Abstract
Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.
Highlights
Healthy resting brain dynamics exhibit several characteristic spatiotemporal features, including structured functional connectivity between brain regions detectable by a variety of different measures
The balance between excitation and inhibition in the brain is believed to play a critical role in brain dynamics, and it is likely that this balance is regulated by homeostatic mechanisms
The aim of this study is to use a biophysical model to investigate whether inhibitory synaptic plasticity (ISP) is successfully able to balance excitation and inhibition for models with ongoing oscillations on electrophysiological timescales, and to investigate the effect that ISP has on population-level dynamics and functional connectivity measured using MEG
Summary
Healthy resting brain dynamics exhibit several characteristic spatiotemporal features, including structured functional connectivity between brain regions detectable by a variety of different measures. There has been considerable interest in using coupled networks of oscillators to relate largescale functional connectivity to network properties including connectivity strength, time delays, and graph structure These oscillator models span very simple oscillators such as the Kuramoto model [1,2,3,4,5,6,7], through to more sophisticated oscillators based on Hopf bifurcations [8,9], and biophysical neural mass models such as the Wilson-Cowan model [10,11,12,13,14,15]. Achieving realistic brain activity in biophysical models typically requires extremely fine tuning of parameters [24] This contrasts with real brains, whose dynamics can be robust to quite large changes in white matter connectivity and grey matter structure, whether through learning, aging, development or disease [25]. The balance between excitation and inhibition is often framed in terms of correlations in excitatory and inhibitory activity when analysing electrophysiological data, but it could be framed in terms of excitatory and inhibitory connection strengths at the connectome level
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