CA1 pyramidal neurons: effects of Alzheimer (Culmone and Migliore 2012)

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Accession:144976
The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper).
Reference:
1 . Culmone V, Migliore M (2012) Progressive effect of beta amyloid peptides accumulation on CA1 pyramidal neurons: a model study suggesting possible treatments Front Comput Neurosci 6:52 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Channel/Receptor; Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Action Potentials; Aging/Alzheimer`s;
Implementer(s): Migliore, Michele [Michele.Migliore at Yale.edu]; Culmone, Viviana [viviana.abigail at hotmail.it];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal cell; AMPA; I Na,t; I A; I K; I h;
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alzheimer
readme.html
distr.mod *
h.mod *
kadist.mod
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
netstims.mod *
10sim.ses
fixnseg.hoc *
geo5038804.hoc
membrane_potential.hoc
mosinit.hoc
screenshot.png
                            
/* 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  }
}