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Dynamic Change of Global and Local Information Processing in Propofol-Induced Loss and Recovery of Consciousness

DOI: 10.1371/journal.pcbi.1003271

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Whether unique to humans or not, consciousness is a central aspect of our experience of the world. The neural fingerprint of this experience, however, remains one of the least understood aspects of the human brain. In this paper we employ graph-theoretic measures and support vector machine classification to assess, in 12 healthy volunteers, the dynamic reconfiguration of functional connectivity during wakefulness, propofol-induced sedation and loss of consciousness, and the recovery of wakefulness. Our main findings, based on resting-state fMRI, are three-fold. First, we find that propofol-induced anesthesia does not bear differently on long-range versus short-range connections. Second, our multi-stage design dissociated an initial phase of thalamo-cortical and cortico-cortical hyperconnectivity, present during sedation, from a phase of cortico-cortical hypoconnectivity, apparent during loss of consciousness. Finally, we show that while clustering is increased during loss of consciousness, as recently suggested, it also remains significantly elevated during wakefulness recovery. Conversely, the characteristic path length of brain networks (i.e., the average functional distance between any two regions of the brain) appears significantly increased only during loss of consciousness, marking a decrease of global information-processing efficiency uniquely associated with unconsciousness. These findings suggest that propofol-induced loss of consciousness is mainly tied to cortico-cortical and not thalamo-cortical mechanisms, and that decreased efficiency of information flow is the main feature differentiating the conscious from the unconscious brain.


[1]  Tononi G (2008) Consciousness as integrated information: a provisional manifesto. Biol Bull 215: 216–242. doi: 10.2307/25470707
[2]  Crick F, Koch C (2003) A framework for consciousness. Nat Neurosci 6: 119–126. doi: 10.1038/nn0203-119
[3]  Engel AK, Singer W (2001) Temporal binding and the neural correlates of sensory awareness. Trends Cogn Sci 5: 16–25. doi: 10.1016/s1364-6613(00)01568-0
[4]  Tallon-Baudry C (2009) The roles of gamma-band oscillatory synchrony in human visual cognition. Front Biosci 14: 321–332. doi: 10.2741/3246
[5]  Dehaene S, Changeux JP (2005) Ongoing spontaneous activity controls access to consciousness: a neuronal model for inattentional blindness. PLoS Biol 3: e141. doi: 10.1371/journal.pbio.0030141
[6]  Baars BJ (2002) The conscious access hypothesis: origins and recent evidence. Trends Cogn Sci 6: 47–52. doi: 10.1016/s1364-6613(00)01819-2
[7]  Baars BJ, Ramsoy TZ, Laureys S (2003) Brain, conscious experience and the observing self. Trends Neurosci 26: 671–5. doi: 10.1016/j.tins.2003.09.015
[8]  Dehaene S, Changeux J (2004) Neural mechanisms for access to consciousness. In: The cognitive neurosciences, New York: Norton. 3rd edition, pp. 1145–57.
