Show simple item record

Disrupted neural variability during propofol‐induced sedation and unconsciousness

dc.contributor.authorHuang, Zirui
dc.contributor.authorZhang, Jun
dc.contributor.authorWu, Jinsong
dc.contributor.authorLiu, Xiaoge
dc.contributor.authorXu, Jianghui
dc.contributor.authorZhang, Jianfeng
dc.contributor.authorQin, Pengmin
dc.contributor.authorDai, Rui
dc.contributor.authorYang, Zhong
dc.contributor.authorMao, Ying
dc.contributor.authorHudetz, Anthony G.
dc.contributor.authorNorthoff, Georg
dc.date.accessioned2018-11-20T15:33:50Z
dc.date.available2020-01-06T16:40:59Zen
dc.date.issued2018-11
dc.identifier.citationHuang, Zirui; Zhang, Jun; Wu, Jinsong; Liu, Xiaoge; Xu, Jianghui; Zhang, Jianfeng; Qin, Pengmin; Dai, Rui; Yang, Zhong; Mao, Ying; Hudetz, Anthony G.; Northoff, Georg (2018). "Disrupted neural variability during propofol‐induced sedation and unconsciousness." Human Brain Mapping 39(11): 4533-4544.
dc.identifier.issn1065-9471
dc.identifier.issn1097-0193
dc.identifier.urihttps://hdl.handle.net/2027.42/146388
dc.description.abstractVariability quenching is a widespread neural phenomenon in which trial‐to‐trial variability (TTV) of neural activity is reduced by repeated presentations of a sensory stimulus. However, its neural mechanism and functional significance remain poorly understood. Recurrent network dynamics are suggested as a candidate mechanism of TTV, and they play a key role in consciousness. We thus asked whether the variability‐quenching phenomenon is related to the level of consciousness. We hypothesized that TTV reduction would be compromised during reduced level of consciousness by propofol anesthetics. We recorded functional magnetic resonance imaging signals of resting‐state and stimulus‐induced activities in three conditions: wakefulness, sedation, and unconsciousness (i.e., deep anesthesia). We measured the average (trial‐to‐trial mean, TTM) and variability (TTV) of auditory stimulus‐induced activity under the three conditions. We also examined another form of neural variability (temporal variability, TV), which quantifies the overall dynamic range of ongoing neural activity across time, during both the resting‐state and the task. We found that (a) TTM deceased gradually from wakefulness through sedation to anesthesia, (b) stimulus‐induced TTV reduction normally seen during wakefulness was abolished during both sedation and anesthesia, and (c) TV increased in the task state as compared to resting‐state during both wakefulness and sedation, but not anesthesia. Together, our results reveal distinct effects of propofol on the two forms of neural variability (TTV and TV). They imply that the anesthetic disrupts recurrent network dynamics, thus prevents the stabilization of cortical activity states. These findings shed new light on the temporal dynamics of neuronal variability and its alteration during anesthetic‐induced unconsciousness.
dc.publisherJohn Wiley & Sons, Inc.
dc.subject.otherfMRI
dc.subject.otherneural variability
dc.subject.otherpropofol
dc.subject.otherrecurrent network
dc.subject.othersedation
dc.subject.othertemporal variability
dc.subject.othertrial‐to‐trial variability
dc.subject.otherconsciousness
dc.subject.otheranesthesia
dc.titleDisrupted neural variability during propofol‐induced sedation and unconsciousness
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelNeurosciences
dc.subject.hlbsecondlevelKinesiology and Sports
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146388/1/hbm24304_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146388/2/hbm24304.pdf
dc.identifier.doi10.1002/hbm.24304
dc.identifier.sourceHuman Brain Mapping
dc.identifier.citedreferenceNogueira, R., Lawrie, S., & Moreno‐Bote, R. ( 2018 ). Neuronal variability as a proxy for network state. Trends in Neurosciences, 41, 170 – 173.
