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Carrasco-Gómez MAutor o coautor

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Traditionally, research in the field of brain stimulation has determined target brain regions based on the positioning of electrodes or coils with respect to the head. Although this approach has yielded valuable insights, it tends to overlook two critical aspects: the magnitude of the electrical field generated within the brain and the intricate non-linear network interactions inherent in brain physiology. In the last three decades' research in non-invasive brain stimulation (NIBS) has made significant advancements, and computational models offer indispensable tools to comprehensively address this complex issue. On the one hand, current propagation models facilitate the prediction of electric field distributions. These predictions rely on structural neuroimaging techniques, such as magnetic resonance imaging (MRI), and employ discretization techniques to calculate the magnitude of the electric field elicited by the stimulation inside the brain. On the other hand, neural activation models simulate brain activity by incorporating structural connectivity data obtained from diffusion-weighted MRI and modeling the activities of neural populations at network nodes, typically through second-order differential equations. The combination of these methodologies holds promise for optimizing stimulation protocols for NIBS techniques, such as transcranial electrical stimulation (tES). This integration accounts for both individualized structural and functional information, thus facilitating hypothesis-driven research and fostering the development of treatment approaches for conditions characterized by widespread alterations in functional or metabolic activity and significant inter-subject variability, such as those associated with healthy aging.

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