EXPLORING BRAIN COMPLEXITY AND ITS MECHANISMS: FROM THEORY TO PRACTICE AND BACK

This thesis explores brain complexity and its structural and functional substrates, linking theoretical principles, empirical TMS–EEG recordings, and multi-scale computational modeling. The central question it addresses is how brain networks sustain complex dynamics and how these dynamics break down in pathology. Across five interconnected studies, it standardizes experimental TMS–EEG data analysis and whole-brain computational pipelines to investigate how structural connectivity, network topology, and dynamical regimes jointly shape brain complexity. First, a standardized analysis framework (TEPpy) is developed to map the temporal and spectral signatures of TMS-evoked potentials, operationalizing local circuit differentiation by characterizing peak-based features and natural frequencies. Second, an integrated whole-brain modeling workflow (TVB–Cobrawap) is introduced to tune connectome-based neural mass models toward biologically realistic regimes that jointly reproduce spontaneous rhythms, features of criticality, and high perturbational complexity in evoked responses, thereby unifying expectations for spontaneous and evoked activity within a single tuned model. Third, local virtual manipulations in a whole-brain computational model show that silencing highly central posterior hubs produces the largest reductions in perturbational complexity, indicating a central role of posterior network hubs in sustaining complex dynamics. Fourth, a multiscale computational model demonstrates that structural disconnection alone is sufficient to generate cortical bistability and slow waves via neuronal disfacilitation, and further shows that their propagation depends on hierarchical topology and the temporal coherence of afferent slow wave activity, providing a mechanistic account of perilesional sleep-like dynamics and large-scale slow wave propagation after focal lesions. Fifth, a computational analysis across rewired networks indicates that perturbational complexity peaks in small-world regimes and remains robust to common-driver confounds, whereas observational measures exhibit systematic biases toward segregation or integration. This clarifies complexity metric sensitivities and provides a principled basis for interpreting complexity markers. Collectively, these contributions provide standardized empirical and computational tools, establish a mechanistic link between criticality and perturbational complexity, and bridge abstract information-theoretic constructs with the neurobiology of brain structure and dynamics. They further delineate translational avenues for assessing brain complexity and modeling slow wave dynamics following brain injury.