dc.identifier.citedreferenceMazzucato, L., Fontanini, A., & La Camera, G. ( 2015 ). Dynamics of multistable states during ongoing and evoked cortical activity. The Journal of Neuroscience 35: 8214 – 8231.
dc.identifier.citedreferenceMcIntosh, A. R., Kovacevic, N., & Itier, R. J. ( 2008 ). Increased brain signal variability accompanies lower behavioral variability in development. PLoS Computational Biology, 4, e1000106 Ed. Karl J. Friston.
dc.identifier.citedreferenceMcIntosh, A. R., Kovacevic, N., Lippe, S., Garrett, D., Grady, C., & Jirsa, V. ( 2010 ). The development of a noisy brain. Archives Italiennes de Biologie, 148, 323 – 337.
dc.identifier.citedreferenceMhuircheartaigh, R. N., Rosenorn‐Lanng, D., Wise, R., Jbabdi, S., Rogers, R., & Tracey, I. ( 2010 ). Cortical and subcortical connectivity changes during decreasing levels of consciousness in humans: A functional magnetic resonance imaging study using propofol. The Journal of Neuroscience, 30, 9095 – 9102.
dc.identifier.citedreferenceMišić, B., Vakorin, V. A., Paus, T., & McIntosh, A. R. ( 2011 ). Functional embedding predicts the variability of neural activity. Frontiers in Systems Neuroscience, 5, 90.
dc.identifier.citedreferenceMonier, C., Chavane, F., Baudot, P., Graham, L. J., & Frégnac, Y. ( 2003 ). Orientation and direction selectivity of synaptic inputs in visual cortical neurons: A diversity of combinations produces spike tuning. Neuron, 37, 663 – 680.
dc.identifier.citedreferenceMoutard, C., Dehaene, S., & Malach, R. ( 2015 ). Spontaneous fluctuations and non‐linear ignitions: Two dynamic faces of cortical recurrent loops. Neuron, 88, 194 – 206.
dc.identifier.citedreferenceMurphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. ( 2009 ). The impact of global signal regression on resting state correlations: Are anti‐correlated networks introduced? NeuroImage, 44, 893 – 905.
dc.identifier.citedreferenceMurphy, K., & Fox, M. D. ( 2017 ). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. NeuroImage, 154, 169 – 173.
dc.identifier.citedreferenceNí Mhuircheartaigh, R., Warnaby, C., Rogers, R., Jbabdi, S., & Tracey, I. ( 2013 ). Slow‐wave activity saturation and thalamocortical isolation during propofol anesthesia in humans. Science Translational Medicine, 5, 208ra148.
dc.identifier.citedreferencePonce‐Alvarez, A., He, B. J., Hagmann, P., & Deco, G. ( 2015 ). Task‐driven activity reduces the cortical activity space of the brain: Experiment and whole‐brain modeling. PLOS Computational Biology, 11, e1004445 Ed. Lyle J. Graham.
dc.identifier.citedreferencePower, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. ( 2012 ). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59, 2142 – 2154.
dc.identifier.citedreferencePower, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. ( 2014 ). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320 – 341.
dc.identifier.citedreferenceQin, P., Liu, Y., Shi, J., Wang, Y., Duncan, N., Gong, Q., … Northoff, G. ( 2012 ). Dissociation between anterior and posterior cortical regions during self‐specificity and familiarity: A combined fMRI‐meta‐analytic study. Human Brain Mapping, 33, 154 – 164.
dc.identifier.citedreferenceRajan, K., Abbott, L. F., & Sompolinsky, H. ( 2010 ). Stimulus‐dependent suppression of chaos in recurrent neural networks. Physical Review E, 82, 11903.
dc.identifier.citedreferenceRamsay, M. A., Savege, T. M., Simpson, B. R., & Goodwin, R. ( 1974 ). Controlled sedation with alphaxalone‐alphadolone. British Medical Journal, 2, 656 – 659.
