OBJECTIVE: Baclofen is a promising treatment for alcohol use disorders (AUD), although its clinical response in humans is mixed. The present study aimed at investigating the impact of baclofen treatment on cue-induced brain activation pattern and its relationship with relapse outcomes. METHODS: Twenty-three inpatients with AUD underwent a functional magnetic resonance imaging cue-reactivity task before beginning medication with baclofen and 2 weeks later. Twelve additional inpatients with AUD, who did not receive any anticraving medications, formed the control group. All subjects were prospectively followed up for 90 days post-discharge or until lapse to first alcohol use. RESULTS: Whole-brain linear mixed effects analysis revealed a significant group-by-time interaction with greater activation of the bilateral dorsolateral pre-frontal cortex and right anterior cingulate cortex (ACC) following baclofen treatment in comparison with the control group. Further, cox regression analysis revealed that increased activation of ACC and deactivation of insular cortex (IC) was associated with longer time to first alcohol use only in the baclofen treatment group but not in the control group. CONCLUSION: This study provides preliminary evidence for the neural predictors of baclofen treatment response in AUD. Baclofen treatment in AUD was associated with changes in cue-reactivity at critical brain regions within the incentive-salience network. Importantly, baclofen treatment-related specific activation of regions involved in cognitive control (ACC) and deactivation of regions involved in reward anticipation (IC) prolonged the time to first alcohol drink.
Baclofen* ; Brain* ; Cerebral Cortex ; Gyrus Cinguli ; Humans ; Inpatients ; Magnetic Resonance Imaging* ; Prospective Studies ; Recurrence ; Reward

Objective: Spontaneous motor tempo (SMT), observed in walking, tapping and clapping, tends to occur around 2 Hz. Initiating and controlling movement can be difficult for people with Parkinson’s (PWP), but studies have not identified whether PWP differ from controls in SMT. For community-based interventions, e.g. dancing, it would be helpful to know a baseline SMT to optimize the tempi of cued activities. Therefore, this study compared finger tapping (FT), toe tapping (TT) and stepping ‘on the spot’ (SS) in PWP and two groups of healthy controls [age-matched controls (AMC) and young healthy controls (YHC)], as SMT is known to change with age. Methods: Participants (PWP; n = 30, AMC; n = 23, YHC; n = 35) were asked to tap or step on the spot at a natural pace for two trials lasting 40 seconds. The central 30 seconds were averaged for analyses using mean inter-onset intervals (IOI) and coefficient of variation (CoV) to measure rate and variability respectively. Results: PWP had faster SMT than both control groups, depending on the movement modality: FT, F(2, 87) = 7.92, p < 0.01 (PWP faster than YHC); TT, F(2, 87) = 4.89, p = 0.01 (PWP faster than AMC); and SS, F(2, 77) = 3.26, p = 0.04 (PWP faster than AMC). PWP had higher CoV (more variable tapping) than AMC in FT only, F(2, 87) = 4.10, p = 0.02. Conclusion This study provides the first direct comparison of SMT between PWP and two control groups for different types of movements. Results suggest SMT is generally faster in PWP than control groups, and more variable when measured with finger tapping compared to stepping on the spot.