Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes - Manos, Thanos; Diaz-Pier, Sandra; Fortel, Igor; Driscoll, Ira; Zhan, Liang; Leow, Alex (Frontiers in Computational Neuroscience, 2023)
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight 1 Manos et al.coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome.