Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivity
- University of Oxford, United Kingdom
- Tel Aviv University, Israel
- Sorbonne Université, CNRS, France
- Max Planck Institute of Neurobiology, Germany
- 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.
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Data from: Neural assemblies uncovered by generative modeling explain whole-brain activity statistics and reflect structural connectivityGIN, https://gin.g-node.org/vdplasthijs/cRBM_zebrafish_spontaneous_data.
Article and author information
Author details
Thijs L van der Plas
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.
Reviewing Editor
- Peter Latham, University College London, United Kingdom
Ethics
Animal experimentation: Experiments were approved by Le Comité d'Ethique pour l'Expérimentation Animale Charles Darwin C2EA-05 (02601.01).
Version history
- Preprint posted: November 11, 2021 (view preprint)
- Received: September 21, 2022
- Accepted: January 15, 2023
- Accepted Manuscript published: January 17, 2023 (version 1)
- Version of Record published: February 20, 2023 (version 2)
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.
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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
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