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Title: Hidden Markov and semi-Markov Models for dynamic connectivity analysis in resting-state fMRI Authors:  Mark Fiecas - University of Minnesota (United States) [presenting]
Abstract: Motivated by a study on adolescent mental health, a dynamic connectivity analysis is conducted using resting-state functional magnetic resonance imaging (fMRI) data. A dynamic connectivity analysis investigates how the interactions between different regions of the brain, represented by the different dimensions of a multivariate time series, change over time. Hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) are common analytic approaches for conducting dynamic connectivity analyses. We will give an overview of HMMs and HSMMs and their utility of dynamic connectivity analysis. We will describe how we can assess model fit using pseudo-residuals. We use these models to conduct a dynamic connectivity analysis on fMRI data obtained from female adolescents, where we show how dwell-time distributions vary across the severity of non-suicidal self-injury (NSSI). We will provide empirical evidence of the limitations of HMMs and HSMMs with respect to model fit, and discuss potential steps for further development of these models for dynamic connectivity analysis.