Dr. Heather Shappell: Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models
Oct 21, 2021 to Oct 21, 2021
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 encourages state switches too often.
Current students and faculty in the epidemiology and biostatistics programs are invited to hear from Dr. Heather Shappell, Assistant Professor of Biostatistics and Data Science at Wake Forest University School of Medicine. Her research interests include developing statistical methods for the analysis of fMRI data, statistical methods to estimate dynamic brain networks, and performing the statistical analyses for clinical trials (especially those involving rare diseases) and observational studies.
Dr. Shappell proposes a hidden semi-Markov model (HSMM) approach for inferring functional brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, she proposes 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. This approach is demonstrated on resting-state fMRI data from a cohort of children with Attention-Deficit/Hyperactivity Disorder (ADHD). Lastly, she develops an extension to the HSMM, where the sojourn distribution may depend on a number of covariates. This extension allows for a direct comparison of sojourn times across patient populations.