EEG; disorders of consciousness; neural activity modeling; neural correlates of consciousness; neural field theory; Experimental and Cognitive Psychology; Clinical Psychology; Neurology; Neurology (clinical); Psychiatry and Mental Health
Abstract :
[en] Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFT's capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness states remains unclear. Here, we fitted a corticothalamic NFT model to the electroencephalography (EEG) data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data across various states of consciousness. The identified correlations not only highlight the model's ability to differentiate between healthy and impaired states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across normal and pathological states of consciousness. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.
Disciplines :
Neurosciences & behavior
Author, co-author :
Polyakov, Daniel ; Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, David Ben Gurion Blvd 1, Be'er-Sheva 8410501, Israel
Robinson, P A; School of Physics, The University of Sydney, Sydney, NSW 2006, Australia
Makbili, Avigail; Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, David Ben Gurion Blvd 1, Be'er-Sheva 8410501, Israel
Laureys, Steven ; Université de Liège - ULiège > Département des sciences cliniques ; Joint International Research Unit on Consciousness, CERVO Brain Research Centre, Laval University, 2601 de la Canardière, G1J 2G3, Québec, Canada
Gosseries, Olivia ✱; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Coma Science Group
Shriki, Oren ✱; Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, David Ben Gurion Blvd 1, Be'er-Sheva 8410501, Israel
✱ These authors have contributed equally to this work.
Language :
English
Title :
Neural field modeling and analysis of consciousness states in the brain.
Abeysuriya RG, Robinson PA. Real-time automated EEG tracking of brain states using neural field theory. J Neurosci Methods 2016;258:28–45. 10.1016/j.jneumeth.2015.09.026
Abeysuriya RG, Rennie CJ, Robinson PA. Physiologically based arousal state estimation and dynamics. J Neurosci Methods 2015;253:55–69. 10.1016/j.jneumeth.2015.06.002
van Albada SJ, Robinson PA. Mean-field modeling of the basal ganglia-thalamocortical system. I. Firing rates in healthy and parkinsonian states. J Theor Biol 2009;257:642–63. 10.1016/j.jtbi.2008.12.018
Annen J, Frasso G, van der Lande GJ. et al. Cerebral electrometabolic coupling in disordered and normal states of consciousness. Cell Rep 2023;42:112854. 10.1016/j.celrep.2023.112854
Assadzadeh S, Annen J, Sanz L. et al. Method for quantifying arousal and consciousness in healthy states and severe brain injury via EEG-based measures of corticothalamic physiology. J Neurosci Methods 2023;398:109958. 10.1016/j.jneumeth.2023.109958
Bai Y, Lin Y, Ziemann U. Managing disorders of consciousness: the role of electroencephalography. J Neurol 2021;268:4033–65. 10.1007/s00415-020-10095-z
Bernat JL. Chronic disorders of consciousness. Lancet Neurol 2006;14:1277–83. 10.3892/etm.2017.4639
Bodien YG, Martens G, Ostrow J. et al. Cognitive impairment, clinical symptoms and functional disability in patients emerging from the minimally conscious state. NeuroRehabilitation 2020;46:65–74. 10.3233/NRE-192860
Boly M, Massimini M, Tsuchiya N. et al. Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? Clinical and neuroimaging evidence. J Neurosci 2017;37:9603–13. 10.1523/JNEUROSCI.3218-16.2017
Braitenberg, V. and Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity. Berlin: Springer, Berlin Heidelberg. 2 edn, 1998. 10.1007/978-3-662-03733-1
Breakspear M, Roberts JA, Terry JR. et al. A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cereb Cortex 2006;16:1296–313. 10.1093/cercor/bhj072
