The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients
Open accessAbstract
Editor’s Perspective: What We Already Know about This Topic: What This Article Tells Us That Is New: Background: Functional connectivity across the cortex has been posited to be important for consciousness and anesthesia, but functional connectivity patterns during the course of surgery and general anesthesia are unknown. The authors tested the hypothesis that disrupted cortical connectivity patterns would correlate with surgical anesthesia. Methods: Surgical patients (n = 53) were recruited for study participation. Whole-scalp (16-channel) wireless electroencephalographic data were prospectively collected throughout the perioperative period. Functional connectivity was assessed using weighted phase lag index. During anesthetic maintenance, the temporal dynamics of connectivity states were characterized via Markov chain analysis, and state transition probabilities were quantified. Results: Compared to baseline (weighted phase lag index, 0.163, ± 0.091), alpha frontal–parietal connectivity was not significantly different across the remaining anesthetic and perioperative epochs, ranging from 0.100 (± 0.041) to 0.218 (± 0.136) (P > 0.05 for all time periods). In contrast, there were significant increases in alpha prefrontal–frontal connectivity (peak = 0.201 [0.154, 0.248]; P < 0.001), theta prefrontal–frontal connectivity (peak = 0.137 [0.091, 0.182]; P < 0.001), and theta frontal–parietal connectivity (peak = 0.128 [0.084, 0.173]; P < 0.001) during anesthetic maintenance. Additionally, shifts occurred between states of high prefrontal–frontal connectivity (alpha, beta) with suppressed frontal–parietal connectivity, and high frontal–parietal connectivity (alpha, theta) with reduced prefrontal–frontal connectivity. These shifts occurred in a nonrandom manner (P < 0.05 compared to random transitions), suggesting structured transitions of connectivity during general anesthesia. Conclusions: Functional connectivity patterns dynamically shift during surgery and general anesthesia but do so in a structured way. Thus, a single measure of functional connectivity will likely not be a reliable correlate of surgical anesthesia.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Still, it is important to note that the anesthetized brain is not just in a fixed state; recent observations found it to be more dynamic than previously considered, with metastable oscillations or connectivity patterns that appear to reflect intrinsic dynamics rather than pharmacokinetic instability or external stimuli (Hudson et al., 2014; Li et al., 2019; Vlisides et al., 2019).”
From Conscious Processing and the Global Neuronal Workspace Hypothesis (2020) · cites this paper as Vlisides et al., 2019
Experimental — accuracy not yet measuredIn the citing paper
“recent studies have demonstrated that corticocortical functional connectivity shifts dynamically during exposure to volatile anesthetics, even when controlling for pharmacokinetic stability and surgical stimulus [147, 148].”
From The Biology of General Anesthesia from Paramecium to Primate (2019) · cites this paper as [147]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
Stam, C. J., Nolte, G., & Daffertshofer, A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20346 · Cited by 2,083
Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F., & Pennartz, C. M. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.055 · Cited by 1,753
Electroencephalogram signatures of loss and recovery of consciousness from propofol
Purdon, P. L., Pierce, E. T., Mukamel, E. A., Prerau, M. J., Walsh, J. L., Wong, K. F. K., Salazar-Gomez, A. F., Harrell, P. G., Sampson, A. L., Cimenser, A., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221180110 · Cited by 854
