Related Experiment Video
Updated: Sep 16, 2025
07:05
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
Published on: August 24, 2017
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Neural Mechanisms of Feedback Processing and Regulation Recalibration During Neurofeedback Training
Gustavo S P Pamplona1,2,3, Jana Zweerings4, Cindy S Lor5
1Department of Ophthalmology/University of Lausanne, SensoriMotorLab, Jules-Gonin Eye Hospital/Fondation Asile Des Aveugles, Lausanne, Switzerland.
Human Brain Mapping
|July 8, 2025
Summary
Functional magnetic resonance imaging (fMRI) neurofeedback learning involves processing feedback in the brain's reward system. This research highlights how performance feedback drives learning and skill acquisition through brain training.
Area of Science:
- Neuroscience
- Cognitive Science
- Brain Imaging
Background:
- Skill acquisition relies on performance feedback for closed-loop learning.
- Functional magnetic resonance imaging (fMRI) neurofeedback offers direct brain activity regulation feedback.
- Understanding neurofeedback learning requires examining feedback evaluation and regulation adjustments.
Purpose of the Study:
- Investigate brain regions associated with feedback processing and regulation recalibration during fMRI neurofeedback training.
- Examine how feedback scores relate to brain activity and connectivity during training.
- Clarify the neural mechanisms underlying neurofeedback-based learning.
Main Methods:
- Mega-analysis of eight pre-registered intermittent fMRI neurofeedback studies (N=153).
- Harmonized feedback scores across studies.
- Parametric general linear model analyses to associate feedback scores with brain activity and connectivity.
Main Results:
- During feedback processing, higher feedback scores correlated with increased activity in the reward system, dorsal attention network, default mode network, and cerebellum.
- Reward system connectivity within the salience network was also linked to feedback scores.
- No significant associations were found between feedback scores and activity or connectivity during regulation recalibration.
Conclusions:
- Neurofeedback processing engages the reward system, supporting reinforcement learning theories for brain training.
- Large-scale network involvement in feedback processing indicates higher-level cognitive functions are crucial for neurofeedback learning.
- Performance-related feedback is a key driver of learning, with implications beyond neurofeedback training.
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