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

Details

Serval ID
serval:BIB_053A93664C61
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
Journal
Human brain mapping
Author(s)
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 R.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., Fukuda M., Sorger B., Kamp T., Liew S.L., Veit R., Spetter M., Weiskopf N., Scharnowski F.
ISSN
1097-0193 (Electronic)
ISSN-L
1065-9471
Publication state
Published
Issued date
01/10/2020
Peer-reviewed
Oui
Volume
41
Number
14
Pages
3839-3854
Language
english
Notes
Publication types: Journal Article ; Meta-Analysis ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
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.
Keywords
Adult, Brain/diagnostic imaging, Brain/physiology, Brain Mapping, Humans, Magnetic Resonance Imaging, Neurofeedback/physiology, Practice, Psychological, Prognosis, fMRI, functional neuroimaging, learning, meta-analysis, neurofeedback, real-time fMRI
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 100014_178841
Swiss National Science Foundation / 32003B_166566
Swiss National Science Foundation / BSSG10_155915
Create date
06/01/2021 12:55
Last modification date
13/04/2024 7:05
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