Mitra, P. P., & Bokil, H. (2007) · doi:10.1093/acprof:oso/9780195178081.001.0001 · Cited by 827
Tognoli, E., & Kelso, J. A. S. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.022 · Cited by 718
Signature of consciousness in the dynamics of resting-state brain activity
Barttfeld, P., Uhrig, L., Sitt, J. D., Sigman, M., Jarraya, B., & Dehaene, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418031112 · Cited by 670
Boveroux, P., Vanhaudenhuyse, A., Bruno, M.-A., Noirhomme, Q., Lauwick, S., Luxen, A., Degueldre, C., Plenevaux, A., Schnakers, C., Phillips, C., et al. (2010) · Anesthesiology · doi:10.1097/aln.0b013e3181f697f5 · Cited by 630
Prevention of Intraoperative Awareness in a High-Risk Surgical Population
Avidan, M. S., Jacobsohn, E., Glick, D. B., Burnside, B. A., Zhang, L., Villafranca, A., Karl, L. R., Kamal, S., Torres, B. A., O'Connor, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1100403 · Cited by 593
Self-citationFast transient networks in spontaneous human brain activity
Baker, A. P., Brookes, M. J., Rezek, I. A., Smith, S. M., Behrens, T. E. J., Probert-Smith, P., & Woolrich, M. W. (2014) · eLife · doi:10.7554/elife.01867 · Cited by 569
Stability-Based Validation of Clustering Solutions
Lange, T., Röth, V., Braun, M. L., & Buhmann, J. M. (2004) · Neural Computation · doi:10.1162/089976604773717621 · Cited by 527
Surrogate data for hypothesis testing of physical systems
Lancaster, G., Iatsenko, D., Pidde, A., Ticcinelli, V., & Stefanovska, A. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.06.001 · Cited by 435
Disruption of frontal-parietal communication by ketamine, propofol, and sevoflurane
Lee, U., Ku, S., Noh, G., Baek, S., Choi, B., & Mashour, G. A. (2013) · Anesthesiology · doi:10.1097/aln.0b013e31829103f5 · Cited by 426
Self-citationDiscovering dynamic brain networks from big data in rest and task
Vidaurre, D., Abeysuriya, R., Becker, R., Quinn, A. J., Alfaro‐Almagro, F., Smith, S. M., & Woolrich, M. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.077 · Cited by 347
Connectivity Changes Underlying Spectral EEG Changes during Propofol-Induced Loss of Consciousness
Boly, M., Moran, R., Murphy, M., Boveroux, P., Bruno, M., Noirhomme, Q., Ledoux, D., Bonhomme, V. L., Brichant, J.-F., Tononi, G., et al. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3769-11.2012 · Cited by 326
Effects of Sevoflurane and Propofol on Frontal Electroencephalogram Power and Coherence
Akeju, O., Westover, M. B., Pavone, K. J., Sampson, A. L., Hartnack, K. E., Brown, E. N., & Purdon, P. L. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000436 · Cited by 298
Invariant Reversible QEEG Effects of Anesthetics
John, E. R., Prichep, L. S., Kox, W., Valdés-Sosa, P., Bosch-Bayard, J., Aubert, E., Tom, M., diMichele, F., & Gugino, L. D. (2001) · Consciousness and Cognition · doi:10.1006/ccog.2001.0507 · Cited by 274
Brain functional integration decreases during propofol-induced loss of consciousness
Schrouff, J., Perlbarg, V., Boly, M., Marrelec, G., Boveroux, P., Vanhaudenhuyse, A., Bruno, M., Laureys, S., Phillips, C. L. M., Pélégrini‐Issac, M., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.020 · Cited by 237
Ku, S. W., Lee, U., Noh, G. J., Jun, I., & Mashour, G. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025155 · Cited by 212
Self-citationBonhomme, V., Vanhaudenhuyse, A., Demertzi, A., Bruno, M.-A., Jaquet, O., Bahri, M. A., Plenevaux, A., Boly, M., Boveroux, P., Soddu, A., et al. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001275 · Cited by 207
Akeju, O., Pavone, K. J., Westover, M. B., Vazquez, R., Prerau, M. J., Harrell, P. G., Hartnack, K. E., Rhee, J., Sampson, A. L., Habeeb, K., et al. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000419 · Cited by 202
