A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition

Whole-brain models are valuable tools for understanding brain dynamics in health and disease by enabling the testing of causal mechanisms and identification of therapeutic targets through dynamic simulations. Among these models, biophysically inspired neural mass models have been widely used to simulate electrophysiological recordings, such as MEG and EEG. However, traditional models face limitations, including susceptibility to over-saturation of the sigmoid function by model hyperexcitability, which constrains their ability to capture the full richness of neural dynamics. Here, we thoroughly characterize a previously introduced multi-frequency Jansen-Rit neural mass model with inhibitory synaptic plasticity (ISP) aimed at overcoming these limitations. The ISP adjusts inhibitory feedback onto pyramidal neurons to clamp their firing rates around a target value. This mechanism allows for fine control of neuronal firing rates, preventing over-saturation in whole-brain simulations. In this model, we analyzed how different model parameters modulate oscillatory frequency and connectivity. As a demonstration, we considered simultaneously fitting EEG and fMRI recordings during NREM sleep. Bifurcation analysis showed that ISP widened the range of parameters in which the model exhibited sustained oscillations; the target firing rate can modulate oscillatory dynamics, producing different oscillatory regimes, from slower (δ, θ and α) to faster (β and γ) oscillations. High-frequency activity emerged from low global coupling, high firing rates, and a high proportion of γ versus α subpopulations. The ISP was necessary in the multi-frequency model to successfully fit EEG functional connectivity across frequency bands. Finally, ISP-controlled reductions in excitability reproduced both the slow-wave activity and the reduced connectivity in NREM sleep. Altogether, our model is compatible with biological evidence of the effects of excitability on modulating brain rhythms and connectivity, as observed in sleep, neurodegeneration, and chemical neuromodulation. This biophysical model with ISP provides a springboard for realistic brain simulations in health and disease.