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A mathematical model of dopamine autoreceptors and uptake inhibitors and their influence on tonic and phasic dopamine signaling

Dopamine (DA) D2-like autoreceptors are an important component in the DA system. But their influence on postsynaptic DA signaling is not well understood. They are, directly or indirectly, involved in drug abuse and in treatment of schizophrenia and attention deficit hyperactive disorder: DA autoreceptors influence the behavioral effect of cocaine and methylphenidate and may be the target of antipsychotic medication such as haloperidol. DA autoreceptors are active at two levels: Somatodendritic autoreceptors mainly influence firing rate of DA neurons and presynaptic autoreceptors control release of neurotransmitter at axonal terminals. Here we develop a mathematical model that captures the dynamics of this dual autoregulation system. Our model predicts a biphasic autoreceptor response between DA terminals and somatodendritic regions that influences the postsynaptic integration of DAergic firing patterns. We applied our model to study how DA uptake inhibition affects the translation of DA cell firing into activation of post synaptic DA receptors. While uptake inhibition increased tonic activation of low affinity postsynaptic receptors, high affinity state receptors saturated and thus became insensitive to phasic DA signaling. This effect had remarkable regional specificity: While high affinity DA receptors in nucleus accumbens saturated at low levels of uptake inhibition they only saturated at higher levels of uptake inhibition in dorsal striatum. Based on high affinity receptor saturation, the model predicted that removal of autoreceptor control would lead to cocaine hypersensitivity.

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Typ:Originalarbeit
Autor:Jakob Kisbye Dreyer
Quelle:J Neurophysiol (October 10, 2012). doi:10.1152/jn.00502.2012
Jahr:2012
Keywords (deutsch):---
Keywords (englisch):Burst, striatum, nucleus accumbens, D2 receptor, D1 receptor, cocaine, methylphenidate, D2 antagonist, D2 agonist, locomotor, ADHD, reward, antipsychotic, modeling, computational
DOI:---