Cortical Hypersynchrony Predicts Breakdown of Sensory Processing during Loss of Consciousness
Supp, G. G., Siegel, M., Hipp, J. F., & Engel, A. K. (2011) · Current Biology · doi:10.1016/j.cub.2011.10.017 · Cited by 183
Jordan, D., Ilg, R., Riedl, V., Schorer, A., Grimberg, S., Neufang, S., Omerovic, A., Berger, S., Untergehrer, G., Preibisch, C., et al. (2013) · Anesthesiology · doi:10.1097/aln.0b013e3182a7ca92 · Cited by 180
Recovery of consciousness is mediated by a network of discrete metastable activity states
Hudson, A. E., Calderon, D. P., Pfaff, D. W., & Proekt, A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408296111 · Cited by 178
Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness
Flores, F. J., Hartnack, K. E., Fath, A. B., Kim, S., Wilson, M. A., Brown, E. N., & Purdon, P. L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1700148114 · Cited by 176
Functional connectivity of the human insular cortex during noxious and innocuous thermal stimulation
Peltz, E., Seifert, F., DeCol, R., Dörfler, A., Schwab, S., & Maihöfner, C. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.012 · Cited by 175
Ma, S., Calhoun, V. D., Phlypo, R., & Adalı, T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.12.063 · Cited by 173
Anterior Shift of the Dominant EEG Rhythm during Anesthesia in the Java Monkey
Tinker, J. H., Sharbrough, F. W., & Michenfelder, J. D. (1977) · Anesthesiology · doi:10.1097/00000542-197704000-00005 · Cited by 156
Reconfiguration of Network Hub Structure after Propofol-induced Unconsciousness
Lee, H., Mashour, G. A., Noh, G., Kim, S., & Lee, U. (2013) · Anesthesiology · doi:10.1097/aln.0b013e3182a8ec8c · Cited by 150
Self-citationRanft, A., Golkowski, D., Kiel, T., Riedl, V., Kohl, P., Rohrer, G., Pientka, J., Berger, S., Thul, A., Maurer, M., et al. (2016) · Anesthesiology · doi:10.1097/aln.0000000000001322 · Cited by 142
Self-citationMoon, J.-Y., Lee, U., Blain-Moraes, S., & Mashour, G. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004225 · Cited by 136
Self-citationResting-state Dynamics as a Cortical Signature of Anesthesia in Monkeys
Uhrig, L., Sitt, J. D., Jacob, A., Tasserie, J., Barttfeld, P., Dupont, M., Dehaene, S., & Jarraya, B. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002336 · Cited by 131
Giattino, C., Gardner, J. E., Sbahi, F. M., Roberts, K., Cooter, M. L., Moretti, E., Browndyke, J. N., Mathew, J. P., Woldorff, M. G., Berger, M., et al. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00024 · Cited by 129
Palanca, B. J. A., Mitra, A., Larson-Prior, L., Snyder, A. Z., Avidan, M. S., & Raichle, M. E. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000731 · Cited by 112
Opioid–Volatile Anesthetic Synergy
Manyam, S. C., Gupta, D. K., Johnson, K. B., et al. (2006) · Anesthesiology · doi:10.1097/00000542-200608000-00009 · Cited by 111
Brain Networks Maintain a Scale-free Organization across Consciousness, Anesthesia, and Recovery
Lee, U., Oh, G., Kim, S., Noh, G., Choi, B., & Mashour, G. A. (2010) · Anesthesiology · doi:10.1097/aln.0b013e3181f229b5 · Cited by 98
Self-citationDrug Interactions: Volatile Anesthetics and Opioids
Glass, P. S. A., Gan, T. J., Howell, S. T., et al. (1997) · Journal of Clinical Anesthesia · doi:10.1016/s0952-8180(97)00122-0 · Cited by 84
Role of Network Science in the Study of Anesthetic State Transitions
Lee, U., & Mashour, G. A. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002228 · Cited by 83
Self-citationKim, H., Hudetz, A. G., Lee, J., Mashour, G. A., Lee, U., & the ReCCognition Study Group (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00042 · Cited by 82
