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The medial septum controls hippocampal supra-theta oscillations

Author

Listed:
  • Bálint Király

    (Institute of Experimental Medicine
    Eötvös Loránd University)

  • Andor Domonkos

    (Institute of Experimental Medicine)

  • Márta Jelitai

    (Institute of Experimental Medicine)

  • Vítor Lopes-dos-Santos

    (University of Oxford)

  • Sergio Martínez-Bellver

    (Institute of Experimental Medicine
    University of Valencia)

  • Barnabás Kocsis

    (Institute of Experimental Medicine
    Pázmány Péter Catholic University)

  • Dániel Schlingloff

    (Institute of Experimental Medicine)

  • Abhilasha Joshi

    (University of Oxford)

  • Minas Salib

    (University of Oxford)

  • Richárd Fiáth

    (Pázmány Péter Catholic University
    Research Centre for Natural Sciences)

  • Péter Barthó

    (Research Centre for Natural Sciences)

  • István Ulbert

    (Pázmány Péter Catholic University
    Research Centre for Natural Sciences)

  • Tamás F. Freund

    (Institute of Experimental Medicine)

  • Tim J. Viney

    (University of Oxford)

  • David Dupret

    (University of Oxford)

  • Viktor Varga

    (Institute of Experimental Medicine)

  • Balázs Hangya

    (Institute of Experimental Medicine)

Abstract

Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.

Suggested Citation

  • Bálint Király & Andor Domonkos & Márta Jelitai & Vítor Lopes-dos-Santos & Sergio Martínez-Bellver & Barnabás Kocsis & Dániel Schlingloff & Abhilasha Joshi & Minas Salib & Richárd Fiáth & Péter Barthó , 2023. "The medial septum controls hippocampal supra-theta oscillations," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41746-0
    DOI: 10.1038/s41467-023-41746-0
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    References listed on IDEAS

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    1. Sanja Mikulovic & Carlos Ernesto Restrepo & Samer Siwani & Pavol Bauer & Stefano Pupe & Adriano B. L. Tort & Klas Kullander & Richardson N. Leão, 2018. "Ventral hippocampal OLM cells control type 2 theta oscillations and response to predator odor," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    2. Guillaume Etter & Suzanne van der Veldt & Frédéric Manseau & Iman Zarrinkoub & Emilie Trillaud-Doppia & Sylvain Williams, 2019. "Optogenetic gamma stimulation rescues memory impairments in an Alzheimer’s disease mouse model," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    3. Lihui Lu & Yuqi Ren & Tao Yu & Zhixiang Liu & Sice Wang & Lubin Tan & Jiawei Zeng & Qiru Feng & Rui Lin & Yang Liu & Qingchun Guo & Minmin Luo, 2020. "Control of locomotor speed, arousal, and hippocampal theta rhythms by the nucleus incertus," Nature Communications, Nature, vol. 11(1), pages 1-16, December.
    4. Laura Lee Colgin & Tobias Denninger & Marianne Fyhn & Torkel Hafting & Tora Bonnevie & Ole Jensen & May-Britt Moser & Edvard I. Moser, 2009. "Frequency of gamma oscillations routes flow of information in the hippocampus," Nature, Nature, vol. 462(7271), pages 353-357, November.
    5. D. Kvitsiani & S. Ranade & B. Hangya & H. Taniguchi & J. Z. Huang & A. Kepecs, 2013. "Distinct behavioural and network correlates of two interneuron types in prefrontal cortex," Nature, Nature, vol. 498(7454), pages 363-366, June.
    6. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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