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REPORT
1Institute of Biophysics, National Research Council, Palermo, Italy; 2Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut; and 3Neuroscience Center of Excellence, Louisiana State University Health Sciences Center, New Orleans, Louisiana
Submitted 9 January 2008; accepted in final form 18 March 2008
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ABSTRACT |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSGRANTSREFERENCES |
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; Grace and Bunney 1983), from pacemaking and single spikes to a bursting activity that is believed to be functionally relevant for higher cognitive functions, such as reward and goal-directed behaviors (Grace et al. 2007; Schultz 2007). It is important to understand the cellular mechanisms underlying pathological dysfunctions in which these neurons are implicated. For example, acute ethanol intoxication is accompanied by an increase in the availability of the neurotransmitter dopamine (Weiss et al. 1992). On the cellular level, it has been experimentally shown (Brodie and Appel 1998; Okamoto et al. 2006) that ethanol increases the excitability of dopaminergic neurons, in the substantia nigra (SN) and ventral tegmental area (VTA), through the modulation of a hyperpolarization-activated nonspecific cation current, Ih. Although there is some evidence for heterogeneity in the dopamine neuron population with respect to the contribution of this current (Neuhoff et al. 2002; Seutin et al. 2001), a recent study established a clear link between the ethanol-induced firing increase of midbrain dopaminergic neurons and this ionic channel. Particularly, ethanol application in brain slices (Okamoto et al. 2006) resulted in an approximately 10% increase of the peak current, a +5-mV shift in the activation curve (Fig. 1A, top), and a reduction in the time constant of activation (measured at –110 mV). A pharmacological treatment that preferentially acts on these channels could thus be devised to (temporarily) reduce or avoid the rewarding and/or euphoric effects of ethanol. The firing rate increase in SN and VTA neurons has also been demonstrated in vivo by intravenous administration of ethanol in unanesthetized rats (Gessa et al. 1985; Mereu et al. 1984).
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; Komendantov et al. 2004; these models are available for public download at http://senselab.med.yale.edu/ModelDB, accession number 84612). As shown in previous works (Canavier and Landry 2006; Komendantov et al. 2004), the model is able to take into account both the single-spike (regular or irregular) firing mode of dopaminergic neurons, as observed in vitro and in vivo (Grace and Bunney 1984; Neuhoff 2002), and the burst-firing mode observed in vivo (Grace and Bunney 1984b) or in vitro after N-methyl-D-aspartate (NMDA) pharmacological manipulation (Johnson et al. 1992). Figure 1B gives an example of the pacemaker-like firing observed in vitro in the absence of synaptic input. In vivo, individual neurons exhibit a great deal of variability in the fraction of spikes fired in bursts (Hyland et al. 2002). The firing modes exhibited in vivo were simulated as follows. Background levels of excitation were modeled as arising from glutamatergic synaptic events generated by a Poisson process. The level of background excitation was adjusted by changing the mean interevent interval. The level of background inhibition was modeled simply by adding a constant 
-aminobutyric acid type A (GABAA) conductance that reversed at –60 mV. A low level of background excitation (large average interevent interval) produces an irregular single-spike firing pattern, whereas a high level produces a bursty firing pattern. The examples of in vivo single-spike firing given in Fig. 1B should be thought of as representing the two extremes of the spectrum of firing patterns observed in experiments in vivo. We further validated the model and its prediction on the role of Ih (Fig. 1A, bottom) against the experimental results in vitro (Okamoto et al. 2006) (Fig. 1C, top). The simulation findings (Fig. 1C, bottom) confirmed that ethanol application results in the Ih-dependent increase in spontaneous firing frequency, a decrease in the afterhyperpolarization, and a decrease in spike amplitude (Brodie and Appel 1998; Okamoto et al. 2006).
