[en] Low level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low level states of consciousness, thus attenuating the differences between hubs and non hubs brain dynamics.
Research center :
Coma Science Group, GIGA-Consciousness
Disciplines :
Neurology
Author, co-author :
López-González, Ane ✱; Universitat Pompeu Fabra > Computational Neuroscience Group, Center for Brain and Cognition
Panda, Rajanikant ✱; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group
Ponce-Alvarez, Adrián; Universitat Pompeu Fabra > Computational Neuroscience Group, Center for Brain and Cognition
Zamora-López, Gorka; Universitat Pompeu Fabra > Computational Neuroscience Group, Center for Brain and Cognition
Escrichs, Anira; Universitat Pompeu Fabra > Computational Neuroscience Group, Center for Brain and Cognition
Martial, Charlotte ; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group
Thibaut, Aurore ; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group
Kringelbach, Morten L; University of Oxford > Department of Psychiatry
Annen, Jitka ; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group ; CHU Liège - Central University Hospital of Liege > Centre du Cerveau²
Laureys, Steven ; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group
Gustavo, Deco; Universitat Pompeu Fabra > Computational Neuroscience Group, Center for Brain and Cognition
✱ These authors have contributed equally to this work.
Language :
English
Title :
Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics
Laureys, S. The neural correlate of (un)awareness: lessons from the vegetative state. Trends Cogn. Sci. 9, 556–559 (2005). DOI: 10.1016/j.tics.2005.10.010
Sporns, O., Tononi, G. & Kötter, R. The human connectome: a structural description of the human brain. PLoS Computat. Biol. 1, e42 (2005). DOI: 10.1371/journal.pcbi.0010042
van den Heuvel, M. P. & Sporns, O. A cross-disorder connectome landscape of brain dysconnectivity. Nat. Rev. Neurosci. 20, 435–446 (2019). DOI: 10.1038/s41583-019-0177-6
Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995). DOI: 10.1002/mrm.1910340409
Greicius, M. D., Krasnow, B., Reiss, A. L. & Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl Acad. Sci. 100, 253–258 (2003). DOI: 10.1073/pnas.0135058100
Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006). DOI: 10.1073/pnas.0601417103
Zamora-López, G., Zhou, C. & Kurths, J. Exploring brain function from anatomical connectivity. Front. Neurosci. 103, 5–83 (2011).
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015). DOI: 10.1038/nrn3963
Deco, G. & Kringelbach, M. L. Hierarchy of information processing in the brain: a novel ‘intrinsic ignition’ framework. Neuron 94, 961–968 (2017). DOI: 10.1016/j.neuron.2017.03.028
Dehaene, S. & Changeux, J.-P. Experimental and theoretical approaches to conscious processing. Neuron 70, 200–227 (2011). DOI: 10.1016/j.neuron.2011.03.018
Tononi, G. & Koch, C. The neural correlates of consciousness: an update. Ann. N. Y. Acad. Sci. 1124, 239–261 (2008). DOI: 10.1196/annals.1440.004
Zamora-López, G. & Brasselet, R. Sizing complex networks. Commun. Phys. 2, 1–10 (2019). DOI: 10.1038/s42005-019-0239-0
Demertzi, A. et al. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci. Adv. 5, eaat7603 (2019). DOI: 10.1126/sciadv.aat7603
Luppi, A. I. et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat. Commun. 10, 1–12 (2019). DOI: 10.1038/s41467-019-12658-9
Panda, R. et al. Temporal dynamics of the default mode network characterize meditation-induced alterations in consciousness. Front. Hum. Neurosci. 10, 372 (2016). DOI: 10.3389/fnhum.2016.00372
Escrichs, A. et al. Characterizing the dynamical complexity underlying meditation. Front. Syst. Neurosci. 13, 27 (2019). DOI: 10.3389/fnsys.2019.00027
Rizkallah, J. et al. Decreased integration of EEG source-space networks in disorders of consciousness. NeuroImage: Clin. 23, 101841 (2019). DOI: 10.1016/j.nicl.2019.101841
Monti, M. M. et al. Dynamic change of global and local information processing in propofol-induced loss and recovery of consciousness. PLoS Comput. Biol. 9, e1003271 (2013).
