broadcasting of information; disorders of consciousness; in-silico exogenous perturbations; integration of information; Neurology (clinical); Neurology; Radiology, Nuclear Medicine and imaging; Radiological and Ultrasound Technology; Anatomy
Abstract :
[en] The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI data, fitted to a linear model of activity propagation. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to severe reduction of glucose metabolism as measured with [18 F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior.
Disciplines :
Neurology
Author, co-author :
Panda, Rajanikant ✱; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
López-González, Ane ✱; Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
Gilson, Matthieu; Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain ; Institut des Neurosciences des Systemes, INSERM-AMU, Marseille, France
Gosseries, Olivia ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Thibaut, Aurore ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Frasso, Gianluca; Wageningen Food Safety Research, Wageningen, The Netherlands
Cecconi, Benedetta ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Escrichs, Anira; Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
Coma Science Group Collaborators
Deco, Gustavo; Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain ; Institució Catalana de la Recerça i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain ; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany ; School of Psychological Sciences, Monash University, Victoria, Australia
Laureys, Steven ; Centre Hospitalier Universitaire de Liège - CHU > > Centre du Cerveau² ; CERVO Research Center, Laval University, Québec, Quebec, Canada
Zamora-López, Gorka ✱; Center for Brain and Cognition, Department of Information and Communication Technologies, Pompeu Fabra University, Barcelona, Spain
Annen, Jitka ✱; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Adhikari, M. H., Griffis, J., Siegel, J. S., Thiebaut de Schotten, M., Deco, G., Insabato, A., Gilson, M., & Corbetta, M. (2021). Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke. Brain Communications, 3, fcab233.
Afrasiabi, M., Redinbaugh, M. J., Phillips, J. M., Kambi, N. A., Mohanta, S., Raz, A., Haun, A. M., & Saalmann, Y. B. (2021). Consciousness depends on integration between parietal cortex, striatum, and thalamus. Cell Systems, 12, 363–373.e11.
Annen, J., Frasso, G., Crone, J. S., Heine, L., Di Perri, C., Martial, C., Cassol, H., Demertzi, A., Naccache, L., & Laureys, S. (2018). Regional brain volumetry and brain function in severely brain-injured patients. Annals of Neurology, 83, 842–853.
Annen, J., Heine, L., Ziegler, E., Frasso, G., Bahri, M., Di Perri, C., Stender, J., Martial, C., Wannez, S., D'ostilio, K., Amico, E., Antonopoulos, G., Bernard, C., Tshibanda, F., Hustinx, R., & Laureys, S. (2016). Function–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Human Brain Mapping, 37, 3720.
Arena, A., Comolatti, R., Thon, S., Casali, A. G., & Storm, J. F. (2021). General anesthesia disrupts complex cortical dynamics in response to intracranial electrical stimulation in rats. eNeuro, 8. https://doi.org/10.1523/ENEURO.0343-20.2021
Armitage, R. (1995). The distribution of EEG frequencies in REM and NREM sleep stages in healthy young adults. Sleep, 18, 334–341.
Barnett, L., Buckley, C. L., & Bullock, S. (2009). Neural complexity and structural connectivity. Physical Review E, 79, 051914.
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2014). Signature of consciousness in the dynamics of resting-state brain activity. Proceedings of the National Academy of Sciences, 112, 201418031. http://www.pnas.org/content/early/2015/01/02/1418031112.short
Bettinardi, R. G., Tort-Colet, N., Ruiz-Mejias, M., Sanchez-Vives, M. V., & Deco, G. (2015). Gradual emergence of spontaneous correlated brain activity during fading of general anesthesia in rats: Evidences from fMRI and local field potentials. NeuroImage, 114, 185–198.
Bodart, O., Gosseries, O., Wannez, S., Thibaut, A., Annen, J., Boly, M., Rosanova, M., Casali, A. G., Casarotto, S., Tononi, G., Massimini, M., & Laureys, S. (2017). Measures of metabolism and complexity in the brain of patients with disorders of consciousness. NeuroImage: Clinical, 14, 354–362.
