Switching Models of Oscillatory Networks Greatly Improve Inference of Dynamic Functional Connectivity

TL;DR

This paper introduces a novel modeling framework for dynamic functional brain connectivity that leverages discrete network modes and oscillatory components, significantly enhancing inference accuracy in rapidly changing neural networks.

Contribution

The paper proposes a new set of models combining switching states and oscillators, with an EM algorithm for improved estimation of dynamic brain networks.

Findings

Models show increased statistical power in simulations.

Effective in capturing rapid network changes.

Robust to some model misspecifications.

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 at each moment as well as how it is shifting through time. In this paper, we describe a general modeling framework and a set of specific models that provides substantially increased statistical power for estimating rhythmic dynamic networks, based on the assumption that for a particular experiment or task, the network state at any moment is chosen from a discrete set of possible network modes. 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 observed activity at each electrode 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 at each 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.