Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task - Vargas, Gabriela; Araya, David; Sepulveda, Pradyumna; Rodriguez-Fernandez, Maria; Friston, Karl J.; Sitaram, Ranganatha; El-Deredy, Wael (Frontiers in Neuroscience, 2023)
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 address this gap, we explore neurofeedback's neural mechanisms of self-regulation learning and investigate the brain processes involved in successful brain self-regulation. By analyzing effective brain connectivity using dynamical causal modeling of real-time functional MRI data associated with neurofeedback training of the supplementary motor area (n=18), we compared top-down hierarchical connectivity models proposed by Active Inference with bottom-up connectivity models like reinforcement learning. Crucially, successful learners evinced a significantly stronger top-down effective connectivity to the target area for self-regulation. Our findings suggest that successful learners engage in a top-down network similar to that observed in goal-oriented and cognitive control studies, highlighting the importance of cognitive processes in self-regulation learning.