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There has been an explosion of interest in functional Magnetic ResonanceImaging (MRI) during the past two decades. Naturally, this has been accompaniedby many major advances in the understanding of the human connectome. Theseadvances have served to pose novel challenges as well as open new avenues forresearch. One of the most promising and exciting of such avenues is the studyof functional MRI in real-time. Such studies have recently gained momentum andhave been applied in a wide variety of settings; ranging from training ofhealthy subjects to self-regulate neuronal activity to being suggested aspotential treatments for clinical populations. To date, the vast majority ofthese studies have focused on a single region at a time. This is due in part tothe many challenges faced when estimating dynamic functional connectivitynetworks in real-time. In this work we propose a novel methodology with whichto accurately track changes in functional connectivity networks in real-time.We adapt the recently proposed SINGLE algorithm for estimating sparse andtemporally homo- geneous dynamic networks to be applicable in real-time. Theproposed method is applied to motor task data from the Human Connectome Projectas well as to real-time data ob- tained while exploring a virtual environment.We show that the algorithm is able to estimate significant task-related changesin network structure quickly enough to be useful in future brain-computerinterface applications.

Measuring the functional connectome "on-the-fly": towards a new control signal for fMRI-based brain-computer interfaces