Motion Correction for fMRI data using Conditional Transition Regime Switching General Autoregressive Conditional Heteroskedasticity Models

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  • March 20, 2019
Creator
  • Henry, Teague
    • Affiliation: College of Arts and Sciences, Department of Psychology and Neuroscience
Abstract
  • This dissertation develops the Conditional Transition Regime Switching General Autoregressive Conditional Heteroskedasticity Model (CTRS-GARCH) for motion correction of functional magnetic resonance imaging (fMRI) region of interest (ROI) time series. This methodology brings together finite mixtures, hidden Markov modeling, proportional odds modeling, and multivariate volatility analysis to develop a non-destructive (in the sense it does not remove time points) method for removing the influence of motion from the correlation matrices that result from functional connectivity analyses on fMRI data. This dissertation develops the analytics and estimation procedures for the CTRS-GARCH, evaluates the performance using simulations, and uses the CTRS-GARCH to evaluate motion artifacts on an empirical dataset.
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Rights statement
  • In Copyright
Advisor
  • Giovanello, Kelly
  • Gates, Kathleen
  • Pipiras, Vladas
  • Thissen, David
  • Curran, Patrick
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2017
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