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Related Experiment Video

Updated: May 11, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI.

Yury Koush1, Maria Joao Rosa2, Fabien Robineau3

  • 1Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Neuroimage
|May 15, 2013
PubMed
Summary

This study introduces a new neurofeedback method using dynamic causal modeling to train control over brain networks, not just single areas. This advance allows for targeted training of functional brain networks, crucial for mental functions and neurological disorders.

Keywords:
Brain connectivityDynamic causal modeling (DCM)Functional magnetic resonance imaging (fMRI)NeurofeedbackReal-time fMRI

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Last Updated: May 11, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

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Published on: March 21, 2019

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Published on: June 30, 2018

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
07:05

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Neuroimaging

Background:

  • Real-time fMRI neurofeedback trains voluntary brain activity control.
  • Current methods primarily target localized brain activity within specific regions.
  • Behavioral effects are specific to the targeted brain area's function.

Purpose of the Study:

  • To overcome limitations of localized training in real-time fMRI neurofeedback.
  • To develop a method for training voluntary control over functional brain networks.
  • To enable direct targeting of brain networks relevant to cognitive functions and disorders.

Main Methods:

  • Introduced near real-time dynamic causal modeling (DCM) for neurofeedback.
  • Provided feedback based on connectivity between brain areas, not just activity.
  • Utilized a visual-spatial attention paradigm with Bayesian model comparison.

Main Results:

  • Participants could voluntarily control a feedback signal based on DCM.
  • The feedback signal reflected connectivity between specific brain regions (e.g., visual and parietal cortices).
  • Demonstrated the ability to train control over specific functional brain networks.

Conclusions:

  • The novel approach allows neurofeedback based on brain network connectivity.
  • This method enables voluntary control training of specific functional brain networks.
  • Represents a significant methodological advancement for targeting brain networks in neuroscience and clinical applications.