Reduced dynamical variability in depression: evidence from EEG and QIF-E network simulation.

Abstract

Alterations in neural signal complexity have been consistently reported in Major Depressive Disorder (MDD), suggesting changes in the underlying dynamics of brain activity. In this study, we investigate whether changes in neural signal complexity observed in MDD can be characterized using multiscale entropy (MSE) analysis, a method that quantifies temporal complexity across multiple scales. We analyzed electroencephalographic (EEG) recordings from individuals diagnosed with MDD and healthy controls, and compared these results with simulations of neural network models incorporating different forms of local electrical coupling. The EEG analysis revealed significant alterations in signal complexity in MDD, characterized by higher mean entropy at broader time scales and reduced inter-individual variability when compared to healthy controls. To explore potential dynamical mechanisms underlying these observations, we employed a Quadratic Integrate-and-Fire (QIF) neuronal network model with tunable local electrical coupling and small-world synaptic topology. Network topology and coupling parameters were systematically varied to assess their impact on signal complexity, without assuming a direct physiological correspondence between model components and specific biological mechanisms. MSE was used to quantify the irregularity and predictability of both empirical EEG signals and simulated network activity across multiple temporal scales. We found that network configurations lacking local electrical coupling reproduced key entropy features observed in the EEG signals of individuals with MDD, including increased mean entropy and reduced dispersion across realizations. In contrast, simulations with local electrical coupling exhibited lower average entropy and greater variability, resembling the entropy patterns observed in healthy control EEG data. These results suggest that differences in local coupling structure can modulate the balance between complexity and variability in network dynamics, potentially influencing the range of accessible dynamical states. Rather than establishing causality, this comparative analysis highlights how simplified models of local electrical coupling can phenomenologically account for entropy alterations observed in MDD, providing a computational framework for exploring links between network dynamics and large-scale brain signal complexity.