Veronica S. Weiner, David W. Zhou https://orcid.org/0000-0002-9635-0472 [email protected], Stiff Kahali, Emily P. Stephen https://orcid.org/0000-0003-1978-9622, Robert A. Peter friend, Linda S. Garlic https://orcid.org/0000-0001-6336-0634, Michael D. Szabo, Mothers N. Eskander, Andrés F. Salazar Gomez, Aaron L. Sampson, Sydney S. Cash https://orcid.org/0000-0002-4557-6391, Emery N. Brownand Patrick L. Purdon https://orcid.org/0000-0003-0080-3340 [email protected]Authors Info & Affiliations
Edited by J. Anthony Movshon, New York University, New York, NY; received May 12, 2022; accepted January 14, 2023
March 10, 2023
120 (11) e2207831120
Significance
Although anesthetic drugs are known to lower arousal, it is unclear how anesthesia impacts perceptual and cognitive processing. Diminished arousal has been associated with prominent brain oscillations such as the slow wave, but functional roles for other anesthesia-induced rhythmic changes have not been proposed. During waking states, brain oscillations are understood to be involved in a variety of sensory and cognitive processes mediated by circuits connecting posterior or prefrontal cortices with the thalamus. This study shows that propofol disrupts alpha oscillations (~10 cycles/s) in posterior circuits that mediate sensory processing and induces an alpha oscillation in prefrontal cognitive circuits that normally operate at higher frequencies.
Abstract
During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.
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Data, Materials, and Software Availability
Acknowledgments
This work was supported by NIH grants P01GM118269 (Brown), 1R01AG056015 (Purdon), R21DA048323 (Purdon), NSF Graduate Research Fellowship (Weiner), Singleton Fellowship (Weiner), T32EB019940 (Zhou), and the Tiny Blue Dot Foundation (Purdon). Data were provided in part by the Human Connectome Project, Washington University-University of Minnesota Consortium [1U54MH091657 (Van Essen, Ugurbil)] funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank Bram Diamond for the 1-mm isotropic Montreal Neurological Institute atlas of thalamic nuclei. We are grateful to Sourish Chakravarty, Brian Edlow, Michael Halassa, Nancy Kopell, Michelle McCarthy, Samuel Snider, and Carmen Varela for their valuable comments and suggestions. Above all, we are deeply indebted to the volunteers who contributed their time and efforts as surgical patients to our research.
Author contributions
V.S.W., E.N.E., S.S.C., E.N.B., and P.L.P. designed research; V.S.W., R.A.P., L.S.A., M.D.S., E.N.E., A.F.S.-G., A.L.S., S.S.C., and P.L.P. performed research; V.S.W., D.W.Z., P.K., E.P.S., and P.L.P. contributed new reagents/analytic tools; V.S.W., D.W.Z., P.K., and P.L.P. analyzed data; and V.S.W., D.W.Z., P.K., and P.L.P. wrote the paper.
Competing interests
The authors have organizational affiliations to disclose, P.L.P. and E.N.B. are co-founders of PASCALL Systems, Inc., a start-up company developing closed-loop physiological control for anesthesiology. The authors have patent filings to disclose, P.L.P. and E.N.B. hold patents in general anesthesia monitoring technology.
Supporting Information
References
1
V. A. Feshchenko, R. A. Veselis, R. A. Reinsel, Propofol-induced alpha rhythm. Neuropsychobiology 50257–266 (2004).
2
S. Ching, A. Cimenser, P. L. Purdon, E. N. Brown, N. J. Kopell, Thalamocortical model for a propofol-induced α-rhythm associated with loss of consciousness. Proc. Natl. Acad. Sci. U.S.A. 10722665–22670 (2010).
3
A. Cimenser et al., Tracking brain states under general anesthesia by using global coherence analysis. Proc. Natl. Acad. Sci. U.S.A. 1088832–8837 (2011).
4
P. L. Purdon et al., Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc. Natl. Acad. Sci. U.S.A. 110E1142–E1151 (2013).
5
F. J. Flores et al., Thalamocortical synchronization during induction and emergence from propofol-induced unconsciousness. Proc. Natl. Acad. Sci. U.S.A. 114E6660–E6668 (2017).
6
L. D. Lewis et al., Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness. Proc. Natl. Acad. Sci. U.S.A. 109E3377–E3386 (2012).
7
J. H. Tinker, F. W. Sharbrough, J. D. Michenfelder, Anterior shift of the dominant EEG rhytham during anesthesia in the Java monkey: Correlation with anesthetic potency. Anesthesiology 46252 (1977).