Rosanova, M. et al. Sleep-like cortical OFF-periods disrupt causality and complexity in the brain of unresponsive wakefulness syndrome patients. Nat. Commun. 9, 4427 (2018). DOI: 10.1038/s41467-018-06871-1
Casali, A. G. et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med. 5, 105–198 (2013). DOI: 10.1126/scitranslmed.3006294
Bodart, O. et al. Global structural integrity and effective connectivity in patients with disorders of consciousness. Brain Stimulation 11, 358–365 (2018). DOI: 10.1016/j.brs.2017.11.006
Boly, M. et al. Preserved feedforward but impaired top-down processes in the vegetative state. Science 332, 858–862 (2011). DOI: 10.1126/science.1202043
Greicius, M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 21, 424–430 (2008). DOI: 10.1097/WCO.0b013e328306f2c5
Barttfeld, P. et al. Signature of consciousness in the dynamics of resting-state brain activity. Proc. Natl Acad. Sci. 112, 887–892 (2015). DOI: 10.1073/pnas.1418031112
Tagliazucchi, E., Crossley, N., Bullmore, E. T. & Laufs, H. Deep sleep divides the cortex into opposite modes of anatomical-functional coupling. Brain Struct. Funct. 221, 4221–4234 (2016). DOI: 10.1007/s00429-015-1162-0
Dehaene, S. & Naccache, L. Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79, 1–37 (2001). DOI: 10.1016/S0010-0277(00)00123-2
Crone, J. S. et al. Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. NeuroImage: Clin. 4, 240–248 (2014). DOI: 10.1016/j.nicl.2013.12.005
Laureys, S. et al. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. 8, 68 (2010).
Giacino, J. T. et al. The minimally conscious state: definition and diagnostic criteria. Neurology 58, 349–353 (2002). DOI: 10.1212/WNL.58.3.349
Ponce-Alvarez, A. et al. Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLOS Comput. Biol. 11, e1004100 (2015). DOI: 10.1371/journal.pcbi.1004100
Deco, G., Kringelbach, M. L., Jirsa, V. K. & Ritter, P. The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Sci. Rep. 7, 1–14 (2017). DOI: 10.1038/s41598-017-03073-5
Glerean, E., Salmi, J., Lahnakoski, J. M., Jääskeläinen, I. P. & Sams, M. Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity. Brain Connectivity 2, 91–101 (2012). DOI: 10.1089/brain.2011.0068
Deco, G. et al. Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states. NeuroImage 169, 46–56 (2018). DOI: 10.1016/j.neuroimage.2017.12.009
Adhikari, M. H. et al. Decreased integration and information capacity in stroke measured by whole brain models of resting state activity. Brain 140, 1068–1085 (2017). DOI: 10.1093/brain/awx021
Rubinov, M. & Sporns, O. Weight-conserving characterization of complex functional brain networks. NeuroImage 56, 2068–2079 (2011). DOI: 10.1016/j.neuroimage.2011.03.069
Shen, X., Tokoglu, F., Papademetris, X. & Constable, R. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage 82, 403–415 (2013). DOI: 10.1016/j.neuroimage.2013.05.081
Saenger, V. M. et al. Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease. Sci. Rep. 7, 9882 (2017). DOI: 10.1038/s41598-017-10003-y
Padilla, N. et al. Breakdown of whole-brain dynamics in preterm-born children. Cereb. Cortex 30, 1159–1170 (2019). DOI: 10.1093/cercor/bhz156
Demertzi, A. et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain: J. Neurol. 138, 2619–2631 (2015). DOI: 10.1093/brain/awv169
Amico, E. et al. Posterior cingulate cortex-related co-activation patterns: a resting state fmri study in propofol-induced loss of consciousness. PLoS ONE 9, e100012 (2014). DOI: 10.1371/journal.pone.0100012
Tagliazucchi, E. et al. Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics. J. R. Soc. Interface 13, 20151027 (2016). DOI: 10.1098/rsif.2015.1027
Chennu, S. et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 140, 2120–2132 (2017). DOI: 10.1093/brain/awx163
Zamora-López, G., Zhou, C. & Kurths, J. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks. Front. Neuroinformatics 4, 1 (2010).
