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

dc.contributor.authorPark, Jun Young
dc.contributor.authorPolzehl, Joerg
dc.contributor.authorChatterjee, Snigdhansu
dc.contributor.authorBrechmann, André
dc.contributor.authorFiecas, Mark
dc.date.accessioned2025-03-11T14:43:11Z
dc.date.available2025-03-11T14:43:11Z
dc.date.issued2020-10-01
dc.description.abstractIn 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.
dc.description.sponsorshipThis research was supported by the National Science Foundation (NSF) [grant numbers # DMS-1622483, # DMS1737918]; the German Science Foundation (DFG) [grant number # BR 2267/9-1]; and European Union EFRE grant [grant number # ZS/2017/10/88785].
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0167947320300979
dc.format.extent14 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m2etdx-wwj9
dc.identifier.citationPark, Jun Young, Joerg Polzehl, Snigdhansu Chatterjee, André Brechmann, and Mark Fiecas. “Semiparametric Modeling of Time-Varying Activation and Connectivity in Task-Based FMRI Data.” Computational Statistics & Data Analysis 150 (October 1, 2020): 107006. https://doi.org/10.1016/j.csda.2020.107006.
dc.identifier.urihttps://doi.org/10.1016/j.csda.2020.107006
dc.identifier.urihttp://hdl.handle.net/11603/37807
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International CC BY-NC-ND 4.0 Deed
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTime-varying activation
dc.subjectDynamic functional connectivity
dc.subjectTVAAC
dc.subjectTask-based fMRI
dc.subjectBootstrap
dc.subjectPenalized splines
dc.titleSemiparametric modeling of time-varying activation and connectivity in task-based fMRI data
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-7986-0470

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