Sevoflurane Induces Coherent Slow-Delta Oscillations in Rats

Jennifer A Guidera, Norman E Taylor, Justin T Lee, Ksenia Y Vlasov, JunZhu Pei, Emily P Stephen, J Patrick Mayo, Emery N Brown, Ken Solt, Jennifer A Guidera, Norman E Taylor, Justin T Lee, Ksenia Y Vlasov, JunZhu Pei, Emily P Stephen, J Patrick Mayo, Emery N Brown, Ken Solt

Abstract

Although general anesthetics are routinely administered to surgical patients to induce loss of consciousness, the mechanisms underlying anesthetic-induced unconsciousness are not fully understood. In rats, we characterized changes in the extradural EEG and intracranial local field potentials (LFPs) within the prefrontal cortex (PFC), parietal cortex (PC), and central thalamus (CT) in response to progressively higher doses of the inhaled anesthetic sevoflurane. During induction with a low dose of sevoflurane, beta/low gamma (12-40 Hz) power increased in the frontal EEG and PFC, PC and CT LFPs, and PFC-CT and PFC-PFC LFP beta/low gamma coherence increased. Loss of movement (LOM) coincided with an abrupt decrease in beta/low gamma PFC-CT LFP coherence. Following LOM, cortically coherent slow-delta (0.1-4 Hz) oscillations were observed in the frontal EEG and PFC, PC and CT LFPs. At higher doses of sevoflurane sufficient to induce loss of the righting reflex, coherent slow-delta oscillations were dominant in the frontal EEG and PFC, PC and CT LFPs. Dynamics similar to those observed during induction were observed as animals emerged from sevoflurane anesthesia. We conclude that the rat is a useful animal model for sevoflurane-induced EEG oscillations in humans, and that coherent slow-delta oscillations are a correlate of sevoflurane-induced behavioral arrest and loss of righting in rats.

Keywords: EEG; anesthesia; coherence; rat; sevoflurane.