dc.identifier.citedreferenceSaad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. ( 2012 ). Trouble at rest: How correlation patterns and group differences become distorted after global signal regression. Brain Connectivity, 2, 25 – 32.
dc.identifier.citedreferenceSarasso, S., Boly, M., Napolitani, M., Gosseries, O., Charland‐Verville, V., Casarotto, S., … Massimini, M. ( 2015 ). Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Current Biology, 25, 3099 – 3105.
dc.identifier.citedreferenceScaglione, A., Moxon, K. A., Aguilar, J., & Foffani, G. ( 2011 ). Trial‐to‐trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus. Proceedings of the National Academy of Sciences of the United States of America, 108, 14956 – 14961. https://doi.org/10.1073/pnas.1103168108
dc.identifier.citedreferenceSchurger, A., Sarigiannidis, I., Naccache, L., Sitt, J. D., & Dehaene, S. ( 2015 ). Cortical activity is more stable when sensory stimuli are consciously perceived. Proceedings of the National Academy of Sciences of the United States of America, 112, E2083 – E2092.
dc.identifier.citedreferenceShew, W. L., Yang, H., Petermann, T., Roy, R., & Plenz, D. ( 2009 ). Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. The Journal of Neuroscience, 29, 15595 – 15600.
dc.identifier.citedreferenceStephens, G. J., Honey, C. J., & Hasson, U. ( 2013 ). A place for time: The spatiotemporal structure of neural dynamics during natural audition. Journal of Neurophysiology, 110, 2019 – 2026.
dc.identifier.citedreferenceSussillo, D., & Abbott, L. F. ( 2009 ). Generating coherent patterns of activity from chaotic neural networks. Neuron, 63, 544 – 557.
dc.identifier.citedreferenceTagliazucchi, E., Chialvo, D. R., Siniatchkin, M., Brichant, J., & Laureys, S. ( 2016 ). Large‐scale signatures of unconsciousness are consistent with a departure from critical dynamics. Journal of the Royal Society, Interface, 13, 1 – 34.
dc.identifier.citedreferenceTononi, G., Boly, M., Massimini, M., & Koch, C. ( 2016 ). Integrated information theory: From consciousness to its physical substrate. Nature Reviews. Neuroscience, 17, 450 – 461. https://doi.org/10.1038/nrn.2016.44%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/27225071
dc.identifier.citedreferenceTononi, G., & Edelman, G. M. ( 1998 ). Consciousness and complexity. Science, 282, 1846 – 1851.
dc.identifier.citedreferenceVakorin, V. A., Lippe, S., & McIntosh, A. R. ( 2011 ). Variability of brain signals processed locally transforms into higher connectivity with brain development. The Journal of Neuroscience, 31, 6405 – 6413.
dc.identifier.citedreferenceVakorin, V. A., Mišić, B., Krakovska, O., & McIntosh, A. R. ( 2011 ). Empirical and theoretical aspects of generation and transfer of information in a neuromagnetic source network. Frontiers in Systems Neuroscience, 5, 96.
dc.identifier.citedreferencevan Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. ( 2012 ). The influence of head motion on intrinsic functional connectivity MRI. NeuroImage, 59, 431 – 438.
dc.identifier.citedreferenceWhite, B., Abbott, L. F., & Fiser, J. ( 2012 ). Suppression of cortical neural variability is stimulus‐ and state‐dependent. Journal of Neurophysiology, 108, 2383 – 2392. https://doi.org/10.1152/jn.00723.2011
dc.identifier.citedreferenceXu, Z., Liu, F., Yue, Y., Ye, T., Zhang, B., Zuo, M., … Che, X. ( 2009 ). C50 for propofol‐remifentanil target‐controlled infusion and bispectral index at loss of consciousness and response to painful stimulus in Chinese patients: A multicenter clinical trial. Anesthesia and Analgesia, 108, 478 – 483.
