOS Comput. Biol. 10, e1003588 (2014).
pubmed: 24811198
pmcid: 4014402
doi: 10.1371/journal.pcbi.1003588
Sarà, M. & Pistoia, F. Complexity loss in physiological time series of patients in a vegetative state. Nonlinear Dyn. Psychol. Life Sci. 14, 1–13 (2010).
Gosseries, O. et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state. Funct. Neurol. 26, 25–30 (2011).
pubmed: 21693085
pmcid: 3814509
King, J.-R. et al. Information sharing in the brain indexes consciousness in noncommunicative patients. Curr. Biol. 23, 1914–1919 (2013).
pubmed: 24076243
pmcid: 5635964
doi: 10.1016/j.cub.2013.07.075
Sarasso, S. et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Curr. Biol. 25, 3099–3105 (2015).
pubmed: 26752078
doi: 10.1016/j.cub.2015.10.014
Schartner, M. et al. Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLOS ONE 10, e0133532 (2015).
pubmed: 26252378
pmcid: 4529106
doi: 10.1371/journal.pone.0133532
Mateos, D. M., Guevara Erra, R., Wennberg, R. & Perez Velazquez, J. L. Measures of entropy and complexity in altered states of consciousness. Cogn. Neurodyn 12, 73–84 (2018).
pubmed: 29435088
doi: 10.1007/s11571-017-9459-8
Casali, A. G. et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med. 5, 198ra105–198ra105 (2013).
pubmed: 23946194
doi: 10.1126/scitranslmed.3006294
Casarotto, S. et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann. Neurol. 80, 718–729 (2016).
pubmed: 27717082
pmcid: 5132045
doi: 10.1002/ana.24779
Edlow, B. L. et al. Measuring consciousness in the intensive care unit. Neurocrit Care 38, 584–590 (2023).
pubmed: 37029315
doi: 10.1007/s12028-023-01706-4
Sitt, J. D., King, J.-R., Naccache, L. & Dehaene, S. Ripples of consciousness. Trends Cogn. Sci. 17, 552–554 (2013).
pubmed: 24094796
doi: 10.1016/j.tics.2013.09.003
Mediano, P. A. M. et al. Integrated information as a common signature of dynamical and information-processing complexity. Chaos 32, 013115 (2022).
pubmed: 35105139
doi: 10.1063/5.0063384
Shew, W. L., Yang, H., Petermann, T., Roy, R. & Plenz, D. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. J. Neurosci. 29, 15595–15600 (2009).
pubmed: 20007483
pmcid: 3862241
doi: 10.1523/JNEUROSCI.3864-09.2009
Shew, W. L., Yang, H., Yu, S., Roy, R. & Plenz, D. Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. J. Neurosci. 31, 55–63 (2011).
pubmed: 21209189
pmcid: 3082868
doi: 10.1523/JNEUROSCI.4637-10.2011
Shew, W. L. & Plenz, D. The functional benefits of criticality in the cortex. Neuroscientist 19, 88–100 (2013).
pubmed: 22627091
doi: 10.1177/1073858412445487
Gervais, C., Boucher, L.-P., Villar, G. M., Lee, U. & Duclos, C. A scoping review for building a criticality-based conceptual framework of altered states of consciousness. Front. Syst. Neurosci. 17, 1085902 (2023).
pubmed: 37304151
pmcid: 10248073
doi: 10.3389/fnsys.2023.1085902
Popiel, N. J. M. et al. The emergence of integrated information, complexity, and ‘consciousness’ at criticality. Entropy 22, 339 (2020).
pubmed: 33286113
pmcid: 7516800
doi: 10.3390/e22030339
Beggs, J. M. & Plenz, D. Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167–11177 (2003).
pubmed: 14657176
pmcid: 6741045
doi: 10.1523/JNEUROSCI.23-35-11167.2003
Friedman, N. et al. Universal critical dynamics in high resolution neuronal avalanche data. Phys. Rev. Lett. 108, 208102 (2012).