Figures

FIGURE 1
FIGURE 1
Overview of recording sites. (A) Schematic of targeted sites. Shapes represent locations where screws (gray circles) and electrodes (black circles, PFC; squares, CT; triangles, PC) were placed. (B) Coronal section containing the tract of an electrode localized to the CT. Tract is indicated with an arrow. Pink color is a dye used to aid with tract visualization. (C) Locations of probes in PFC, PC, and CT determined by histological analysis. Shapes same as in (A). The center of each shape marks the tip of one electrode. Numbers indicate anterior–posterior position (mm) relative to bregma. cg1, cingulate cortex area 1; CM, central medial thalamic nucleus; IMD, intermediodorsal thalamic nucleus; M1, primary motor cortex; M2, secondary motor cortex; MDL, mediodorsal thalamic nucleus, lateral part; MDM, mediodorsal thalamic nucleus, medial part; MPtA, medial parietal association cortex; PCT, paracentral thalamic nucleus. Drawings in (A) and (C) are from Paxinos and Watson (2005).
FIGURE 2
FIGURE 2
Overview of oscillatory dynamics observed during the 1.6–2.8% sevoflurane ramp. (A) Spectrogram of the frontal EEG from a representative animal during the sevoflurane ramp. Yellow blocks mark periods during which sevoflurane was set to the indicated dose. Arrows point to oscillatory dynamics. From this animal, (B) PFC (left) and CT (right) LFPs from four 5-s periods.
FIGURE 3
FIGURE 3
Dynamics during induction. (A) Group EMG power and group difference spectrogram of the frontal EEG aligned to the start of sevoflurane administration. Red rectangle with text marks the period analyzed in (B–D). (B) Group power difference for PFC (red), PC (green), and CT (blue) LFPs. Shading indicates 95% confidence intervals, and red, green, and blue horizontal lines mark ranges of significant power increase from awake baseline for PFC, PC, and CT LFPs, respectively. (C) Group coherence between LFPs during the 1.6% sevoflurane induction (red) and awake (blue) states. Shading indicates 95% confidence intervals, and horizontal lines mark ranges of significant coherence increase from awake baseline. (D) Left: PFC (black) and CT (red) LFPs from a representative animal filtered in the range of significant PFC–CT LFP coherence increase shown in (C). Right: group PFC–CT LFP phase relationship in the range of significant PFC–CT LFP coherence increase shown in (C), in terms of phase angle (left y-axis, red line) and time offset (right y-axis, blue dotted line). Shading indicates 95% confidence intervals for phase angle. A positive phase relationship indicates that the first signal lags the second at the indicated frequency.
FIGURE 4
FIGURE 4
Dynamics around the time of loss of movement. (A) Group EMG power and group difference spectrogram of the frontal EEG aligned to LOM. Red rectangles mark the periods analyzed in (B,C,E,F). (B,C) Group power difference between 30-s periods immediately before (B) or 30 s after (C) LOM and awake baseline for PFC (red), PC (green), and CT (blue) LFPs. Shading indicates 95% confidence intervals. Red, green, and blue horizontal lines mark ranges of significant power increase relative to awake baseline for PFC, PC, and CT LFPs, respectively. (D) Group difference coherograms for pairs of LFPs aligned to LOM. (E,F) Group coherence during awake baseline (blue) and periods before (E) or after (F) LOM (red). Shading indicates 95% confidence intervals, and horizontal lines mark ranges of significant coherence increase from awake baseline.
FIGURE 5
FIGURE 5
