Cell type-specific connectome predicts distributed working memory activity in the mouse brain
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
-
A mesoscale connectome of the mouse brain.https://doi.org/10.1038/nature13186.
-
The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlashttps://doi.org/10.1016/j.cell.2020.04.007.
-
Hierarchical organization of cortical and thalamic connectivityhttps://doi.org/10.1038/s41586-019-1716-z.
-
Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphismhttps://doi.org/10.1016/j.cell.2017.09.020.
-
A Cell Atlas for the Mouse Brainhttps://doi.org/10.3389/fninf.2018.00084.
Article and author information
Author details
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
-
- 2,668
- views
-
- 447
- downloads
-
- 9
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Citations by DOI
-
- 9
- citations for umbrella DOI https://doi.org/10.7554/eLife.85442