We then investigated the possible effects of ethanol application in vivo, simulating single-spike or burst-firing activity of dopaminergic neurons under control and after ethanol (Fig. 2 ). The typical somatic traces shown in Fig. 2, A and B already suggest that regular firing is not affected by ethanol (Fig. 2A), in contrast with the burst firing, that appears to be somewhat perturbed (Fig. 2B), with shorter interburst silent periods. To make a more quantitative measure of the differences, we calculated the distribution of the interspike intervals (ISIs) from five (70-s-long) simulations under different conditions (Fig. 2C). In all, 1,158 ISIs (1,184 after ethanol) were analyzed for the single-spike mode. The results suggest that ethanol had no effects on the irregular single-spike behavior observed in vivo [Fig. 2C, left, average ISI = 258 ± 141 ms (control), 253 ± 150 ms (ethanol), Mann–Whitney U test, P = 0.0528]. For the bursting mode (Fig. 2C, right) 5,073 intervals were analyzed (5,901 after ethanol). As expected, the ISIs show a bimodal distribution, separating intra- (<80 ms, 
85% of the total) from interburst (>80 ms) spike intervals (Grace and Bunney 1983), with about 85% being short intraburst intervals. Interburst ISIs were significantly shorter after ethanol (Fig. 2C, right, Mann–Whitney U test, P = 1.8 x 10–134), with average (±SD) values for interburst intervals of 269 ± 73 ms (control) and 174 ± 44 ms (ethanol), with little effect on the coefficient of variation (CV = 0.27 vs. 0.25). The distributions of intraburst intervals were significantly different (Mann–Whitney U test, P = 1 x 10–52), although their average values (ISI = 31 ± 7 and 33 ± 8 ms, for control and ethanol, respectively) were very similar. The large proportion of shorter intraburst ISIs and the much longer interburst intervals dominated the average and the SD calculated considering all ISIs (59 ± 81 ms, control, and 54 ± 54, ethanol), although the difference was statistically significant (Mann–Whitney U test, P = 1.1 x 10–44). To show that a stronger Ih activation during the bursting mode could underlie these effects, we plotted in Fig. 2D (left) the time course of Ih activation during the two simulations in Fig. 2B. As can be clearly seen from the curves, the Ih was consistently higher than control during the simulation modeling ethanol application (Fig. 2D, grey trace). The average values under the different conditions, calculated from all simulations (Fig. 2D, right), confirmed a higher Ih activation after ethanol, although only during bursting conditions it was strong enough to result in a different ISI distribution (Fig. 2C).
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; Schultz 2001). Bursts that are time-locked to a stimulus are thought to communicate temporally precise information about reward, but the frequency and pattern of background firing, which contains a highly variable percentage of spikes fired in bursts (Hyland et al. 2002), may serve to modulate network activity patterns on a more prolonged timescale by regulating the level of dopamine in projection areas. The dopamine level reached in the various areas receiving dopaminergic input is then important for controlling a number of possibly abnormal behaviors. It has been experimentally shown that dopaminergic transmission is regulated by different kinetics in different brain regions (Garris and Wightman 1994). In particular, the extracellular dopamine clearance was shown to follow mainly two different time constants: a long (2- to 3-s) decay in the medial prefrontal cortex and in the basal lateral amygdaloid nucleus and a much shorter (40- to 70-ms) decay in the nucleus accumbens and caudate-putamen. We hypothesized that ethanol, through the different ISI distribution resulting from the simulation of burst firing in vivo, might differentially affect dopamine release in the various regions. To investigate this issue, we calculated the average level reached by dopamine during 60 s of burst firing under different conditions (control and ethanol) and using different Vmax, the maximal velocity for uptake (0.5 and 5 µM/s). The results from the first 20 s of a typical simulation from a bursting condition (Fig. 3) clearly show that ethanol application would result in an approximately 50% higher level of dopamine in those regions with a slower clearance rate (average ± SE increase calculated from 300 s of simulation: 49.78 ± 0.03%, P = 1.9 x 10–191), with a much smaller effect for regions with a faster rate (10.49 ± 0.11%, P = 2.9 x 10–8). In single-spike mode (not shown), the dopamine increase was small but significant for the slow rate (2.8 ± 0.06%, P = 0.006) and negligible for the fast rate (1.55 ± 0.25%, P = 0.21). We also modeled a 50 mM ethanol concentration (halving the parameters used to model the results for the higher concentration), to test the average level of dopamine that could be reached in this case. The results (a typical trace is shown in Fig. 3, blue lines) confirmed that even a much smaller ethanol concentration could result in a significant increase in the average dopamine level during bursting activity for the slow rate (+18.52 ± 0.03%, P = 1.1 x 10–88), whereas it was much smaller for the fast rate (+3.19 ± 0.12%, P = 0.034). These results represent the upper limit of the effects of alcohol in humans, for which the legal limit of blood alcohol level for driving in many countries is 17 mM. Our results demonstrate that at least part of the effects can be caused by a change in the Ih kinetics.
