Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models

• Published in: NeuroImage (Orlando, Fla.) Vol. 191; pp. 243 - 257
• Main Authors: Shappell, Heather, Caffo, Brian S., Pekar, James J., Lindquist, Martin A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2019; Elsevier Limited
• Subjects: Adult; Anxiety; Brain; Brain - physiology; Brain mapping; Brain networks; Connectome - methods; Dynamic functional connectivity; Estimates; Female; fMRI; Functional magnetic resonance imaging; Hidden Markov models; Hidden semi-Markov models; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Markov analysis; Markov Chains; Models, Neurological; Nerve Net - physiology; Neural networks; Neuroimaging; Sojourn distribution; Time series; Wavelet transforms; Hidden semi-Markov models; fMRI; Sojourn distribution; Dynamic functional connectivity; Brain networks; Hidden Markov models
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.02.013

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms. •The HSMM and HMM perform similarly when estimating dynamic brain states.•The HSMM is more flexible than the HMM when estimating sojourn times.•The HMM and HSMM estimate different empirical sojourn distributions in rs-fMRI.•Multidimensional scaling reveals rs-fMRI states are reproducible.•Sojourn distributions are associated with sustained attention scores.