Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data

TL;DR

This paper introduces a non-parametric Bayesian model for analyzing dynamic functional connectivity in fMRI data, avoiding fixed window lengths and state numbers, and investigates what factors influence these dynamic states.

Contribution

It presents a novel non-parametric generative model for dynamic FC that does not require predefining window sizes or number of states, advancing analysis flexibility.

Findings

Number of states varies with subject and preprocessing

States are mainly driven by task or rest conditions

Model can discriminate between task and rest within and across subjects

Abstract

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.