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Real-time fMRI data for testing OpenNFT functionality


Koush, Yury; Ashburner, John; Prilepin, Evgeny; Sladky, Ronald; Zeidman, Peter; Bibikov, Sergei; Scharnowski, Frank; Nikonorov, Artem; Van De Ville, Dimitri (2017). Real-time fMRI data for testing OpenNFT functionality. Data in Brief, 14:344-347.

Abstract

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.

Abstract

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.

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Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
04 Faculty of Medicine > Neuroscience Center Zurich
04 Faculty of Medicine > Center for Integrative Human Physiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Uncontrolled Keywords:Activity, Connectivity, Multivariate pattern analysis, Neurofeedback, OpenNFT, Real-time fMRI
Language:English
Date:October 2017
Deposited On:25 Sep 2017 15:46
Last Modified:19 Aug 2018 10:21
Publisher:Elsevier
ISSN:2352-3409
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.dib.2017.07.049
PubMed ID:28795112
Project Information:
  • : FunderSNSF
  • : Grant IDP300PB_161083
  • : Project TitleReal-time functional MRS of lactate-based neurofeedback: controlling metabolic activity
  • : FunderSNSF
  • : Grant ID32003B_166566
  • : Project TitleEnhancing functional connectivity in prefrontal networks to test and improve self-control mechanisms in decision-making
  • : FunderSNSF
  • : Grant IDBSSGI0_155915
  • : Project TitleTreatment of Human Brain Dysfunction with Neurofeedback.
  • : FunderSNSF
  • : Grant IDPZ00P3_131932
  • : Project TitleModulating Human Brain Function and Dysfunction with Neurofeedback

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