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Updated: Jun 4, 2026
A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
Published on: August 24, 2017
Detection of cerebral reorganization induced by real-time fMRI feedback training of insula activation: a multivariate
Sangkyun Lee1, Sergio Ruiz, Andrea Caria
1University of Tübingen, Tübingen, Germany. lee.sangkyun@gmail.com
Background:
Studies with real-time functional magnetic resonance imaging (fMRI) demonstrate that humans volitionally regulate hemodynamic signals from circumscribed regions of the brain, leading to area-specific behavioral consequences. Methods to better determine the nature of dynamic functional interactions between different brain regions and plasticity due to self-regulation training are still in development.
Objective:
The authors investigated changes in brain states while training 6 healthy participants to self-regulate insular cortex by real-time fMRI feedback.
Method:
The authors used multivariate pattern analysis to observe spatial pattern changes and a multivariate Granger causality model to show changes in temporal interactions in multiple brain areas over the course of 5 repeated scans per subject during positive and negative emotional imagery with feedback about the level of insular activation.
Results:
Feedback training leads to more spatially focused recruitment of areas relevant for learning and emotion. Effective connectivity analysis reveals that initial training is associated with an increase in network density; further training "prunes" presumably redundant connections and "strengthens" relevant connections.
Conclusions:
The authors demonstrate the application of multivariate methods for assessing cerebral reorganization during the learning of volitional control of local brain activity. The findings provide insight into mechanisms of training-induced learning techniques for rehabilitation. The authors anticipate that future studies, specifically designed with this hypothesis in mind, may be able to construct a universal index of cerebral reorganization during skill learning based on multiple similar criteria across various skilled tasks. These techniques may be able to discern recovery from compensation, dose-response curves related to training, and ways to determine whether rehabilitation training is actively engaging necessary networks.
