Human Brain MappingNeurosciencePeer-reviewed journal article
Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions
Zhiying ZhaoiD, Shuxia YaoiD, Jana ZweeringsiD, Xinqi ZhouiD, Feng ZhouiD, Keith Maurice KendrickiD, Huafu CheniD, Klaus MathiakiD, Benjamin BeckeriD
Abstract
Real-time fMRI guided neurofeedback training has gained increasing interest as a non-invasive brain regulation technique with the potential to normalize functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI datasets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three datasets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning the finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.
- Citations
- 37
- FWCI
- 3.68
- Field percentile
- top 6%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?
Amelie Haugg, Ronald Sladky, … Frank Scharnowski·Human Brain MappingPeer-reviewed·Jul 30, 2020
- Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis
Amelie Haugg, Fabian M. Renz, … David Steyrl·bioRxiv·Oct 21, 2020
- Neural Mechanisms of Feedback Processing and Regulation Recalibration during Neurofeedback Training
Gustavo Santo Pedro Pamplona, Jana Zweerings, … Frank Scharnowski·Human Brain MappingPeer-reviewed·Jul 1, 2025
- Online self-evaluation of fMRI-based neurofeedback performance
Santiago Muñoz-Moldes, Anita Tursic, … Rainer Goebel·Philosophical Transactions of the Royal Society B Biological SciencesPeer-reviewed·Oct 21, 2024
- Motion and physiological noise effects on amygdala real-time fMRI neurofeedback learning
Lydia Hellrung, Viola Borchardt, … Johan N. van der Meer·bioRxiv·Jul 13, 2018
- The role of superstition of cognitive control during neurofeedback training
Doris Grössinger, Florian Ph. S. Fischmeister, … Guilherme Maia de Oliveira Wood·bioRxiv·Sep 14, 2021