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Global dynamics of neural mass models

PLoS Computational Biology, ISSN: 1553-7358, Vol: 19, Issue: 2, Page: e1010915
2023
  • 7
    Citations
  • 0
    Usage
  • 25
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    7
  • Captures
    25
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Great Ormond Street Hospital for Children NHS Foundation Trust Reports Findings in Science (Global dynamics of neural mass models)

2023 FEB 27 (NewsRx) -- By a News Reporter-Staff News Editor at Math Daily News -- New research on Science is the subject of a

Article Description

Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing slow changes in the type of cortical activity, i.e. dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations in a relatively simple mathematical format providing analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. This work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as the transition between interictal, pre-ictal and ictal activity in epilepsy.

Bibliographic Details

Gerald Kaushallye Cooray; Richard Ewald Rosch; Karl John Friston; Lyle J. Graham

Public Library of Science (PLoS)

Agricultural and Biological Sciences; Mathematics; Environmental Science; Biochemistry, Genetics and Molecular Biology; Neuroscience; Computer Science

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