Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity

  1. Thijs L van der Plas
  2. Jérôme Tubiana
  3. Guillaume Le Goc
  4. Geoffrey Migault
  5. Michael Kunst
  6. Herwig Baier
  7. Volker Bormuth
  8. Bernhard Englitz
  9. Georges Debrégeas  Is a corresponding author
  1. University of Oxford, United Kingdom
  2. Tel Aviv University, Israel
  3. Sorbonne Université, CNRS, France
  4. Max Planck Institute of Neurobiology, Germany
  5. Radboud University Nijmegen, Netherlands

Abstract

Patterns of endogenous activity in the brain reflect a stochastic exploration of the neuronal state space that is constrained by the underlying assembly organization of neurons. Yet it remains to be shown that this interplay between neurons and their assembly dynamics indeed suffices to generate whole-brain data statistics. Here we recorded the activity from ∼ 40, 000 neurons simultaneously in zebrafish larvae, and show that a data-driven generative model of neuron-assembly interactions can accurately reproduce the mean activity and pairwise correlation statistics of their spontaneous activity. This model, the compositional Restricted Boltzmann Machine (cRBM), unveils ∼200 neural assemblies, which compose neurophysiological circuits and whose various combinations form successive brain states. We then performed in silico perturbation experiments to determine the interregional functional connectivity, which is conserved across individual animals and correlates well with structural connectivity. Our results showcase how cRBMs can capture the coarse-grained organization of the zebrafish brain. Notably, this generative model can readily be deployed to parse neural data obtained by other large-scale recording techniques.

Data availability

The cRBM model has been developed in Python 3.7 and is available at:https://github.com/jertubiana/PGM. An extensive example notebook that implements this model is also provided.Calcium imaging data pre-processing was performed in MATLAB (Mathworks) using previously published protocols and software (Panier et al., 2013; Wolf et al., 2017; Migault et al., 2018; Tubiana et al., 2020). The functional data recordings, the trained cRBM models and the structural and functional connectivity matrix are available at https://gin.g-node.org/vdplasthijs/cRBM_zebrafish_spontaneous_data .Figures of neural assemblies or neurons (Figure 1, 3) were made using the Fishualizer, which is a 4D (space + time) data visualization software package that we have previously published (Migault et al., 2018), available at https://bitbucket.org/benglitz/fishualizer_publicMinor updates were implemented to tailor the Fishualizer for viewing assemblies, which can be found at https://bitbucket.org/benglitz/fishualizer_public/src/assembly_viewer/All other data analysis and visualization was performed in Python 3.7 using standard packages (numpy (Harris et al., 2020), scipy (Virtanen et al., 2020), scikit-learn (Pedregosa et al., 2011), matplotlib (Hunter, 2007), pandas (McKinney et al., 2010), seaborn (Waskom, 2021), h5py). The corresponding code is available at https://github.com/vdplasthijs/zf-rbm.

The following data sets were generated

Article and author information

Author details

  1. Thijs L van der Plas

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5490-1785
  2. Jérôme Tubiana

    Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8878-5620
  3. Guillaume Le Goc

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6946-1142
  4. Geoffrey Migault

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  5. Michael Kunst

    Department of Genes, Circuits, Behavior, Max Planck Institute of Neurobiology, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Herwig Baier

    Department of Genes, Circuits, Behavior, Max Planck Institute of Neurobiology, Martinsried, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7268-0469
  7. Volker Bormuth

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
  8. Bernhard Englitz

    Donders Center for Neuroscience, Radboud University Nijmegen, Nijmegen, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9106-0356
  9. Georges Debrégeas

    Laboratoire Jean Perrin, Sorbonne Université, CNRS, Paris, France
    For correspondence
    georges.debregeas@sorbonne-universite.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3698-4497

Funding

Erasmus+

  • Thijs L van der Plas

Biotechnology and Biological Sciences Research Council (BB/M011224/1)

  • Thijs L van der Plas

Edmond J. Safra Center for Bioinformatics at Tel Aviv University

  • Jérôme Tubiana

Human Frontier Science Program (LT001058/2019-C)

  • Jérôme Tubiana

NWO-VIDI

  • Bernhard Englitz

ERC (715980)

  • Volker Bormuth

HFSP (RGP0060/2017)

  • Georges Debrégeas

Nederlandse Organisatie voor Wetenschappelijk Onderzoek) (016.VIDI.189.052)

  • Bernhard Englitz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: Experiments were approved by Le Comité d'Ethique pour l'Expérimentation Animale Charles Darwin C2EA-05 (02601.01).

Copyright

© 2023, van der Plas et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,129
    views
  • 302
    downloads
  • 18
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Thijs L van der Plas
  2. Jérôme Tubiana
  3. Guillaume Le Goc
  4. Geoffrey Migault
  5. Michael Kunst
  6. Herwig Baier
  7. Volker Bormuth
  8. Bernhard Englitz
  9. Georges Debrégeas
(2023)
Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity
eLife 12:e83139.
https://doi.org/10.7554/eLife.83139

Share this article

https://doi.org/10.7554/eLife.83139

Further reading

    1. Neuroscience
    Vincent Huson, Wade G Regehr
    Research Article

    Unipolar brush cells (UBCs) are excitatory interneurons in the cerebellar cortex that receive mossy fiber (MF) inputs and excite granule cells. The UBC population responds to brief burst activation of MFs with a continuum of temporal transformations, but it is not known how UBCs transform the diverse range of MF input patterns that occur in vivo. Here, we use cell-attached recordings from UBCs in acute cerebellar slices to examine responses to MF firing patterns that are based on in vivo recordings. We find that MFs evoke a continuum of responses in the UBC population, mediated by three different types of glutamate receptors that each convey a specialized component. AMPARs transmit timing information for single stimuli at up to 5 spikes/s, and for very brief bursts. A combination of mGluR2/3s (inhibitory) and mGluR1s (excitatory) mediates a continuum of delayed, and broadened responses to longer bursts, and to sustained high frequency activation. Variability in the mGluR2/3 component controls the time course of the onset of firing, and variability in the mGluR1 component controls the duration of prolonged firing. We conclude that the combination of glutamate receptor types allows each UBC to simultaneously convey different aspects of MF firing. These findings establish that UBCs are highly flexible circuit elements that provide diverse temporal transformations that are well suited to contribute to specialized processing in different regions of the cerebellar cortex.

    1. Neuroscience
    Eleni Hackwell, Sharon R Ladyman ... David R Grattan
    Research Article

    The specific role that prolactin plays in lactational infertility, as distinct from other suckling or metabolic cues, remains unresolved. Here, deletion of the prolactin receptor (Prlr) from forebrain neurons or arcuate kisspeptin neurons resulted in failure to maintain normal lactation-induced suppression of estrous cycles. Kisspeptin immunoreactivity and pulsatile LH secretion were increased in these mice, even in the presence of ongoing suckling stimulation and lactation. GCaMP fibre photometry of arcuate kisspeptin neurons revealed that the normal episodic activity of these neurons is rapidly suppressed in pregnancy and this was maintained throughout early lactation. Deletion of Prlr from arcuate kisspeptin neurons resulted in early reactivation of episodic activity of kisspeptin neurons prior to a premature return of reproductive cycles in early lactation. These observations show dynamic variation in arcuate kisspeptin neuronal activity associated with the hormonal changes of pregnancy and lactation, and provide direct evidence that prolactin action on arcuate kisspeptin neurons is necessary for suppressing fertility during lactation in mice.