Disorders of consciousness; High-density electroencephalography; Functional brain networks; Unresponsive wakefulness syndrome; Minimally conscious state
Abstract :
[en] Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between
distant brain areas. However, to which extent the balance of brain network segregation and integration is
modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of
our study was to characterize the local and global topological changes of DOC patients' functional brain networks.
Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering
from coma with various levels of consciousness (EMCS (n=6), MCS+ (n=29), MCS- (n=17) and
UWS (n=9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency
bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph
theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as
group differences between healthy volunteers and DOC patient groups.
Results showed that networks in DOC patients are characterized by impaired global information processing
(network integration) and increased local information processing (network segregation) as compared to controls.
The large-scale functional brain networks had integration decreasing with lower level of consciousness.
Disciplines :
Neurology
Author, co-author :
Rizkallah, Jennifer; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Annen, Jitka ; Université de Liège - ULiège > Consciousness-Coma Science Group
Modolo, Julien; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Gosseries, Olivia ; Université de Liège - ULiège > Consciousness-Coma Science Group
Benquet, Pascal; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Mortaheb, Sepehr ; Université de Liège - ULiège > Consciousness-Coma Science Group
Amoud, Hassan; Lebanese University > Azm Center for Research in Biotechnology and its Applications
Cassol, Helena ; Université de Liège - ULiège > Consciousness-Coma Science Group
Mheich, Ahmad; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Thibaut, Aurore ; Université de Liège - ULiège > Consciousness-Coma Science Group
Chatelle, Camille ; Université de Liège - ULiège > Consciousness-Coma Science Group
Hassan, Mahmoud; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Panda, Rajanikant ; Université de Liège - ULiège > Consciousness-Coma Science Group
Wendling, Fabrice; Université de Rennes > Laboratoire Traitement du Signal et de l'Image
Laureys, Steven ; Université de Liège - ULiège > Consciousness-Coma Science Group
Amico, E., et al. Mapping the functional connectome traits of levels of consciousness. NeuroImage 148 (2017), 201–211.
Annen, J., et al. Function–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Hum. Brain Mapp. 37 (2016), 3707–3720.
Annen, J., et al. Regional brain volumetry and brain function in severely brain-injured patients. Ann. Neurol. 83 (2018), 842–853.
Bassett, D.S., et al. Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. 108 (2011), 7641–7646.
Bassett, D.S., et al. Robust detection of dynamic community structure in networks. Chaos: Interdisc. J. Nonlinear Sci., 23, 2013, 013142.
Bassett, D.S., et al. Learning-induced autonomy of sensorimotor systems. Nat. Neurosci. 18 (2015), 744–751.
Blondel, V.D., et al. Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp., 2008, 2008 P10008.
Bodien, Y.G., Chatelle, C., Edlow, B.L., Functional networks in disorders of consciousness. Seminars in Neurology, vol. 37, 2017, Thieme Medical Publishers, 485–502.
Boly, M., et al. Brain connectivity in disorders of consciousness. Brain Connectivity 2 (2012), 1–10.
Brookes, M.J., Woolrich, M.W., Barnes, G.R., Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage 63 (2012), 910–920.
Bullmore, E., Sporns, O., Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10 (2009), 186–198.
Casali, A.G., et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med., 5, 2013, 198ra105.
Casarotto, S., et al. Stratification of unresponsive patients by an independently validated index of brain complexity. Ann. Neurol. 80 (2016), 718–729.
Chennu, S., et al. Spectral signatures of reorganised brain networks in disorders of consciousness. PLoS Comput. Biol., 10, 2014, e1003887.
Chennu, S., et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 140 (2017), 2120–2132.
Colclough, G.L., et al. A symmetric multivariate leakage correction for MEG connectomes. NeuroImage 117 (2015), 439–448.
Crone, J.S., et al. Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. NeuroImage: Clinical 4 (2014), 240–248.
