Adult; Anesthesia, General; Consciousness; Electroencephalography; Female; Humans; Ketamine; Male; Propofol; Unconsciousness; Young Adult; Medicine (miscellaneous); Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all)
Abstract :
[en] 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.
Disciplines :
Neurosciences & behavior
Author, co-author :
Maschke, Charlotte; Montreal General Hospital, McGill University Health Centre, Montreal, Canada ; Integrated Program in Neuroscience, McGill University, Montreal, Canada ; Cognitive & amp, Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Canada
O’Byrne, Jordan; Cognitive & amp, Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Canada ; MILA (Québec Artificial Intelligence Institute), Montréal, Canada
Colombo, Michele Angelo; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
Boly, Mélanie ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie ; Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, United States
Gosseries, Olivia ; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Coma Science Group
Laureys, Steven ; Université de Liège - ULiège > Département des sciences cliniques ; CERVO Brain Research Centre, Laval University, Laval, Canada ; Consciousness Science Institute, Hangzhou Normal University, Hangzhou, China
Rosanova, Mario; Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
Jerbi, Karim ; Cognitive & amp, Computational Neuroscience Lab, Psychology Department, University of Montreal, Montreal, Canada ; MILA (Québec Artificial Intelligence Institute), Montréal, Canada ; Centre UNIQUE (Union Neurosciences & amp, Intelligence Artificielle), Montréal, Canada
Blain-Moraes, Stefanie ; Montreal General Hospital, McGill University Health Centre, Montreal, Canada ; School of Physical and Occupational Therapy, McGill University, Montreal, Canada
Language :
English
Title :
Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity
CM is funded through Fonds de recherche du Qu\u00E9bec\u2014Sant\u00E9 (FRQS). JOB is supported by the Canadian Institutes of Health Research (CIHR) and FRQS. OG is research associate and SL is research director at F.R.S-FNRS. SL is Research Director at the Belgian National Fund for Scientific Research, Chairholder of the Canada Excellence Research Chair in Integrative Neuroscience for Sustainable Mental Health and funded by European Foundation of Biomedical Research FERB Onlus. K.J. was supported by funding from the Canada Research Chairs Program and a Discovery Grant (grant no. RGPIN-2015-04854) from the Natural Sciences and Engineering Research Council of Canada (NSERC), a New Investigators Award from the Fonds de recherche du Qu\u00E9bec en nature et technologies (FRQNT) (grant no. 2018-NC-206005), and an Institut de valorisation des donn\u00E9es (IVADO) fundamental research project grant funded through the Canada First Research Excellence Fund (CFREF) program. SBM is supported by the Canada Research Chairs Program (Tier II). This research was funded through the FRQNT Strategic Clusters Program (2020-RS4-265502\u2014Centre UNIQUE\u2014Union Neurosciences & Artificial Intelligence\u2014Quebec, an NSERC Discovery Grant (RGPIN-2023-03619), the Canada Excellence Research Chairs Program (#215063), the Canadian Institutes of Health Research (#408004), the ERA-Net FLAG-ERA JTC2021 project ModelDXConsciousness (Human Brain Project Partnering Project) and the NINDS grant 1K23NS112473. This research was undertaken thanks to funding from Fondazione Regionale per la Ricerca Biomedica (Regione Lombardia), Project PerBrain, call ERAPERMED2019\u2013101, GA 779282 (to MR), the Canada First Research Excellence Fund and Fonds de recherche du Que\u0301bec, awarded to the Healthy Brains, Healthy Lives initiative at McGill University.
P. Bak C. Tang K. Wiesenfeld Self-organized criticality: An explanation of the 1/f noise Phys. Rev. Lett. 1987 59 381 384 1:STN:280:DC%2BC2sfotlSqug%3D%3D 10035754 10.1103/PhysRevLett.59.381
OByrne, J. & Jerbi, K. How critical is brain criticality? Trends in Neurosci. https://doi.org/10.1016/j.tins.2022.08.007 (2022).
Zimmern, V. Why brain criticality is clinically relevant: a scoping review. Front. Neural Circ. 14, (2020).
G. Solovey et al. Loss of consciousness is associated with stabilization of cortical activity J. Neurosci. 2015 35 10866 10877 1:CAS:528:DC%2BC2MXhsVGksrjI 26224868 4518057 10.1523/JNEUROSCI.4895-14.2015
H. Kim U. Lee Criticality as a determinant of integrated information Φ in human brain networks Entropy 2019 21 981 7514311 10.3390/e21100981
Toker, D. et al. Consciousness is supported by near-critical slow cortical electrodynamics. PNAS119, e2024455119 (2022).
Walter, N. & Hinterberger, T. Self-organized criticality as a framework for consciousness: A review study. Front. Psychol. 13, 911620 (2022).
