Directly Exploring the Neural Correlates of Feedback-Related Reward Saliency and Valence During Real-Time fMRI-Based Neurofeedback

Introduction: The potential therapeutic efficacy of real-time fMRI Neurofeedback has received increasing attention in a variety of psychological and neurological disorders and as a tool to probe cognition. Despite its growing popularity, the success rate varies significantly, and the underlying neural mechanisms are still a matter of debate. The question whether an individually tailored framework positively influences neurofeedback success remains largely unexplored.Methods: To address this question, participants were trained to modulate the activity of a target brain region, the visual motion area hMT+/V5, based on the performance of three imagery tasks with increasing complexity: imagery of a static dot, imagery of a moving dot with two and with four opposite directions. Participants received auditory feedback in the form of vocalizations with either negative, neutral or positive valence. The modulation thresholds were defined for each participant according to the maximum BOLD signal change of their target region during the localizer run.Results: We found that 4 out of 10 participants were able to modulate brain activity in this region-of-interest during neurofeedback training. This rate of success (40%) is consistent with the neurofeedback literature. Whole-brain analysis revealed the recruitment of specific cortical regions involved in cognitive control, reward monitoring, and feedback processing during neurofeedback training. Individually tailored feedback thresholds did not correlate with the success level. We found region-dependent neuromodulation profiles associated with task complexity and feedback valence.Discussion: Findings support the strategic role of task complexity and feedback valence on the modulation of the network nodes involved in monitoring and feedback control, key variables in neurofeedback frameworks optimization. Considering the elaborate design, the small sample size here tested (N = 10) impairs external validity in comparison to our previous studies. Future work will address this limitation. Ultimately, our results contribute to the discussion of individually tailored solutions, and justify further investigation concerning volitional control over brain activity.

Functional heterogeneity of perceived control in feedback processing

Perceived control is a fundamental psychological function that can either boost positive affect or buffer negative affect. The current study addressed the electrophysiological correlates underlying perceived control, as exercised by choice, in the processing of feedback valence. Thirty-six participants performed an EEG choice task during which they received positive or negative feedback following choices made either by themselves or by a computer. Perceived control resulted in an enhanced reward positivity for positive feedback but increased theta power for negative feedback. Further, perceived control led to greater feedback P3 amplitude and delta power, regardless of feedback valence. These results suggest functional heterogeneity of perceived control in feedback processing as diverse as magnifying the reward signal, enhancing the need for control and increasing the motivational salience of outcome irrespective of valence.

Effect of Feedback Valence on Family Resemblance Category Learning

This research uses ERPs method to study how different valence of feedback influence family resemblance category learning. The results showed that at the behavioral level, participants in the negative feedback condition received higher test scores than those in the positive feedback condition; at the physiological level, the four kinds of ERPs evoked by both negative and positive feedback are P200, P300, and FRN. Compared with the non-feedback condition, P300 is more sensitive to negative feedback, and negative feedback induces larger amplitude, while P300 is not sensitive to positive feedback. For both negative and positive feedback, when feedback was presented after 200-300ms, the reaction to errors induced FRN production, and FRN produced under negative feedback condition led to more activation. This research has deepened our understanding of the influence of feedback valence on category learning from the brain mechanisms as well.