| Cocaine addiction is a serious public health problem in both the Netherlands and the USA. Until recently, there existed no efficacious medication for the treatment of cocaine dependence. Disruptions in the dopaminergic system are implicated in etiology and relapse in cocaine addiction. Alterations of dopamine D2 receptors and/or transporters are involved in the neurobiology of cocaine addiction; e.g.striatal D2 receptor availability in chronic cocaine users is lower than in controls and low D2 receptor availability promotes cocaine self-administration in non-human primates. Therefore, if striatal D2 receptor availability were increased, then relapse may be prevented. There is a drug, Rimonabant, that show promise in this regard. Rimonabant is a selective cannabinoid receptor 1 (CB1) receptor antagonist that may increase availability of D2 receptors; e.g. increased availability of dopamine D2 receptors in CB1 knockout mice. Furthermore, it has been shown that Rimonabant attenuated relapse induced by cocaine-associated cues. Thus, Rimonabant could be a strategy to prevent relapse in cocaine addicts. The neurobiology of drug addiction and the effects of Rimonabant may be elucidated by functional magnetic resonance imaging (fMRI), electroencephalography (EEG), single photon emission computed tomography (SPECT), and/or positron emission tomography (PET). In that manner, the prevalence of D2 availability in abstinent cocaine addicts maintained on Rimonabant can be compared to drug-free control subjects. Additionally, the relationship between D2 availability and performance on cognitive/behavioral tasks will be examined in both groups. The goals of the current proposal are to a) investigate effects of prolonged treatment with Rimonabant on availability of D2 receptors in abstinent cocaine addicts; b) examine effects of Rimonabant on impulse control, motivational strength of drug cues, and brain activation of cocaine-addicted patients compared to non-addicted controls; and c) examine the extent to which these processes predict relapse. |
