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Real-time fMRI data for testing OpenNFT functionality
Yury Koush1,2,3, John Ashburner4, Evgeny Prilepin5
1Department of Radiology and Medical Imaging, Yale University, New Haven, USA.
Data in Brief
|August 11, 2017
Summary
This study provides real-time fMRI data for the Open NeuroFeedback Training (OpenNFT) framework. The dataset enables testing neurofeedback simulations and software functionality without an MRI scanner.
Area of Science:
- Neuroscience
- Neuroimaging
- Computational Neuroscience
Background:
- Real-time functional magnetic resonance imaging (fMRI) is crucial for neurofeedback.
- Open-source software facilitates reproducible neurofeedback research.
- Testing neurofeedback protocols requires accessible, standardized data.
Purpose of the Study:
- To provide a real-time fMRI dataset for testing the Open NeuroFeedback Training (OpenNFT) framework.
- To enable simulation of various neurofeedback paradigms.
- To facilitate evaluation of software performance and data processing times.
Main Methods:
- The dataset includes real-time fMRI runs from three participants.
- Structural scans, ROIs, masks, and weights are provided.
- Data supports intermittent activation-based, DCM-based connectivity, and SVM-based classification feedback.
Main Results:
- The dataset allows for offline simulation of neurofeedback experiments.
- It facilitates exploration of OpenNFT software functionality.
- Processing times can be measured on local hardware.
Conclusions:
- The OpenNFT dataset supports the testing and development of open-source neurofeedback tools.
- Accessible data promotes reproducible research in real-time fMRI neurofeedback.
- This resource aids in understanding the performance of different feedback modalities.
