Matched neurofeedback during fMRI differentially activates reward‐related circuits in active and sham groups

Abstract

Background and Purpose: Functional MRI neurofeedback (fMRI‐nf) leverages the brain's ability to self‐regulate its own activity. However, self‐regulation processes engaged during fMRI‐nf are incompletely understood. Here, we used matched feedback in an fMRI‐nf experimental protocol to investigate whether brain processes recognize true neurofeedback signals. Methods: We implemented an existing fMRI‐nf protocol to train lateralized motor activity using a finger‐tap task in conjunction with real‐time feedback. Twelve healthy, right‐handed, adult participants were assigned into age‐ and sex‐matched active and sham study groups. Matched participant pairs received the same visual feedback, based on brain activity of the participant from the active group. We compared group‐averaged activation maps before, during, and after neurofeedback, and analyzed changes in lateralized motor activity due to neurofeedback. Results: Active and sham groups demonstrated different brain activation to the same feedback during neurofeedback. In particular, there was higher activation in visual cortex, secondary somatosensory cortex, and right inferior frontal gyrus in the active group compared to the sham group. Conversely, sham participants demonstrated higher activation in anterior cingulate cortex, left frontal pole, and posterior superior temporal gyrus. Despite differing brain activations during neurofeedback, neither group demonstrated significant improvement in lateralized motor activity from pre to postfeedback scan in the same session. We also observed no significant difference between pre and postfeedback activation maps, suggesting that no significant finger‐tap related functional reorganization had occurred. Conclusions: These findings suggest that fMRI neurofeedback paradigms that monitor or incorporate activity from regions reported here would provide enhanced efficacy for research investigation and clinical intervention.

Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback

“…8k results from an in vitro rerun of data from pilot 4 is shown, where feedback was shifted from the parietal mask from [91] to personalized rIFG. The result from this analysis also indicates that rIFG is enhanced through the rtfMRI-nf sessions, in accordance with [33].…”

“…As rIFG may be trained as a consequence of the rtfMRInf method ( [33]), we chose to provide feedback from SMA and measure resulting effects on both SMA and rIFG. Reinforcing both ROIs could aid both conditions; the primary interest is the relative importance of the ROIs for the benefit of the patients.…”

“…Included in the development was the simulation of the rtfMRI-nf experience through VR, the sham-feedback itself was based on screen-recordings of participants, as used in e.g. [28,33].…”

“…This may take the form of, for example, a developing graph, a bar/thermometer, clarity of images, and/or a rocket man, depending on the study's goal and the sample's participants [28,16,29,30,31]. The feedback may be calculated based on a region of interest (ROI) ( [28,32,16,33,30]), brain connectivity ( [34,35]), and/or networks [36].…”

Motor Imagery-based Neurofeedback in Physiotherapy Practice

“…The anterior insula [19] and anterior cingulate cortex [20] are associated with a conscious perception of reward, whereas the ventral striatum is associated with an unconscious perception of reward [21,22]. The reward-processing network has been reported to be activated by the NFB [23], which is also known to regulate brain activity. Thus, the knowledge of results (KR) for regulating brain activity conferred by the NFB may be involved in the process of recognizing the accomplishment of the task, and these neural networks have been proposed to be related to NFB.…”