G. Tononi G.M. Edelman Consciousness and complexity Science 1998 282 1846 1851 1:CAS:528:DyaK1cXotVSktrg%3D 9836628 10.1126/science.282.5395.1846
G. Tononi An information integration theory of consciousness BMC Neurosci. 2004 5 15522121 543470 10.1186/1471-2202-5-42
M. Oizumi L. Albantakis G. Tononi From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0 PLOS Comput. Biol. 2014 10 e1003588 24811198 4014402 10.1371/journal.pcbi.1003588
M. Sarà F. Pistoia Complexity loss in physiological time series of patients in a vegetative state Nonlinear Dyn. Psychol. Life Sci. 2010 14 1 13
O. Gosseries et al. Automated EEG entropy measurements in coma, vegetative state/unresponsive wakefulness syndrome and minimally conscious state Funct. Neurol. 2011 26 25 30 21693085 3814509
J.-R. King et al. Information sharing in the brain indexes consciousness in noncommunicative patients Curr. Biol. 2013 23 1914 1919 1:CAS:528:DC%2BC3sXhsFemtLbO 24076243 5635964 10.1016/j.cub.2013.07.075
S. Sarasso et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine Curr. Biol. 2015 25 3099 3105 1:CAS:528:DC%2BC2MXhvVOqsbvE 26752078 10.1016/j.cub.2015.10.014
M. Schartner et al. Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia PLOS ONE 2015 10 e0133532 26252378 4529106 10.1371/journal.pone.0133532
D.M. Mateos R. Guevara Erra R. Wennberg J.L. Perez Velazquez Measures of entropy and complexity in altered states of consciousness Cogn. Neurodyn 2018 12 73 84 1:STN:280:DC%2BC1Mrgsl2jsg%3D%3D 29435088 10.1007/s11571-017-9459-8
A.G. Casali et al. A theoretically based index of consciousness independent of sensory processing and behavior Sci. Transl. Med. 2013 5 198ra105 198ra105 23946194 10.1126/scitranslmed.3006294
S. Casarotto et al. Stratification of unresponsive patients by an independently validated index of brain complexity Ann. Neurol. 2016 80 718 729 27717082 5132045 10.1002/ana.24779
B.L. Edlow et al. Measuring consciousness in the intensive care unit Neurocrit Care 2023 38 584 590 37029315 10.1007/s12028-023-01706-4
J.D. Sitt J.-R. King L. Naccache S. Dehaene Ripples of consciousness Trends Cogn. Sci. 2013 17 552 554 24094796 10.1016/j.tics.2013.09.003
P.A.M. Mediano et al. Integrated information as a common signature of dynamical and information-processing complexity Chaos 2022 32 013115 35105139 10.1063/5.0063384
W.L. Shew H. Yang T. Petermann R. Roy D. Plenz Neuronal avalanches imply maximum dynamic range in cortical networks at criticality J. Neurosci. 2009 29 15595 15600 1:CAS:528:DC%2BD1MXhsFOgsb3I 20007483 3862241 10.1523/JNEUROSCI.3864-09.2009
W.L. Shew H. Yang S. Yu R. Roy D. Plenz Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches J. Neurosci. 2011 31 55 63 1:CAS:528:DC%2BC3MXls1yitQ%3D%3D 21209189 3082868 10.1523/JNEUROSCI.4637-10.2011
W.L. Shew D. Plenz The functional benefits of criticality in the cortex Neuroscientist 2013 19 88 100 22627091 10.1177/1073858412445487
C. Gervais L.-P. Boucher G.M. Villar U. Lee C. Duclos A scoping review for building a criticality-based conceptual framework of altered states of consciousness Front. Syst. Neurosci. 2023 17 1085902 37304151 10248073 10.3389/fnsys.2023.1085902
N.J.M. Popiel et al. The emergence of integrated information, complexity, and ‘consciousness’ at criticality Entropy 2020 22 339 33286113 7516800 10.3390/e22030339
J.M. Beggs D. Plenz Neuronal avalanches in neocortical circuits J. Neurosci. 2003 23 11167 11177 1:CAS:528:DC%2BD3sXpvV2gtbg%3D 14657176 6741045 10.1523/JNEUROSCI.23-35-11167.2003
