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Related Experiment Video

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Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex.

Pok Him Siu1, Eli Müller1, Valerio Zerbi2,3

  • 1School of Physics, The University of Sydney, Camperdown, NSW, Australia.

Frontiers in Computational Neuroscience
|May 13, 2022
PubMed
Summary

Researchers developed a neural-mass model to link microscale brain physiology to macroscale dynamics. Simple models accurately fit resting-state fMRI data, showing how neural connections constrain brain activity.

Keywords:
brain dynamicscell densitiesdynamical systemsmouse cortexneural mass model

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Area of Science:

  • Computational neuroscience
  • Systems neuroscience
  • Neuroimaging

Background:

  • High-resolution brain atlases reveal cell density variations across the cortex.
  • Bridging microscale physiology and macroscale brain dynamics remains a challenge.
  • Physiologically based models can explain brain dynamics but are often complex.

Purpose of the Study:

  • To develop a neural-mass model of the mouse cortex.
  • To use bifurcation diagrams to interpret whole-brain dynamics.
  • To link microscale physiology to macroscale brain activity.

Main Methods:

  • Developed a neural-mass model of the mouse cortex.
  • Utilized bifurcation diagrams to analyze local and whole-brain dynamics.
  • Constrained model parameters using cell-density data and resting-state fMRI.

Main Results:

  • Simple dynamical regimes accurately fit resting-state fMRI data.
  • Structural connections strongly constrain functional connectivity in anesthetized mice.
  • Perturbations in coupling strengths yield spatially dependent cortical activity patterns.

Conclusions:

  • Bifurcation diagrams aid in understanding complex neural dynamics.
  • Physiologically grounded models can be simplified for mechanistic interpretation.
  • This approach facilitates building explanatory models of large-scale brain activity.