8
S. Vijayan, S. Ching, P. L. Purdon, E. N. Brown, N. J. Kopell, Thalamocortical mechanisms for the anteriorization of α rhythms during propofol-induced unconsciousness. J. Neurosci. 3311070–11075 (2013).
9
D. Contreras, A. Destexhe, T. J. Sejnowski, M. Steriade, Control of spatiotemporal coherence of a thalamic oscillation by corticothalamic feedback. Science 274771–774 (1996).
10
E. G. Jones, The thalamic matrix and thalamocortical synchrony. Trends Neurosci. 24595–601 (2001).
11
J. W. Scannell, G. A. Burns, C. C. Hilgetag, M. A. O’Neil, M. P. Young, The connectional organization of the cortico-thalamic system of the cat. Cereb Cortex. 9277–299 (1999).
12
T. E. J. Behrens et al., Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6750–757 (2003).
13
D. Zhang, A. Z. Snyder, J. S. Shimony, M. D. Fox, M. E. Raichle, Noninvasive functional and structural connectivity mapping of the human thalamocortical system. Cereb Cortex. 201187–1194 (2010).
14
P. Mitra, Observed Brain Dynamics (Oxford University Press, 2007).
15
M. Malekmohammadi, C. M. Price, A. E. Hudson, J. A. T. DiCesare, N. Pouratian, Propofol-induced loss of consciousness is associated with a decrease in thalamocortical connectivity in humans. Brain 1422288–2302 (2019), https://doi.org/10.1093/brain/awz169.
17
A. M. Bastos et al., Neural effects of propofol-induced unconsciousness and its reversal using thalamic stimulation. Elife 10e60824 (2021), https://doi.org/10.7554/eLife.60824.
18
D. C. Van Essen et al., The WU-Minn Human Connectome Project: An overview. Neuroimage 8062–79 (2013).
19
M. Rosanova et al., Natural frequencies of human corticothalamic circuits. J. Neurosci. 297679–7685 (2009).
20
E. Barzegaran, V. Y. Vildavski, M. G. Knyazeva, Fine structure of posterior alpha rhythm in human EEG: Frequency components, their cortical sources, and temporal behavior. Sci. Rep. 78249 (2017).
21
S. Vijayan, N. J. Kopell, Thalamic model of awake alpha oscillations and implications for stimulus processing. Proc. Natl. Acad. Sci. U.S.A. 10918553–18558 (2012).
22
D. Bai, P. S. Pennefather, J. F. MacDonald, B. A. Orser, The general anesthetic propofol slows deactivation and desensitization of GABA(A) receptors. J. Neurosci. 1910635–10646 (1999).
23
S.-W. Ying, S. Y. Abbas, N. L. Harrison, P. A. Goldstein, Propofol block of Ih contributes to the suppression of neuronal excitability and rhythmic burst firing in thalamocortical neurons: Propofol inhibition of thalamic Ih. Eur J. Neurosci. 23465–480 (2006).
24
A. K. Lyashchenko, K. J. Redd, J. Yang, G. R. Tibbs, Propofol inhibits HCN1 pacemaker channels by selective association with the closed states of the membrane embedded channel core. J. Physiol. 58337–56 (2007).
25
S. W. Hughes et al., Synchronized oscillations at α and θ frequencies in the lateral geniculate nucleus. Neuron 42253–268 (2004).
26
S. W. Hughes et al., Thalamic gap junctions control local neuronal synchrony and influence macroscopic oscillation amplitude during EEG alpha rhythms. Front. Psychol. 2193 (2011).
27
F. H. Lopes da Silva, J. E. Vos, J. Mooibroek, A. Van Rotterdam, Relative contributions of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroencephalogr. Clin. Neurophysiol. 50449–456 (1980).
28
Y. B. Saalmann, M. A. Pinsk, L. Wang, S. Kastner, X. Li, The pulvinar regulates information transmission between cortical areas based on attention demands. Science 337753–756 (2012).
29
S. Parnaudeau et al., Inhibition of mediodorsal thalamus disrupts thalamofrontal connectivity and cognition. Neuron 771151–1162 (2013).
30
N. A. Ketz, O. Jensen, R. C. O’Reilly, Thalamic pathways underlying prefrontal cortex-medial temporal lobe oscillatory interactions. Trends Neurosci. 383–12 (2015).
31
Y. B. Saalmann, Intralaminar and medial thalamic influence on cortical synchrony, information transmission and cognition. Front Syst. Neurosci. 883 (2014).
32
T. M. Preuss, P. S. Goldman-Rakic, Crossed corticothalamic and thalamocortical connections of macaque prefrontal cortex. J. Comp. Neurol. 257269–281 (1987).
