Browsing by Author "El-Deredy, Wael"

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    Brain self-regulation learning in the neurocomputational framework of active inference
    (2023) Vargas González, Gabriela Adriana; Rodríguez Fernández, María; El-Deredy, Wael; Pontificia Universidad Católica de Chile. Instituto de Ingeniería Biológica y Médica
    Neurofeedback (NF), a cutting-edge technique in the realm of brain-computer interfaces (BCI), has proven to be a powerful tool for both scientific exploration and clinical rehabilitation. NF provides individuals with real-time information about their neural processes, enabling them to modulate and regulate their brain activity—a phenomenon known as 'brain self-regulation learning'. While NF holds great promise, it faces an efficiency hurdle. Remarkably, only 50% of participants successfully achieve self-regulation, limiting its clinical adoption. Existing models have struggled to fully elucidate the intricate interplay between reward mechanisms and cognitive functions, without fully succeeding. Herein lies the significance of Active Inference—a theoretical framework that illuminates this complex relationship. To address this gap, we propose using the framework of Active inference to understand the neural processes underlying self-regulation learning. Active inference provides a statistical model of the brain and a combination of computational modeling and neuroimaging techniques. By analyzing real-time functional MRI data and implementing agent-based simulations, we identify that learners exhibit a hierarchical computational anatomy as the neural substrate that supports the internal dynamics of the brain. Our findings underscore the importance of cognitive processes in self-regulation learning and provide insights for optimizing NF protocols.
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    Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
    (2023) Vargas, Gabriela; Araya, David; Sepulveda, Pradyumna; Rodriguez-Fernandez, Maria; Friston, Karl J.; Sitaram, Ranganatha; El-Deredy, Wael
    Introduction Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.Methods We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.Results Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.Discussion The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.