Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons

Citation
Jk. Seamans et al., Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons, J NEUROSC, 21(10), 2001, pp. 3628-3638
Citations number
54
Categorie Soggetti
Neurosciences & Behavoir
Journal title
JOURNAL OF NEUROSCIENCE
ISSN journal
02706474 → ACNP
Volume
21
Issue
10
Year of publication
2001
Pages
3628 - 3638
Database
ISI
SICI code
0270-6474(20010515)21:10<3628:BDMOGI>2.0.ZU;2-C
Abstract
Dopamine regulates the activity of neural networks in the prefrontal cortex that process working memory information, but its precise biophysical actio ns are poorly understood. The present study characterized the effects of do pamine on GABAergic inputs to prefrontal pyramidal neurons using whole- cel l patch-clamp recordings in vitro. In most pyramidal cells, dopamine had a temporally biphasic effect on evoked IPSCs, producing an initial abrupt dec rease in amplitude followed by a delayed increase in IPSC amplitude. Using receptor subtype- specific agonists and antagonists, we found that the init ial abrupt reduction was D2 receptor- mediated, whereas the late, slower de veloping enhancement was D1 receptor- mediated. Linearly combining the effe cts of the two agonists could reproduce the biphasic dopamine effect. Becau se D1 agonists enhanced spontaneous (sIPSCs) but did not affect miniature ( mIPSCs) IPSCs, it appears that D1 agonists caused larger evoked IPSCs by in creasing the intrinsic excitability of interneurons and their axons. In con trast, D2 agonists had no effects on sIPSCs but did produce a significant r eduction in mIPSCs, suggestive of a decrease in GABA release probability. I n addition, D2 agonists reduced the postsynaptic response to a GABA(A) agon ist. D1 and D2 receptors therefore regulated GABAergic activity in opposite manners and through different mechanisms in prefrontal cortex (PFC) pyrami dal cells. This bidirectional modulation could have important implications for the computational properties of active PFC networks.