  Dynamic functional connectivity (FC) has in recent years become a topic of
interest in the neuroimaging community. Several models and methods exist for
both functional magnetic resonance imaging (fMRI) and electroencephalography
(EEG), and the results point towards the conclusion that FC exhibits dynamic
changes. The existing approaches modeling dynamic connectivity have primarily
been based on time-windowing the data and k-means clustering. We propose a
non-parametric generative model for dynamic FC in fMRI that does not rely on
specifying window lengths and number of dynamic states. Rooted in Bayesian
statistical modeling we use the predictive likelihood to investigate if the
model can discriminate between a motor task and rest both within and across
subjects. We further investigate what drives dynamic states using the model on
the entire data collated across subjects and task/rest. We find that the number
of states extracted are driven by subject variability and preprocessing
differences while the individual states are almost purely defined by either
task or rest. This questions how we in general interpret dynamic FC and points
to the need for more research on what drives dynamic FC.
