Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data(2018)
In functional magnetic resonance imaging (fMRI), there is a rise in evidence that the temporal change in the synchronization of brain activity, known as dynamic functional connectivity (dFC) or time-varying connectivity, provides additional information on brain networks not captured by measures of connectivity that is static over time. While there have been many developments for statistical models for dFC when the study participants are at rest, there remains a gap in the literature on how to simultaneously model both dFC and time-varying activation when the study participants are undergoing an experimental task designed to probe at a cognitive process of interest. We propose a method to estimate the dFC between two regions of interest (ROI) in task-based fMRI where the activation effects are also allowed to vary over time. Our method uses penalized splines to model both time-varying activation effects and time-varying connectivity, and uses the bootstrap for statistical inference. We validate our approach using simulations and show that ignoring time-varying activation effects would lead to poor estimation of dFC. Our proposed model, called TVAAC (time-varying activation and connectivity), can estimate the both static and time-varying activation and functional connectivity. We give an empirical illustration of both time-varying activation and connectivity by using our proposed method to analyze two subjects in an event-related fMRI learning experiment. R codes for implementing our method are publicly available at https://github.com/mfiecas/tvaac.
- Dynamic functional connectivity,
- Penalized Splines,
- Task-based fMRI,
- Time-varying activation
Citation InformationJun Young Park, Joerg Polzehl, Snigdhansu Chatterjee, Andre Brechmann, et al.. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data" (2018)
Available at: http://works.bepress.com/mfiecas/20/