Dataset
The DecNef data collection: fMRI data from closed-loop decoded neurofeedback experiments
- 1Computational Neuroscience Labs, ATR Institute International, Kyoto - Japan
- 2Cognitive Mechanisms Labs, ATR Institute International, Kyoto - Japan
- 3CiNet, Osaka - Japan
- 4Computational Neuroscience Labs, ATR Institute International, Kyoto - Japan; CiNet, Osaka - Japan; Sony Computer Science Laboratory, Inc., Tokyo - Japan
- 5Department of Psychology, UCLA, Los Angeles - USA
- 6Computational Neuroscience Labs, ATR Institute International, Kyoto - Japan; Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence - USA
- 7RIKEN CBS, Saitama - Japan
Published 13 Apr. 2020 | License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
Description
Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds high promises for clinical applications. Yet, currently only a few labs have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyze and investigate some of the factors enabling the manipulation of brain dynamics. We release here the full collection of DecNef studies, consisting of 6 separate fMRI datasets, each with multiple sessions recorded per subject. For each subject 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 90 subjects, will be very useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics.
Keywords
| Neuroscience | Neuroimaging | fMRI | Neurofeedback | Neural Reinforcement | Decoding | Cognition | Non-invasive neural modulation | DecNef |References
- Shibata et al. (2011) Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation (Science 2011). https://doi.org/10.1126/science.1212003
- Shibata et al. (2016) Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States (PLoS Biology 2016). https://doi.org/10.1371/journal.pbio.1002546
- Amano et al. (2016) Learning to Associate Orientation with Color in Early Visual Areas by Associative Decoded fMRI Neurofeedback (Current Biology 2016). https://doi.org/10.1016/j.cub.2016.05.014
- Koizumi et al. (2016) Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure (Nature Human Behaviour 2016). https://doi.org/10.1038/s41562-016-0006
- Cortese et al. (2016) Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance (Nature Communications 2016). https://doi.org/10.1038/ncomms13669
- Taschereau-Dumouchel et al. (2018) Towards an unconscious neural reinforcement intervention for common fears (PNAS 2018). https://doi.org/10.1073/pnas.1721572115
Funding
- AMED JP18dm0307008
- JSPS 19H01041
- JST-ERATO JPMJER1801
- NIH R01NS088628
- NIMH R61MH113772