Self-citationBlain‐Moraes, S., Tarnal, V., Vanini, G., Bel‐Bahar, T., Janke, E., Picton, P., Golmirzaie, G., Palanca, B. J. A., Avidan, M. S., Kelz, M. B., et al. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00328 · Cited by 72
Self-citationDynamic Connectivity Patterns in Conscious and Unconscious Brain
Ma, Y., Hamilton, C. M., & Zhang, N. (2016) · Brain Connectivity · doi:10.1089/brain.2016.0464 · Cited by 71
Hambrecht‐Wiedbusch, V. S., Li, D., & Mashour, G. A. (2017) · Anesthesiology · doi:10.1097/aln.0000000000001512 · Cited by 68
Self-citationFunctional diversity of brain networks supports consciousness and verbal intelligence
Naci, L., Haugg, A., MacDonald, A., Anello, M., Houldin, E., Naqshbandi, S., Gonzalez-Lara, L. E., Arango, M., Harle, C., Cusack, R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-31525-z · Cited by 66
Reducing the bias of causality measures
Papana, A., Kugiumtzis, D., & Larsson, P. G. (2011) · Physical Review E · doi:10.1103/physreve.83.036207 · Cited by 50
Metastability of Neuronal Dynamics during General Anesthesia: Time for a Change in Our Assumptions?
Hudson, A. E. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2017.00058 · Cited by 34
Mashour, G. A., Esaki, R. K., Vandervest, J. C., et al. (2009) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-009-9193-9 · Cited by 28
Self-citationFronto-parietal connectivity changes following noxious stimulation during anesthesia.
Pavel, B., Daneasa, A., Roşca, A. E., Călin, A., Zahiu, D., Panaitescu, A. M., Zăgrean, A., & Zăgrean, L. (2014) · Europe PMC (PubMed Central) · Cited by 2
Cited by
Sorted by citations
Conscious Processing and the Global Neuronal Workspace Hypothesis
Mashour, G. A., Roelfsema, P., Changeux, J.-P., & Dehaene, S. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.026 · Cited by 1,164
“Review with metastable oscillations or connectivity patterns that appear to reflect intrinsic dynamics rather than pharmacokinetic instability or external stimuli (Hudson et al., 2014; Li et al., 2019; Vli-sides et al., 2019).”
BackgroundSelf-citationTemporal circuit of macroscale dynamic brain activity supports human consciousness
Huang, Z., Zhang, J., Wu, J., Mashour, G. A., & Hudetz, A. G. (2020) · Science Advances · doi:10.1126/sciadv.aaz0087 · Cited by 217
Self-citationThe Biology of General Anesthesia from Paramecium to Primate
Kelz, M. B., & Mashour, G. A. (2019) · Current Biology · doi:10.1016/j.cub.2019.09.071 · Cited by 151
Self-citationAnesthesia and the neurobiology of consciousness
Mashour, G. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.03.002 · Cited by 127
Self-citation
Metadata
- Authors
- Vlisides, P. E., Li, D., Zierau, M., Lapointe, A. P., Ip, K. I., McKinney, A. M., & Mashour, G. A.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Li, Duan
- Venue
- Anesthesiology
- Published
- 2019-06
- Volume
- 130
- Issue
- 6
- Pages
- 885-897
- ISSN
- 0003-3022 (ISSN-L)
- PMC
- PMC6520139
- OpenAlex
- W2925881161
- Cited by
- 66
- References
- 48
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 14 · 2020: 10 · 2019: 10
- FWCI
- 15.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Anesthesiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM098578, T32GM103730)
- Topics
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Anesthesia and Sedative Agents
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- general anesthesia, cortical functional connectivity, weighted phase lag index, Markov chain state transition, frontoparietal connectivity
- MeSH terms
- Adult, Aged, Anesthesia, General, Cerebral Cortex, Electroencephalography, Female, Humans, Male, Middle Aged, Nerve Net, Prospective Studies, Anesthetics, Inhalation, Anesthetics, Intravenous
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review