Computational approaches to the neurobiology of drug addiction.

S. H. Ahmed, M. Graupner, B. Gutkin
Pharmacopsychiatry. 2009-05-01; 42(S 01): S144-S152
DOI: 10.1055/s-0029-1216345

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1. Pharmacopsychiatry. 2009 May;42 Suppl 1:S144-52. doi: 10.1055/s-0029-1216345.
Epub 2009 May 11.

Computational approaches to the neurobiology of drug addiction.

Ahmed SH(1), Graupner M, Gutkin B.

Author information:
(1)University Bordeaux 2, University Bordeaux 1, CNRS UMR 5227, Bordeaux, France.

To increase our understanding of drug addiction–notably its pharmacological and
neurobiological determinants–researchers have begun to formulate computational
models of drug self-administration. Currently, one can roughly distinguish
between three classes of models which all have in common to attribute to brain
dopamine signaling a key role in addiction. The first class of models contains
quantitative pharmacological models that describe the influence of
pharmacokinetic and pharmacodynamic factors on drug self-administration. These
models fail, however, to explain how the drug self-administration behavior is
acquired and how it eventually becomes rigid and compulsive with extended drug
use. Models belonging to the second class circumvent some of these limitations by
modeling how drug use usurps the function of dopamine in reinforcement learning
and action selection. However, despite their behavioral plausibility, these
latter models lack neurobiological plausibility and ignore the potential role of
opponent processes in addiction. The third class of models attempts to surmount
these pitfalls by providing a more realistic picture of the midbrain dopamine
circuitry and of the complex action of drugs of abuse in the output of this
circuitry. Here we provide a brief overview of these different models to
illustrate the potential contribution of mathematical modeling to our
understanding of the neurobiology of drug addiction.

DOI: 10.1055/s-0029-1216345
PMID: 19434552 [Indexed for MEDLINE]


Auteurs Bordeaux Neurocampus