A1157
Title: Changepoint analysis in a mixed model framework, with applications to fMRI time series
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. Changes in the distributional properties of the data can be captured by identifying the changepoints in the time series data. The presence of changepoints in the fMRI data suggests that the connectivity between different regions of the brain changes over time. An overview of changepoint analysis and the utility of dynamic connectivity analysis is given. The novel approach for changepoint analysis that uses a mixed model framework is then described, thereby leveraging the spatial structure of the brain. The mixed model is embedded in a dynamic programming algorithm for detecting multiple changepoints in the fMRI data. The results of the proposed changepoint model in a dynamic connectivity analysis on fMRI are shown on data obtained from female adolescents.
