Theta-Nested Gamma Oscillations in Next Generation Neural Mass Models.

Marco Segneri, Hongjie Bi, Simona Olmi, Alessandro Torcini
Author Information
  1. Marco Segneri: Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France.
  2. Hongjie Bi: Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France.
  3. Simona Olmi: Inria Sophia Antipolis Méditerranée Research Centre, Valbonne, France.
  4. Alessandro Torcini: Laboratoire de Physique Théorique et Modélisation, Université de Cergy-Pontoise, CNRS, UMR 8089, Cergy-Pontoise, France.

Abstract

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling (CFC) among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: namely, the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms always display phase amplitude coupling. However, two different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behavior; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing intensity and which are clearly modulated by the theta phase. Furthermore, analogously to the experiments, the gamma power and the frequency of the gamma-power peak increase with the forcing amplitude. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in our model, only by incrementing, at the same time, also the stimulation power. An effect achieved by increasing the amplitude either of the noise or of the forcing term proportionally to the theta frequency. On the basis of our analysis both the PING and the ING mechanism give rise to theta-nested gamma oscillations with almost identical features.

Keywords

References

  1. PLoS Comput Biol. 2017 Dec 29;13(12):e1005881 [PMID: 29287081]
  2. PLoS One. 2014 Aug 19;9(8):e102591 [PMID: 25136855]
  3. Hippocampus. 2005;15(7):913-22 [PMID: 16161035]
  4. Hippocampus. 2013 Dec;23(12):1269-79 [PMID: 23832676]
  5. Phys Rev E. 2017 Oct;96(4-1):042311 [PMID: 29347566]
  6. J Neurosci. 1995 Jan;15(1 Pt 1):47-60 [PMID: 7823151]
  7. Neuron. 2014 Jan 8;81(1):140-52 [PMID: 24333053]
  8. Neuron. 2013 Jan 9;77(1):141-54 [PMID: 23312522]
  9. Curr Opin Neurobiol. 2015 Apr;31:45-50 [PMID: 25168855]
  10. Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Jul;90(1):010901 [PMID: 25122239]
  11. Nat Neurosci. 2012 May;15(5):763-8 [PMID: 22466505]
  12. Brain Res. 1998 Jun 15;796(1-2):327-31 [PMID: 9689489]
  13. Nature. 1995 Feb 16;373(6515):612-5 [PMID: 7854418]
  14. Elife. 2016 Dec 07;5: [PMID: 27925581]
  15. Neuron. 2013 Mar 20;77(6):1002-16 [PMID: 23522038]
  16. Nat Rev Neurosci. 2007 Jan;8(1):45-56 [PMID: 17180162]
  17. Cereb Cortex. 2016 Jan;26(1):268-278 [PMID: 25316340]
  18. J Neurophysiol. 2019 Feb 1;121(2):444-458 [PMID: 30517044]
  19. Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Apr;81(4 Pt 2):046119 [PMID: 20481798]
  20. J Neurosci. 2012 Jan 11;32(2):423-35 [PMID: 22238079]
  21. J Neurosci. 1998 Jan 1;18(1):388-98 [PMID: 9412515]
  22. J Neurosci. 2012 May 23;32(21):7373-83 [PMID: 22623683]
  23. Trends Neurosci. 2015 Nov;38(11):725-740 [PMID: 26549886]
  24. Neuron. 2006 Oct 5;52(1):155-68 [PMID: 17015233]
  25. Chaos. 2020 May;30(5):053121 [PMID: 32491891]
  26. Neural Comput. 2013 Dec;25(12):3207-34 [PMID: 24047318]
  27. J Neurosci. 2016 Apr 13;36(15):4155-69 [PMID: 27076416]
  28. J Psychiatry Neurosci. 2010 Mar;35(2):75-7 [PMID: 20184803]
  29. Biophys J. 1972 Jan;12(1):1-24 [PMID: 4332108]
  30. Proc Natl Acad Sci U S A. 2019 Apr 9;116(15):7477-7482 [PMID: 30910984]
  31. Neuron. 2009 Oct 29;64(2):281-9 [PMID: 19874794]
  32. J Physiol Paris. 2011 Jan-Jun;105(1-3):2-15 [PMID: 21907800]
  33. Annu Rev Neurosci. 2012;35:203-25 [PMID: 22443509]
  34. Phys Rev E. 2018 Jan;97(1-1):012209 [PMID: 29448391]
  35. Chaos. 2018 Oct;28(10):101101 [PMID: 30384615]
  36. Trends Neurosci. 2007 Jul;30(7):309-16 [PMID: 17555828]
  37. PLoS One. 2017 Apr 5;12(4):e0173776 [PMID: 28380064]
  38. Neural Comput. 1999 Oct 1;11(7):1621-71 [PMID: 10490941]
  39. Science. 2006 Sep 15;313(5793):1626-8 [PMID: 16973878]
  40. Biol Cybern. 2008 Aug;99(2):139-57 [PMID: 18688638]
  41. Trends Cogn Sci. 2010 Nov;14(11):506-15 [PMID: 20932795]
  42. Phys Rev Lett. 2018 Sep 21;121(12):128301 [PMID: 30296134]
  43. Nature. 2009 Nov 19;462(7271):353-7 [PMID: 19924214]
  44. PLoS Comput Biol. 2019 May 9;15(5):e1007019 [PMID: 31071085]
  45. Neuron. 2009 Sep 24;63(6):727-32 [PMID: 19778503]
  46. Trends Cogn Sci. 2007 Jul;11(7):267-9 [PMID: 17548233]
  47. Phys Rev Lett. 2018 Jun 29;120(26):264101 [PMID: 30004770]
  48. Eur J Neurosci. 2018 Oct;48(8):2795-2806 [PMID: 29356162]

Word Cloud

Created with Highcharts 10.0.0gammaoscillationsthetaexperimentsfrequencycouplingneuralforcingrhythmsINGtheta-nestedphaseamplitudemechanismstimulationfeaturescross-frequencyreproducefindingsmassmodelsabledynamicstwoset-upsinterneuronalnetworkPINGcanonelockinganalysisfrequenciesproportionallypowergamma-powerpeakincreasingeffectTheta-nestedreportedmanyareasbrainbelievedrepresentfundamentaltransferinformationacrossspatialtemporalscalesseriesrecentpossiblereplicateoptogeneticseveralCFCamongobservedbehavinganimalsordermainconsiderednewclassexactlymacroscopicspikingnetworksframeworkexaminedsupportcollectiveoscillations:namelypyramidalobserveemergencedrivingsystemsinusoidaltheta-forcingproximityHopfbifurcationmixedalwaysdisplayHoweverdifferenttypesnestedidentified:characterizedperfectcorrespondingoverallperiodicbehavioranotherimperfectquasi-periodicevenchaoticemergeslockedstatesfrequentset-upagreementfindrange[1:10]HzwhoseamplitudesgrowintensityclearlymodulatedFurthermoreanalogouslyincreasevarianceexperimentalshifthigherobtainedmodelincrementingtimealsoachievedeithernoisetermbasisgiverisealmostidenticalTheta-NestedGammaOscillationsNextGenerationNeuralMassModelshippocampusphase-amplitudequadraticintegrate-and-fireneuron

Similar Articles

Cited By (18)