Resumen de Model-based analyses of spatiotemporal brain activity during resting-state and low-level states of consciousness
The brain's resting-state activity displays complex spatiotemporal patterns of activity that are constantly formed and dissolved. The study of these dynamic collective patterns has increased our understanding of the brain's organization principles during function and dysfunction. However, the underlying mechanisms remain unknown. Theoretical whole-brain models explore this question by combining neural networks and brain activity/connectivity data. In the present thesis, I studied whole-brain models of resting-state activity during conscious wakefulness and low-level states of consciousness. I analyzed neuroimaging data (fMRI and EEG) from subjects during wakefulness and anesthesia, and from patients with disorders of consciousness due to brain lesions. I interpreted the results using diverse models to disentangle the contribution of brain dynamics, network connectivity, and temporal scales. The results suggest that loss of consciousness is driven by altered network interactions, more homogeneous and structurally constrained local dynamics, and less stability of the network's topological core compared to conscious states.
