Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity?

Abstract

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.

Putamen volume predicts real‐time fMRI neurofeedback learning success across paradigms and neurofeedback target regions

“…A previous meta‐analysis of NF studies aimed at determining whether pre‐training activation within the target regions of the training collected from more than 400 participants could predict subsequent learning success. However, this study did not find a common functional MRI‐based predictor for neurofeedback efficacy, suggesting an examination of more stable alternative predictors of neurofeedback learning success (Haugg et al, 2020). Against this background, the current study explored whether regional brain volume could predict learning success during rt‐fMRI NF training in healthy subjects.…”

“…The underlying mechanisms and factors that contribute to individual differences in NF learning success have been rarely examined. A recent meta‐analysis that aimed at identifying functional brain markers has failed to find one that could reliably predict neurofeedback learning success across studies (Haugg et al, 2020). In the light of these efforts, we asked whether individual variations in brain structure could predict NF learning outcome in three samples and found a positive association between volume of the dorsal striatum, specifically the putamen, and learning success.…”

“…However, this study did not find a common functional MRI-based predictor for neurofeedback efficacy, suggesting an examination of more stable alternative predictors of neurofeedback learning success (Haugg et al, 2020). Against this background, the current study explored whether regional brain volume could predict learning success during rt-fMRI NF training in healthy subjects.…”

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

“…For comparisons with existing related studies, (Scheinost et al, 2014) and (Haugg et al, 2020b) predicted the appropriateness of NFs from brain indices of resting-state FCs and brain activity in the localizer tasks, respectively. In both studies, the brain indices used for prediction corresponded to each NF target.…”

“…At present, the most extensive report on the predictions of individual NF aptitudes based on brain activity is the work by Haugg et al (2020b), they gathered a large dataset from various types of fMRI-NF studies and tried to predict individual NF performances based on brain activation during localizer tasks in each experiment. More preferably, we wish to screen patients before NF training using a task-free setting, such as their resting-states.…”