N. Friedman et al. Universal critical dynamics in high resolution neuronal avalanche data Phys. Rev. Lett. 2012 108 208102 23003192 10.1103/PhysRevLett.108.208102
M.M. Monti et al. Willful modulation of brain activity in disorders of consciousness N. Engl. J. Med. 2010 362 579 589 1:CAS:528:DC%2BC3cXisVegtLY%3D 20130250 10.1056/NEJMoa0905370
R.D. Sanders G. Tononi S. Laureys J.W. Sleigh D.S. Warner Unresponsiveness ≠ Unconsciousness Anesthesiology 2012 116 946 959 22314293 10.1097/ALN.0b013e318249d0a7
M.A. Colombo et al. The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine NeuroImage 2019 189 631 644 1:CAS:528:DC%2BC1MXis1Wrsrg%3D 30639334 10.1016/j.neuroimage.2019.01.024
O. Shriki et al. Neuronal avalanches in the resting MEG of the human brain J. Neurosci. 2013 33 7079 7090 1:CAS:528:DC%2BC3sXhtlOjs7zE 23595765 3665287 10.1523/JNEUROSCI.4286-12.2013
T.F. Varley O. Sporns A. Puce J. Beggs Differential effects of propofol and ketamine on critical brain dynamics PLOS Comput. Biol. 2020 16 e1008418 1:CAS:528:DC%2BB3MXhsFahsL0%3D 33347455 7785236 10.1371/journal.pcbi.1008418
Z. Ma G.G. Turrigiano R. Wessel K.B. Hengen Cortical circuit dynamics are homeostatically tuned to criticality in vivo Neuron 2019 104 655 664.e4 1:CAS:528:DC%2BC1MXhvFertLjI 31601510 6934140 10.1016/j.neuron.2019.08.031
J. Wilting V. Priesemann 25 years of criticality in neuroscience — established results, open controversies, novel concepts Curr. Opin. Neurobiol. 2019 58 105 111 1:CAS:528:DC%2BC1MXhslOhtbjK 31546053 10.1016/j.conb.2019.08.002
P. Sorrentino et al. Flexible brain dynamics underpins complex behaviours as observed in Parkinson’s disease Sci. Rep. 2021 11 1:CAS:528:DC%2BB3MXkvFejtLY%3D 33602980 7892831 10.1038/s41598-021-83425-4
D. Dahmen S. Grün M. Diesmann M. Helias Second type of criticality in the brain uncovers rich multiple-neuron dynamics PNAS 2019 116 13051 13060 1:CAS:528:DC%2BC1MXht1ers7zM 31189590 6600928 10.1073/pnas.1818972116
K. Kanders T. Lorimer R. Stoop Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks Chaos: Interdiscip. J. Nonlinear Sci. 2017 27 047408 10.1063/1.4978998
G.A. Gottwald I. Melbourne On the Implementation of the 0–1 Test for Chaos SIAM J. Appl. Dyn. Syst. 2009 8 129 145 10.1137/080718851
M.T. Rosenstein J.J. Collins C.J. De Luca A practical method for calculating largest Lyapunov exponents from small data sets Phys. D: Nonlinear Phenom. 1993 65 117 134 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).
S. Yoon M. Sorbaro Sindaci A.V. Goltsev J.F.F. Mendes 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. 2015 91 032814 1:STN:280:DC%2BC2Mbht1ShtQ%3D%3D 25871164 10.1103/PhysRevE.91.032814
M.M. Schartner R.L. Carhart-Harris A.B. Barrett A.K. Seth S.D. Muthukumaraswamy Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin Sci. Rep. 2017 7 1:CAS:528:DC%2BC2sXmsVSqtbk%3D 28422113 5396066 10.1038/srep46421
M. Rosanova et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients Brain 2012 135 1308 1320 22226806 3326248 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).
D. Momi Z. Wang J.D. Griffiths TMS-evoked responses are driven by recurrent large-scale network dynamics eLife 2023 12 1:CAS:528:DC%2BB2cXmtFahs7g%3D 37083491 10121222 10.7554/eLife.83232
M. Lee et al. Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning Nat. Commun. 2022 13 1:CAS:528:DC%2BB38XltFWhsr8%3D 35217645 8881479 10.1038/s41467-022-28451-0
T. Ezaki E. Fonseca dos Reis T. Watanabe M. Sakaki N. Masuda Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence Commun. Biol. 2020 3 1 9 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).