Casali, A. G., Gosseries, O., Rosanova, M., Boly, M., Sarasso, S., Casali, K. R., Casarotto, S., Bruno, M. A., Laureys, S., Tononi, G., & Massimini, M. (2013). A theoretically based index of consciousness independent of sensory processing and behavior. Science Translational Medicine, 5, 198ra105.
Damasio, A., & Meyer, K. (2009). Consciousness: An overview of the phenomenon and of its possible neural basis. The Neurology of Consciousness: Cognitive neuroscience and neuropathology, 3–14.
Deco, G., & Kringelbach, M. L. (2017). Hierarchy of information processing in the brain: A novel ‘intrinsic ignition’ framework. Neuron, 94, 961–968.
Deco, G., Kringelbach, M. L., Jirsa, V. K., & Ritter, P. (2017). The dynamics of resting fluctuations in the brain: Metastability and its dynamical cortical core. Scientific Reports, 7, 3095.
Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70, 200–227.
Dehaene, S., Changeux, J. P., & Naccache, L. (2011). The global neuronal workspace model of conscious access: From neuronal architectures to clinical applications. Research and Perspectives in Neurosciences, 18, 55–84.
Demertzi, A., Antonopoulos, G., Heine, L., Voss, H. U., Crone, J. S., De Los, A. C., Bahri, M. A., Di Perri, C., Vanhaudenhuyse, A., Charland-Verville, V., Kronbichler, M., Trinka, E., Phillips, C., Gomez, F., Tshibanda, L., Soddu, A., Schiff, N. D., Whitfield-Gabrieli, S., & Laureys, S. (2015). Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain, 138, 2619–2631.
Demertzi, A., Tagliazucchi, E., Dehaene, S., Deco, G., Barttfeld, P., Raimondo, F., Martial, C., Fernández-Espejo, D., Rohaut, B., Voss, H. U., Schiff, N. D., Owen, A. M., Laureys, S., Naccache, L., & Sitt, J. D. (2019). Human consciousness is supported by dynamic complex patterns of brain signal coordination. Science Advances, 5, eaat7603.
Di Perri, C., Bastianello, S., Bartsch, A. J., Pistarini, C., Maggioni, G., Magrassi, L., Imberti, R., Pichiecchio, A., Vitali, P., Laureys, S., & Di Salle, F. (2013). Limbic hyperconnectivity in the vegetative state. Neurology, 81, 1417–1424.
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., Todd Constable, R., & Author, N. N. (2015). Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity HHS public access Author manuscript. Nature Neuroscience, 18, 1664–1671.
Galán, R. (2008). On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS One, 3, e2148.
Giacino, J. T., Kalmar, K., & Whyte, J. (2004). The JFK coma recovery scale-revised: Measurement characteristics and diagnostic utility. Archives of Physical Medicine and Rehabilitation, 85, 2020–2029.
Giacino, J. T., Katz, D. I., Schiff, N. D., Whyte, J., Ashman, E. J., Ashwal, S., Barbano, R., Hammond, F. M., Laureys, S., Ling, G. S. F., Nakase-Richardson, R., Seel, R. T., Yablon, S., Getchius, T. S. D., Gronseth, G. S., & Armstrong, M. J. (2018). Practice guideline update recommendations summary: Disorders of consciousness: Report of the guideline development, dissemination, and implementation Subcommittee of the American Academy of Neurology; the American Congress of Rehabilitation Medicine; and the National Institute on Disability, Independent Living, and Rehabilitation Research. Archives of Physical Medicine and Rehabilitation, 99, 1699–1709.
Gilson, M., Kouvaris, N. E., Deco, G., Mangin, J. F., Poupon, C., Lefranc, S., Rivière, D., & Zamora-López, G. (2019). Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability. NeuroImage, 201, 116007.
