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A mean field model for movement induced changes in the beta rhythm.
Áine Byrne1, Matthew J Brookes2, Stephen Coombes3
1Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK. aine.byrne@nottingham.ac.uk.
Journal of Computational Neuroscience
|July 28, 2017
Summary
A new model explains brain signal changes during movement. It details the movement-related beta decrease (MRBD) and post-movement beta rebound (PMBR), offering insights into neural synchrony dynamics.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Systems Neuroscience
Background:
- Brain signal amplitude changes reflect neuronal population synchrony.
- Movement-related beta decrease (MRBD) and post-movement beta rebound (PMBR) are key oscillatory phenomena observed during movement and its cessation.
Purpose of the Study:
- To develop a parsimonious model for neuronal synchrony dynamics.
- To replicate and explain the observed MRBD and PMBR phenomena in human MEG data.
- To elucidate the underlying causes of the time-lag and duration of PMBR.
Main Methods:
- Developed a synaptically coupled spiking network model.
- Derived an exact mean-field description using four ordinary differential equations.
- Compared model output to human MEG power spectrograms.
Main Results:
- The model successfully replicated the transition from MRBD to PMBR.
- The mean-field model provided insights into the time-lag (∼0.5s) from movement termination to PMBR onset.
- The model explained the long duration (∼1-10s) of PMBR.
Conclusions:
- This is the first model to predict MRBD and PMBR phenomena.
- The model offers a key step in understanding these robust neural dynamics in health and disease.
- The mean-field approach provides valuable insights into complex neural network behavior.