Brown EN, Lydic R, Schiff ND. General anesthesia, sleep, and coma. N Engl J Med 2010;363:2638–50. 10.1056/NEJMra0808281
Bruno MA, Vanhaudenhuyse A, Thibaut A. et al. From unresponsive wakefulness to minimally conscious PLUS and functional locked-in syndromes: recent advances in our understanding of disorders of consciousness. J Neurol 2011;258:1373–84. 10.1007/s00415-011-6114-x
Carrière M, Cassol H, Aubinet C. et al. Auditory localization should be considered as a sign of minimally conscious state based on multimodal findings. Brain Commun 2020;2:1–15. 10.1093/braincomms/fcaa195
Casali AG, Gosseries O, Rosanova M. et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med 2013;5. 10.1126/scitranslmed.3006294
Colombo MA, Napolitani M, Boly M. et al. The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine. NeuroImage 2019;189:631–44. 10.1016/j.neuroimage.2019.01.024
Crick F, Koch C. Towards a neurobiological theory of consciousness. Semin Neurosci 1990;25:251–62. 10.3828/sj.2016.25.2.9
Darracq M, Funk CM, Polyakov D. et al. Evoked alpha power is reduced in disconnected consciousness during sleep and anesthesia. Sci Rep 2018;8:1–10. 10.1038/s41598-018-34957-9
Deco G, Jirsa VK, Robinson PA. et al. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol 2008;4. 10.1371/journal.pcbi.1000092
Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9–21. 10.1016/j.jneumeth.2003.10.009
Demertzi A, Tagliazucchi E, Dehaene S. et al. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv 2019;5:eaat7603–11. 10.1126/sciadv.aat7603
Donoghue T, Haller M, Peterson EJ. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci 2020;23:1655–65. 10.1038/s41593-020-00744-x
Fekete T, Omer DB, O’Hashi K. et al. Critical dynamics, anesthesia and information integration: lessons from multi-scale criticality analysis of voltage imaging data. NeuroImage 2018;183:919–33. 10.1016/j.neuroimage.2018.08.026
Freeman WJ, Zhai J. Simulated power spectral density (PSD) of background electrocorticogram (ECoG). Cogn Neurodyn 2009;3:97–103. 10.1007/s11571-008-9064-y
Fulcher BD, Phillips AJ, Robinson PA. Modeling the impact of impulsive stimuli on sleep-wake dynamics. Phys Rev E 2008;78:1–14. 10.1103/PhysRevE.78.051920
Gao R, Peterson EJ, Voytek B. Inferring synaptic excitation/inhibition balance from field potentials. NeuroImage 2017;158:70–8. 10.1016/j.neuroimage.2017.06.078
Gervais C, Boucher LP, Villar GM. et al. A scoping review for building a criticality-based conceptual framework of altered states of consciousness. Front Syst Neurosci 2023;17. 10.3389/fnsys.2023.1085902
Giacino JT, Kalmar K, Whyte J. The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004;85:2020–9. 10.1016/j.apmr.2004.02.033
Gosseries O, Bruno MA, Chatelle C. et al. Disorders of consciousness: what’s in a name? NeuroRehabilitation 2011;28:3–14. 10.3233/NRE-2011-0625
Groppe DM, Urbach TP, Kutas M. Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. Psychophysiology 2011;48:1711–25. 10.1111/j.1469-8986.2011.01273.x
Haun AM, Oizumi M, Kovach CK. et al. Conscious perception as integrated information patterns in human electrocorticography. eNeuro 2017;4:ENEURO.0085–17.2017. 10.1523/ENEURO.0085-17.2017
Jansen BH, Rit VG. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern 1995;73:357–66. 10.1007/BF00199471
Koch C, Tsuchiya N. Attention and consciousness: two distinct brain processes. Trends Cogn Sci 2007;11:16–22. 10.1016/j.tics.2006.10.012
Koch C, Massimini M, Boly M. et al. Neural correlates of consciousness: progress and problems. Nat Rev Neurosci 2016;17:307–21. 10.1038/nrn.2016.22
Lau H, Rosenthal D. Empirical support for higher-order theories of conscious awareness. Trends Cogn Sci 2011;15:365–73. 10.1016/j.tics.2011.05.009
Laufs H, Vuust P, Deco G. et al. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun 2019;10:1–14. 10.1038/s41467-019-08934-3
