Corticostriatal and Dopaminergic Response to Beer Flavor with Both fMRI and [(11) C]raclopride Positron Emission Tomography.

BACKGROUND: Cue-evoked drug-seeking behavior likely depends on interactions between frontal activity and ventral striatal (VST) dopamine (DA) transmission. Using [(11) C]raclopride (RAC) positron emission tomography (PET), we previously demonstrated that beer flavor (absent intoxication) elicited VST DA release in beer drinkers, inferred by RAC displacement. Here, a subset of subjects from this previous RAC-PET study underwent a similar paradigm during functional magnetic resonance imaging (fMRI) to test how orbitofrontal cortex (OFC) and VST blood oxygenation level-dependent (BOLD) responses to beer flavor are related to VST DA release and motivation to drink. METHODS: Male beer drinkers (n = 28, age = 24 ± 2, drinks/wk = 16 ± 10) from our previous PET study participated in a similar fMRI paradigm wherein subjects tasted their most frequently consumed brand of beer and Gatorade(®) (appetitive control). We tested for correlations between BOLD activation in fMRI and VST DA responses in PET, and drinking-related variables. RESULTS: Compared to Gatorade, beer flavor increased wanting and desire to drink, and induced BOLD responses in bilateral OFC and right VST. Wanting and desire to drink correlated with both right VST and medial OFC BOLD activation to beer flavor. Like the BOLD findings, beer flavor (relative to Gatorade) again induced right VST DA release in this fMRI subject subset, but there was no correlation between DA release and the magnitude of BOLD responses in frontal regions of interest. CONCLUSIONS: Both imaging modalities showed a right-lateralized VST response (BOLD and DA release) to a drug-paired conditioned stimulus, whereas fMRI BOLD responses in the VST and medial OFC also reflected wanting and desire to drink. The data suggest the possibility that responses to drug-paired cues may be rightward biased in the VST (at least in right-handed males) and that VST and OFC responses in this gustatory paradigm reflect stimulus wanting.

Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level.

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.