Homeodynamic feedback inhibition control in whole-brain simulations.

Simulations of large-scale brain dynamics are often impacted by overexcitation resulting from heavy-tailed structural network distributions, leading to biologically implausible simulation results. We implement a homeodynamic plasticity mechanism, known from other modeling work, in the widely used Jansen-Rit neural mass model for The Virtual Brain (TVB) simulation framework. We aim at heterogeneously adjusting the inhibitory coupling weights to reach desired dynamic regimes in each brain region. We show that, by using this dynamic approach, we can control the target activity level to obtain biologically plausible brain simulations, including post-synaptic potentials and blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) activity. We demonstrate that the derived dynamic Feedback Inhibitory Control (dFIC) can be used to enable increased variability of model dynamics. We derive the conditions under which the simulated brain activity converges to a predefined target level analytically and via simulations. We highlight the benefits of dFIC in the context of fitting the TVB model to static and dynamic measures of fMRI empirical data, accounting for global synchronization across the whole brain. The proposed novel method helps computational neuroscientists, especially TVB users, to easily "tune" brain models to desired dynamical regimes depending on the specific requirements of each study. The presented method is a steppingstone towards increased biological realism in brain network models and a valuable tool to better understand their underlying behavior. Author summary: We introduce the dynamic inhibitory plasticity mechanism (dFIC) in the widely used Jansen-Rit brain network model. The mechanism allows for adapting inhibitory coupling weights based on a synaptic plasticity-inspired rule. Our method effectively balances long-range excitation and local feedback inhibition, allowing better control over the brain network model's dynamics and analysis of the tuning process. We study the conditions, boundaries, and consequences of using the proposed method on different scales and modalities. We demonstrate that under certain conditions dFIC leads to improved variability of behavior, more biologically plausible simulation results and better fits to empirical data. Our solution is presented as an effective method for improving the fitting simulated to empirical data, by allowing computational neuroscientists to set the activity according to specific study requirements. [ABSTRACT FROM AUTHOR]