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

Amelie Haugg, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob N. Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Jeff MacInnes, R. Alison Adcock, Kathryn Dickerson, Nan Kuei Chen, Kymberly Young, Jerzy Bodurka, Shuxia YaoBenjamin Becker, Tibor Auer, Renate Schweizer, Gustavo Pamplona, Kirsten Emmert, Sven Haller, Dimitri Van De Ville, Maria Laura Blefari, Dong Youl Kim, Jong Hwan Lee, Theo Marins, Megumi Fukuda, Bettina Sorger, Tabea Kamp, Sook Lei Liew, Ralf Veit, Maartje Spetter, Nikolaus Weiskopf, Frank Scharnowski

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3839-3854
Number of pages16
JournalHuman Brain Mapping
Volume41
Issue number14
DOIs
Publication statusPublished - 2020 Oct 1

Keywords

  • fMRI
  • functional neuroimaging
  • learning
  • meta-analysis
  • neurofeedback
  • real-time fMRI

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

Fingerprint Dive into the research topics of 'Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?'. Together they form a unique fingerprint.

Cite this