Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

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dc.contributor.authorVargas, Gabriela
dc.contributor.authorAraya, David
dc.contributor.authorSepulveda, Pradyumna
dc.contributor.authorRodriguez-Fernandez, Maria
dc.contributor.authorFriston, Karl J.
dc.contributor.authorSitaram, Ranganatha
dc.contributor.authorEl-Deredy, Wael
dc.date.accessioned2024-05-30T18:41:03Z
dc.date.available2024-05-30T18:41:03Z
dc.date.issued2024
dc.descriptionIndexación: Scopus.
dc.description.abstractIntroduction: 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.
dc.description.urihttps://www-scopus-com.recursosbiblioteca.unab.cl/record/display.uri?eid=2-s2.0-85169325476&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=0389911cdd8d502d962e4876b3764a51&sot=aff&sdt=cl&cluster=scofreetoread%2c%22all%22%2ct&sl=61&s=AF-ID%28%22Universidad+Andr%c3%a9s+Bello%22+60002636%29+AND+SUBJAREA%28NEUR%29&relpos=17&citeCnt=0&searchTerm=
dc.identifier.citationFrontiers in Neuroscience Open Access Volume 172023 Article number 1212549
dc.identifier.doi10.3389/fnins.2023.1212549
dc.identifier.issn16624548
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/57212
dc.language.isoen
dc.publisherFrontiers Media SA
dc.rights.licenseCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectActive Inference
dc.subjectbrain-computer interface
dc.subjectfMRI
dc.subjectneurofeedback
dc.subjectself-regulation learning
dc.titleSelf-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
dc.typeArtículo
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