Slow-delta dynamics during 2.2% sevoflurane. (A) Group difference spectrogram of the frontal EEG during slow-delta dominated periods at 2.2% sevoflurane. The same period is analyzed in (B–G). (B) Group power difference between 2.2% sevoflurane and awake baseline for PFC (red), PC (green), and CT (blue) LFPs. Shading indicates 95% confidence intervals. Red, green, and blue horizontal lines mark ranges of significant power increase relative to awake baseline for PFC, PC, and CT LFPs, respectively. (C) Group coherence during the awake (blue) and 2.2% sevoflurane (red) states for pairs of LFPs. Shading indicates 95% confidence intervals, and horizontal lines mark ranges of significant coherence increase from awake baseline. (D–G) Left: LFPs from a representative animal filtered in the frequency range shown in the corresponding plot on the right. Right: Group phase relationship between two LFPs shown in terms of phase angle (left y-axis, red line) and time offset (right y-axis, blue dotted line). A positive phase relationship indicates that the first signal lags the second, and a negative phase relationship indicates that the first signal leads the second. (D) Group PFC–CT LFP phase relationship in the range of significant PFC–CT LFP coherence increase shown in (C). (E) Group PFC–CT LFP phase relationship in the slow-delta range (note: PFC–CT coherence did not significantly increase in this range). (F) Group PC–CT LFP relationship in the range of significant PC–CT LFP coherence increase shown in (C). (G) Group PFC–PC LFP phase relationship in the range of significant PFC–PC LFP coherence increase within the slow-delta range shown in (C).
FIGURE 6
FIGURE 6
Spectrograms of the PFC LFP from three animals aligned to ROM.
FIGURE 7
FIGURE 7
Dynamics during the last minute of elevated slow-delta power. (A) Group difference spectrogram of the frontal EEG during the last minute of elevated slow-delta power. The same period is analyzed in (B–D). (B) Group power difference for PFC (red), PC (green), and CT (blue) LFPs. Shading indicates 95% confidence intervals. Red, green, and blue horizontal lines mark ranges of significant power increase relative to awake baseline for PFC, PC, and CT LFPs, respectively. (C) Group coherence between LFPs during the awake baseline (blue) and last minute of elevated slow-delta power (red) states. Shading indicates 95% confidence intervals, and horizontal lines mark ranges of significant coherence increase from awake baseline. (D) Left: PFC (black) and PC (red) LFPs from a representative animal filtered in the range of significant PFC–PC LFP coherence increase within the slow-delta range shown in (C). Right: Group PFC–PC LFP phase relationship shown for the range of significant PFC–PC LFP coherence increase shown in (C), in terms of phase angle (left y-axis, red line) and time offset (right y-axis, blue dotted line). Shading indicates 95% confidence intervals for phase angle. A negative phase relationship indicates that the first signal leads the second.
FIGURE 8
FIGURE 8
Dynamics following ROR. (A) Group difference spectrogram of the frontal EEG during the first artifact-free minute following righting. The same period is analyzed in (B–D). (B) Group power difference for PFC (red), PC (green), and CT (blue) LFPs. Shading indicates 95% confidence intervals. Red, green, and blue horizontal lines mark ranges of significant power increase relative to awake baseline for PFC, PC, and CT LFPs, respectively. (C) Group coherence between LFPs during awake baseline (blue) and post-righting (red) states. Shading indicates 95% confidence intervals, and horizontal lines mark ranges of significant coherence increase from awake baseline. (D) Left: PFC (black) and CT (red) LFPs from a representative animal filtered in the range of significant PFC–CT LFP coherence increase shown in (C). Right: Group PFC–CT LFP phase relationship shown for the range of significant PFC–CT LFP coherence increase shown in (C), in terms of phase angle (left y-axis, red line) and time offset (right y-axis, blue dotted line). Shading indicates 95% confidence intervals for phase angle. A positive phase relationship indicates that the first signal lags the second.