dc.identifier.citedreferenceXue, G., Dong, Q., Chen, C., Lu, Z., Mumford, J. A., & Poldrack, R. A. ( 2010 ). Greater neural pattern similarity across repetitions is associated with better memory. Science, 330, 97 – 101.
dc.identifier.citedreferenceYan, C.‐G., Craddock, R. C., Zuo, X.‐N., Zang, Y.‐F., & Milham, M. P. ( 2013 ). Standardizing the intrinsic brain: Towards robust measurement of inter‐individual variation in 1000 functional connectomes. NeuroImage, 80, 246 – 262.
dc.identifier.citedreferenceZang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., … Wang, Y. F. ( 2007 ). Altered baseline brain activity in children with ADHD revealed by resting‐state functional MRI. Brain & Development, 29, 83 – 91.
dc.identifier.citedreferenceZhang, J., Huang, Z., Chen, Y., Zhang, J., Ghinda, D., Nikolova, Y., … Northoff, G. ( 2018 ). Breakdown in the temporal and spatial organization of spontaneous brain activity during general anesthesia. Human Brain Mapping, 39, 2035 – 2046.
dc.identifier.citedreferenceAdapa, R. M., Davis, M. H., Stamatakis, E. A., Absalom, A. R., & Menon, D. K. ( 2014 ). Neural correlates of successful semantic processing during propofol sedation. Human Brain Mapping, 35, 2935 – 2949. https://doi.org/10.1002/hbm.22375
dc.identifier.citedreferenceAlkire, M. T., Hudetz, A. G., & Tononi, G. ( 2008 ). Consciousness and anesthesia. Science, 322, 876 – 880.
dc.identifier.citedreferenceArazi, A., Gonen‐Yaacovi, G., & Dinstein, I. ( 2017 ). The magnitude of trial‐by‐trial neural variability is reproducible over time and across tasks in humans. eNeuro, ENEURO.0292‐17.
dc.identifier.citedreferenceArieli, A., Sterkin, A., Grinvald, A., & Aertsen, A. ( 1996 ). Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science, 273, 1868 – 1871.
dc.identifier.citedreferenceAzouz, R., & Gray, C. M. ( 1999 ). Cellular mechanisms contributing to response variability of cortical neurons in vivo. The Journal of Neuroscience, 19, 2209 – 2223.
dc.identifier.citedreferenceBaria, A. T., Centeno, M. V., Ghantous, M. E., Chang, P. C., Procissi, D., & Apkarian, A. V. ( 2018 ). BOLD temporal variability differentiates wakefulness from anesthesia‐induced unconsciousness. Journal of Neurophysiology, 119, 834 – 848.
dc.identifier.citedreferenceBaria, A. T., Maniscalco, B., & He, B. J. ( 2017 ). Initial‐state‐dependent, robust, transient neural dynamics encode conscious visual perception. PLOS Computational Biology, 13, e1005806 Ed. Ole Jensen.
dc.identifier.citedreferenceBarttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., & Jarraya, B. ( 2015 ). Signature of consciousness in the dynamics of resting‐state brain activity. Proceedings of the National Academy of Sciences of the United States of America, 112, E5219 – E5220. https://doi.org/10.1073/pnas.1515029112
dc.identifier.citedreferenceBecker, R., Reinacher, M., Freyer, F., Villringer, A., & Ritter, P. ( 2011 ). How ongoing neuronal oscillations account for evoked fMRI variability. The Journal of Neuroscience, 31, 11016 – 11027.
dc.identifier.citedreferenceBoly, M., Garrido, M. I., Gosseries, O., Bruno, M.‐A., Boveroux, P., Schnakers, C., … Friston, K. ( 2011 ). Preserved feedforward but impaired top‐down processes in the vegetative state. Science, 332, 858 – 862.
