The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.
The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments.
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DOI：
10.1038/s41597-021-00845-7
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发表时间：
2021-02-23
期刊：
影响因子：
9.8
通讯作者：
Kawato M
中科院分区：
文献类型：
--
作者：
Cortese A;Tanaka SC;Amano K;Koizumi A;Lau H;Sasaki Y;Shibata K;Taschereau-Dumouchel V;Watanabe T;Kawato M
解码神经反馈（DecNef）是一种结合机器学习方法的闭环功能磁共振成像（fMRI）形式，具有一定的临床应用前景。然而，目前只有少数研究小组有机会进行这样的实验;此外，科学家还没有现有的公共数据集来分析和研究一些能够操纵大脑动力学的因素。我们在这里发布的数据来自已发表的DecNef研究，包括5个独立的fMRI数据集，每个数据集都记录了每个参与者的多个会话。对于每个参与者，数据包括一个用于主实验的训练机器学习解码器的会话，以及几个（3到10个）闭环fMRI神经强化会话。这个大型数据集目前包括60多名参与者，将对整个fMRI社区以及试图了解脑动力学非侵入性调制机制的研究人员有用。最后，随着时间的推移，数据收集规模将增加，因为将添加新运行的DecNef研究的数据。描述报告数据的机器可访问元数据文件：10.6084/m9.figshare.13578008
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. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13578008
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