Switching functional network models of oscillatory brain dynamics
OA Version
Citation
Abstract
Functional brain networks can change rapidly as a function of stimuli or cognitive shifts. Tracking dynamic functional connectivity is particularly challenging as it requires estimating the structure of the network and its changes moment to moment. In this dissertation, we describe a general modeling framework and a set of specific models that provide substantially increased statistical power for estimating rhythmic dynamic networks, based on the assumption that for a particular experiment or task there are a discrete set of networks that are expressed at any moment. Each model is comprised of three components: (1) a set of latent switching states that represent transitions between the expression of each network mode; (2) a set of latent oscillators, each characterized by an estimated mean oscillation frequency and an instantaneous phase and amplitude at each time point; and (3) an observation model that relates the activity at each recorded network node to a linear combination of the latent oscillators. We develop an expectation-maximization procedure to estimate the network structure for each switching state and the probability of each state being expressed moment to moment. We conduct a set of simulation studies to illustrate the application of these models and quantify their statistical power, even in the face of model misspecification. Additionally, we demonstrate the application of this model framework through an analysis of EEG data from a subject undergoing general anesthesia.
Description
2024
License
Attribution 4.0 International