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 Addiction Biology 15(3):266-76 [PubMed]
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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;
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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
                            
/* Sets nseg in each section to an odd value
   so that its segments are no longer than 
     d_lambda x the AC length constant
   at frequency freq in that section.

   Be sure to specify your own Ra and cm before calling geom_nseg()

   To understand why this works, 
   and the advantages of using an odd value for nseg,
   see  Hines, M.L. and Carnevale, N.T.
        NEURON: a tool for neuroscientists.
        The Neuroscientist 7:123-135, 2001.
*/

// these are reasonable values for most models
freq = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1

func lambda_f() { local i, x1, x2, d1, d2, lam
        if (n3d() < 2) {
                return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
        }
// above was too inaccurate with large variation in 3d diameter
// so now we use all 3-d points to get a better approximate lambda
        x1 = arc3d(0)
        d1 = diam3d(0)
        lam = 0
        for i=1, n3d()-1 {
                x2 = arc3d(i)
                d2 = diam3d(i)
                lam += (x2 - x1)/sqrt(d1 + d2)
                x1 = x2   d1 = d2
        }
        //  length of the section in units of lambda
        lam *= sqrt(2) * 1e-5*sqrt(4*PI*$1*Ra*cm)

        return L/lam
}

proc geom_nseg() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }
}