De Pasquale, F., et al. Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl. Acad. Sci. 107 (2010), 6040–6045.
Delorme, A., Makeig, S., EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134 (2004), 9–21.
Demertzi, A., et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex 52 (2014), 35–46.
Demertzi, A., et al. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain 138 (2015), 2619–2631.
Desikan, R.S., et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31 (2006), 968–980.
Di Perri, C., et al. Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study. Lancet Neurol. 15 (2016), 830–842.
Douw, L., et al. Consistency of magnetoencephalographic functional connectivity and network reconstruction using a template versus native MRI for co-registration. Hum. Brain Mapp. 39 (2018), 104–119.
Engemann, D.A., et al. Robust EEG-based cross-site and cross-protocol classification of states of consciousness. Brain 141 (2018), 3179–3192.
Fernández-Espejo, D., et al. A role for the default mode network in the bases of disorders of consciousness. Ann. Neurol. 72 (2012), 335–343.
Fornito, A., Zalesky, A., Bullmore, E., Fundamentals of Brain Network Analysis. 2016.
Fries, P., Rhythms for cognition: communication through coherence. Neuron 88 (2015), 220–235.
Garrison, K.A., et al. The (in) stability of functional brain network measures across thresholds. Neuroimage 118 (2015), 651–661.
Giacino, J.T., et al. The minimally conscious state definition and diagnostic criteria. Neurology 58 (2002), 349–353.
Giacino, J.T., Kalmar, K., Whyte, J., The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch. Phys. Med. Rehabil. 85 (2004), 2020–2029.
Giacino, J.T., et al. Behavioral assessment in patients with disorders of consciousness: gold standard or fool's gold?. Prog. Brain Res. 177 (2009), 33–48.
Giacino, J.T., et al. Disorders of consciousness after acquired brain injury: the state of the science. Nat. Rev. Neurol. 10 (2014), 99–114.
Good, B.H., de Montjoye, Y.-A., Clauset, A., Performance of modularity maximization in practical contexts. Phys. Rev. E, 81, 2010, 046106.
Gosseries, O., Zasler, N.D., Laureys, S., Recent advances in disorders of consciousness: focus on the diagnosis. Brain Inj. 28 (2014), 1141–1150.
Gottfried, J.A., O'doherty, J., Dolan, R.J., Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science 301 (2003), 1104–1107.
Gramfort, A., et al. OpenMEEG: opensource software for quasistatic bioelectromagnetics. Biomed. Eng. Online, 9, 2010, 45.
Guimera, R., Amaral, L.A.N., Functional cartography of complex metabolic networks. Nature 433 (2005), 895–900.
Hassan, M., Wendling, F., Electroencephalography source connectivity: aiming for high resolution of brain networks in time and space. IEEE Signal Process. Mag. 35 (2018), 81–96.
Hassan, M., et al. EEG source connectivity analysis: from dense array recordings to brain networks. PLoS One, 9, 2014, e105041.
Hassan, M., et al. Dynamic reorganization of functional brain networks during picture naming. Cortex 73 (2015), 276–288.
Hassan, M., et al. Identification of interictal epileptic networks from dense-EEG. Brain Topogr., 2016, 1–17.
Hipp, J.F., et al. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci., 15, 2012, 884.
Holmes, C.J., et al. Enhancement of MR images using registration for signal averaging. J. Comput. Assist. Tomogr. 22 (1998), 324–333.
Jonckheere, A.R., A distribution-free k-sample test against ordered alternatives. Biometrika 41 (1954), 133–145.
Kabbara, A., et al. The dynamic functional core network of the human brain at rest. Sci. Rep., 7, 2017.
Kabbara, A., et al. Reduced integration and improved segregation of functional brain networks in Alzheimer's disease. J. Neural Eng., 15, 2018, 026023.
Kjaer, T., et al. Precuneus–prefrontal activity during awareness of visual verbal stimuli. Conscious. Cogn. 10 (2001), 356–365.
