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.

解码神经反馈（DecNef）是闭环功能磁共振成像（fMRI）与机器学习方法结合的一种形式，为临床应用带来了一些希望。但是，目前只有少数研究小组有机会进行此类实验。此外，目前还没有公开的数据集供科学家分析和研究一些能够操纵大脑动力学的因素。我们在这里发布来自已发表的DecNef研究的数据，该研究由5个独立的fMRI数据集组成，每个数据集每个参与者记录了多个会话。对于每个参与者，数据由一个在主要实验中用于训练机器学习解码器的会话以及几个（从3到10个）闭环fMRI神经强化会话组成。大型数据集 目前由60多名参与者组成，这将对整个fMRI社区以及试图了解无创调节脑部动力学机制的研究人员有用。最后，随着新运行的DecNef研究数据的添加，数据收集规模将随着时间的推移而增加。