Abstract
Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels—group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.
Highlights
The assessment of dynamic functional connectivity, estimated by finding the time-varying association between time series of brain region pairs, is a recent expansion of functional connectivity (FC)
We examined a model of non-linearly changing time-dependent associations between the time series of representative pairs of brain regions (Figure 3)
To validate Dynamic functional connectivity (dFC) estimates, we assessed associations of the right sensorimotor cortex (SMC; precentral gyrus region) to three other somatomotor network (SMN) regions, which showed expected positive associations for both flavors and no significant differences between flavors (Figures 3D–F)
Summary
The assessment of dynamic functional connectivity (dFC), estimated by finding the time-varying association between time series of brain region pairs, is a recent expansion of functional connectivity (FC). Recent studies pointed out non-stationary nature of functional connectivity that was changing during task-related activity and while resting (Hutchison et al, 2012), and indicated that population-based static networks are less informative to uncover neurological illness (Hutchison et al, 2012; Jones et al, 2012). Others addressed the dynamic nature of brain activity in both task-based and task-free designs (Debener et al, 2006; Sadaghiani et al, 2009; Doucet et al, 2012; Cribben et al, 2013; Allen et al, 2014), suggesting an important role for dFC analyses in quantifying time-varying network behavior. DFC is estimated solely in a resting state, without any emitted behavior (Turk-Browne, 2013)
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