dc.identifier.citedreferenceBrown, E. N., Purdon, P. L., & Van Dort, C. J. ( 2011 ). General anesthesia and altered states of arousal: A systems neuroscience analysis. Annual Review of Neuroscience 34: 601 – 628.
dc.identifier.citedreferenceChai, X. J., Castañán, A. N., Öngür, D., & Whitfield‐Gabrieli, S. ( 2012 ). Anticorrelations in resting state networks without global signal regression. NeuroImage, 59, 1420 – 1428.
dc.identifier.citedreferenceChang, M. H., Armstrong, K. M., & Moore, T. ( 2012 ). Dissociation of response variability from firing rate effects in frontal eye field neurons during visual stimulation, working memory, and attention. The Journal of Neuroscience, 32, 2204 – 2216.
dc.identifier.citedreferenceChennu, S., Finoia, P., Kamau, E., Allanson, J., Williams, G. B., Monti, M. M., … Bekinschtein, T. A. ( 2014 ). Spectral signatures of reorganised brain networks in disorders of consciousness. PLoS Computational Biology, 10, e1003887 Ed. Bard Ermentrout.
dc.identifier.citedreferenceChurchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., … Shenoy, K. V. ( 2010 ). Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nature Neuroscience, 13, 369 – 378.
dc.identifier.citedreferenceCox, R. W., Chen, G., Glen, D. R., Reynolds, R. C., & Taylor, P. A. ( 2017 ). FMRI clustering in AFNI: False‐positive rates Redux. Brain Connectivity, 7, 152 – 171.
dc.identifier.citedreferenceDai, R., Huang, Z., Tu, H., Wang, L., Tanabe, S., Weng, X., … Li, D. ( 2016 ). Interplay between heightened temporal variability of spontaneous brain activity and task‐evoked hyperactivation in the blind. Frontiers in Human Neuroscience, 10, 632.
dc.identifier.citedreferenceDavis, M. H., Coleman, M. R., Absalom, A. R., Rodd, J. M., Johnsrude, I. S., Matta, B. F., … Menon, D. K. ( 2007 ). Dissociating speech perception and comprehension at reduced levels of awareness. Proceedings of the National Academy of Sciences of the United States of America, 104, 16032 – 16037.
dc.identifier.citedreferenceDeco, G., & Hugues, E. ( 2012 ). Neural network mechanisms underlying stimulus driven variability reduction. PLoS Computational Biology, 8, e1002395 Ed. Tim Behrens.
dc.identifier.citedreferenceDeco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., … Jirsa, V. K. ( 2014 ). Identification of optimal structural connectivity using functional connectivity and neural modeling. The Journal of Neuroscience, 34, 7910 – 7916.
dc.identifier.citedreferenceDiFrancesco, M. W., Robertson, S. A., Karunanayaka, P., & Holland, S. K. ( 2013 ). BOLD fMRI in infants under sedation: Comparing the impact of pentobarbital and propofol on auditory and language activation. Journal of Magnetic Resonance Imaging, 38, 1184 – 1195.
dc.identifier.citedreferenceDueck, M. H., Petzke, F., Gerbershagen, H. J., Paul, M., Hesselmann, V., Girnus, R., … Boerner, U. ( 2005 ). Propofol attenuates responses of the auditory cortex to acoustic stimulation in a dose‐dependent manner: A FMRI study. Acta Anaesthesiologica Scandinavica, 49, 784 – 791.
dc.identifier.citedreferenceFaisal, A. A., Selen, L. P. J., & Wolpert, D. M. ( 2008 ). Noise in the nervous system. Nature Reviews. Neuroscience, 9, 292 – 303.
dc.identifier.citedreferenceFerri, F., Costantini, M., Huang, Z., Perrucci, M. G., Ferretti, A., Romani, G. L., & Northoff, G. ( 2015 ). Intertrial variability in the premotor cortex accounts for individual differences in peripersonal space. The Journal of Neuroscience, 35, 16328 – 16339.