pubmed: 23003192
doi: 10.1103/PhysRevLett.108.208102
Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579–589 (2010).
pubmed: 20130250
doi: 10.1056/NEJMoa0905370
Sanders, R. D., Tononi, G., Laureys, S., Sleigh, J. W. & Warner, D. S. Unresponsiveness ≠ Unconsciousness. Anesthesiology 116, 946–959 (2012).
pubmed: 22314293
doi: 10.1097/ALN.0b013e318249d0a7
Colombo, M. A. et al. The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine. NeuroImage 189, 631–644 (2019).
pubmed: 30639334
doi: 10.1016/j.neuroimage.2019.01.024
Shriki, O. et al. Neuronal avalanches in the resting MEG of the human brain. J. Neurosci. 33, 7079–7090 (2013).
pubmed: 23595765
pmcid: 3665287
doi: 10.1523/JNEUROSCI.4286-12.2013
Varley, T. F., Sporns, O., Puce, A. & Beggs, J. Differential effects of propofol and ketamine on critical brain dynamics. PLOS Comput. Biol. 16, e1008418 (2020).
pubmed: 33347455
pmcid: 7785236
doi: 10.1371/journal.pcbi.1008418
Sethna, J. P., Dahmen, K. A. & Myers, C. R. Crackling noise. Nature 410, 242–250 (2001).
pubmed: 11258379
doi: 10.1038/35065675
Ma, Z., Turrigiano, G. G., Wessel, R. & Hengen, K. B. Cortical circuit dynamics are homeostatically tuned to criticality in vivo. Neuron 104, 655–664.e4 (2019).
pubmed: 31601510
pmcid: 6934140
doi: 10.1016/j.neuron.2019.08.031
Wilting, J. & Priesemann, V. 25 years of criticality in neuroscience — established results, open controversies, novel concepts. Curr. Opin. Neurobiol. 58, 105–111 (2019).
pubmed: 31546053
doi: 10.1016/j.conb.2019.08.002
Sorrentino, P. et al. Flexible brain dynamics underpins complex behaviours as observed in Parkinson’s disease. Sci. Rep. 11, 4051 (2021).
pubmed: 33602980
pmcid: 7892831
doi: 10.1038/s41598-021-83425-4
Dahmen, D., Grün, S., Diesmann, M. & Helias, M. Second type of criticality in the brain uncovers rich multiple-neuron dynamics. PNAS 116, 13051–13060 (2019).
pubmed: 31189590
pmcid: 6600928
doi: 10.1073/pnas.1818972116
Kanders, K., Lorimer, T. & Stoop, R. Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks. Chaos: Interdiscip. J. Nonlinear Sci. 27, 047408 (2017).
doi: 10.1063/1.4978998
Gottwald, G. A. & Melbourne, I. On the Implementation of the 0–1 Test for Chaos. SIAM J. Appl. Dyn. Syst. 8, 129–145 (2009).
doi: 10.1137/080718851
Rosenstein, M. T., Collins, J. J. & De Luca, C. J. A practical method for calculating largest Lyapunov exponents from small data sets. Phys. D: Nonlinear Phenom. 65, 117–134 (1993).
doi: 10.1016/0167-2789(93)90009-P
Frohlich, J., Toker, D. & Monti, M. M. Consciousness among delta waves: a paradox? Brain https://doi.org/10.1093/brain/awab095 (2021).
Yoon, S., Sorbaro Sindaci, M., Goltsev, A. V. & Mendes, J. F. F. Critical behavior of the relaxation rate, the susceptibility, and a pair correlation function in the Kuramoto model on scale-free networks. Phys. Rev. E Stat. Nonlin Soft Matter Phys. 91, 032814 (2015).
pubmed: 25871164
doi: 10.1103/PhysRevE.91.032814
Schartner, M. M., Carhart-Harris, R. L., Barrett, A. B., Seth, A. K. & Muthukumaraswamy, S. D. Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin. Sci. Rep. 7, 46421 (2017).
pubmed: 28422113
pmcid: 5396066
doi: 10.1038/srep46421
Rosanova, M. et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain 135, 1308–1320 (2012).
pubmed: 22226806
pmcid: 3326248
doi: 10.1093/brain/awr340
Breyton, M. et al. Large-scale brain signatures of fluid dynamics and responsiveness linked to consciousness. 2023.04.18.537321 Preprint at https://doi.org/10.1101/2023.04.18.537321 (2023).