Li D, Mashour GA. Cortical dynamics during psychedelic and anesthetized states induced by ketamine. NeuroImage 2019;196:32–40. 10.1016/j.neuroimage.2019.03.076
Li G, Liu Y, Zheng Y. et al. Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder. NeuroImage Clin 2021;31:102758. 10.1016/j.nicl.2021.102758
Markram H. The Blue Brain Project. Nat Rev Neurosci 2006;7:153–60. 10.1038/nrn1848
Martens G, Ibáñez-Soria D, Barra A. et al. A novel closed-loop EEG-tDCS approach to promote responsiveness of patients in minimally conscious state: a study protocol. Behav Brain Res 2021;409:113311. 10.1016/j.bbr.2021.113311
Maschke C, Duclos C, Owen AM. et al. Aperiodic brain activity and response to anesthesia vary in disorders of consciousness. Neuroimage 2023;275:120154. 10.1016/j.neuroimage.2023.120154
Mashour GA, Hudetz AG. Neural correlates of unconsciousness in large-scale brain networks. Trends Neurosciences 2018;41:150–60. 10.1016/j.tins.2018.01.003
Mateos DM, Guevara Erra R, Wennberg R. et al. Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn 2018;12:73–84. 10.1007/s11571-017-9459-8
Medel V, Irani M, Crossley N. et al. Complexity and 1/f slope jointly reflect brain states. Sci Rep 2023;13:21700–12. 10.1038/s41598-023-47316-0
Mohanta S, Afrasiabi M, Casey CP. et al. Predictive feedback, early sensory representations, and fast responses to predicted stimuli depend on NMDA receptors. J Neurosci 2021;41:10130–47. 10.1523/jneurosci.1311-21.2021
Müller EJ, van Albada SJ, Kim JW. et al. Unified neural field theory of brain dynamics underlying oscillations in Parkinson’s disease and generalized epilepsies. J Theor Biol 2017;428:132–46. 10.1016/j.jtbi.2017.06.016
Muller EJ, Munn BR, Redinbaugh MJ. et al. The non-specific matrix thalamus facilitates the cortical information processing modes relevant for conscious awareness. Cell Rep 2023;42:112844. 10.1016/j.celrep.2023.112844
Nevado-Holgado AJ, Marten F, Richardson MP. et al. Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: application to epilepsy seizure evolution. NeuroImage 2012;59:2374–92. 10.1016/j.neuroimage.2011.08.111
Nunez PL. Neocortical Dynamics and Human EEG Rhythms. New York, NY, USA: Oxford University Press, 1995.
O’Connor SC, Robinson PA. Spatially uniform and nonuniform analyses of electroencephalographic dynamics, with application to the topography of the alpha rhythm. Phys Rev E 2004;70:19. 10.1103/PhysRevE.70.011911
O’Connor SC, Robinson PA. Analysis of the electroencephalographic activity associated with thalamic tumors. J Theor Biol 2005;233:271–86. 10.1016/j.jtbi.2004.10.009
Ouyang G, Li J, Liu X. et al. Dynamic characteristics of absence EEG recordings with multiscale analysispermutation entropy. Epilepsy Res 2013;104:246–52. 10.1016/j.eplepsyres.2012.11.003
Pal D, Li D, Dean JG. et al. Level of consciousness is dissociable from electroencephalographic measures of cortical connectivity, slow oscillations, and complexity. J Neurosci 2019;40:605–18. 10.1523/JNEUROSCI.1910-19.2019
Penfield, W. and Jasper, H. Epilepsy and the Functional Anatomy of the Human Brain. Boston: Brown. Vol. 47, 704, 1954. 10.1097/00007611-195407000-00024
Piarulli A, Bergamasco M, Thibaut A. et al. EEG ultradian rhythmicity differences in disorders of consciousness during wakefulness. J Neurol 2016;263:1746–60. 10.1007/s00415-016-8196-y
Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage 2019;198:181–97. 10.1016/j.neuroimage.2019.05.026
Polyakov D, Robinson PA, Muller EJ. et al. Personalized stimulation therapies for disorders of consciousness: a computational approach to inducing healthy-like brain activity based on neural field theory. J Neural Eng 2025;22:1–24. 10.1088/1741-2552/addd48
Rennie CJ, Robinson PA, Wright JJ. Unified neurophysical model of EEG spectra and evoked potentials. Biol Cybern 2002;86:457–71. 10.1007/s00422-002-0310-9
