Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training

Abstract

The acquisition of new skills can be facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that utilizes feedback processing and behavioral adaptation following feedback to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is a specific instantiation of this principle, where the brain is trained directly by providing feedback of its self-regulation. Neurofeedback is unique in that it is the most direct form of brain training and it trains something we do not normally have conscious access to - our brain activity. To understand how learning with neurofeedback or other forms of feedback is accomplished, it is essential to understand how the feedback is evaluated and how behavior is adjusted following guidance from the feedback signal. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions whose activity and connectivity are associated with feedback processing and behavioral adaptation to feedback during neurofeedback training. We converted and harmonized feedback scores across studies, and computed their linear associations with brain activity and connectivity in parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in key regions of the reward system, as well as the dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity in the salience network. During behavioral adaptation (i.e., regulation after feedback), no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results demonstrate that neurofeedback is processed in the reward system, thereby endorsing the theory that reinforcement learning shapes this form of brain training towards behavioral change. In addition, the association of large-scale networks with feedback suggests that higher-level processing, involving the continuous transition between the evaluation of external feedback and the subsequent internal evaluation of the adopted cognitive state, is also involved in this type of learning. Our findings highlight the pivotal role of performance-related feedback as a driving force during learning, a conclusion that can potentially be extended to other processes beyond neurofeedback training.