Momi, D., Wang, Z. & Griffiths, J. D. TMS-evoked responses are driven by recurrent large-scale network dynamics. eLife 12, e83232 (2023).
pubmed: 37083491
pmcid: 10121222
doi: 10.7554/eLife.83232
Lee, M. et al. Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning. Nat. Commun. 13, 1064 (2022).
pubmed: 35217645
pmcid: 8881479
doi: 10.1038/s41467-022-28451-0
Ezaki, T., Fonseca dos Reis, E., Watanabe, T., Sakaki, M. & Masuda, N. Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence. Commun. Biol. 3, 1–9 (2020).
doi: 10.1038/s42003-020-0774-y
Haldeman, C. & Beggs, J. M. Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys. Rev. Lett. 94, 058101 (2005).
Franks, N. P., Dickinson, R., de Sousa, S. L. M., Hall, A. C. & Lieb, W. R. How does xenon produce anaesthesia? Nature 396, 324–324 (1998).
pubmed: 9845069
doi: 10.1038/24525
Sanders, R. D., Franks, N. P. & Maze, M. Xenon: no stranger to anaesthesia. BJA: Br. J. Anaesth. 91, 709–717 (2003).
pubmed: 14570795
doi: 10.1093/bja/aeg232
Hirota, K. Special cases: Ketamine, nitrous oxide and xenon. Best. Pract. Res. Clin. Anaesthesiol. 20, 69–79 (2006).
pubmed: 16634415
doi: 10.1016/j.bpa.2005.08.014
Steyn-Ross, M. L., Steyn-Ross, D. A. & Sleigh, J. W. Interacting turing-hopf instabilities drive symmetry-breaking transitions in a mean-field model of the cortex: a mechanism for the slow oscillation. Phys. Rev. X 3, 021005 (2013).
Toker, D. et al. Criticality supports cross-frequency cortical-thalamic information transfer during conscious states. eLife 13, e86547 (2024).
pubmed: 38180472
pmcid: 10805384
doi: 10.7554/eLife.86547
Varley, T. F. et al. Consciousness & brain functional complexity in propofol anaesthesia. Sci. Rep. 10, 1–13 (2020).
doi: 10.1038/s41598-020-57695-3
Ruiz de Miras, J. et al. Fractal dimension analysis of states of consciousness and unconsciousness using transcranial magnetic stimulation. Computer Methods Prog. Biomed. 175, 129–137 (2019).
doi: 10.1016/j.cmpb.2019.04.017
von Wegner, F. et al. Complexity measures for EEG microstate sequences - concepts and algorithms. https://www.researchsquare.com . https://doi.org/10.21203/rs.3.rs-2878411/v1 (2023).
Lee, H. et al. Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks. NeuroImage 188, 228–238 (2019).
pubmed: 30529630
doi: 10.1016/j.neuroimage.2018.12.011
Colombo, M. A. et al. Beyond alpha power: EEG spatial and spectral gradients robustly stratify disorders of consciousness. Cerebral Cortex. https://doi.org/10.1093/cercor/bhad031 (2023).
Maschke, C., Duclos, C., Owen, A. M., Jerbi, K. & Blain-Moraes, S. Aperiodic brain activity and response to anesthesia vary in disorders of consciousness. NeuroImage 275, 120154 (2023).
pubmed: 37209758
doi: 10.1016/j.neuroimage.2023.120154
Alstott, J., Bullmore, E. & Plenz, D. powerlaw: a python package for analysis of heavy-tailed distributions. PLOS ONE 9, e85777 (2014).
pubmed: 24489671
pmcid: 3906378
doi: 10.1371/journal.pone.0085777
Gottwald, G. A. & Melbourne, I. Testing for chaos in deterministic systems with noise. Phys. D: Nonlinear Phenom. 212, 100–110 (2005).
doi: 10.1016/j.physd.2005.09.011
Liu, X., Ward, B. D., Binder, J. R., Li, S.-J. & Hudetz, A. G. Scale-free functional connectivity of the brain is maintained in anesthetized healthy participants but not in patients with unresponsive wakefulness syndrome. PLOS ONE 9, e92182 (2014).
pubmed: 24647227
pmcid: 3960221
doi: 10.1371/journal.pone.0092182
Massimini, M. & Laureys, S. Rest EEG recordings in healthy subjects during wakefulness, sleep and anesthesia with ketamine, propofol, and xenon. Zenodo https://doi.org/10.5281/zenodo.806176 (2017).
Fagerholm, E. D. et al. Cascades and cognitive state: focused attention incurs subcritical dynamics. J. Neurosci. 35, 4626–4634 (2015).
pubmed: 25788679
pmcid: 4363389
doi: 10.1523/JNEUROSCI.3694-14.2015
Clauset, A., Shalizi, C. R. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009).
doi: 10.1137/070710111
Girardi-Schappo, M. Brain criticality beyond avalanches: open problems and how to approach them. J. Phys. Complex. 2, 031003 (2021).
doi: 10.1088/2632-072X/ac2071
Priesemann, V. et al. Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front. Syst. Neurosci. 8, 108 (2014).
pubmed: 25009473
pmcid: 4068003
doi: 10.3389/fnsys.2014.00108
Gabbiani, F. & Cox, S. J. Chapter 17 - Quantification of Spike Train Variability. In Mathematics for Neuroscientists (Second Edition) (eds. Gabbiani, F. & Cox, S. J.) 321–334 https://doi.org/10.1016/B978-0-12-801895-8.00017-8 (Academic Press, San Diego, 2017).
Wilting, J. & Priesemann, V. Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation. Cereb. Cortex 29, 2759–2770 (2019).
pubmed: 31008508
pmcid: 6519697
doi: 10.1093/cercor/bhz049
Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655–1665 (2020).
pubmed: 33230329
pmcid: 8106550
doi: 10.1038/s41593-020-00744-x
Makowski, D. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behav. Res. 53, 1689–1696 (2021).
doi: 10.3758/s13428-020-01516-y
Morales, G. B., di Santo, S. & Muñoz, M. A. Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics. Proc. Natl Acad. Sci. 120, e2208998120 (2023).
pubmed: 36827262
pmcid: 9992863
doi: 10.1073/pnas.2208998120
Hardstone, R. et al. Detrended fluctuation analysis: a scale-free view on neuronal oscillations. Front. Physiol. 3, 450 (2012).
pubmed: 23226132
pmcid: 3510427
doi: 10.3389/fphys.2012.00450
Donoghue, T., Schaworonkow, N. & Voytek, B. Methodological considerations for studying neural oscillations. Eur. J. Neurosci. https://doi.org/10.1111/ejn.15361 (2021).
Zhang, Y., Hao, J., Zhou, C. & Chang, K. Normalized Lempel-Ziv complexity and its application in bio-sequence analysis. J. Math. Chem. 46, 1203–1212 (2009).
doi: 10.1007/s10910-008-9512-2
Costa, M., Goldberger, A. L. & Peng, C.-K. Multiscale entropy analysis of complex physiologic time series. Phys. Rev. Lett. 89, 068102 (2002).
pubmed: 12190613
doi: 10.1103/PhysRevLett.89.068102
Lau, Z. J., Pham, T., Chen, S. H. A. & Makowski, D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur. J. Neurosci. 56, 5047–5069 (2022).
pubmed: 35985344
pmcid: 9826422
doi: 10.1111/ejn.15800
O’Byrne, J. edgeofpy. https://github.com/jnobyrne/edgeofpy (2023).