Gilson, M., Kouvaris, N. E., Deco, G., & Zamora-López, G. (2018). Framework based on communicability and flow to analyze complex network dynamics. Physical Review E, 97. https://doi.org/10.1103/physreve.97.052301
Gilson, M., Moreno-Bote, R., Ponce-Alvarez, A., Ritter, P., & Deco, G. (2016). Estimation of directed effective connectivity from fMRI functional connectivity hints at asymmetries of cortical connectome. PLoS Computational Biology, 12, e1004762.
Gilson, M., Zamora-López, G., Pallarés, V., Adhikari, M. H., Senden, M., Tauste-Campo, A., Mantini, D., Corbetta, M., Deco, G., & Insabato, A. (2020). Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Network Neuroscience, 4(2), 338–373.
Griffanti, L., Douaud, G., Bijsterbosch, J., Evangelisti, S., Alfaro-Almagro, F., Glasser, M. F., Duff, E. P., Fitzgibbon, S., Westphal, R., Carone, D., Beckmann, C. F., & Smith, S. M. (2017). Hand classification of fMRI ICA noise components. NeuroImage, 154, 188–205.
Hasson, U., Yang, E., Vallines, I., Heeger, D. J., & Rubin, N. (2008). A hierarchy of temporal receptive windows in human cortex. The Journal of Neuroscience, 28, 2539–2550.
Herbet, G., Lafargue, G., de Champfleur, N. M., Moritz-Gasser, S., le Bars, E., Bonnetblanc, F., & Duffau, H. (2014). Disrupting posterior cingulate connectivity disconnects consciousness from the external environment. Neuropsychologia, 56, 239–244.
Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G. (2016). Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage, 127, 242–256. https://doi.org/10.1016/j.neuroimage.2015.11.055
Ishizawa, Y., Ahmed, O. J., Patel, S. R., Gale, J. T., Sierra-Mercado, D., Brown, E., & Eskandar, E. N. (2016). Dynamics of Propofol-induced loss of consciousness across primate neocortex. The Journal of Neuroscience, 36, 7718–7726.
Kondziella, D., Bender, A., Diserens, K., van Erp, W., Estraneo, A., Formisano, R., Laureys, S., Naccache, L., Ozturk, S., Rohaut, B., Sitt, J. D., Stender, J., Tiainen, M., Rossetti, A. O., Gosseries, O., & Chatelle, C. (2020). European academy of neurology guideline on the diagnosis of coma and other disorders of consciousness. European Journal of Neurology, 27, 741–756.
Kroeger, D., & Amzica, F. (2007). Hypersensitivity of the anesthesia-induced comatose brain. The Journal of Neuroscience, 27, 10597–10607.
Krom, A. J., Marmelshtein, A., Gelbard-Sagiv, H., Tankus, A., Hayat, H., Hayat, D., Matot, I., Strauss, I., Fahoum, F., Soehle, M., Bostrom, J., Mormann, F., Fried, I., & Nir, Y. (2020). Anesthesia-induced loss of consciousness disrupts auditory responses beyond primary cortex. Proceedings of the National Academy of Sciences of the United States of America, 117, 11780.
Laureys, S. (2005). The neural correlate of (un)awareness: Lessons from the vegetative state. Trends in Cognitive Sciences, 9, 556–559.
Laureys, S., Owen, A. M., & Schiff, N. D. (2004). Brain function in coma, vegetative state, and related disorders. Lancet Neurology, 3, 537–546.
López-González, A., Panda, R., Ponce-Alvarez, A., Zamora-López, G., Escrichs, A., Martial, C., Thibaut, A., Gosseries, O., Kringelbach, M. L., Annen, J., Laureys, S., & Deco, G. (2021). Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Communications Biology, 4, 2020.11.20.391482. https://doi.org/10.1101/2020.11.20.391482v1
Luppi, A. I., Craig, M. M., Pappas, I., Finoia, P., Williams, G. B., Allanson, J., Pickard, J. D., Owen, A. M., Naci, L., Menon, D. K., & Stamatakis, E. A. (2019). Consciousness-specific dynamic interactions of brain integration and functional diversity. Nature Communications, 10, 4616.
