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Enhancing fMRI Decoded Neurofeedback with Co-adaptive Training: Simulation and Proof-of-principle Evidence.

Najmeddine Abdennour1, Pedro Margolles2, David Soto3

  • 1Basque Center on Cognition, Brain and Language, Paseo Mikeletegi 69, 2nd Floor, 20009, San Sebastian, Spain. n.abdennour@bcbl.eu.

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Summary
This summary is machine-generated.

This study introduces a co-adaptation method to improve real-time fMRI neurofeedback (DecNef) training. This adaptive decoder enhances participants' ability to achieve target brain states, improving DecNef precision and reliability.

Keywords:
Co-adaptationDecoded neurofeedbackMachine learning.Real-time functional magnetic resonance imaging

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Neurofeedback training, particularly fMRI-based decoded neurofeedback (DecNef), faces challenges in participants learning to control specific brain patterns.
  • Discrepancies between decoder training data and real-time neurofeedback data, including noise and differing contexts, contribute to learning difficulties.

Purpose of the Study:

  • To develop and validate a co-adaptation procedure to enhance participant performance in DecNef training.
  • To improve the precision and reliability of DecNef protocols for targeting specific brain representations.

Main Methods:

  • Developed a co-adaptation procedure using standard machine learning algorithms with a real-time adaptive decoder.
  • Tested the procedure using simulations on a previous DecNef dataset.
  • Validated the co-adaptation approach with real-time fMRI data from DecNef training sessions.

Main Results:

  • Simulations demonstrated that decoder co-adaptation significantly improves performance during neurofeedback training.
  • Drift analysis confirmed the stability of the co-adapted decoder throughout training sessions.
  • Real-time fMRI data provided proof-of-concept evidence that co-adaptation enhances participants' ability to induce target brain states.

Conclusions:

  • Personalized decoders created through co-adaptation can enhance the effectiveness of DecNef training protocols.
  • This approach offers improved precision and reliability for targeting specific brain representations, with potential translational research applications.
  • The developed tools are openly available to the scientific community.