dc.identifier.citedreferenceFinn, I. M., Priebe, N. J., & Ferster, D. ( 2007 ). The emergence of contrast‐invariant orientation tuning in simple cells of cat visual cortex. Neuron, 54, 137 – 152.
dc.identifier.citedreferenceFox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. ( 2005 ). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America 102: 9673 – 9678.
dc.identifier.citedreferenceFox, M. D., Snyder, A. Z., Zacks, J. M., & Raichle, M. E. ( 2006 ). Coherent spontaneous activity accounts for trial‐to‐trial variability in human evoked brain responses. Nature Neuroscience, 9, 23 – 25.
dc.identifier.citedreferenceFranks, N. P. ( 2008 ). General anaesthesia: From molecular targets to neuronal pathways of sleep and arousal. Nature Reviews. Neuroscience, 9, 370 – 386.
dc.identifier.citedreferenceFrölich, M. A., Banks, C., & Ness, T. J. ( 2017 ). The effect of sedation on cortical activation: A randomized study comparing the effects of sedation with midazolam, propofol, and dexmedetomidine on auditory processing. Anesthesia and Analgesia, 124, 1603 – 1610.
dc.identifier.citedreferenceGarrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. ( 2010 ). Blood oxygen level‐dependent signal variability is more than just noise. The Journal of Neuroscience, 30, 4914 – 4921.
dc.identifier.citedreferenceGarrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. ( 2011 ). The importance of being variable. The Journal of Neuroscience, 31, 4496 – 4503.
dc.identifier.citedreferenceGarrett, D. D., Kovacevic, N., McIntosh, A. R., & Grady, C. L. ( 2013 ). The modulation of BOLD variability between cognitive states varies by age and processing speed. Cerebral Cortex, 23, 684 – 693.
dc.identifier.citedreferenceGarrett, D. D., McIntosh, A. R., & Grady, C. L. ( 2011 ). Moment‐to‐moment signal variability in the human brain can inform models of stochastic facilitation now. Nature Reviews. Neuroscience, 12, 612.
dc.identifier.citedreferenceGarrett, D. D., Samanez‐Larkin, G. R., MacDonald, S. W. S., Lindenberger, U., McIntosh, A. R., & Grady, C. L. ( 2013 ). Moment‐to‐moment brain signal variability: A next frontier in human brain mapping? Neuroscience and Biobehavioral Reviews, 37, 610 – 624.
dc.identifier.citedreferenceHe, B. J. ( 2011 ). Scale‐free properties of the functional magnetic resonance imaging signal during rest and task. The Journal of Neuroscience, 31, 13786 – 13795.
dc.identifier.citedreferenceHe, B. J. ( 2013 ). Spontaneous and task‐evoked brain activity negatively interact. The Journal of Neuroscience, 33, 4672 – 4682.
dc.identifier.citedreferenceHe, B. J., & Zempel, J. M. ( 2013 ). Average is optimal: An inverted‐U relationship between trial‐to‐trial brain activity and behavioral performance. PLoS Computational Biology, 9, e1003348.Ed. Olaf Sporns. https://doi.org/10.1371/journal.pcbi.1003348
dc.identifier.citedreferenceHuang, Z., Dai, R., Wu, X., Yang, Z., Liu, D., Hu, J., … Northoff, G. ( 2014 ). The self and its resting state in consciousness: An investigation of the vegetative state. Human Brain Mapping, 35, 1997 – 2008. https://doi.org/10.1002/hbm.22308
dc.identifier.citedreferenceHuang, Z., Liu, X., Mashour, G. A., & Hudetz, A. G. ( 2018 ). Timescales of intrinsic BOLD signal dynamics and functional connectivity in pharmacologic and neuropathologic states of unconsciousness. The Journal of Neuroscience, 38, 2304 – 2317.
