  There has been an explosion of interest in functional Magnetic Resonance
Imaging (MRI) during the past two decades. Naturally, this has been accompanied
by many major advances in the understanding of the human connectome. These
advances have served to pose novel challenges as well as open new avenues for
research. One of the most promising and exciting of such avenues is the study
of functional MRI in real-time. Such studies have recently gained momentum and
have been applied in a wide variety of settings; ranging from training of
healthy subjects to self-regulate neuronal activity to being suggested as
potential treatments for clinical populations. To date, the vast majority of
these studies have focused on a single region at a time. This is due in part to
the many challenges faced when estimating dynamic functional connectivity
networks in real-time. In this work we propose a novel methodology with which
to accurately track changes in functional connectivity networks in real-time.
We adapt the recently proposed SINGLE algorithm for estimating sparse and
temporally homo- geneous dynamic networks to be applicable in real-time. The
proposed method is applied to motor task data from the Human Connectome Project
as 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 changes
in network structure quickly enough to be useful in future brain-computer
interface applications.