33
M. Boly et al., Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? Clinical and neuroimaging evidence. J. Neurosci. 379603–9613 (2017).
34
B. Odegaard, R. T. Knight, H. Lau, Should a few null findings falsify prefrontal theories of conscious perception? J. Neurosci. 379593–9602 (2017).
35
D. Pal et al., Differential role of prefrontal and parietal cortices in controlling level of consciousness. Curr Biol. 282145–2152.e5 (2018).
36
O. Jensen, A. Mazaheri, Shaping functional architecture by oscillatory alpha activity: Gating by inhibition. Front Hum. Neurosci. 4186 (2010).
37
N. A. Busch, J. Dubois, R. VanRullen, The phase of ongoing EEG oscillations predicts visual perception. J. Neurosci. 297869–7876 (2009).
38
J. Samaha, B. R. Postle, The speed of alpha-band oscillations predicts the temporal resolution of visual perception. Curr Biol. 252985–2990 (2015).
39
J. Misselhorn, U. Friese, A. K. Engel, Frontal and parietal alpha oscillations reflect attentional modulation of cross-modal matching. Sci. Rep. 95030 (2019).
40
R. Sokoliuk et al., Two spatially distinct posterior alpha sources fulfill different functional roles in attention. J. Neurosci. 397183–7194 (2019).
41
M. Lundqvist et al., Gamma and beta bursts underlie working memory. Neuron 90152–164 (2016).
42
S. T. Williams et al., Common resting brain dynamics indicate a possible mechanism underlying zolpidem response in severe brain injury. Elife 2e01157 (2013).
43
Z. Chen, R. D. Wimmer, M. A. Wilson, M. M. Halassa, Thalamic circuit mechanisms link sensory processing in sleep and attention. Front. Neural. Circuits 983 (2015).
44
M. J. Prerau et al., Tracking the sleep onset process: An empirical model of behavioral and physiological dynamics. PLoS Comput. Biol. 10e1003866 (2014).
45
H. C. Hemmings Jr., et al., Towards a comprehensive understanding of anesthetic mechanisms of action: A decade of discovery. Trends Pharmacol. Sci. 40464–481 (2019).
46
P. E. Vlisides et al., Neurophysiologic correlates of ketamine sedation and anesthesia: A high-density electroencephalography study in healthy volunteers. Anesthesiology 12758–69 (2017).
47
B. Moghaddam, B. Adams, A. Verma, D. Daly, Activation of glutamatergic neurotransmission by ketamine: A novel step in the pathway from NMDA receptor blockade to dopaminergic and cognitive disruptions associated with the prefrontal cortex. J. Neurosci. 172921–2927 (1997), https://doi.org/10.1523/jneurosci.17-08-02921.1997.
48
N. Gass et al., Sub-anesthetic ketamine modulates intrinsic BOLD connectivity within the hippocampal-prefrontal circuit in the rat. Neuropsychopharmacology 39895–906 (2014).
49
A. R. Dykstra et al., Individualized localization and cortical surface-based registration of intracranial electrodes. Neuroimage 593563–3570 (2012).
50
B. Fischl et al., Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33341–355 (2002).
51
B. Fischl, Automatically parcellating the human cerebral cortex. Cereb Cortex 1411–22 (2004).
52
JK Mai, M. Majtanik, G. Paxinos, Atlas of the Human Brain (Academic Press, 2015).
53
G. Postelnicu, L. Zollei, B. Fischl, Combined volumetric and surface registration. IEEE Trans Med. Imaging 28508–522 (2009).
54
L. Zöllei, A. Stevens, K. Huber, S. Kakunoori, B. Fischl, Improved tractography alignment using combined volumetric and surface registration. Neuroimage 51206–213 (2010).
55
L. S. Hamilton, D. L. Chang, M. B. Lee, E. F. Chang, Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography. Front. Neuroinform. 1162 (2017).
56
M. F. Glasser et al., The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80105–124 (2013).
57
T. E. J. Behrens, H. J. Berg, S. Jbabdi, M. F. S. Rushworth, M. W. Woolrich, Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34144–155 (2007).
58
S. Ren et al., Nonparametric bootstrapping for hierarchical data. J. Appl. Stat. 371487–1498 (2010).
59
V. Saravanan, G. J. Berman, S. J. Sober, Application of the hierarchical bootstrap to multi-level data in neuroscience. Neuron Behav. Data Anal. Theory.3 (2020).
