Study of brain rhythms in cognitive and neuropathological processes: analysis through EEG data processing and simulation using neurocomputational models

The brain's ability to integrate, process, and store information is essential to guide our behavior, yet its underlying neural mechanisms remain largely unknown. Neural oscillations have long been associated with cognitive processes; so-called brain rhythms are crucial for understanding healthy brain function and identifying early markers of cognitive disorders. Electroencephalography (EEG) is widely used in clinical and research settings because it provides high temporal resolution data in a non-invasive and relatively inexpensive manner. However, EEG has some inherent limitations, such as poor spatial resolution and low signal-to-noise ratio. Advanced techniques such as cortical source reconstruction, time-frequency analysis, and functional connectivity estimation are crucial to overcome these limitations. Moreover, computational modeling represents a complementary tool to investigate the neural mechanisms underlying brain rhythms. Neural mass models (NMMs) simulate large-scale dynamics by balancing biological accuracy and computational efficiency, linking neuronal activity to macroscopically detectable signals. By altering internal parameters, NMMs can mimic changes in neural oscillations under different conditions and be compared with experimental data. The research presented in this Dissertation advances our understanding of neural dynamics in cognitive and pathological processes through complementary EEG analysis and neurocomputational modeling. Analysis of EEG signals collected during a fear conditioning task provided insights into conditioned learning, while alterations in the rhythmic transmission of information were observed in subjects with high schizotypal traits. Moreover, implemented models of semantic, working and episodic memory have highlighted the crucial role of theta and gamma oscillations in different memory stages. In summary, this Dissertation contributes to the understanding of brain rhythms and their role in cognitive and pathological processes. The combination of advanced EEG analysis and computational modeling has narrowed the gap between neural oscillations and cognitive processes, advancing theory and offering early insights for the development of treatment strategies.