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

  • 2,162
    views
  • 304
    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
    William T Redman, Santiago Acosta-Mendoza ... Michael J Goard
    Research Article

    Although grid cells are one of the most well-studied functional classes of neurons in the mammalian brain, whether there is a single orientation and spacing value per grid module has not been carefully tested. We analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the encoding of local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that grid property variability of a similar magnitude to the analyzed data leads to significantly decreased decoding error. This holds even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.

    1. Genetics and Genomics
    2. Neuroscience
    Monique Marylin Alves de Almeida, Yves De Repentigny ... Rashmi Kothary
    Research Article

    Spinal muscular atrophy (SMA) is caused by mutations in the Survival Motor Neuron 1 (SMN1) gene. While traditionally viewed as a motor neuron disorder, there is involvement of various peripheral organs in SMA. Notably, fatty liver has been observed in SMA mouse models and SMA patients. Nevertheless, it remains unclear whether intrinsic depletion of SMN protein in the liver contributes to pathology in the peripheral or central nervous systems. To address this, we developed a mouse model with a liver-specific depletion of SMN by utilizing an Alb-Cre transgene together with one Smn2B allele and one Smn1 exon 7 allele flanked by loxP sites. Initially, we evaluated phenotypic changes in these mice at postnatal day 19 (P19), when the severe model of SMA, the Smn2B/- mice, exhibit many symptoms of the disease. The liver-specific SMN depletion does not induce motor neuron death, neuromuscular pathology or muscle atrophy, characteristics typically observed in the Smn2B/- mouse at P19. However, mild liver steatosis was observed, although no changes in liver function were detected. Notably, pancreatic alterations resembled that of Smn2B/-mice, with a decrease in insulin-producing β-cells and an increase in glucagon-producingα-cells, accompanied by a reduction in blood glucose and an increase in plasma glucagon and glucagon-like peptide (GLP-1). These changes were transient, as mice at P60 exhibited recovery of liver and pancreatic function. While the mosaic pattern of the Cre-mediated excision precludes definitive conclusions regarding the contribution of liver-specific SMN depletion to overall tissue pathology, our findings highlight an intricate connection between liver function and pancreatic abnormalities in SMA.