[9]  Tononi G (2004) An information integration theory of consciousness. BMC Neurosci 5: 42. doi: 10.1186/1471-2202-5-42
[10]  Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, et al. (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98: 676–682. doi: 10.1073/pnas.98.2.676
[11]  Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8: 700–711. doi: 10.1038/nrn2201
[12]  Martuzzi R, Ramani R, Qiu M, Rajeevan N, Constable RT (2010) Functional connectivity and alterations in baseline brain state in humans. Neuroimage 49: 823–834. doi: 10.1016/j.neuroimage.2009.07.028
[13]  Schrouff J, Perlbarg V, Boly M, Marrelec G, Boveroux P, et al. (2011) Brain functional integration decreases during propofol-induced loss of consciousness. Neuroimage 57: 198–205. doi: 10.1016/j.neuroimage.2011.04.020
[14]  Larson-Prior LJ, Zempel JM, Nolan TS, Prior FW, Snyder AZ, et al. (2009) Cortical network functional connectivity in the descent to sleep. Proc Natl Acad Sci U S A 106: 4489–4494. doi: 10.1073/pnas.0900924106
[15]  Boly M, Moran R, Murphy M, Boveroux P, Bruno MA, et al. (2012) Connectivity changes underlying spectral eeg changes during propofol-induced loss of consciousness. J Neurosci 32: 7082–7090. doi: 10.1523/jneurosci.3769-11.2012
[16]  Uehara T, Yamasaki T, Okamoto T, Koike T, Kan S, et al. (2013) Efficiency of a “Small-World” brain network depends on consciousness level: A resting-state fMRI study. Cereb Cortex [Epub ahead of print]. doi: 10.1093/cercor/bht004
[17]  Boly M, Massimini M, Tononi G (2009) Theoretical approaches to the diagnosis of altered states of consciousness. Prog Brain Res 177: 383–398. doi: 10.1016/s0079-6123(09)17727-0
[18]  Fernández-Espejo D, Soddu A, Cruse D, Palacios EM, Junque C, et al. (2012) A role for the default mode network in the bases of disorders of consciousness. Ann Neurol 72: 335–343. doi: 10.1002/ana.23635
[19]  Boly M, Faymonville ME, Schnakers C, Peigneux P, Lambermont B, et al. (2008) Perception of pain in the minimally conscious state with PET activation: an observational study. Lancet Neurol 7: 1013–1020. doi: 10.1016/s1474-4422(08)70219-9
[20]  Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJF, Bruno MA, Boveroux P, et al. (2010) Default network connectivity reects the level of consciousness in non-communicative brain-damaged patients. Brain 133: 161–171. doi: 10.1093/brain/awp313
[21]  Boveroux P, Vanhaudenhuyse A, Bruno MA, Noirhomme Q, Lauwick S, et al. (2010) Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 113: 1038–1053. doi: 10.1097/aln.0b013e3181f697f5
[22]  Blumenfeld H, Westerveld M, Ostroff RB, Vanderhill SD, Freeman J, et al. (2003) Selective frontal, parietal, and temporal networks in generalized seizures. Neuroimage 19: 1556–1566. doi: 10.1016/s1053-8119(03)00204-0
[23]  Pyka M, Burgmer M, Lenzen T, Pioch R, Dannlowski U, et al. (2011) Brain correlates of hypnotic paralysis-a resting-state fmri study. Neuroimage 56: 2173–2182. doi: 10.1016/j.neuroimage.2011.03.078
[24]  Boly M, Tshibanda L, Vanhaudenhuyse A, Noirhomme Q, Schnakers C, et al. (2009) Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 30: 2393–400. doi: 10.1002/hbm.20672
[25]  Schr?ter MS, Spoormaker VI, Schorer A, Wohlschl?ger A, Czisch M, et al. (2012) Spatiotemporal reconfiguration of large-scale brain functional networks during propofol-induced loss of consciousness. J Neurosci 32: 12832–12840. doi: 10.1523/jneurosci.6046-11.2012
[26]  Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12: 512–523. doi: 10.1177/1073858406293182
[27]  Stam CJ, Reijneveld JC (2007) Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys 1: 3. doi: 10.1186/1753-4631-1-3
[28]  Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52: 1059–1069. doi: 10.1016/j.neuroimage.2009.10.003
[29]  McQuillan JM (1977) Graph theory applied to optimal connectivity in computer networks. SIGCOMM Comput Commun Rev 7: 13–41. doi: 10.1145/1024857.1024860
[30]  Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442. doi: 10.1038/30918
