Full Citation

Title: Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data

Citation Type: Journal Article

Publication Year: 2020

ISBN:

ISSN: 01679473

DOI: 10.1016/j.csda.2020.107006

NSFID:

PMCID:

PMID:

Abstract: In functional magnetic resonance imaging (fMRI), there is a rise in evidence that time-varying functional connectivity, or dynamic functional connectivity (dFC), which measures changes in the synchronization of brain activity, provides additional information on brain networks not captured by time-invariant (i.e., static) functional connectivity. While there have been many developments for statistical models of dFC in resting-state fMRI, there remains a gap in the literature on how to simultaneously model both dFC and time-varying activation when the study participants are undergoing experimental tasks designed to probe at a cognitive process of interest. A method is proposed to estimate dFC between two regions of interest (ROIs) in task-based fMRI where the activation effects are also allowed to vary over time. The proposed method, called TVAAC (time-varying activation and connectivity), uses penalized splines to model both time-varying activation effects and time-varying functional connectivity and uses the bootstrap for statistical inference. Simulation studies show that TVAAC can estimate both static and time-varying activation and functional connectivity, while ignoring time-varying activation effects would lead to poor estimation of dFC. An empirical illustration is provided by applying TVAAC to analyze two subjects from an event-related fMRI learning experiment.

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Authors: Park, Jun Young; Polzehl, Joerg; Chatterjee, Snigdhansu; Brechmann, André; Fiecas, Mark

Periodical (Full): Computational Statistics and Data Analysis

Issue:

Volume: 150

Pages: 107006

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