The DecNef data collection: fMRI data from closed-loop decoded neurofeedback experiments

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.