[31]  Freeman LC (1978) Centrality in social networks conceptual clarification. Social Networks 215–239. doi: 10.1016/0378-8733(78)90021-7
[32]  Bassett D, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E (2006) Adaptive reconfiguration of fractal small-world human brain functional networks. Proceedings of the National Academy of Sciences 103: 19518–19523. doi: 10.1073/pnas.0606005103
[33]  Liu Y, Liang M, Zhou Y, He Y, Hao Y, et al. (2008) Disrupted small-world networks in schizophrenia. Brain 131: 945–961. doi: 10.1093/brain/awn018
[34]  van den Heuvel MP, Stam CJ, Boersma M, Hulshoff Pol HE (2008) Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage 43: 528–539. doi: 10.1016/j.neuroimage.2008.08.010
[35]  van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29: 7619–7624. doi: 10.1523/jneurosci.1443-09.2009
[36]  Fair DA, Cohen AL, Power JD, Dosenbach NUF, Church JA, et al. (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5: e1000381. doi: 10.1371/journal.pcbi.1000381
[37]  Liang Z, King J, Zhang N (2012) Intrinsic organization of the anesthetized brain. J Neurosci 32: 10183–10191. doi: 10.1523/jneurosci.1020-12.2012
[38]  Rubinov M, Knock SA, Stam CJ, Micheloyannis S, Harris AWF, et al. (2009) Small-world properties of nonlinear brain activity in schizophrenia. Hum Brain Mapp 30: 403–416. doi: 10.1002/hbm.20517
[39]  Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87: 198701. doi: 10.1103/physrevlett.87.198701
[40]  Alkire MT, Gruver R, Miller J, McReynolds JR, Hahn EL, et al. (2008) Neuroimaging analysis of an anesthetic gas that blocks human emotional memory. Proc Natl Acad Sci U S A 105: 1722–1727. doi: 10.1073/pnas.0711651105
[41]  Boly M, Perlbarg V, Marrelec G, Schabus M, Laureys S, et al. (2012) Hierarchical clustering of brain activity during human nonrapid eye movement sleep. Proc Natl Acad Sci U S A 109: 5856–5861. doi: 10.1073/pnas.1111133109
[42]  Adams JH, Graham DI, Jennett B (2000) The neuropathology of the vegetative state after an acute brain insult. Brain 123 (Pt 7) 1327–38. doi: 10.1093/brain/123.7.1327
[43]  Fernández-Espejo D, Bekinschtein T, Monti MM, Pickard JD, Junque C, et al. (2011) Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. Neuroimage 54: 103–12. doi: 10.1016/j.neuroimage.2010.08.035
[44]  Laureys S, Faymonville ME, Degueldre C, Fiore GD, Damas P, et al. (2000) Auditory processing in the vegetative state. Brain 123 (Pt 8) 1589–601. doi: 10.1016/s1053-8119(00)91701-4
[45]  Schiff ND (2010) Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends in neurosciences 33: 1–9. doi: 10.1016/j.tins.2009.11.002
[46]  Giacino JT, Schnakers C, Rodriguez-Moreno D, Kalmar K, Schiff N, et al. (2009) Behavioral assessment in patients with disorders of consciousness: gold standard or fool's gold? Prog Brain Res 177: 33–48. doi: 10.1016/s0079-6123(09)17704-x
[47]  Monti MM (2012) Cognition in the vegetative state. Annu Rev Clin Psychol 8: 431–454. doi: 10.1146/annurev-clinpsy-032511-143050
[48]  Tomasi D, Volkow ND (2011) Association between functional connectivity hubs and brain networks. Cereb Cortex 21: 2003–2013. doi: 10.1093/cercor/bhq268
[49]  Laureys S, Owen AM, Schiff ND (2004) Brain function in coma, vegetative state, and related disorders. Lancet Neurol 3: 537–46. doi: 10.1016/s1474-4422(04)00852-x
[50]  Laureys S, Giacino JT, Schiff ND, Schabus M, Owen AM (2006) How should functional imaging of patients with disorders of consciousness contribute to their clinical rehabilitation needs? Curr Opin Neurol 19: 520–7. doi: 10.1097/wco.0b013e3280106ba9
[51]  Voss HU, Ulu? AM, Dyke JP, Watts R, Kobylarz EJ, et al. (2006) Possible axonal regrowth in late recovery from the minimally conscious state. J Clin Invest 116: 2005–2011. doi: 10.1172/jci27021
[52]  Laureys S, Perrin F, Brédart S (2007) Self-consciousness in non-communicative patients. Conscious Cogn 16: 722–41 discussion 742-5. doi: 10.1016/j.concog.2007.04.004
