A Connectome-Constrained Jansen-Rit Framework for Inferring Cortical Gain Control and Ensemble Stability

Abstract

Understanding how local circuit dynamics give rise to large-scale stability and instability of brain activity is a central challenge in computational neuroscience, with direct relevance for disorders characterized by disrupted excitatory-inhibitory balance, including schizophrenia spectrum disorder (SSD). Here, we introduce a principled methodology for recovering local neural parameters and low-dimensional dynamical biomarkers from a connectome-constrained Jansen-Rit (JR) neural mass model using variational free-energy inversion and sliding-window analysis. Each cortical region is modeled as a canonical excitatory-inhibitory microcircuit embedded within a whole-brain network whose long-range interactions are factorized into pyramidal-pyramidal, pyramidal- excitatory, and pyramidal-inhibitory subnetworks. Across 80 independent simulations, the inversion framework reliably recovered both microcircuit parameters and emergent biomarkers derived from neural states, including the mean-variance slope ({beta}1), its spatial variability, and the lag-1 autocorrelation ({rho}1). These quantities capture complementary aspects of cortical ensemble dynamics--gain sensitivity, regional heterogeneity, and temporal persistence associated with proximity to criticality--and were consistently estimated with minimal bias and high reliability. The recovered slope hierarchy [Formula] revealed an interpretable gain-control architecture in which inhibitory channels regulate damping, excitatory channels gate resonance, and pyramidal populations integrate network drive into stable output. Together, these results demonstrate that the JR model provides a tractable and biophysically grounded framework for linking synaptic parameters, network structure, and ensemble-level stability. Although motivated by questions surrounding psychosis risk and SSD, the proposed approach is general and establishes a foundation for future applications in model-based inference, network control, and adaptive neuromodulation.