Mesoscale cortex-wide neural dynamics predict goal-directed, but not random actions in mice several seconds prior to movement

  1. Catalin Mitelut  Is a corresponding author
  2. Yongxu Zhang
  3. Yuki Sekino
  4. Jamie D Boyd
  5. Federico Bollanos
  6. Nicholas V Swindale
  7. Greg Silasi
  8. Shreya Saxena
  9. Timothy H Murphy  Is a corresponding author
  1. University of British Columbia, Canada
  2. University of Florida, United States
  3. University of Ottawa, Canada

Abstract

Volition - the sense of control or agency over one's voluntary actions - is widely recognized as the basis of both human subjective experience and natural behavior in non-human animals. Several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls could be predicted between 3 to 5 seconds (or more in some cases) prior to movement. We found inhibition of motor cortex starting at approximately 5sec prior to lever pulls and activation of motor cortex starting at approximately 2sec prior to a random unrewarded left limb movement. We show that mice, like humans, are biased towards commencing self-initiated actions during specific phases of neural activity but that the pre-movement neural code changes over time in some mice and is widely distributed as behavior prediction improved when using all vs single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding self-initiated action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated action could be preserved between mice and humans.

Data availability

Code for generating all figures is provided here:https://github.com/catubc/elife_self_init_paperDatasets are provided on Dryad under the information below:Mitelut, Catalin (2022), Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement, Dryad,Dataset, https://doi.org/10.5061/dryad.ttdz08m0z

The following data sets were generated

Article and author information

Author details

  1. Catalin Mitelut

    Department of Psychiatry, University of British Columbia, Vancouver, Canada
    For correspondence
    mitelutco@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0471-9816
  2. Yongxu Zhang

    Department of Engineering, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Yuki Sekino

    Department of Psychiatry, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2038-274X
  4. Jamie D Boyd

    Department of Psychiatry, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  5. Federico Bollanos

    Department of Psychiatry, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
  6. Nicholas V Swindale

    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7106-5114
  7. Greg Silasi

    Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada
    Competing interests
    The authors declare that no competing interests exist.
  8. Shreya Saxena

    Department of Engineering, University of Florida, Gainesville, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Timothy H Murphy

    Department of Psychiatry, University of British Columbia, Vancouver, Canada
    For correspondence
    thmurphy@mail.ubc.ca
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0093-4490

Funding

Canadian Institutes of Health Research (MOP-15360)

  • Catalin Mitelut
  • Yongxu Zhang
  • Yuki Sekino
  • Jamie D Boyd
  • Federico Bollanos
  • Nicholas V Swindale
  • Greg Silasi
  • Timothy H Murphy

Canadian Institutes of Health Research (MOP-12675)

  • Catalin Mitelut
  • Yongxu Zhang
  • Yuki Sekino
  • Jamie D Boyd
  • Federico Bollanos
  • Nicholas V Swindale
  • Greg Silasi
  • Shreya Saxena
  • Timothy H Murphy

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

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Ethics

Animal experimentation: Mouse protocols were approved by the University of British Columbia Animal Care Committee and followed the Canadian Council on Animal Care and Use guidelines (protocols A13-0336 and A14-0266).

Version history

  1. Received: December 18, 2021
  2. Preprint posted: December 20, 2021 (view preprint)
  3. Accepted: November 2, 2022
  4. Accepted Manuscript published: November 3, 2022 (version 1)
  5. Version of Record published: November 17, 2022 (version 2)

Copyright

© 2022, Mitelut 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

  • 1,246
    views
  • 188
    downloads
  • 7
    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. Catalin Mitelut
  2. Yongxu Zhang
  3. Yuki Sekino
  4. Jamie D Boyd
  5. Federico Bollanos
  6. Nicholas V Swindale
  7. Greg Silasi
  8. Shreya Saxena
  9. Timothy H Murphy
(2022)
Mesoscale cortex-wide neural dynamics predict goal-directed, but not random actions in mice several seconds prior to movement
eLife 11:e76506.
https://doi.org/10.7554/eLife.76506

Share this article

https://doi.org/10.7554/eLife.76506

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Weichen Song, Yongyong Shi, Guan Ning Lin
    Tools and Resources

    We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

    1. Computational and Systems Biology
    Qianmu Yuan, Chong Tian, Yuedong Yang
    Tools and Resources

    Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven’t fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.