|
Abstract:
|
The study of functional brain networks has grown tremendously over the past decade. Most functional connectivity (FC) analyses assume that FC networks are stationary across time. However, there is interest in studying changes in FC over time. Hidden Markov models (HMMs) are a useful modeling approach for FC. However, a severe limitation is that HMMs assume the sojourn time (number of consecutive time points in a state) is geometrically distributed. This may encourage state switches too often. We propose a hidden semi-Markov model (HSMM) approach for inferring functional 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, the cumulative time in each state, and the networks associated with each state. The estimated model provides additional information, including the probabilities of transitioning from one state to another, sojourn distributions, and the initial state probabilities. We demonstrate our approach on fMRI data from an anxiety-inducing experiment, where the algorithm was agnostic to alignment, and yet discovered the alignment near perfectly.
|
