Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data
dc.contributor.author | Park, Jun Young | |
dc.contributor.author | Polzehl, Joerg | |
dc.contributor.author | Chatterjee, Snigdhansu | |
dc.contributor.author | Brechmann, André | |
dc.contributor.author | Fiecas, Mark | |
dc.date.accessioned | 2025-03-11T14:43:11Z | |
dc.date.available | 2025-03-11T14:43:11Z | |
dc.date.issued | 2020-10-01 | |
dc.description.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. | |
dc.description.sponsorship | This 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.uri | https://www.sciencedirect.com/science/article/pii/S0167947320300979 | |
dc.format.extent | 14 pages | |
dc.genre | journal articles | |
dc.genre | postprints | |
dc.identifier | doi:10.13016/m2etdx-wwj9 | |
dc.identifier.citation | Park, 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.uri | https://doi.org/10.1016/j.csda.2020.107006 | |
dc.identifier.uri | http://hdl.handle.net/11603/37807 | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Mathematics and Statistics Department | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International CC BY-NC-ND 4.0 Deed | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Time-varying activation | |
dc.subject | Dynamic functional connectivity | |
dc.subject | TVAAC | |
dc.subject | Task-based fMRI | |
dc.subject | Bootstrap | |
dc.subject | Penalized splines | |
dc.title | Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data | |
dc.type | Text | |
dcterms.creator | https://orcid.org/0000-0002-7986-0470 |
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