HighlightsHumans are able to gradually self-regulate regional brain activation by applying cognitive strategies.Providing rtfMRI neurofeedback can enhance the gradual self-regulation ability.Findings are generalizable to various mental tasks and clinical MR field strengths.Novel parametric activation paradigm enriches spectrum of rtfMRI-neurofeedback and BCI methodology.

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Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs (i.e., self‐regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain‐based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Introduction Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self‐regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience , 18, 86). Methods To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self‐regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self‐regulation with and without providing neurofeedback. Ten participants freely chose one self‐regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real‐time based on activity in task‐related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain‐activation levels in the target region. Results During gradual self‐regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self‐regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time‐resolved single‐trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self‐regulation. Conclusion Overall, these findings support that neurofeedback contributes to self‐regulation through task‐general regions involved in feedback and reward processing.

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies.Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.