N.P. Franks R. Dickinson S.L.M. de Sousa A.C. Hall W.R. Lieb How does xenon produce anaesthesia? Nature 1998 396 324 324 1:CAS:528:DyaK1cXnvVamt74%3D 9845069 10.1038/24525
R.D. Sanders N.P. Franks M. Maze Xenon: no stranger to anaesthesia BJA: Br. J. Anaesth. 2003 91 709 717 1:CAS:528:DC%2BD3sXos1antLg%3D 14570795 10.1093/bja/aeg232
K. Hirota Special cases: Ketamine, nitrous oxide and xenon Best. Pract. Res. Clin. Anaesthesiol. 2006 20 69 79 1:CAS:528:DC%2BD28Xis1SrtLs%3D 16634415 10.1016/j.bpa.2005.08.014
M.L. Steyn-Ross D.A. Steyn-Ross J.W. Sleigh 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 2013 3 021005 1:CAS:528:DC%2BC3sXpsFaqu7k%3D
D. Toker et al. Criticality supports cross-frequency cortical-thalamic information transfer during conscious states eLife 2024 13 38180472 10805384 10.7554/eLife.86547
T.F. Varley et al. Consciousness & brain functional complexity in propofol anaesthesia Sci. Rep. 2020 10 1 13 10.1038/s41598-020-57695-3
J. Ruiz de Miras et al. Fractal dimension analysis of states of consciousness and unconsciousness using transcranial magnetic stimulation Computer Methods Prog. Biomed. 2019 175 129 137 1:STN:280:DC%2BB3M7ntlGjug%3D%3D 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).
H. Lee et al. Relationship of critical dynamics, functional connectivity, and states of consciousness in large-scale human brain networks NeuroImage 2019 188 228 238 30529630 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).
C. Maschke C. Duclos A.M. Owen K. Jerbi S. Blain-Moraes Aperiodic brain activity and response to anesthesia vary in disorders of consciousness NeuroImage 2023 275 120154 1:CAS:528:DC%2BB3sXhtFGnsrvJ 37209758 10.1016/j.neuroimage.2023.120154
J. Alstott E. Bullmore D. Plenz powerlaw: a python package for analysis of heavy-tailed distributions PLOS ONE 2014 9 e85777 24489671 3906378 10.1371/journal.pone.0085777
G.A. Gottwald I. Melbourne Testing for chaos in deterministic systems with noise Phys. D: Nonlinear Phenom. 2005 212 100 110 10.1016/j.physd.2005.09.011
X. Liu B.D. Ward J.R. Binder S.-J. Li A.G. Hudetz Scale-free functional connectivity of the brain is maintained in anesthetized healthy participants but not in patients with unresponsive wakefulness syndrome PLOS ONE 2014 9 e92182 24647227 3960221 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).
E.D. Fagerholm et al. Cascades and cognitive state: focused attention incurs subcritical dynamics J. Neurosci. 2015 35 4626 4634 1:CAS:528:DC%2BC2MXpvFOjtLo%3D 25788679 4363389 10.1523/JNEUROSCI.3694-14.2015
A. Clauset C.R. Shalizi M.E.J. Newman Power-law distributions in empirical data SIAM Rev. 2009 51 661 703 10.1137/070710111
M. Girardi-Schappo Brain criticality beyond avalanches: open problems and how to approach them J. Phys. Complex. 2021 2 031003 10.1088/2632-072X/ac2071
V. Priesemann et al. Spike avalanches in vivo suggest a driven, slightly subcritical brain state Front. Syst. Neurosci. 2014 8 108 25009473 4068003 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).
J. Wilting V. Priesemann Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation Cereb. Cortex 2019 29 2759 2770 1:STN:280:DC%2BB3M%2FnslOlsg%3D%3D 31008508 6519697 10.1093/cercor/bhz049
T. Donoghue et al. Parameterizing neural power spectra into periodic and aperiodic components Nat. Neurosci. 2020 23 1655 1665 1:CAS:528:DC%2BB3cXisVSlu7bI 33230329 8106550 10.1038/s41593-020-00744-x
D. Makowski et al. NeuroKit2: A Python toolbox for neurophysiological signal processing Behav. Res. 2021 53 1689 1696 10.3758/s13428-020-01516-y
G.B. Morales S. di Santo M.A. Muñoz Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics Proc. Natl Acad. Sci. 2023 120 e2208998120 1:CAS:528:DC%2BB3sXlvVKjsLk%3D 36827262 9992863 10.1073/pnas.2208998120
R. Hardstone et al. Detrended fluctuation analysis: a scale-free view on neuronal oscillations Front. Physiol. 2012 3 450 23226132 3510427 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).
Y. Zhang J. Hao C. Zhou K. Chang Normalized Lempel-Ziv complexity and its application in bio-sequence analysis J. Math. Chem. 2009 46 1203 1212 1:CAS:528:DC%2BD1MXht1Wgsr7N 10.1007/s10910-008-9512-2
M. Costa A.L. Goldberger C.-K. Peng Multiscale entropy analysis of complex physiologic time series Phys. Rev. Lett. 2002 89 068102 12190613 10.1103/PhysRevLett.89.068102
Z.J. Lau T. Pham S.H.A. Chen D. Makowski Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations Eur. J. Neurosci. 2022 56 5047 5069 1:CAS:528:DC%2BB38XitlWhs7rK 35985344 9826422 10.1111/ejn.15800
O’Byrne, J. edgeofpy. https://github.com/jnobyrne/edgeofpy (2023).