van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011). DOI: 10.1523/JNEUROSCI.3539-11.2011
Demertzi, A., Soddu, A. & Laureys, S. Consciousness supporting networks. Curr. Opin. Neurobiol. 23, 239–244 (2013). DOI: 10.1016/j.conb.2012.12.003
Tononi, G. & Koch, C. The neural correlates of consciousness. Ann. N. Y. Acad. Sci. 1124, 239–261 (2008). DOI: 10.1196/annals.1440.004
Deco, G. & Kringelbach, M. L. Metastability and coherence: extending the communication through coherence hypothesis using a whole-brain computational perspective. Trends Neurosci. 39, 125–135 (2016). DOI: 10.1016/j.tins.2016.01.001
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015). DOI: 10.1038/nrn3963
Gómez-Gardeñes, J., Zamora-López, G., Moreno, Y. & Arenas, A. From modular to centralized organization of synchronization in functional areas of the cat cerebral cortex. PLoS ONE 5, 12313 (2010). DOI: 10.1371/journal.pone.0012313
Gollo, L. L., Zalesky, A., Matthew Hutchison, R., Van Den Heuvel, M. & Breakspear, M. Dwelling quietly in the rich club: Brain network determinants of slow cortical fluctuations. Philos. Trans. Roy. Soc. B: Biol. Sci. 370, 20140165 (2015).
Van Den Heuvel, M. P., Kahn, R. S., Goñi, J. & Sporns, O. High-cost, high-capacity backbone for global brain communication. Proc. Natl Acad. Sci. USA 109, 11372–11377 (2012). DOI: 10.1073/pnas.1203593109
van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683–96 (2013). DOI: 10.1016/j.tics.2013.09.012
Gu, S. et al. Functional hypergraph uncovers novel covariant structures over neurodevelopment. Hum. Brain Mapp. 38, 3823–3835 (2017). DOI: 10.1002/hbm.23631
Bassett, D. S. et al. Task-based core-periphery organization of human brain dynamics. PLoS Comput. Biol. 9, e1003171 (2013).
Schiff, N. D. Recovery of consciousness after brain injury: a mesocircuit hypothesis (2010). Trends Neurosci. 33, 1–9 (2010). DOI: 10.1016/j.tins.2009.11.002
Massimini, M. et al. Breakdown of cortical effective connectivity during sleep. Science 309, 2228–2232 (2005). DOI: 10.1126/science.1117256
Thibaut, A., Schiff, N., Giacino, J., Laureys, S. & Gosseries, O. Therapeutic interventions in patients with prolonged disorders of consciousness. Lancet Neurol. 18, 600–614 (2019). DOI: 10.1016/S1474-4422(19)30031-6
Deco, G. et al. Awakening: predicting external stimulation to force transitions between different brain states. Proc. Natl Acad. Sci. USA 116, 18088–18097 (2019). DOI: 10.1073/pnas.1905534116
Brown, E. N., Lydic, R. & Schiff, N. D. General anesthesia, sleep, and coma. N. Engl. J. Med. 363, 2638–2650 (2010). DOI: 10.1056/NEJMra0808281
Aru, J., Suzuki, M. & Larkum, M. E. Cellular mechanisms of conscious processing. Trends Cogn. Sci. 24, 814–825 (2020). DOI: 10.1016/j.tics.2020.07.006
Suzuki, M. & Larkum, M. E. General anesthesia decouples cortical pyramidal neurons. Cell 180, 666–676 (2020). DOI: 10.1016/j.cell.2020.01.024
Bayne, T. & Carter, O. Dimensions of consciousness and the psychedelic state. Neurosci. Consciousness 2018, niy008 (2018).
