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Connectome-based neurofeedback: A pilot study to improve sustained attention.

Dustin Scheinost1, Tiffany W Hsu2, Emily W Avery3

  • 1Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Child Study Center, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA.

Neuroimage
|March 2, 2020
PubMed
Summary

Connectome-based neurofeedback uses real-time fMRI to train complex brain networks. This novel approach shows technical feasibility for real-time whole-brain functional connectivity but did not improve attention in a pilot study.

Keywords:
AttentionConnectome-based predictive modelingFunctional connectivityNeurofeedbackReal-time fMRI

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback offers non-invasive therapeutic potential for behavioral training and symptom alleviation.
  • Existing rt-fMRI methods typically focus on limited brain regions or connections, despite growing evidence of complex network interactions in behavior and symptoms.

Purpose of the Study:

  • To introduce and demonstrate the technical feasibility of connectome-based neurofeedback, a novel rt-fMRI method utilizing whole-brain functional networks.
  • To assess the accuracy of this method in providing feedback on a specific network model of sustained attention (saCPM).

Main Methods:

  • Development and validation of a real-time whole-brain functional connectivity calculation for intermittent neurofeedback.
  • Application of connectome-based neurofeedback targeting the sustained attention Connectome-based Predictive Model (saCPM) during task performance.

Main Results:

  • Demonstrated the technical feasibility of calculating whole-brain functional connectivity in real-time.
  • Showed that the connectome-based approach could accurately provide feedback on saCPM strength during tasks.
  • In a pilot sample, saCPM-based neurofeedback did not significantly improve out-of-scanner attention task performance.

Conclusions:

  • Connectome-based neurofeedback is technically feasible for real-time assessment of complex brain network dynamics.
  • Further research is needed to optimize network targets, training duration, and feedback parameters to enhance the efficacy of rt-fMRI for clinical applications.