Mashour, G. A., Roelfsema, P., Changeux, J. P., & Dehaene, S. (2020). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105, 776–798.
Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005). Breakdown of cortical effective connectivity during sleep. Science, 309, 2228–2232.
Messé, A., Rudrauf, D., Benali, H., & Marrelec, G. (2014). Relating structure and function in the human brain: Relative contributions of anatomy, stationary dynamics, and non-stationarities. PLoS Computational Biology, 10, e1003530.
Moeller, F., Siebner, H. R., Wolff, S., Muhle, H., Granert, O., Jansen, O., Stephani, U., & Siniatchkin, M. (2008). Simultaneous EEG-fMRI in drug-naive children with newly diagnosed absence epilepsy. Epilepsia, 49, 1510–1519.
Murray, J. D., Bernacchia, A., Freedman, D. J., Romo, R., Wallis, J. D., Cai, X., Padoa-Schioppa, C., Pasternak, T., Seo, H., Lee, D., & Wang, X. J. (2014). A hierarchy of intrinsic timescales across primate cortex. Nature Neuroscience, 17, 1661–1663.
Nagel, T. (1974). What is it like to Be a bat? Philosophical Review, 83, 435.
Northoff, G., & Lamme, V. (2020). Neural signs and mechanisms of consciousness: Is there a potential convergence of theories of consciousness in sight? Neuroscience and Biobehavioral Reviews, 118, 568–587.
Northoff, G., Wainio-Theberge, S., & Evers, K. (2020). Is temporo-spatial dynamics the “common currency” of brain and mind? In quest of “spatiotemporal neuroscience”. Physics of Life Reviews, 33, 34–54.
Oizumi, M., Albantakis, L., & Tononi, G. (2014). From the phenomenology to the mechanisms of consciousness: Integrated information theory 3.0. PLoS Computational Biology, 10, e1003588.
Owen, A. M., & Coleman, M. R. (2008). Functional neuroimaging of the vegetative state. Nature Reviews Neuroscience, 9, 235–243.
Pavone, K. J., Su, L., Gao, L., Eromo, E., Vazquez, R., Rhee, J., Hobbs, L. E., Ibala, R., Demircioglu, G., Purdon, P. L., Brown, E. N., & Akeju, O. (2017). Lack of responsiveness during the onset and offset of Sevoflurane anesthesia is associated with decreased awake-alpha oscillation power. Frontiers in Systems Neuroscience, 11, 38.
Phillips, C. L., Bruno, M. A., Maquet, P., Boly, M., Noirhomme, Q., Schnakers, C., Vanhaudenhuyse, A., Bonjean, M., Hustinx, R., Moonen, G., Luxen, A., & Laureys, S. (2011). “Relevance vector machine” consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients. NeuroImage, 56, 797–808.
Portas, C. M., Krakow, K., Allen, P., Josephs, O., Armony, J. L., & Frith, C. D. (2000). Auditory processing across the sleep-wake cycle: Simultaneous EEG and fMRI monitoring in humans. Neuron, 28, 991–999.
Schiff, N. D. (2010). Recovery of consciousness after brain injury: A mesocircuit hypothesis. Trends in Neurosciences, 33, 1–9.
Sela, Y., Vyazovskiy, V. V., Cirelli, C., Tononi, G., & Nir, Y. (2016). Responses in rat Core auditory cortex are preserved during sleep spindle oscillations. Sleep, 39, 1069–1082.
Selçuk Bayin, S. (2006). Green's functions. In Mathematical methods in science and engineering. John Wiley & Sons, Inc.
