Dynamic functional brain network analysis using hidden semi-Markov models
On Thursday, April 22 at 2 pm EDT join the Department of Computer Science at BU MET for its next research seminar with Dr. Heather Shappell, assistant professor of biostatistics and data science at Wake Forest University School of Medicine. Entitled “Dynamic functional brain network analysis using hidden semi-Markov models,” this virtual seminar will be moderated by Dr. Kia Teymourian, assistant professor of computer science and coordinator of programming languages.
The abstract for “Dynamic functional brain network analysis using hidden semi-Markov models” is as follows:
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.
Archived seminars from this series can be found on the Department of Computer Science events web page.