dc.identifier.citedreferenceHuang, Z., Wang, Z., Zhang, J., Dai, R., Wu, J., Li, Y., … Northoff, G. ( 2014 ). Altered temporal variance and neural synchronization of spontaneous brain activity in anesthesia. Human Brain Mapping, 35, 5368 – 5378. https://doi.org/10.1002/hbm.22556
dc.identifier.citedreferenceHuang, Z., Zhang, J., Longtin, A., Dumont, G., Duncan, N. W., Pokorny, J., … Northoff, G. ( 2017 ). Is there a nonadditive interaction between spontaneous and evoked activity? Phase‐dependence and its relation to the temporal structure of scale‐free brain activity. Cerebral Cortex, 27, 1037 – 1059.
dc.identifier.citedreferenceHuang, Z., Zhang, J., Wu, J., Qin, P., Wu, X., Wang, Z., … Northoff, G. ( 2016 ). Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia. NeuroImage, 124, 693 – 703.
dc.identifier.citedreferenceHudetz, A. G., Liu, X., & Pillay, S. ( 2015 ). Dynamic repertoire of intrinsic brain states is reduced in propofol‐induced unconsciousness. Brain Connectivity, 5, 10 – 22.
dc.identifier.citedreferenceImas, O. A., Ropella, K. M., Ward, B. D., Wood, J. D., & Hudetz, A. G. ( 2005 ). Volatile anesthetics disrupt frontal‐posterior recurrent information transfer at gamma frequencies in rat. Neuroscience Letters, 387, 145 – 150.
dc.identifier.citedreferenceKelso, J. A. S. ( 2012 ). Multistability and metastability: Understanding dynamic coordination in the brain. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 367, 906 – 918. https://doi.org/10.1098/rstb.2011.0351
dc.identifier.citedreferenceKing, J.‐R., & Dehaene, S. ( 2014 ). Characterizing the dynamics of mental representations: The temporal generalization method. Trends in Cognitive Sciences, 18, 203 – 210.
dc.identifier.citedreferenceLamme, V. A., & Roelfsema, P. R. ( 2000 ). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23, 571 – 579.
dc.identifier.citedreferenceLee, U., Ku, S., Noh, G., Baek, S., Choi, B., & Mashour, G. a. ( 2013 ). Disruption of frontal‐parietal communication by ketamine, propofol, and sevoflurane. Anesthesiology, 118, 1264 – 1275.
dc.identifier.citedreferenceLipsman, N., Nakao, T., Kanayama, N., Krauss, J. K., Anderson, A., Giacobbe, P., … Northoff, G. ( 2014 ). Neural overlap between resting state and self‐relevant activity in human subcallosal cingulate cortex – Single unit recording in an intracranial study. Cortex, 60, 139 – 144.
dc.identifier.citedreferenceLitwin‐Kumar, A., & Doiron, B. ( 2012 ). Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscience, 15, 1498 – 1505.
dc.identifier.citedreferenceLiu, X., Lauer, K. K., Ward, B. D., Rao, S. M., Li, S. J., & Hudetz, A. G. ( 2012 ). Propofol disrupts functional interactions between sensory and high‐order processing of auditory verbal memory. Human Brain Mapping, 33, 2487 – 2498.
dc.identifier.citedreferenceMarsh, B., Morton, N., & Kenny, G. N. C. ( 1991 ). Pharmacokinetic model driven infusion of propofol in children. British Journal of Anaesthesia, 67, 41 – 48.
dc.identifier.citedreferenceMashour, G. A. ( 2014 ). Top‐down mechanisms of anesthetic‐induced unconsciousness. Frontiers in Systems Neuroscience, 8, 115.
dc.identifier.citedreferenceMashour, G. A., & Hudetz, A. G. ( 2018 ). Neural correlates of unconsciousness in large‐scale brain networks. Trends in Neurosciences, 41, 150 – 160.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.