Robinson PA. The balanced and introspective brain. J R Soc Interface 2017;14:1–7. 10.1098/rsif.2016.0994
Robinson PA. Physical brain connectomics. Phys Rev E 2019;99:23–5. 10.1103/PhysRevE.99.012421
Robinson PA. Neural field theory of neural avalanche exponents. Biol Cybern 2021;115:237–43. 10.1007/s00422-021-00875-9
Robinson PA, Rennie CJ, Wright JJ. Propagation and stability of waves of electrical activity in the cerebral cortex. Phys Rev E 1997;56:826–40. 10.1103/physreve.56.826
Robinson PA, Rennie CJ, Wright JJ. et al. Prediction of electroencephalographic spectra from neurophysiology. Phys Rev E 2001;63:0219031–02190318. 10.1103/PhysRevE.63.021903
Robinson PA, Rennie CJ, Rowe DL. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys Rev E 2002;65:9. 10.1103/PhysRevE.65.041924
Robinson PA, Rennie CJ, Rowe DL. et al. Estimation of multiscale neurophysiologic parameters by electroencephalographic means. Hum Brain Mapp 2004;23:53–72. 10.1002/hbm.20032
Robinson PA, Rennie CJ, Rowe DL. et al. Multiscale brain modelling. Phil Trans R Soc B Biol Sci 2005;360:1043–50. 10.1098/rstb.2005.1638
Robinson PA, Phillips AJ, Fulcher BD. et al. Quantitative modelling of sleep dynamics. Philos Trans R Soc A Math Phys Eng Sci 2011;369:3840–54. 10.1098/rsta.2011.0120
Robinson PA, Sarkar S, Pandejee GM. et al. Determination of effective brain connectivity from functional connectivity with application to resting state connectivities. Phys Rev E 2014;90:1–6. 10.1103/PhysRevE.90.012707
Rowe DL, Robinson PA, Rennie CJ. Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics. J Theor Biol 2004;231:413–33. 10.1016/j.jtbi.2004.07.004
Sanchez-Vives MV, Massimini M, Mattia M. Shaping the default activity pattern of the cortical network. Neuron 2017;94:993–1001. 10.1016/j.neuron.2017.05.015
Sanz-Leon P, Knock SA, Woodman MM. et al. The virtual brain: a simulator of primate brain network dynamics. Front Neuroinform 2013;7. 10.3389/fninf.2013.00010
Sanz-Leon P, Robinson PA, Knock SA. et al. NFTsim: theory and simulation of multiscale neural field dynamics. PLoS Comput Biol 2018;14:e1006387–37. 10.1371/journal.pcbi.1006387
Schartner MM, Carhart-harris RL, Barrett AB, Seth AK. Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and Psilocybin. Sci Rep 2017;7:46421. 10.1038/srep46421
Sergent C, Corazzol M, Labouret G. et al. Bifurcation in brain dynamics reveals a signature of conscious processing independent of report. Nat Commun 2021;12:1149. 10.1038/s41467-021-21393-z
Shriki O, Yellin D. Optimal information representation and criticality in an adaptive sensory recurrent neuronal network. PLoS Comput Biol 2016;12:e1004698–19. 10.1371/journal.pcbi.1004698
Thibaut A, Panda R, Annen J. et al. Preservation of brain activity in unresponsive patients identifies MCS star. Ann Neurol 2021;90:89–100. 10.1002/ana.26095
Tononi G. Consciousness as integrated information: a provisional manifesto. Biol Bull 2008;215:216–42. 10.2307/25470707
Tononi G, Boly M, Gosseries O. et al. The Neurology of Consciousness: An Overview. 2nd edn. Elsevier Ltd, 2016a.
Tononi G, Boly M, Massimini M. et al. Integrated information theory: from consciousness to its physical substrate. Nat Rev Neurosci 2016b;17:450–61. 10.1038/nrn.2016.44
Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ. et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010;133:161–71. 10.1093/brain/awp313
Wang HE, Triebkorn P, Breyton M. et al. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024;11. 10.1093/nsr/nwae079
Whyte CJ, Redinbaugh MJ, Shine JM. et al. Thalamic contributions to the state and contents of consciousness. Neuron 2024;112:1611–25. 10.1016/j.neuron.2024.04.019
Wilson HR, Cowan JD. A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 1973;13:55–80. 10.1007/BF00288786