60
B. Efron, R. Tibshirani, Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 154–75 (1986).
61
K. F. K. Wong et al., Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.4725–4728 (2011).
62
S. N. Lahiri, Resampling Methods for Dependent Data (Springer Science & Business Media, 2013).
63
I. T. Jolliffe, Principal Component Analysis (Springer Science & Business Media, 2013).
64
C. A. Field, A. H. Welsh, Bootstrapping clustered data. J. R. Stat. Soc. Series B Stat. Methodol. 69369–390 (2007).
65
J. E. Iglesias et al., A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 183314–326 (2018).
66
S. De Simoni et al., Altered caudate connectivity is associated with executive dysfunction after traumatic brain injury. Brain 141148–164 (2018).
67
T. Mitsuhashi et al., Dynamic tractography-based localization of spike sources and animation of spike propagations. Epilepsy 622372–2384 (2021).
68
E. G. Jones, The Thalamus (Springer Science & Business Media, 2012).
69
V. S. Weiner et al., 10-Hz cross-spectral matrices from intracranial electrode recordings before and after loss of consciousness during propofol-induced general anesthesia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7657814. Deposited 20 February 2023.
Information & Authors
Information
Published in
Proceedings of the National Academy of Sciences
Vol. 120 | No. 11
March 14, 2023
Classifications
Copyright
Data, Materials, and Software Availability
Submission history
Received: May 12, 2022
Accepted: January 14, 2023
Published online: March 10, 2023
Published in issue: March 14, 2023
Keywords
- propofol
- alpha
- synchrony
- thalamocortical
- intracranial EEG
Acknowledgments
This work was supported by NIH grants P01GM118269 (Brown), 1R01AG056015 (Purdon), R21DA048323 (Purdon), NSF Graduate Research Fellowship (Weiner), Singleton Fellowship (Weiner), T32EB019940 (Zhou), and the Tiny Blue Dot Foundation (Purdon). Data were provided in part by the Human Connectome Project, Washington University-University of Minnesota Consortium [1U54MH091657 (Van Essen, Ugurbil)] funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research and by the McDonnell Center for Systems Neuroscience at Washington University. We would like to thank Bram Diamond for the 1-mm isotropic Montreal Neurological Institute atlas of thalamic nuclei. We are grateful to Sourish Chakravarty, Brian Edlow, Michael Halassa, Nancy Kopell, Michelle McCarthy, Samuel Snider, and Carmen Varela for their valuable comments and suggestions. Above all, we are deeply indebted to the volunteers who contributed their time and efforts as surgical patients to our research.
Author Contributions
V.S.W., E.N.E., S.S.C., E.N.B., and P.L.P. designed research; V.S.W., R.A.P., L.S.A., M.D.S., E.N.E., A.F.S.-G., A.L.S., S.S.C., and P.L.P. performed research; V.S.W., D.W.Z., P.K., E.P.S., and P.L.P. contributed new reagents/analytic tools; V.S.W., D.W.Z., P.K., and P.L.P. analyzed data; and V.S.W., D.W.Z., P.K., and P.L.P. wrote the paper.
Competing Interests
The authors have organizational affiliations to disclose, P.L.P. and E.N.B. are co-founders of PASCALL Systems, Inc., a start-up company developing closed-loop physiological control for anesthesiology. The authors have patent filings to disclose, P.L.P. and E.N.B. hold patents in general anesthesia monitoring technology.
Notes
This article is a PNAS Direct Submission.
Authors
Affiliations
Veronica S. Weiner1
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Center for Neurotechnology and Recovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
Present address: Carney Institute for Brain Science, Brown University, Providence, RI 02906.
Stiff Kahali1
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Present address: Department of Neurology, Keck Medical Center, University of Southern California, Los Angeles, CA 90033.
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
Present address: Department of Math and Statistics, Boston University, Boston, MA 02215.
Robert A. Peter friend
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Harvard Medical School, Boston, MA 02115
Harvard Medical School, Boston, MA 02115
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA 02115
Michael D. Szabo
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Mothers N. Eskander
Harvard Medical School, Boston, MA 02115
Department of Neurological Surgery, Massachusetts General Hospital, Boston, MA 02114
Present address: Department of Neurological Surgery, Albert Einstein College of Medicine—Montefiore Medical Center, Bronx, NY 10467.
Andrés F. Salazar Gomez
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Present address: Open Learning, Massachusetts Institute of Technology, Cambridge, MA 02139.
Aaron L. Sampson
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Present address: Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218.
Center for Neurotechnology and Recovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
Harvard Medical School, Boston, MA 02115
Emery N. Brown
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Harvard Medical School, Boston, MA 02115
Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology, Cambridge, MA 02139
Institute of Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
Harvard Medical School, Boston, MA 02115
Notes
1
V.S.W., D.W.Z., and P.K. contributed equally to this work.
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