Dynamic functional brain network analysis using hidden semi-Markov models
Dynamic functional brain network analysis using hidden semi-Markov models
Speaker: Dr. Heather Shappell, Assistant Professor of Biostatistics and Data Science, Wake Forest University School of Medicine
Moderated by Kia Teymourian
April 22, 2021
Abstract: A great deal of evidence now supports the theory that the brain is a system of interacting regions that produce complex behaviors. Functional magnetic resonance imaging (fMRI) data can be used to measure whole brain activity and extract information on which regions or components of the brain interact/communicate (i.e., are functionally connected). While much of the previous brain network literature is based on one average network constructed using data from the entire fMRI scan (i.e., static connectivity), emerging evidence suggests brain network topology exhibits meaningful variations within an fMRI experiment (i.e., dynamic connectivity). I propose a hidden semi-Markov model (HSMM) approach for inferring functional brain networks from fMRI data. Specifically, I propose using HSMMs to find each subject’s most probable series of network states during the course of a scan, the cumulative time spent in each state, and the probabilities of transitioning from one state to another. I will discuss findings from a study where we applied this analysis approach on fMRI data from a cohort of children with Attention-Deficit/Hyperactivity Disorder (ADHD). Finally, I will conclude with a discussion of future research questions and directions.
Speaker Bio: Heather Shappell is currently an Assistant Professor of Biostatistics and Data Science at Wake Forest University. She completed a postdoctoral fellowship in July of 2020 in the Department of Biostatistics at Johns Hopkins University, Bloomberg School of Public Health, where she was awarded a Provost Fellowship to continue her research efforts developing statistical methods to analyze fMRI data. She earned her PhD in Biostatistics from Boston University in 2017, as well as her Masters in Biostatistics from Boston University in 2013. Heather obtained her Bachelor’s degree in Mathematics and Computer Science from Arcadia University in 2011. Her current research interests include the statistical analysis of network data, with a particular focus on applications to neuroscience and brain networks. She has also been involved in the statistical analyses for several clinical trials, including clinical trials for treatment of the rare disease, Progeria, as well as in the analyses for observational studies on mental illness.