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OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity,

Yury Koush1, John Ashburner2, Evgeny Prilepin3

  • 1Department of Radiology and Medical Imaging, Yale University, New Haven, USA; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

Neuroimage
|June 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces OpenNFT, a new open-source framework for real-time fMRI (rt-fMRI) neurofeedback. It enables advanced training of brain activity and connectivity, facilitating wider access to neurofeedback research and clinical applications.

Keywords:
ActivityConnectivityMultivariate pattern analysisNeurofeedbackOpenNFTReal-time fMRI

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Area of Science:

  • Neuroscience
  • Neuroimaging
  • Computational Neuroscience

Background:

  • Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is an emerging technique for training brain activity and connectivity.
  • Rapid advancements in MRI technology and computing power enable new methodological developments in rt-fMRI neurofeedback.
  • Existing frameworks may lack the flexibility or integration needed for cutting-edge research.

Purpose of the Study:

  • To introduce Open NeuroFeedback Training (OpenNFT), a novel open-source framework for rt-fMRI neurofeedback.
  • To provide a modular, extensible, and user-friendly platform for developing and implementing advanced neurofeedback protocols.
  • To facilitate the integration of rt-fMRI neurofeedback with widely used analysis tools like Statistical Parametric Mapping (SPM).

Main Methods:

  • Development of a Python and Matlab-based open-source neurofeedback framework (OpenNFT).
  • Integration with Statistical Parametric Mapping (SPM) for enhanced data analysis capabilities.
  • Demonstration of framework functionality through case studies using diverse neurofeedback protocols.

Main Results:

  • OpenNFT offers high modularity, extendibility, and an intuitive interface for rt-fMRI neurofeedback.
  • The framework supports various neurofeedback approaches, including activity levels, effective connectivity, and pattern classification.
  • Case studies confirm the framework's capability to implement and test novel neurofeedback paradigms.

Conclusions:

  • OpenNFT provides a robust and adaptable platform for advancing rt-fMRI neurofeedback research.
  • The open-source nature of OpenNFT promotes collaboration and accessibility, enabling broader engagement in neurofeedback development.
  • This framework supports the development of innovative neurofeedback methods and their dissemination to the research community.