Neural population dynamics underlying evidence accumulation in multiple rat brain regions

  1. Brian DePasquale  Is a corresponding author
  2. Carlos D Brody  Is a corresponding author
  3. Jonathan W Pillow
  1. Princeton University, United States
  2. Princeton University, Howard Hughes Medical Institute, United States

Abstract

Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions - the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS) - while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal's choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally-inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.

Data availability

All data analyzed during this study and original analysis computer code has been deposited at https://github.com/Brody-Lab/DePasquale-eLife-2022 and is publicly available. Additional original analysis code has been deposited at https://github.com/Brody-Lab/PulseInputDDM and is publicly available.

The following previously published data sets were used

Article and author information

Author details

  1. Brian DePasquale

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    For correspondence
    depasquale@princeton.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3830-3184
  2. Carlos D Brody

    Princeton University, Howard Hughes Medical Institute, Princeton, United States
    For correspondence
    brody@princeton.edu
    Competing interests
    Carlos D Brody, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4201-561X
  3. Jonathan W Pillow

    Department of Psychology, Princeton University, Princeton, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3638-8831

Funding

Simons Foundation (SCGB AWD543027 and AWD542593)

  • Carlos D Brody

National Institute of Neurological Disorders and Stroke (BRAIN Initiative Award 5U19NS104648-02)

  • Carlos D Brody

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

Copyright

© 2024, DePasquale 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

  • 978
    views
  • 273
    downloads
  • 0
    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. Brian DePasquale
  2. Carlos D Brody
  3. Jonathan W Pillow
(2024)
Neural population dynamics underlying evidence accumulation in multiple rat brain regions
eLife 13:e84955.
https://doi.org/10.7554/eLife.84955

Share this article

https://doi.org/10.7554/eLife.84955

Further reading

    1. Cell Biology
    2. Computational and Systems Biology
    Sarah De Beuckeleer, Tim Van De Looverbosch ... Winnok H De Vos
    Research Article

    Induced pluripotent stem cell (iPSC) technology is revolutionizing cell biology. However, the variability between individual iPSC lines and the lack of efficient technology to comprehensively characterize iPSC-derived cell types hinder its adoption in routine preclinical screening settings. To facilitate the validation of iPSC-derived cell culture composition, we have implemented an imaging assay based on cell painting and convolutional neural networks to recognize cell types in dense and mixed cultures with high fidelity. We have benchmarked our approach using pure and mixed cultures of neuroblastoma and astrocytoma cell lines and attained a classification accuracy above 96%. Through iterative data erosion, we found that inputs containing the nuclear region of interest and its close environment, allow achieving equally high classification accuracy as inputs containing the whole cell for semi-confluent cultures and preserved prediction accuracy even in very dense cultures. We then applied this regionally restricted cell profiling approach to evaluate the differentiation status of iPSC-derived neural cultures, by determining the ratio of postmitotic neurons and neural progenitors. We found that the cell-based prediction significantly outperformed an approach in which the population-level time in culture was used as a classification criterion (96% vs 86%, respectively). In mixed iPSC-derived neuronal cultures, microglia could be unequivocally discriminated from neurons, regardless of their reactivity state, and a tiered strategy allowed for further distinguishing activated from non-activated cell states, albeit with lower accuracy. Thus, morphological single-cell profiling provides a means to quantify cell composition in complex mixed neural cultures and holds promise for use in the quality control of iPSC-derived cell culture models.

    1. Computational and Systems Biology
    2. Structural Biology and Molecular Biophysics
    Bin Zheng, Meimei Duan ... Peng Zheng
    Research Article

    Viral adhesion to host cells is a critical step in infection for many viruses, including monkeypox virus (MPXV). In MPXV, the H3 protein mediates viral adhesion through its interaction with heparan sulfate (HS), yet the structural details of this interaction have remained elusive. Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. This conserved domain, found across orthopoxviruses, was experimentally validated and shown to be critical for viral adhesion, making it an ideal target for antiviral drug development. Targeting this domain, we designed a protein inhibitor, which disrupted the H3-HS interaction, inhibited viral infection in vitro and viral replication in vivo, offering a promising antiviral candidate. Our findings reveal a novel therapeutic target of MPXV, demonstrating the potential of combination of AI-driven methods and MD simulations to accelerate antiviral drug discovery.