Real-time fMRI data for testing OpenNFT functionality

Yury Koush; John Ashburner; Evgeny Prilepin; Ronald Sladky; Peter Zeidman; Sergei Bibikov; Frank Scharnowski; Artem Nikonorov; Dimitri Van De Ville

doi:10.1016/j.dib.2017.07.049

Real-time fMRI data for testing OpenNFT functionality

Data Brief. 2017 Jul 26:14:344-347. doi: 10.1016/j.dib.2017.07.049. eCollection 2017 Oct.

Authors

Yury Koush^{1

2

3}, John Ashburner⁴, Evgeny Prilepin⁵, Ronald Sladky^{6

7

8}, Peter Zeidman⁴, Sergei Bibikov⁹, Frank Scharnowski^{6

7

8}, Artem Nikonorov^{5

9}, Dimitri Van De Ville^{2

3}

Affiliations

¹ Department of Radiology and Medical Imaging, Yale University, New Haven, USA.
² Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.
³ Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
⁴ Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
⁵ Aligned Research Group, 20 S Santa Cruz Ave 300, 95030 Los Gatos, CA, USA.
⁶ Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, Switzerland.
⁷ Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057 Zürich, Switzerland.
⁸ Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland.
⁹ Supercomputers and Computer Science Department, Samara National Research University, Moskovskoe shosse str., 34, 443086 Samara, Russia.

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.

Keywords: Activity; Connectivity; Multivariate pattern analysis; Neurofeedback; OpenNFT; Real-time fMRI.