References

    1. Akeju O., Westover M. B., Pavone K. J., Sampson A. L., Hartnack K. E., Brown E. N., et al. (2014). Effects of sevoflurane and propofol on frontal electroencephalogram power and coherence. Anesthesiology 121 990–998. 10.1097/ALN.0000000000000436
    1. Alkire M. T., Asher C. D., Franciscus A. M., Hahn E. L. (2009). Thalamic microinfusion of antibody to a voltage-gated potassium channel restores consciousness during anesthesia. Anesthesiology 110 766–773. 10.1097/ALN.0b013e31819c461c
    1. Alonso L. M., Proekt A., Schwartz T. H., Pryor K. O., Cecchi G. A., Magnasco M. O. (2014). Dynamical criticality during induction of anesthesia in human ECoG recordings. Front. Neural Circuits 8:20 10.3389/fncir.2014.00020
    1. Baker R., Gent T. C., Yang Q., Parker S., Vyssotski A. L., Wisden W., et al. (2014). Altered activity in the central medial thalamus precedes changes in the neocortex during transitions into both sleep and propofol anesthesia. J. Neurosci. 34 13326–13335. 10.1523/JNEUROSCI.1519-14.2014
    1. Blain-Moraes S., Tarnal V., Vanini G., Alexander A., Rosen D., Shortal B., et al. (2015). Neurophysiological correlates of sevoflurane-induced unconsciousness. Anesthesiology 122 307–316. 10.1097/ALN.0000000000000482
    1. Brown E. N., Lydic R., Schiff N. D. (2010). General anesthesia, sleep, and coma. N. Engl. J. Med. 363 2638–2650. 10.1056/NEJMra0808281
    1. Brown E. N., Purdon P. L., Van Dort C. J. (2011). General anesthesia and altered states of arousal: a systems neuroscience analysis. Annu. Rev. Neurosci. 34 601–628. 10.1146/annurev-neuro-060909-153200
    1. Chander D., García P. S., MacColl J. N., Illing S., Sleigh J. W. (2014). Electroencephalographic variation during end maintenance and emergence from surgical anesthesia. PLoS ONE 9:e106291 10.1371/journal.pone.0106291
    1. Chauvette S., Crochet S., Volgushev M., Timofeev I. (2011). Properties of slow oscillation during slow-wave sleep and anesthesia in cats. J. Neurosci. 31 14998–15008. 10.1523/JNEUROSCI.2339-11.2011
    1. Ching S., Cimenser A., Purdon P. L., Brown E. N., Kopell N. J. (2010). Thalamocortical model for a propofol-induced α-rhythm associated with loss of consciousness. Proc. Natl. Acad. Sci. U.S.A. 107 22665–22670. 10.1073/pnas.1017069108
    1. Cohen M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice (Issues in Clinical and Cognitive Neuropsychology). Cambridge, MA: MIT Press.
    1. Eckenhoff R. G. (2001). Promiscuous ligands and attractive cavities: how do the inhaled anesthetics work? Mol. Interv. 1 258–268.
    1. Eger E. I., II, Koblin D. D., Harris R. A., Kendig J. J., Pohorille A., Halsey M. J., et al. (1997). Hypothesis: inhaled anesthetics produce immobility and amnesia by different mechanisms at different sites. Anesth. Analg. 84 915–918.
    1. Franks N. P. (2006). Molecular targets underlying general anaesthesia. Br. J. Pharmacol. 147(Suppl. 1) S72–S81.
    1. Franks N. P. (2008). General anaesthesia: from molecular targets to neuronal pathways of sleep and arousal. Nat. Rev. Neurosci. 9 370–386. 10.1038/nrn2372
    1. Friedman E. B., Sun Y., Moore J. T., Hung H. T., Meng Q. C., Perera P., et al. (2010). A conserved behavioral state barrier impedes transitions between anesthetic-induced unconsciousness and wakefulness: evidence for neural inertia. PLoS ONE 5:e11903 10.1371/journal.pone.0011903
    1. Grasshoff C., Rudolph U., Antkowiak B. (2005). Molecular and systemic mechanisms of general anaesthesia: the ‘multi-site and multiple mechanisms’ concept. Curr. Opin. Anaesthesiol. 18 386–391.
    1. Gugino L. D., Chabot R. J., Prichep L. S., John E. R., Formanek V., Aglio L. S. (2001). Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers anaesthetized with propofol or sevoflurane. Br. J. Anaesth. 87 421–428. 10.1093/bja/87.3.421
    1. Hemmings H. C., Jr., Akabas M. H., Goldstein P. A., Trudell J. R., Orser B. A., Harrison N. L. (2005). Emerging molecular mechanisms of general anesthetic action. Trends Pharmacol. Sci. 26 503–510.
    1. Hight D. F., Dadok V. M., Szeri A. J., García P. S., Voss L., Sleigh J. W. (2014). Emergence from general anesthesia and the sleep-manifold. Front. Syst. Neurosci. 8:146 10.3389/fnsys.2014.00146
    1. Hudson A. E., Calderon D. P., Pfaff D. W., Proekt A. (2014). Recovery of consciousness is mediated by a network of discrete metastable activity states. Proc. Natl. Acad. Sci. U.S.A. 111 9283–9288. 10.1073/pnas.1408296111
    1. Ishizawa Y., Ahmed O. J., Patel S. R., Gale J. T., Sierra-Mercado D., Brown E. N., et al. (2016). Dynamics of propofol-induced loss of consciousness across primate neocortex. J. Neurosci. 36 7718–7726. 10.1523/JNEUROSCI.4577-15.2016
