Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
This work addresses the interpretation of dynamic functional connectivity in neuroimaging, highlighting methodological challenges for researchers in the field, though it is incremental as it builds on existing models.
The authors tackled the problem of modeling dynamic functional connectivity in fMRI data without predefined window lengths or state numbers, finding that extracted states are driven by subject variability and preprocessing differences, while individual states are defined by task or rest conditions.
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.