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.
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