Seth, A. K., Barrett, A. B., & Barnett, L. (2011). Causal density and integrated information as measures of conscious level. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369, 3748–3767.
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415. https://doi.org/10.1016/j.neuroimage.2013.05.081
Signorelli, C. M., Wang, Q., & Khan, I. (2021). A compositional model of consciousness based on consciousness-only. Entropy, 23, 308.
Silva, A., Cardoso-Cruz, H., Silva, F., Galhardo, V., & Antunes, L. (2010). Comparison of anesthetic depth indexes based on thalamocortical local field potentials in rats. Anesthesiology, 112, 355–363.
Stender, J., Gosseries, O., Bruno, M. A., Charland-Verville, V., Vanhaudenhuyse, A., Demertzi, A., Chatelle, C., Thonnard, M., Thibaut, A., Heine, L., Soddu, A., Boly, M., Schnakers, C., Gjedde, A., & Laureys, S. (2014). Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: A clinical validation study. Lancet, 384, 514–522.
Stender, J., Kupers, R., Rodell, A., Thibaut, A., Chatelle, C., Bruno, M. A., Gejl, M., Bernard, C., Hustinx, R., Laureys, S., & Gjedde, A. (2015). Quantitative rates of brain glucose metabolism distinguish minimally conscious from vegetative state patients. Journal of Cerebral Blood Flow and Metabolism, 35, 58–65.
Stender, J., Mortensen, K. N. N., Thibaut, A., Darkner, S., Laureys, S., Gjedde, A., & Kupers, R. (2016). The minimal energetic requirement of sustained awareness after brain injury. Current Biology, 26, 1494–1499.
Thibaut, A., Panda, R., Annen, J., Sanz, L. R. D., Naccache, L., Martial, C., Chatelle, C., Aubinet, C., Bonin, E. A. C., Barra, A., Briand, M. M., Cecconi, B., Wannez, S., Stender, J., Laureys, S., & Gosseries, O. (2021). Preservation of brain activity in unresponsive patients identifies MCS star. Annals of Neurology, 90, 89-100.
Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5, 42. https://bmcneurosci.biomedcentral.com/articles/10.1186/1471-2202-5-42
Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17, 450–461.
Tononi, G., & Sporns, O. (1994). A measure for brain complexity: Relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences of the United States of America, 91, 5033–5037.
Tononi, G., Sporns, O., & Edelman, G. M. (1996). A complexity measure for selective matching of signals by the brain. Proceedings of the National Academy of Sciences, 93, 3422–3427. https://doi.org/10.1073/pnas.93.8.3422
Wenzel, M., Han, S., Smith, E. H., Hoel, E., Greger, B., House, P. A., & Yuste, R. (2019). Reduced repertoire of cortical microstates and neuronal ensembles in medically induced loss of consciousness. Cell Systems, 8, 467–474.
Winters, J. J. (2020). The temporally-integrated causality landscape: A theoretical framework for consciousness and meaning. Consciousness and Cognition, 83, 102976.
Wollstadt, P., Sellers, K. K., Rudelt, L., Priesemann, V., Hutt, A., Fröhlich, F., & Wibral, M. (2017). Breakdown of local information processing may underlie isoflurane anesthesia effects. PLoS Computational Biology, 13, e1005511.
Yeshurun, Y., Nguyen, M., & Hasson, U. (2017). Amplification of local changes along the timescale processing hierarchy. Proceedings of the National Academy of Sciences of the United States of America, 114, 9475–9480.
Zamora-López, G., Chen, Y., Deco, G., Kringelbach, M. L., & Zhou, C. S. (2016). Functional complexity emerging from anatomical constraints in the brain: The significance of network modularity and rich-clubs. Scientific Reports, 6, 1–18.
Zamora-López, G., Zhou, C. S., & Kurths, J. (2010). Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Frontiers in Neuroinformatics, 4, 1.