    1. Joiner W. J., Friedman E. B., Hung H.-T., Koh K., Sowcik M., Sehgal A., et al. (2013). Genetic and anatomical basis of the barrier separating wakefulness and anesthetic-induced unresponsiveness. PLoS Genet. 9:e1003605 10.1371/journal.pgen.1003605
    1. Kafashan M., Ching S., Palanca B. J. A. (2016). Sevoflurane alters spatiotemporal functional connectivity motifs that link resting-state networks during wakefulness. Front. Neural Circuits 10:107 10.3389/fncir.2016.00107
    1. Kajikawa Y., Schroeder C. E. (2011). How local is the local field potential? Neuron 72 847–858. 10.1016/j.neuron.2011.09.029
    1. Kashimoto S., Furuya A., Nonaka A., Oguchi T., Koshimizu M., Kumazawa T. (2006). The minimum alveolar concentration of sevoflurane in rats. Eur. J. Anaesthesiol. 14 359–361. 10.1046/j.1365-2346.1997.00092.x
    1. Kenny J., Chemali J., Cotten J., Md P., Van Dort C., Kim S.-E., et al. (2016). Physostigmine and methylphenidate induce distinct arousal states during isoflurane general anesthesia in rats. Anesth. Analg. 123 1210–1219.
    1. Ku S.-W., Lee U., Noh G.-J., Jun I.-G., Mashour G. A. (2011). Preferential inhibition of frontal-to-parietal feedback connectivity is a neurophysiologic correlate of general anesthesia in surgical patients. PLoS ONE 6:e25155 10.1371/journal.pone.0025155
    1. Lewis L. D., Weiner V. S., Mukamel E. A., Donoghue J. A., Eskandar E. N., Madsen J. R., et al. (2012). Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness. Proc. Natl. Acad. Sci. U.S.A. 109 E3377–E3386. 10.1073/pnas.1210907109
    1. Lindén H., Tetzlaff T., Potjans C. T., Klas Pettersen H., Grün S., Diesmann M., et al. (2011). Modeling the spatial reach of the LFP. Neuron 72 859–872. 10.1016/j.neuron.2011.11.006
    1. Lioudyno M. I., Birch A. M., Tanaka B. S., Sokolov Y., Goldin A. L., Chandy K. G., et al. (2013). Shaker-related potassium channels in the central medial nucleus of the thalamus are important molecular targets for arousal suppression by volatile general anesthetics. J. Neurosci. 33 16310–16322. 10.1523/JNEUROSCI.0344-13.2013
    1. Mashour G. A., Alkire M. T. (2013). Consciousness, anesthesia, and the thalamocortical system. Anesthesiology 118 13–15. 10.1097/ALN.0b013e318277a9c6
    1. McCarthy M. M., Brown E. N., Kopell N. (2008). Potential network mechanisms mediating electroencephalographic beta rhythm changes during propofol-induced paradoxical excitation. J. Neurosci. 28 13488–13504. 10.1523/JNEUROSCI.3536-08.2008
    1. Mitra P., Bokil H. (2008). Observed Brain Dynamics. New York, NY: Oxford University Press.
    1. Palanca B. J. A., Mitra A., Larson-Prior L., Snyder A. Z., Avidan M. S., Raichle M. E. (2015). Resting-state functional magnetic resonance imaging correlates of sevoflurane-induced unconsciousness. Anesthesiology 123 346–356. 10.1097/ALN.0000000000000731
    1. Paxinos G., Watson C. (2005). The Rat Brain in Stereotaxic Coordinates. Amsterdam: Elsevier Academic Press.
    1. Pilge S., Jordan D., Kreuzer M., Kochs E. F., Schneider G. (2014). Burst suppression-MAC and burst suppression-CP50 as measures of cerebral effects of anaesthetics. Br. J. Anaesth. 112 1067–1074. 10.1093/bja/aeu016
    1. Purdon P. L., Pierce E. T., Mukamel E. A., Prerau M. J., Walsh J. L., Wong K. F. K., et al. (2013). Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc. Natl. Acad. Sci. U.S.A. 110 E1142–E1151. 10.1073/pnas.1221180110
    1. Purdon P. L., Sampson A., Pavone K. J., Brown E. N. (2015). Clinical electroencephalography for anesthesiologists: part I: background and basic signatures. Anesthesiology 123 937–960. 10.1097/ALN.0000000000000841
    1. Rudolph U., Antkowiak B. (2004). Molecular and neuronal substrates for general anaesthetics. Nat. Rev. Neurosci. 5 709–720. 10.1038/nrn1496
    1. Schiff N. D. (2008). Central thalamic contributions to arousal regulation and neurological disorders of consciousness. Ann. N. Y. Acad. Sci. 1129 105–118. 10.1196/annals.1417.029
    1. Silva A., Cardoso-Cruz H., Silva F., Galhardo V., Antunes L. (2010). Comparison of anesthetic depth indexes based on thalamocortical local field potentials in rats. Anesthesiology 112 355–363. 10.1097/ALN.0b013e3181ca3196
    1. Steriade M., McCormick D. A., Sejnowski T. J. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science 262 679–685.
    1. Tononi G. (2008). Consciousness as integrated information: a provisional manifesto. Biol. Bull. 215 216–242. 10.2307/25470707
    1. Vanderwolf C. H. (1969). Hippocampal electrical activity and voluntary movement in the rat. Electroencephalogr. Clin. Neurophysiol. 26 407–418. 10.1016/0013-4694(69)90092-3

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