Resting-state neuroimaging modalities such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) collect data in the form of time series which represent the activity in the brain at rest. This resting-state behavior can be analyzed in different ways to address different research questions and is thought to represent the intrinsic activity of the brain. We discuss three potential avenues of analysis. First, we propose a permutation-based method which tests the longitudinal functional connectivity of fMRI data collected from cognitively normal participants and Alzheimer’s patients. Next, we propose a Bayesian nonparametric model to jointly perform spectral time series analysis on EEG data from 1,116 twins from the Minnesota Twin Family Study (MTFS) and discuss a novel heritability estimator for features of the estimated spectral density curves. Finally, we propose another Bayesian nonparametric model to perform EEG microstate analysis of the MTFS data at the twin pair level. Each method discussed views the resting-state time series data from a different angle. Additionally, in each of these scenarios, we jointly analyze data collected from many different participants while accounting for the design of the study in which the data was collected. Regardless of the analysis method chosen, accounting for the within and between-participant dependence structure yields improved results.
University of Minnesota Ph.D. dissertation. May 2019. Major: Biostatistics. Advisors: Mark Fiecas, Lynn Eberly. 1 computer file (PDF); xv, 107 pages.
Methods for Analyzing Multi-Subject Resting-State Neuroimaging Time Series Data.
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