Modulations of the neuronal subthreshold activity, giving rise to rhythms at high firing rate, represent the high signal complexity of the brain dynamic repertoire. Together with neural network oscillations, they are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. Here we combine structural information from non-invasive brain imaging with mathematical modeling, thus leveraging an in-silico platform for the exploration of causal mechanisms of brain function and clinical hypothesis testing. In particular we use a recently derived set of exact mean-field equations for networks of quadratic integrate-and-fire neurons to provide a comprehensive study of the effect of external drives or perturbations on neuronal networks exhibiting multistability in order to investigate the role played by the neuroanatomical connectivity matrix in shaping the emergent dynamics. We demonstrate, along the example of 20 diffusion-weighted magnetic resonance imaging (MRI) connectomes of healthy subjects, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. Moreover we studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of MRI and diffusion tensor weighted imaging (DTI), while each patient’s virtual brain was further personalized through the integration of the clinically hypothesized epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate. Across patients, it turns out that patient-specific network connectivity is predictive for the subsequent seizure propagation pattern thus opening the possibility of improving diagnosis and surgery outcome.
bioRxiv Subject Collection: Neuroscience