UC Riverside

There has been recent growth in using hidden Markov models (HMMs) to chronicle the latent spatiotemporal dynamics of brain activity acquired from functional magnetic resonance imaging (fMRI). This technique can be used to model resting state data or dynamic processing, such as attention. The locus coeruleus (LC), a small subcortical structure, is the main source of norepinephrine throughout the brain and therefore is involved in modulating attention and arousal. An HMM can be used to identify latent fMRI-based brain states comprised of a combination of networks, and to determine how the behavior of these brain states change as a function of attentional perturbations.The purpose of this investigation is two pronged. We aimed to create a comprehensive theoretical and empirical overview of various HMM subtypes in effort to make informed decisions about which should be used under different circumstances for future investigations. We then fit this probabilistic model to an fMRI dataset optimized to image the effects of the LC to gain insight into its dynamic relationship with attention and arousal. To accomplish this, we first theoretically contrasted three HMM subtypes, then applied them to an fMRI resting state dataset to obtain an empirical understanding of the strengths and weaknesses of each one. One model type, an activation-based HMM, was applied to a pseudo-resting state dataset where LC activity was noninvasively up-regulated via a handgrip task. This aimed to analyze how HMM-related measure focusing on attentional networks changed as a function of actively squeezing a squeeze-ball. We found that an activation-based HMM is ideal for capturing temporal dynamics and is preferred if activation and connectivity state patterns are to be analyzed in conjunction. An HMM where functional connectivity values were summed at each time point is advantageous if a study’s goal is to examine the general connectedness between nodes. We also noted that the HMM subtype fitted to all unique correlation values from a sliding window analysis is preferred if an investigation wishes to explore specific nodal connectivity. Our findings from HMM-based measures extricated from the LC-focused dataset show potential evidence of norepinephrine depletion and attentional allocation.