Real-time fMRI data for testing OpenNFT functionality
Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-10-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340917303517 |
| _version_ | 1818440960857604096 |
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| author | Yury Koush John Ashburner Evgeny Prilepin Ronald Sladky Peter Zeidman Sergei Bibikov Frank Scharnowski Artem Nikonorov Dimitri Van De Ville |
| author_facet | Yury Koush John Ashburner Evgeny Prilepin Ronald Sladky Peter Zeidman Sergei Bibikov Frank Scharnowski Artem Nikonorov Dimitri Van De Ville |
| author_sort | Yury Koush |
| collection | DOAJ |
| description | Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware. In accordance with the descriptions in our main article, we provide data of (1) periodically displayed (intermittent) activation-based feedback; (2) intermittent effective connectivity feedback, based on dynamic causal modeling (DCM) estimations; and (3) continuous classification-based feedback based on support-vector-machine (SVM) estimations. The data is available on our public GitHub repository:
https://github.com/OpenNFT/OpenNFT_Demo/releases. |
| first_indexed | 2024-12-14T18:20:40Z |
| format | Article |
| id | doaj.art-759d4945e9be431ea1e62975232eeadf |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-14T18:20:40Z |
| publishDate | 2017-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-759d4945e9be431ea1e62975232eeadf2022-12-21T22:52:05ZengElsevierData in Brief2352-34092017-10-0114C34434710.1016/j.dib.2017.07.049Real-time fMRI data for testing OpenNFT functionalityYury Koush0John Ashburner1Evgeny Prilepin2Ronald Sladky3Peter Zeidman4Sergei Bibikov5Frank Scharnowski6Artem Nikonorov7Dimitri Van De Ville8Department of Radiology and Medical Imaging, Yale University, New Haven, USAWellcome Trust Centre for Neuroimaging, University College London, London, UKAligned Research Group, 20 S Santa Cruz Ave 300, 95030 Los Gatos, CA, USADepartment of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, SwitzerlandWellcome Trust Centre for Neuroimaging, University College London, London, UKSupercomputers and Computer Science Department, Samara National Research University, Moskovskoe shosse str., 34, 443086 Samara, RussiaDepartment of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, SwitzerlandAligned Research Group, 20 S Santa Cruz Ave 300, 95030 Los Gatos, CA, USAInstitute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, SwitzerlandHere, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware. In accordance with the descriptions in our main article, we provide data of (1) periodically displayed (intermittent) activation-based feedback; (2) intermittent effective connectivity feedback, based on dynamic causal modeling (DCM) estimations; and (3) continuous classification-based feedback based on support-vector-machine (SVM) estimations. The data is available on our public GitHub repository: https://github.com/OpenNFT/OpenNFT_Demo/releases.http://www.sciencedirect.com/science/article/pii/S2352340917303517OpenNFTNeurofeedbackReal-time fMRIActivityConnectivityMultivariate pattern analysis |
| spellingShingle | Yury Koush John Ashburner Evgeny Prilepin Ronald Sladky Peter Zeidman Sergei Bibikov Frank Scharnowski Artem Nikonorov Dimitri Van De Ville Real-time fMRI data for testing OpenNFT functionality Data in Brief OpenNFT Neurofeedback Real-time fMRI Activity Connectivity Multivariate pattern analysis |
| title | Real-time fMRI data for testing OpenNFT functionality |
| title_full | Real-time fMRI data for testing OpenNFT functionality |
| title_fullStr | Real-time fMRI data for testing OpenNFT functionality |
| title_full_unstemmed | Real-time fMRI data for testing OpenNFT functionality |
| title_short | Real-time fMRI data for testing OpenNFT functionality |
| title_sort | real time fmri data for testing opennft functionality |
| topic | OpenNFT Neurofeedback Real-time fMRI Activity Connectivity Multivariate pattern analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2352340917303517 |
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