[53]  van Wijk BCM, Stam CJ, Daffertshofer A (2010) Comparing brain networks of different size and connectivity density using graph theory. PLoS One 5: e13701. doi: 10.1371/journal.pone.0013701
[54]  Bullmore ET, Bassett DS (2011) Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol 7: 113–140. doi: 10.1146/annurev-clinpsy-040510-143934
[55]  Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks. Neuroimage 56: 2068–2079. doi: 10.1016/j.neuroimage.2011.03.069
[56]  Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, et al. (2012) Impact of inscanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage 60: 623–632. doi: 10.1016/j.neuroimage.2011.12.063
[57]  Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity mri networks arise from subject motion. Neuroimage 59: 2142–2154. doi: 10.1016/j.neuroimage.2011.10.018
[58]  Zalesky A, Fornito A, Harding IH, Cocchi L, Ycel M, et al. (2010) Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50: 970–983. doi: 10.1016/j.neuroimage.2009.12.027
[59]  Hayasaka S, Laurienti PJ (2010) Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. Neuroimage 50: 499–508. doi: 10.1016/j.neuroimage.2009.12.051
[60]  Fornito A, Zalesky A, Bullmore ET (2010) Network scaling effects in graph analytic studies of human resting-state fmri data. Front Syst Neurosci 4: 22. doi: 10.3389/fnsys.2010.00022
[61]  Sepulcre J, Liu H, Talukdar T, Martincorena I, Yeo BTT, et al. (2010) The organization of local and distant functional connectivity in the human brain. PLoS Comput Biol 6: e1000808. doi: 10.1371/journal.pcbi.1000808
[62]  Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1: 1–47. doi: 10.1093/cercor/1.1.1
[63]  Mesulam MM (1998) From sensation to cognition. Brain 121 (Pt 6) 1013–1052. doi: 10.1093/brain/121.6.1013
[64]  Wig GS, Schlaggar BL, Petersen SE (2011) Concepts and principles in the analysis of brain networks. Ann N Y Acad Sci 1224: 126–146. doi: 10.1111/j.1749-6632.2010.05947.x
[65]  Fair DA, Nigg JT, Iyer S, Bathula D, Mills KL, et al. (2012) Distinct neural signatures detected for adhd subtypes after controlling for micro-movements in resting state functional connectivity mri data. Front Syst Neurosci 6: 80. doi: 10.3389/fnsys.2012.00080
[66]  Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, et al. (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76: 183–201. doi: 10.1016/j.neuroimage.2013.03.004
[67]  Ramsay MA, Savege TM, Simpson BR, Goodwin R (1974) Controlled sedation with alphaxalone-alphadolone. Br Med J 2: 656–659. doi: 10.1136/bmj.2.5920.656
[68]  Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44: 893–905. doi: 10.1016/j.neuroimage.2008.09.036
[69]  Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, et al. (2012) Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2: 25–32. doi: 10.1089/brain.2012.0080
[70]  Zhang D, Snyder AZ, Fox MD, Sansbury MW, Shimony JS, et al. (2008) Intrinsic functional relations between human cerebral cortex and thalamus. J Neurophysiol 100: 1740–1748. doi: 10.1152/jn.90463.2008
[71]  Sch?lvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA (2010) Neural basis of global resting-state fmri activity. Proc Natl Acad Sci U S A 107: 10238–10243. doi: 10.1073/pnas.0913110107
[72]  Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW (2010) Mapping sources of correlation in resting state fmri, with artifact detection and removal. Neuroimage 52: 571–582. doi: 10.1016/j.neuroimage.2010.04.246
[73]  Schwarz AJ, McGonigle J (2011) Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage 55: 1132–1146. doi: 10.1016/j.neuroimage.2010.12.047
[74]  Liang X, Wang J, Yan C, Shu N, Xu K, et al. (2012) Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional mri study. PLoS One 7: e32766. doi: 10.1371/journal.pone.0032766
[75]  Telesford QK, Burdette JH, Laurienti PJ (2013) An exploration of graph metric reproducibility in complex brain networks. Front Neurosci 7: 67. doi: 10.3389/fnins.2013.00067
