The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments
Open Access
- 23 February 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Data
- Vol. 8 (1), 1-9
- https://doi.org/10.1038/s41597-021-00845-7
Abstract
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.Funding Information
- Japan Agency for Medical Research and Development (JP18dm0307008, JP18dm0307008)
- MEXT | JST | Exploratory Research for Advanced Technology (JPMJER1801, JPMJER1801)
- U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R61MH113772)
- U.S. Department of Health & Human Services | National Institutes of Health (R01NS088628)
- MEXT | Japan Society for the Promotion of Science (19H01041)
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