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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number107006
JournalComputational Statistics and Data Analysis
Volume150
DOIs
StatePublished - Oct 2020

Bibliographical note

Funding Information:
The authors would like to thank co-editor, associate editor, and reviewers for their helpful and constructive comments. The research was supported by the Minnesota Supercomputing Institute (MSI).

Funding Information:
This research was supported by the National Science Foundation (NSF) [grant numbers # DMS-1622483 , # DMS-1737918 ]; the German Science Foundation (DFG) [grant number # BR 2267/9-1 ]; and European Union EFRE grant [grant number # ZS/2017/10/88785 ].

Funding Information:
The authors would like to thank co-editor, associate editor, and reviewers for their helpful and constructive comments. The research was supported by the Minnesota Supercomputing Institute (MSI). This research was supported by the National Science Foundation (NSF) [grant numbers # DMS-1622483, # DMS-1737918]; the German Science Foundation (DFG) [grant number # BR 2267/9-1]; and European Union EFRE grant [grant number # ZS/2017/10/88785].

Publisher Copyright:
© 2020 Elsevier B.V.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Bootstrap
  • Dynamic functional connectivity
  • Penalized splines
  • TVAAC
  • Task-based fMRI
  • Time-varying activation

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