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), as well as other effects of ethanol on dopamine neurons. For example, the acute application of 20–120 mM ethanol inhibits the KM current in VTA dopamine neurons, which accounts for some but not most of the increase in the firing frequency of dopamine neurons in vitro (Koyama et al. 2007). The KA-type current may be reduced as well (Melis et al. 2007) and VTA dopamine neurons may also be excited by the enhancement of opioid-mediated inhibition of local GABA interneurons (Xiao et al. 2007). In addition, chronic ethanol exposure down-regulates the SK conductance in dopamine neurons (Hopf et al. 2007). However, we hypothesize that the rewarding effects of acute ethanol exposure are at least partially mediated by the Ih current. From this perspective, these results open a new possibility for selective pharmacological treatments targeting the effects of ethanol. Two factors, in particular, may play an important role to ensure that the actions of an external pharmacological modulation of Ih, reversing ethanol effects, would selectively affect specific brain regions, thus limiting collateral effects. One of these is the different time constant for dopamine clearance observed in the various regions. This would restrict the effects of Ih modulation to those regions where dopamine decay after release is slow. Another factor that might further limit collateral effects is the possibility of designing a drug that acts specifically on the proteins coded by the specific HCN genes that control Ih expression in dopaminergic neurons (Santoro et al. 2000). |
METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSGRANTSREFERENCES |
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). The model and simulation files are available for public download under the ModelDB section of the Senselab database (http://senselab.med.yale.edu).
The model (Canavier and Landry 2006; Komendantov et al. 2004) consisted of a stylized, symmetric model neuron with a soma and four primary and eight secondary dendrites. All compartments are capable of spiking and contain a fast sodium current (INa), a delayed rectifying potassium channel (IK,DR), a transient outward potassium current (IK,A), a leak current (IL), and a sodium pump (INaP). The soma also contains calcium dynamics and a calcium balance that includes the voltage-activated T-, N-, and L-type calcium currents (ICa,T, ICa,N, and ICa,L), a calcium component of the leak current (IL), and a calcium pump (ICaP). Calcium entry in the soma activates the SK channel current (IK,SK). All compartments also contain sodium dynamics and a sodium balance. To adapt the model to the specific properties of the experimental traces obtained in vitro (Fig. 1C), with respect to the original implementation (Canavier and Landry 2006), we reduced the peak Na conductance (from 55 to 25 pS/µm2) to reduce the spike height, and the SK current (from 0.8 to 0.45 pS/µm2) to obtain a biphasic behavior during the interspike interval. All other parameters are as in Canavier and Landry (2006), except that the synaptic conductances and permeabilities for GABAA, 
-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and NMDA were set to zero in the in vitro simulations (Fig. 1, B, top and C, bottom) to model the lack of afferent inputs under these conditions.
The model simulates the pacemaking activity observed in vivo as well as the slow oscillatory potential observed in the presence of tetrodotoxin and can emulate NMDA-induced burst firing in vitro. Randomly timed events evoke both NMDA- and AMPA-mediated excitatory postsynaptic conductances (Destexhe et al. 1995).
The hyperpolarization-activated cation current Ih was inserted in all compartments and modeled as 
, where 
, Erev = –30 mV, and v is the membrane potential. The voltage dependence of the activation gate variable under control conditions was modeled as n = 1/{1 + exp[–(V – V1/2)/k]}, with V1/2 = –95 mV (Okamoto et al. 2006), and k = 8 (Arencibia-Albite et al. 2007). The time constant of activation was modeled as 
n = 0 exp[0.075(V – Vt)]/{1 + exp[0.083(V – Vt)]}, with Vt = –112 mV and 0 = 625 ms. To model the experimentally observed changes in the activation and kinetics of Ih after ethanol application (Okamoto et al. 2006), we used 
, V1/2 = –91 mV, k = 10, and 0 = 455 ms (Fig. 1A). The level of dopamine during a simulation was calculated assuming an instantaneous and constant release at each stimulus, and a Michaelis–Menten uptake with an affinity constant Km = 0.2 µM and a maximal velocity for uptake Vmax of 0.5 or 5 µM/s (Heien and Wightman 2006; Wightman et al. 1988). The concentration of dopamine released at each stimulus was adjusted to qualitatively match the release observed experimentally in the nucleus accumbens and basal lateral amygdaloid nucleus (Garris and Wightman 1994). Statistical analyses were carried out using MathLab functions after combining the last 60 s from each simulation, a total of 300 s for each case (control and ethanol).
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GRANTS |
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FOOTNOTES |
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Address for reprint requests and other correspondence: M. Migliore, CNR-IBF, via U. La Malfa 153, 90146 Palermo, Italy (E-mail: michele.migliore{at}pa.ibf.cnr.it)
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REFERENCES |
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