Can we predict real-time fMRI neurofeedback learning success from pre-training 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 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.

The potential of functional near-infrared spectroscopy-based neurofeedback – a systematic review and recommendations for best practice

“…Lastly, it will be crucial to further develop standards and agreements, particularly for neurofeedback success measures, in order to have comparable outcome variables in the future (Haugg et al, 2020). We repeat suggestions by Paret et al, (2019) that a basic science approach should be employed, systematically exploring and optimizing neurofeedback protocols and real-time signal-processing methods, which can then inform translational work in the field.…”

A systematic review of fMRI neurofeedback reporting and effects in clinical populations

“…Next, studies with large sample sizes are required for stratification, i.e., dividing a clinical population into subpopulations based on certain traits or symptoms. Although so far unsuccessful ( Haugg et al, 2020 , Weber et al, 2020 ), partly due to small sample sizes and small number of studies per clinical population, the increasing data and knowledge of the existing and future studies could hopefully be used to identify subpopulations that are successful responders in the future. Although neuroimaging studies usually heavily rely on group results, individual results might prove to be informative as well, in order to estimate how many participants respond to treatment, and furthermore, to extract any characteristics of potential responders.…”

The Potential of Functional Near-Infrared Spectroscopy-Based Neurofeedback—A Systematic Review and Recommendations for Best Practice

“…Lastly, it will be crucial to further develop standards and agreements, particularly for neurofeedback success measures, in order to have comparable outcome variables in the future (Haugg et al, 2020 ). We repeat suggestions by Paret et al ( 2019 ) that a basic science approach should be employed, systematically exploring and optimizing neurofeedback protocols and real-time signal-processing methods, which can then inform translational work in the field.…”