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

Simple item page
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.accessioned2025-01-20T20:07:11Z
dc.date.available2025-01-20T20:07:11Z
dc.date.issued2023
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.fuente.origenWOS
dc.identifier.doi10.3389/fnins.2023.1212549
dc.identifier.eissn1662-453X
dc.identifier.urihttps://doi.org/10.3389/fnins.2023.1212549
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91773
dc.identifier.wosidWOS:001057202400001
dc.language.isoen
dc.revistaFrontiers in neuroscience
dc.rightsacceso restringido
dc.subjectneurofeedback
dc.subjectbrain-computer interface
dc.subjectfMRI
dc.subjectActive Inference
dc.subjectself-regulation learning
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSelf-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
dc.typeartículo
dc.volumen17
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas