Cell type-specific connectome predicts distributed working memory activity in the mouse brain

  1. Xingyu Ding
  2. Sean Froudist-Walsh
  3. Jorge Jaramillo
  4. Junjie Jiang
  5. Xiao-Jing Wang  Is a corresponding author
  1. New York University, United States
  2. University of Bristol, United Kingdom
  3. University of Göttingen, Germany
  4. Xi'an Jiaotong University, China

Abstract

Recent advances in connectome and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the mouse multiregional brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for inter-areal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.

Data availability

The current manuscript is predominantly a computational study. Consequently, no new data was generated. However, the modeling code that underpins our findings has been made publicly available on GitHub, accessible via https://github.com/XY-DIng/mouse_dist_wm

The following previously published data sets were used

Article and author information

Author details

  1. Xingyu Ding

    Center for Neural Science, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Sean Froudist-Walsh

    Bristol Computational Neuroscience Unit, University of Bristol, Bristol, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Jorge Jaramillo

    Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
    Competing interests
    The authors declare that no competing interests exist.
  4. Junjie Jiang

    Institute of Health and Rehabilitation Science, Xi'an Jiaotong University, Xi'an, China
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2930-7770
  5. Xiao-Jing Wang

    Center for Neural Science, New York University, New York, United States
    For correspondence
    xjwang@nyu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3124-8474

Funding

National Institutes of Health (R01MH062349)

  • Xiao-Jing Wang

Office of Naval Research (N00014)

  • Xiao-Jing Wang

National Science Foundation (NeuroNex grant,2015276)

  • Xiao-Jing Wang

Simons Foundation (543057SPI)

  • Xiao-Jing Wang

National Institutes of Health (U19NS123714)

  • Xiao-Jing Wang

Biotechnology and Biological Sciences Research Council (BB/X013243/1)

  • Sean Froudist-Walsh

University of Bristol (Neuroscience of Mental Health Award)

  • Sean Froudist-Walsh

National Institutes of Health (U19NS123714)

  • Jorge Jaramillo

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

Copyright

© 2024, Ding 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

  • 2,313
    views
  • 409
    downloads
  • 5
    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. Xingyu Ding
  2. Sean Froudist-Walsh
  3. Jorge Jaramillo
  4. Junjie Jiang
  5. Xiao-Jing Wang
(2024)
Cell type-specific connectome predicts distributed working memory activity in the mouse brain
eLife 13:e85442.
https://doi.org/10.7554/eLife.85442

Share this article

https://doi.org/10.7554/eLife.85442

Further reading

    1. Neuroscience
    Lisa Reisinger, Gianpaolo Demarchi ... Nathan Weisz
    Research Article

    Phantom perceptions like tinnitus occur without any identifiable environmental or bodily source. The mechanisms and key drivers behind tinnitus are poorly understood. The dominant framework, suggesting that tinnitus results from neural hyperactivity in the auditory pathway following hearing damage, has been difficult to investigate in humans and has reached explanatory limits. As a result, researchers have tried to explain perceptual and potential neural aberrations in tinnitus within a more parsimonious predictive-coding framework. In two independent magnetoencephalography studies, participants passively listened to sequences of pure tones with varying levels of regularity (i.e. predictability) ranging from random to ordered. Aside from being a replication of the first study, the pre-registered second study, including 80 participants, ensured rigorous matching of hearing status, as well as age, sex, and hearing loss, between individuals with and without tinnitus. Despite some changes in the details of the paradigm, both studies equivalently reveal a group difference in neural representation, based on multivariate pattern analysis, of upcoming stimuli before their onset. These data strongly suggest that individuals with tinnitus engage anticipatory auditory predictions differently to controls. While the observation of different predictive processes is robust and replicable, the precise neurocognitive mechanism underlying it calls for further, ideally longitudinal, studies to establish its role as a potential contributor to, and/or consequence of, tinnitus.

    1. Neuroscience
    Sam E Benezra, Kripa B Patel ... Randy M Bruno
    Research Article

    Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.