Herzog, R. et al. A mechanistic model of the neural entropy increase elicited by psychedelic drugs. Sci. Rep. 10, 17725 (2020). DOI: 10.1038/s41598-020-74060-6
Jobst, B. M. et al. Increased stability and breakdown of brain effective connectivity during slow-wave sleep: mechanistic insights from whole-brain computational modelling. Sci. Rep. 7, 4634 (2017). DOI: 10.1038/s41598-017-04522-x
Honey, C. J. et al. Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423–434 (2012). DOI: 10.1016/j.neuron.2012.08.011
Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661–1663 (2014). DOI: 10.1038/nn.3862
Deco, G. et al. Whole-brain multimodal neuroimaging model using serotonin receptor maps explains non-linear functional effects of LSD. Curr. Biol.: CB 28, 3065–3074 (2018). DOI: 10.1016/j.cub.2018.07.083
Demirtaş, M. et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101, 1181–1194 (2019). DOI: 10.1016/j.neuron.2019.01.017
Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. USA 113, 12574–12579 (2016). DOI: 10.1073/pnas.1608282113
Giacino, J. T. The minimally conscious state: defining the borders of consciousness. Prog. Brain Res. 150, 381–395 (2005). DOI: 10.1016/S0079-6123(05)50027-X
Wannez, S., Heine, L., Thonnard, M., Gosseries, O. & Laureys, S. The repetition of behavioral assessments in diagnosis of disorders of consciousness. Ann. Neurol. 81, 883–889 (2017). DOI: 10.1002/ana.24962
Boveroux, P. et al. Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 113, 1038–1053 (2010). DOI: 10.1097/ALN.0b013e3181f697f5
Marsh, B., White, M., Morton, N. & Kenny, G. N. Pharmacokinetic model driven infusion of propofol in children. Br. J. Anaesth. 67, 41–48 (1991). DOI: 10.1093/bja/67.1.41
Ramsay, M. A., Savege, T. M., Simpson, B. R. & Goodwin, R. Controlled sedation with Alphaxalone-Alphadolone. Br. Med. J. 2, 656–659 (1974). DOI: 10.1136/bmj.2.5920.656
Beckmann, C. & Smith, S. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004). DOI: 10.1109/TMI.2003.822821
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002). DOI: 10.1006/nimg.2002.1132
Smith, S. M. Fast robust automated brain extraction. Hum. Brain Mapp. 17, 143–155 (2002). DOI: 10.1002/hbm.10062
Griffanti, L. et al. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95, 232–247 (2014). DOI: 10.1016/j.neuroimage.2014.03.034
Salimi-Khorshidi, G. et al. Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014). DOI: 10.1016/j.neuroimage.2013.11.046
Griffanti, L. et al. Hand classification of fMRI ICA noise components. NeuroImage 154, 188–205 (2017). DOI: 10.1016/j.neuroimage.2016.12.036
Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001). DOI: 10.1016/S1361-8415(01)00036-6
Andersson, J. L. R. et al. Non-linear registration aka spatial normalisation FMRIB Technial Report TR07JA2. 5, 143–156 (2007).
Gong, G. et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524–536 (2009). DOI: 10.1093/cercor/bhn102
Cao, Q. et al. Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. J. Neurosci. 33, 10676–10687 (2013). DOI: 10.1523/JNEUROSCI.4793-12.2013
Muthuraman, M. et al. Structural brain network characteristics can differentiate CIS from early RRMS. Front. Neurosci. 10, 14 (2016). DOI: 10.3389/fnins.2016.00014
Andersson, J. L. R., Graham, M. S., Zsoldos, E. & Sotiropoulos, S. N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage 141, 556–572 (2016). DOI: 10.1016/j.neuroimage.2016.06.058
Leemans, A. & Jones, D. K. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349 (2009). DOI: 10.1002/mrm.21890
Behrens, T., Rohr, K. & Stiehl, H. Robust segmentation of tubular structures in 3-D medical images by parametric object detection and tracking. IEEE Trans. Syst. Man Cybern. B: Cybern. 33, 554–561 (2003). DOI: 10.1109/TSMCB.2003.814305
Behrens, T., Berg, H. J., Jbabdi, S., Rushworth, M. & Woolrich, M. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144–155 (2007). DOI: 10.1016/j.neuroimage.2006.09.018
Newman, M. E. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006). DOI: 10.1073/pnas.0601602103
Hansen, E. C., Battaglia, D., Spiegler, A., Deco, G. & Jirsa, V. K. Functional connectivity dynamics: modeling the switching behavior of the resting state. NeuroImage 105, 525–535 (2015). DOI: 10.1016/j.neuroimage.2014.11.001
Landau, L. D. On the problem of turbulence. Dokl. Akad. Nauk USSR 44, 311 (1944).
Stuart, J. T. On the non-linear mechanics of wave disturbances in stable and unstable parallel flows Part 1. The basic behaviour in plane Poiseuille flow. J. Fluid Mech. 9, 353–370 (1960). DOI: 10.1017/S002211206000116X
Kuznetsov, Y. A. Elements of Applied Bifurcation Theory. Vol. 112 of Applied Mathematical Sciences (Springer New York, 2004).
Pikovsky, A., Rosenblum, M., Kurths, J. & Hilborn, R. C. Synchronization: a universal concept in nonlinear science. Am. J. Phys. 70, 655–655 (2002). DOI: 10.1119/1.1475332
Zhou, S. & Mondragón, R. J. The rich-club phenomenon in the internet topology. IEEE Commun. Lett. 8, 180–182 (2004). DOI: 10.1109/LCOMM.2004.823426
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc.: Ser. B: Methodol. 57, 289–300 (1995).