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A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
Published on: August 24, 2017
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Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox
João R Sato1, Rodrigo Basilio, Fernando F Paiva
1Cognitive and Behavioral Neuroscience Unit and Neuroinformatics Workgroup, D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil ; Center of Mathematics, Computation and Cognition, Universidade Federal do ABC, Santo André, Brazil.
Plos One
|December 7, 2013
Summary
This study introduces FRIEND, a user-friendly software package for functional MRI (fMRI) neurofeedback and brain decoding. It enables researchers to easily conduct advanced neurofeedback experiments using real-time fMRI data.
Area of Science:
- Neuroscience
- Neuroimaging
- Computational Neuroscience
Background:
- Human ability to modulate brain activity via neurofeedback offers significant potential for neuroscience research and applications.
- While electroencephalography (EEG)-based neurofeedback is established, functional magnetic resonance imaging (fMRI)-based neurofeedback provides superior spatial resolution for deep brain region analysis.
- Advancements in computational methods like Support Vector Machines (SVM) for pattern recognition enhance fMRI neurofeedback and brain decoding capabilities, driving innovation in neuromodulation and functional plasticity.
Purpose of the Study:
- To introduce FRIEND, an open-source, user-friendly graphical interface package designed for fMRI neurofeedback and real-time multivoxel pattern decoding.
- To provide researchers with an integrated, accessible tool for conducting advanced neuroimaging experiments.
- To facilitate the expansion of fMRI neurofeedback applications through accessible and flexible software solutions.
Main Methods:
- FRIEND integrates real-time image preprocessing, Region of Interest (ROI)-based feedback (BOLD signal and functional connectivity), and SVM-based brain decoding.
- The package utilizes optimized procedures and widely validated external packages like FSL and libSVM.
- A user-defined visual neurofeedback module allows for flexible experiment design using ROI-based or multivariate classification approaches.
Main Results:
- FRIEND offers an intuitive graphical interface for designing and executing fMRI neurofeedback experiments.
- The software supports both ROI-based and multivariate classification feedback methods.
- It provides real-time processing capabilities for image preprocessing, feedback, and brain decoding.
Conclusions:
- FRIEND is a valuable, open-source tool for the neuroimaging community, simplifying complex fMRI neurofeedback and brain decoding tasks.
- The package promotes accessibility and ease of use for researchers, fostering further development in neuromodulation and functional plasticity studies.
- Availability of tutorials and documentation ensures user support and facilitates adoption.
