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

Frontiers in Neuroscience

Neuroscience

David Araya¹  Wael El-Deredy²  Maria Rodriguez-Fernandez³  Gabriela Vargas⁴  Pradyumna Sepulveda⁵  Ranganatha Sitaram⁶  Karl J. Friston⁷ 

[1] Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile;Instituto de Tecnología para la Innovación en Salud y Bienestar, Facultad de Ingeniería, Universidad Andrés Bello, Viña del Mar, Chile;Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile;Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain;Department of Electronic Engineering, School of Engineering, Universitat de València, Valencia, Spain;Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile;Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Catolica de Chile, Santiago, Chile;Brain Dynamics Lab, Universidad de Valparaíso, Valparaiso, Chile;Institute of Cognitive Neuroscience, University College London, London, United Kingdom;Department of Psychiatry, Columbia University, New York, NY, United States;St. Jude Children's Research Hospital, Memphis, TN, United States;Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom;

关键词: neurofeedback;    brain-computer interface;    fMRI;    Active Inference;    self-regulation learning;   

DOI  :  10.3389/fnins.2023.1212549

 received in 2023-04-26, accepted in 2023-07-12,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionLearning 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.MethodsWe 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.ResultsOur 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.DiscussionThe 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.

【 授权许可】

Unknown   
Copyright © 2023 Vargas, Araya, Sepulveda, Rodriguez-Fernandez, Friston, Sitaram and El-Deredy.