Characterization of cerebellar electrocortical dynamics under ether anesthesia in rats: a preliminary study using linear spectral and non-linear fractal analyses

Authors

  • Jelena Podgorac Kojadinović Department of Neurophysiology, Institute for Biological Research “Siniša Stanković” – National Institute of the Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, 11108 Belgrade, Serbia https://orcid.org/0000-0003-0393-5023
  • Branka Petković Department of Neurophysiology, Institute for Biological Research “Siniša Stanković” – National Institute of the Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, 11108 Belgrade, Serbia https://orcid.org/0000-0001-7817-4092
  • Aleksandar Kalauzi Department for Life Sciences, Institute for Multidisciplinary Research, University of Belgrade, Kneza Višeslava 1, 11030 Belgrade, Serbia https://orcid.org/0000-0003-4833-0757
  • Ljiljana Martać Department of Neurophysiology, Institute for Biological Research “Siniša Stanković” – National Institute of the Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, 11108 Belgrade, Serbia https://orcid.org/0000-0002-9931-7328
  • Slobodan Sekulić 1. Faculty of Medicine, University of Novi Sad, Hajduk Veljkova 3, 21000 Novi Sad, Serbia; 2. Department of Neurology, Clinical Center of Vojvodina, Hajduk Veljkova 1-7, 21000 Novi Sad, Serbia https://orcid.org/0000-0001-7889-310X
  • Gordana Stojadinović Department of Neurophysiology, Institute for Biological Research “Siniša Stanković” – National Institute of the Republic of Serbia, University of Belgrade, Bulevar despota Stefana 142, 11108 Belgrade, Serbia https://orcid.org/0000-0001-7155-165X

Keywords:

ether anesthesia, cerebellum, electrocortical activity, wireless recording

Abstract

Paper description:

  • Ether, as a general anesthetic, influences the activity of cortical and subcortical regions.
  • Cerebellar electrocortical activity of rats after ether anesthesia was recorded telemetrically with a 16-channel microelectrode system and evaluated by linear and non-linear signal analysis.
  • A transitional period between different depths of anesthesia was evident between the 17th and 19th minute post-induction, suggesting progressive neural reorganization associated with the emergence from the anesthetized state.
  • Monitoring anesthetic depth using linear and non-linear signal analysis could assess patterns of electrocortical activity in the cerebellum and other brain regions.

Abstract: The temporal evolution of cerebellar electrocortical activity during emergence from ether anesthesia was investigated using both linear and non-linear analytical methods. Adult male rats underwent operative craniotomy and implantation of a 16-channel microelectrode array targeting the paravermal cerebellar cortex. Following a 7-day recovery period, animals were exposed to ether via inhalation, and cerebellar electrocortical signals were recorded using a wireless acquisition system. Data were quantified through relative spectral power (RSP) across defined frequency bands, Higuchi’s fractal dimension (HFD), and the Hurst exponent (H). A transitional phase between anesthetic depths was identified between the 17th and 19th min post-exposure. This period was characterized by a significant increase in RSP within low-frequency bands and a corresponding decrease in high-frequency bands beginning at the 17th min. Additionally, a marked decrease in HFD and an increase in H were observed at the 19th min, followed by a moderate rebound in HFD and a reduction in H. These findings suggest that non-linear dynamic measures, particularly HFD and H, may offer greater temporal precision in identifying the onset of awakening compared to conventional spectral analysis, highlighting their potential utility in monitoring anesthetic depth.

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References

Hudetz AG, Mashour GA. Disconnecting consciousness: Is there a common anesthetic end point? Anesth Analg. 2016;123(5):1228-40. https://doi.org/10.1213/ANE.0000000000001353

Son Y. Molecular mechanisms of general anesthesia. Korean J Anesthesiol. 2010;59(1):3-8. https://doi.org/10.4097/kjae.2010.59.1.3

Klonowski W, Stepien P, Stepien R. Complexity measures of brain electrophysiological activity: In consciousness, under anesthesia, during epileptic seizure, and in physiological sleep. J Psychophysiol. 2010;24(2):131-5. https://doi.org/10.1027/0269-8803/a000024

Bonhomme V, Staquet C, Montupil J, Defresne A, Kirsch M, Martial C, Vanhaudenhuyse A, Chatelle C, Larroque SK, Raimondo F, Demertzi A, Bodart O, Laureys S, Gosseries O. General anesthesia: A probe to explore consciousness. Front Syst Neurosci. 2019;13:36. https://doi.org/10.3389/fnsys.2019.00036

Alkire MT, Hudetz AG, Tononi G. Consciousness and anesthesia. Science. 2008;322(5903):876-80. https://doi.org/10.1126/science.1149213

Herculano-Houzel S. The human brain in numbers: a linearly scaled-up primate brain. Front Hum Neurosci. 2009;3:31. https://doi.org/10.3389/neuro.09.031.2009

Herculano-Houzel S. Coordinated scaling of cortical and cerebellar numbers of neurons. Front Neuroanat. 2010;4:12. https://doi.org/10.3389/fnana.2010.00012

Clausi S, Iacobacci C, Lupo M, Olivito G, Molinari M, Leggio M. The role of the cerebellum in unconscious and conscious processing of emotions: A review. Appl Sci. 2017;7(5):521. https://doi.org/10.3390/app7050521

Tononi G, Koch C. Consciousness: here, there and everywhere?. Philos Trans R Soc Lond B Biol Sci. 2015;370(1668):20140167. https://doi.org/10.1098/rstb.2014.0167

Tononi G. Consciousness, information integration, and the brain. Prog Brain Res. 2005;150:109-26. https://doi.org/10.1016/S0079-6123(05)50009-8

Dorokhov VB, Malakhov DG, Orlov VA, Ushakov VL. Experimental model of study of consciousness at the awakening: FMRI, EEG and behavioral methods. In: Samsonovich A, editor. Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing Vol. 848. Cham: Springer; 2019. p. 82-7. https://doi.org/10.1007/978-3-319-99316-4_11

Forman SA, Chin VA. General anesthetics and molecular mechanisms of unconsciousness. Int Anesthesiol Clin. 2008;46(3):43-53. https://doi.org/10.1097/AIA.0b013e3181755da5

Dow RS. The electrical activity of the cerebellum and its functional significance. J Physiol. 1938;94(1):67-86. https://doi.org/10.1113/jphysiol.1938.sp003663

Swank RL, Watson CW. Effects of barbiturates and ether on spontaneous electrical activity of dog brain. J Neurophysiol. 1949;12(2):137-60. https://doi.org/10.1152/jn.1949.12.2.137

Domino EF, Ueki S. Differential effects of general anesthetics on spontaneous electrical activity of neocortical and rhinencephalic brain systems of the dog. J Pharmacol Exp Ther. 1959;127:288-304.

Persson A, Peterson E, Wåhlin A. EEG-changes during general anaesthesia with enflurane (Efrane) in comparison with ether. Acta Anaesthesiol Scand. 1978;22(4):339-48. https://doi.org/10.1111/j.1399-6576.1978.tb01309.x

Jameson LC, Sloan TB. Using EEG to monitor anesthesia drug effects during surgery. J Clin Monit Comput. 2006;20(6):445-72. https://doi.org/10.1007/s10877-006-9044-x

Johansen JW. Update on bispectral index monitoring. Best Pract Res Clin Anaesthesiol. 2006;20(1):81-99. https://doi.org/10.1016/j.bpa.2005.08.004

Animal Care and Use Committee. Use of Ether for Animal Anesthesia at Johns Hopkins University (Revised by the JHU Joint Heath Safety and Environment/Animal Care and Use Committee 02/22/06), 2006. http://web.jhu.edu/animalcare/policies/ether.html

Quiroga RQ, Arnhold J, Grassberger P. Learning driver-response relationships from synchronization patterns. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000;61(5 Pt A):5142-8. https://doi.org/10.1103/physreve.61.5142

Higuchi T. Approach to an irregular time series on the basis of the fractal theory. Physica D. 1988;31(2):277-83. https://doi.org/10.1016/0167-2789(88)90081-4

Spasic S, Kalauzi A, Grbic G, Martac L, Culic M. Fractal analysis of rat brain activity after injury. Med Biol Eng Comput. 2005;43(3):345-8. https://doi.org/10.1007/BF02345811

Klonowski W, Olejarczyk E, Stepien R, Jalowiecki P, Rudner R. Monitoring the depth of anaesthesia using fractal complexity method. In: Novak MN, editor. Complexus mundi. emergent patterns in nature. New Jersey, London, Singapore: World Scientific, 2006. p. 333-42. https://doi.org/10.1142/9789812774217_0031

Mandelbrot B, Wallis JR. Robustness of the rescaled range R/S in the measurement of noncyclic long-run statistical dependence. Water Resour Res. 1969;5(5):967-88. https://doi.org/10.1029/WR005i005p00967

Carbone A. Algorithm to estimate the Hurst exponent of high-dimensional fractals. Phys Rev E Stat Nonlin Soft Matter Phys. 2007;76(5 Pt 2):056703. https://doi.org/10.1103/PhysRevE.76.056703

Natarajan K, Acharya U R, Alias F, Tiboleng T, Puthusserypady SK. Nonlinear analysis of EEG signals at different mental states. Biomed Eng Online. 2004;3(1):7. https://doi.org/10.1186/1475-925X-3-7

Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol. 2005;116(10):2266-301. https://doi.org/10.1016/j.clinph.2005.06.011

Widman G, Schreiber T, Rehberg B, Hoeft A, Elger CE. Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000;62(4 Pt A):4898-903. https://doi.org/10.1103/physreve.62.4898

Otto KA. EEG power spectrum analysis for monitoring depth of anaesthesia during experimental surgery. Lab Anim. 2008;42(1):45-61. https://doi.org/10.1258/la.2007.006025

Hagihira S. Changes in the electroencephalogram during anaesthesia and their physiological basis. Br J Anaesth. 2015;115 Suppl 1:i27-i31. https://doi.org/10.1093/bja/aev212

Cartailler J, Parutto P, Touchard C, Vallée F, Holcman D. Alpha rhythm collapse predicts iso-electric suppressions during anesthesia. Commun Biol. 2019;2:327. https://doi.org/10.1038/s42003-019-0575-3

Hudetz AG, Vizuete JA, Pillay S. Differential effects of isoflurane on high-frequency and low-frequency γ oscillations in the cerebral cortex and hippocampus in freely moving rats. Anesthesiology. 2011;114(3):588-95. https://doi.org/10.1097/ALN.0b013e31820ad3f9

Sorrenti V, Cecchetto C, Maschietto M, Fortinguerra S, Buriani A, Vassanelli S. Understanding the Effects of Anesthesia on Cortical Electrophysiological Recordings: A Scoping Review. Int J Mol Sci. 2021;22(3):1286. https://doi.org/10.3390/ijms22031286

Nguyen-Ky T, Wen P, Li Y. Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods. IET Signal Process. 2014;8(9):907-17. https://doi.org/10.1049/iet-spr.2013.0113

Varley TF, Craig M, Adapa R, Finoia P, Williams G, Allanson J, Pickard J, Menon DK, Stamatakis EA. Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness. PLoS One. 2020;15(2):e0223812. https://doi.org/10.1371/journal.pone.0223812

Daly I, Williams D, Hwang F, Kirke A, Miranda ER, Nasuto SJ. Electroencephalography reflects the activity of sub-cortical brain regions during approach-withdrawal behaviour while listening to music. Sci Rep. 2019;9(1):9415. https://doi.org/10.1038/s41598-019-45105-2

Phillips JR, Hewedi DH, Eissa AM, Moustafa AA. The cerebellum and psychiatric disorders. Front Public Health. 2015;3:66. https://doi.org/10.3389/fpubh.2015.00066

Ros H, Sachdev RN, Yu Y, Sestan N, McCormick DA. Neocortical networks entrain neuronal circuits in cerebellar cortex. J Neurosci. 2009;29(33):10309-20. https://doi.org/10.1523/JNEUROSCI.2327-09.2009

Kekovic G, Stojadinovic G, Martac L, Podgorac J, Sekulic S, Culic M. Spectral and fractal measures of cerebellar and cerebral activity in various types of anesthesia. Acta Neurobiol Exp (Wars). 2010;70(1):67-75. https://doi.org/10.55782/ane-2010-1775

Purdon PL, Sampson A, Pavone KJ, Brown EN. Clinical electroencephalography for anesthesiologists: Part I: Background and basic signatures. Anesthesiology. 2015;123(4):937-60. https://doi.org/10.1097/ALN.0000000000000841

De Zeeuw CI, Hoebeek FE, Schonewille M. Causes and consequences of oscillations in the cerebellar cortex. Neuron. 2008;58(5):655-8. https://doi.org/10.1016/j.neuron.2008.05.019

Gandolfi D, Lombardo P, Mapelli J, Solinas S, D'Angelo E. θ-Frequency resonance at the cerebellum input stage improves spike timing on the millisecond time-scale. Front Neural Circuits. 2013;7:64. https://doi.org/10.3389/fncir.2013.00064

Spasic S, Kalauzi A, Kesic S, Obradovic M, Saponjic J. Surrogate data modeling the relationship between high frequency amplitudes and Higuchi fractal dimension of EEG signals in anesthetized rats. J Theor Biol. 2011;289:160-6. https://doi.org/10.1016/j.jtbi.2011.08.037

Zappasodi F, Marzetti L, Olejarczyk E, Tecchio F, Pizzella V. Age-related changes in electroencephalographic signal complexity. PLoS One. 2015;10(11):e0141995. https://doi.org/10.1371/journal.pone.0141995

Kalauzi A, Bojić T, Vuckovic A. Modeling the relationship between Higuchi's fractal dimension and Fourier spectra of physiological signals. Med Biol Eng Comput. 2012;50(7):689-99. https://doi.org/10.1007/s11517-012-0913-9

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Published

2025-06-26

How to Cite

1.
Podgorac Kojadinović J, Petković B, Kalauzi A, Martać L, Sekulić S, Stojadinović G. Characterization of cerebellar electrocortical dynamics under ether anesthesia in rats: a preliminary study using linear spectral and non-linear fractal analyses. Arch Biol Sci [Internet]. 2025Jun.26 [cited 2025Aug.27];77(2):137-45. Available from: https://www.serbiosoc.org.rs/arch/index.php/abs/article/view/10824

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Vol. 77 No. 2 (2025)

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Copyright (c) 2025 Jelena Podgorac, Branka Petković, Aleksandar Kalauzi, Ljiljana Martać, Slobodan Sekulić, Gordana Stojadinović

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