Lachaux, J.-P., et al. Measuring phase synchrony in brain signals. Hum. Brain Mapp. 8 (1999), 194–208.
Lachaux, J.-P., et al. Studying single-trials of phase synchronous activity in the brain. Int. J. Bifurcation Chaos 10 (2000), 2429–2439.
Lai, M., et al. A comparison between scalp-and source-reconstructed EEG networks. Sci. Rep., 8, 2018, 12269.
Lancichinetti, A., Fortunato, S., Consensus clustering in complex networks. Sci. Rep., 2, 2012, 336.
Laureys, S., et al. Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med., 8, 2010, 68.
Mucha, P.J., et al. Community structure in time-dependent, multiscale, and multiplex networks. Science 328 (2010), 876–878.
Naro, A., et al. Moving toward conscious pain processing detection in chronic disorders of consciousness: anterior cingulate cortex neuromodulation. J. Pain 16 (2015), 1022–1031.
Naro, A., et al. How far can we go in chronic disorders of consciousness differential diagnosis? The use of neuromodulation in detecting internal and external awareness. Neuroscience 349 (2017), 165–173.
Owen, A.M., Schiff, N.D., Laureys, S., A new era of coma and consciousness science. Prog. Brain Res. 177 (2009), 399–411.
Palva, J.M., et al. Ghost interactions in MEG/EEG source space: a note of caution on inter-areal coupling measures. Neuroimage 173 (2018), 632–643.
Pascual-Marqui, R.D., et al. Innovations Orthogonalization: A Solution to the Major Pitfalls of EEG/MEG “Leakage Correction”. 2017 arXiv preprint arXiv:1708.05931.
Ren, Y., et al. Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval. Nat. Commun., 9, 2018, 4875.
Rizkallah, J., et al. Dynamic reshaping of functional brain networks during visual object recognition. J. Neural Eng., 15, 2018, 056022.
Rudebeck, P.H., Murray, E.A., The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84 (2014), 1143–1156.
Schiff, N.D., Cognitive motor dissociation following severe brain injuries. JAMA Neurol. 72 (2015), 1413–1415.
Schiff, N.D., Fins, J.J., Brain death and disorders of consciousness. Curr. Biol. 26 (2016), R572–R576.
Schoffelen, J.M., Gross, J., Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30 (2009), 1857–1865.
Sitt, J.D., et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain 137 (2014), 2258–2270.
Stender, J., et al. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet 384 (2014), 514–522.
Tadel, F., et al. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci., 2011, 2011, 8.
Terpstra, T., The asymptotic normality and consistency of Kendall's test against trend, when ties arepresent in one ranking. Proc. Kon. Ned. Akad. v. Wetensch. A. 55 (1952), 327–333 Terpstra32755Proc.
Thibaut, A., et al. tDCS in patients with disorders of consciousness: sham-controlled randomized double-blind study. Neurology 82 (2014), 1112–1118.
Tononi, G., An information integration theory of consciousness. BMC Neurosci., 5, 2004, 42.
Tononi, G., et al. Integrated information theory: from consciousness to its physical substrate. Nat. Rev. Neurosci., 17, 2016, 450.
Van de Steen, F., et al. Critical comments on EEG sensor space dynamical connectivity analysis. Brain Topogr., 2016, 1–12.
van den Heuvel, M.P., et al. Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: issues and recommendations. Neuroimage 152 (2017), 437–449.
Vinck, M., et al. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55 (2011), 1548–1565.
Vogt, B.A., Laureys, S., Posterior cingulate, precuneal and retrosplenial cortices: cytology and components of the neural network correlates of consciousness. Prog. Brain Res. 150 (2005), 205–217.
Wannez, S., et al. The repetition of behavioral assessments in diagnosis of disorders of consciousness. Ann. Neurol. 81 (2017), 883–889.
