NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)

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Accession:126640
Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
Reference:
1 . Spiga S, Lintas A, Migliore M, Diana M (2010) Altered architecture and functional consequences of the mesolimbic dopamine system in cannabis dependence. Addict Biol 15:266-76 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite;
Brain Region(s)/Organism: Basal ganglia;
Cell Type(s): Nucleus accumbens spiny projection neuron;
Channel(s): I Na,t; I A; I Potassium; I A, slow; I Krp;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Activity Patterns; Active Dendrites; Detailed Neuronal Models; Action Potentials; Synaptic Integration; Addiction;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu];
Search NeuronDB for information about:  AMPA; I Na,t; I A; I Potassium; I A, slow; I Krp; Glutamate;
/
withdrawal
tau_tables
readme.html
kaf.mod *
kas.mod *
krp.mod *
naf.mod *
netstimd.mod
after.ses
after-withdrawal.hoc
all_tau_vecs.hoc
control.hoc
control.ses
fixnseg.hoc *
mosinit.hoc
screenshot.jpg
screenshot2.jpg
soma17.hoc
                            
load_file("nrngui.hoc")
cvode_active(1)

numsyn=216
numstim=1000
objref syn[numsyn], stim[numsyn], nc[numsyn], rs, rd, rf, apc, count, curr
strdef dirstr

load_file("all_tau_vecs.hoc")
Vrest = -90
dt = 0.08
gna = 0.25
gk = 0.003
gka = 0.003
gkrp=0.003
weight=0.15
celsius = 35.0  

Cm    = 1
Rm = 7000/Cm

RaAll= 150

tstop=4000

use_mcell_ran4()
mcell_ran4_init()
rs = new Random()
rs.uniform(0,numsyn-1)
rs.MCellRan4(2^32-1)
rd = new Random()
rd.MCellRan4(2^32-1)
rd.uniform(0,250)

load_file("soma17.hoc")
load_file("fixnseg.hoc")

forall {
	insert pas e_pas=Vrest g_pas=1/Rm Ra=RaAll cm=Cm
	insert naf gnabar_naf=gna
	insert kaf gkbar_kaf=gka
	insert kas gkbar_kas = gk
	insert krp gkbar_krp = gkrp
}

geom_nseg()

for k=0,numsyn-1 dend_9[k] {
	syn[k] = new Exp2Syn(.5)
	syn[k].e=0
	syn[k].tau1=0.5
	syn[k].tau2=3

	stim[k]=new NetStimd(.5)
	stim[k].start=rd.repick()
	stim[k].number=10000
	stim[k].interv1=1000/3   
	stim[k].interv2=1000/40   
	stim[k].swd=1000   
	stim[k].swu=400   
	stim[k].noise=1

	nc[k]=new NetCon(stim[k], syn[k],0,0,weight*1e-3)	
		
}

proc init() {
	finitialize(Vrest)
        fcurrent()
	finitialize(Vrest)
        forall for(x) {e_pas(x)=v(x)+(ina(x)+ik(x))/g_pas(x)}
	cvode.re_init()
}

load_file("control.ses")


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