Abstract

Ethanol dependent rats show, upon suspension of treatment, a lowered electro-physiological activity of mesolimbic dopaminergic neurons as indexed by a reduction in spontaneous firing rate, burst rate and spikes/burst. Further, the neuronal activity persists reduced well after the somatic signs of ethanol withdrawal have returned to control levels. This hampers the possibility that the reduction in mesolimbic dopaminergic activity may play a role in the genesis of ethanol physical dependence. Rather it would suggest a role for dopamine in the negative motivational aspects (i. e. dysphoria) of dependence.Similarly, morphine dependent rats display an abrupt abatement of dopaminergic neuronal activity in the mesolimbic system, 24 hours after last morphine administration. The reduction in neuronal activity is still evident 7 days after morphine abstinence, when physical signs of dependence have disappeared.The results, irrespective of the substance abused, are interpreted as indicating a common neurobiological substrate (i. e. the mesolimbic dopaminergic system) which becomes hypofunctional upon suspension of chronic administration of an addicting substance. Since the reduction in dopaminergic neuronal activity was found to last longer than signs of physical dependence for both substances (ethanol and morphine), these findings may be viewed as the neurophysiological correlate of the dysphoria associated to drug withdrawal in humans rather than indicating a role for mesolimbic dopamine in the physical aspects of dependence. The dysphoric status induced by a tonic reduction in dopaminergic activity, in turn, may bear important consequences on drug craving, drug seeking and drug taking behavior, which ultimately result in relapse: the major problem posed by drug addiction in humans.KeywordsDrug WithdrawalMorphine AdministrationMorphine WithdrawalDopaminergic NeurotransmissionEthanol WithdrawalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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