Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Haugg, A.; Sladky, R.; Skouras, S.; McDonald, A.; Craddock, C.; Kirschner, M.; Herdener, M.; Koush, Y.; Papoutsi, M.; Keynan, J. N.; Hendler, T.; Cohen Kadosh, K.; Zich, C.; MacInnes, J.; Adcock, A.; Dickerson, K.; Chen, N.-K.; Young, K.; Bodurka, J.; Yao, S.; Becker, B.; Auer, T.; Schweizer, R.; Pamplona, G.; Emmert, K.; Haller, S.; Van De Ville, D.; Blefari, M. L.; Kim, D.-Y.; Lee, J.-H.; Marins, T. F.; Megumi, F.; Sorger, B.; Kamp, T.; Liew, S.-L.; Veit, R.; Spetter, M.; Weiskopf, N.; Scharnowski, F.

doi:10.1101/2020.01.15.906388

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bioRxiv · 10.1101/2020.01.15.906388

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

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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.

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Haugg, A., Sladky, R., Skouras, S., McDonald, A., Craddock, C., Kirschner, M., Herdener, M., Koush, Y., Papoutsi, M., Keynan, J. N., Hendler, T., Cohen Kadosh, K., Zich, C., MacInnes, J., Adcock, A., Dickerson, K., Chen, N.-K., Young, K., Bodurka, J., Yao, S., Becker, B., Auer, T., Schweizer, R., Pamplona, G., Emmert, K., Haller, S., Van De Ville, D., Blefari, M. L., Kim, D.-Y., Lee, J.-H., Marins, T. F., Megumi, F., Sorger, B., Kamp, T., Liew, S.-L., Veit, R., Spetter, M., Weiskopf, N., Scharnowski, F.. 2020-01-15. Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?. https://doi.org/10.1101/2020.01.15.906388

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