ABSTRACT
Objective@#Smoking cue-(SC) elicited craving can lead to relapse in SC-vulnerable individuals. Thus, identifying treatments that target SC-elicited craving is a top research priority. Reduced drug cue neural activity is associated with recovery and is marked by a profile of greater tonic (resting) activation in executive control regions, and increased connectivity between executive and salience regions. Evidence suggests the GABA-B agonist baclofen can reduce drug cue-elicited neural activity, potentially through its actions on the resting brain. Based on the literature, we hypothesize that baclofen’s effects in the resting brain can predict its effects during SC exposure. @*Methods@#In this longitudinal, double blind, placebo-controlled neuropharmacological study 43 non-abstinent, sated treatment-seeking cigarette smokers (63% male) participated in an fMRI resting-state scan and a SC-reactivity task prior to (T1) and 3 weeks following randomization (T2; baclofen: 80 mg/day; n = 21). Subjective craving reports were acquired before and after SC exposure to explicitly examine SC-induced craving. @*Results@#Whole-brain full-factorial analysis revealed a group-by-time interaction with greater resting brain activation of the right dorsolateral prefrontal cortex (dlPFC) at T2 in the baclofen group (BAC) (pFWEcorr = 0.02), which was associated with reduced neural responses to SCs in key cue-reactive brain regions; the anterior ventral insula and ventromedial prefrontal cortex (pFWEcorr ＜ 0.01). BAC, but not the placebo group reported decreased SC-elicited craving (p = 0.02). @*Conclusion@#Results suggest that baclofen mitigates the reward response to SCs through an increase in tonic activation of the dlPFC, an executive control region. Through these mechanisms, baclofen may offer SC-vulnerable smokers protection from SC-induced relapse.
ABSTRACT
Conventional neuroimaging methods primarily focus on functional localization, through which specific cognitive functions are localized to specific brain regions. However, fully understanding the human brain function requires characterization of functional integration within and among the functionally specialized regions in addition to functional localization. Functional connectivity and effective connectivity analyses have been developed to investigate functional integration in human brain. Several approaches for modeling functional connectivity and effective connectivity, including the time-series correlation, psychophysiological interaction (PPI), structural equation modeling (SEM), dynamic casual modeling (DCM), and diffusion tensor imaging (DTI) are reviewed. The applications of functional connectivity analysis to the studies of object representation, motor coordinate, language, and autism are demonstrated. Functional connectivity study will highly enrich our knowledge about the dynamic integration in the human brain.