[76]  Craddock RC, James GA, Holtzheimer PE, Hu XP, Mayberg HS (2012) A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33: 1914–1928. doi: 10.1002/hbm.21333
[77]  Kriegeskorte N, Simmons WK, Bellgowan PSF, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12: 535–540. doi: 10.1038/nn.2303
[78]  Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10: 186–198. doi: 10.1038/nrn2575
[79]  Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, et al. (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273–289. doi: 10.1006/nimg.2001.0978
[80]  Kennedy DN, Lange N, Makris N, Bates J, Meyer J, et al. (1998) Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cereb Cortex 8: 372–384. doi: 10.1093/cercor/8.4.372
[81]  Makris N, Meyer JW, Bates JF, Yeterian EH, Kennedy DN, et al. (1999) MRI-based topographic parcellation of human cerebral white matter and nuclei II. rationale and applications with systematics of cerebral connectivity. Neuroimage 9: 18–45. doi: 10.1006/nimg.1998.0384
[82]  Mour?o-Miranda J, Bokde ALW, Born C, Hampel H, Stetter M (2005) Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 28: 980–995. doi: 10.1016/j.neuroimage.2005.06.070
[83]  Lemm S, Blankertz B, Dickhaus T, Mller KR (2011) Introduction to machine learning for brain imaging. Neuroimage 56: 387–399. doi: 10.1016/j.neuroimage.2010.11.004
[84]  Mour?o-Miranda J, Reinders AATS, Rocha-Rego V, Lappin J, Rondina J, et al. (2012) Individualized prediction of illness course at the first psychotic episode: A support vector machine MRI study. Psychol Med 42: 1037–1047. doi: 10.1017/s0033291711002005
[85]  Johnson JD, McDuff SGR, Rugg MD, Norman KA (2009) Recollection, familiarity, and cortical reinstatement: a multivoxel pattern analysis. Neuron 63: 697–708. doi: 10.1016/j.neuron.2009.08.011
[86]  McDuff SGR, Frankel HC, Norman KA (2009) Multivoxel pattern analysis reveals increased memory targeting and reduced use of retrieved details during single-agenda source monitoring. J Neurosci 29: 508–516. doi: 10.1523/jneurosci.3587-08.2009
[87]  Marquand A, Howard M, Brammer M, Chu C, Coen S, et al. (2010) Quantitative prediction of subjective pain intensity from whole-brain fMRI data using gaussian processes. Neuroimage 49: 2178–2189. doi: 10.1016/j.neuroimage.2009.10.072
[88]  Newman MEJ (2004) Analysis of weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys 70: 056131.
[89]  Onnela JP, Saramki J, Kertsz J, Kaski K (2005) Intensity and coherence of motifs in weighted complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 71: 065103. doi: 10.1103/physreve.71.065103
[90]  Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103: 8577–8582. doi: 10.1073/pnas.0601602103
[91]  Sporns O, Zwi JD (2004) The small world of the cerebral cortex. Neuroinformatics 2: 145–162. doi: 10.1385/ni:2:2:145
[92]  Girden E (1992) ANOVA: Repeated Measures, volume 84 of Quantitative application in social sciences. Newbery Park, CA: SAGE University Papers.
[93]  Keren G, Lewis C (1979) Partial omega squared for ANOVA designs. Educational and Psychological Measurement 39: 119–128. doi: 10.1177/001316447903900116
[94]  Ferguson CJ (2009) An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice 40: 532. doi: 10.1037/a0015808
[95]  Pierce CA, Block RA, Aguinis H (2004) Cautionary note on reporting eta-squared values from multifactor anova designs. Educational and psychological measurement 64: 916–924. doi: 10.1177/0013164404264848
[96]  Kirk RE (1996) Practical significance: A concept whose time has come. Educational and Psychological Measurement 56: 746–759. doi: 10.1177/0013164496056005002
[97]  Yekutieli D, Benjamini Y (1999) Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. Journal of Statistical Planning and Inference 82: 171–196. doi: 10.1016/s0378-3758(99)00041-5
[98]  Van Essen DC, Drury HA, Dickson J, Harwell J, Hanlon D, et al. (2001) An integrated software suite for surface-based analyses of cerebral cortex. J Am Med Inform Assoc 8: 443–459. doi: 10.1136/jamia.2001.0080443


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