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

Jun Young Park, Joerg Polzehl, Snigdhansu Chatterjee, André Brechmann and Mark Fiecas

Computational Statistics & Data Analysis, 2020, vol. 150, issue C

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.

Keywords: Bootstrap; Dynamic functional connectivity; Penalized splines; Task-based fMRI; Time-varying activation; TVAAC (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:150:y:2020:i:c:s0167947320300979

DOI: 10.1016/j.csda.2020.107006

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