Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)

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Accession:87284
The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
Reference:
1 . Morse TM, Carnevale NT, Mutalik PG, Migliore M, Shepherd GM (2010) Abnormal Excitability of Oblique Dendrites Implicated in Early Alzheimer's: A Computational Study. Front Neural Circuits [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Active Dendrites; Detailed Neuronal Models; Pathophysiology; Aging/Alzheimer`s;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu]; Morse, Tom [Tom.Morse at Yale.edu];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; I Na,t; I L high threshold; I N; I T low threshold; I A; I K; I h; I K,Ca;
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CA1_abeta
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ipulse2.mod *
kadist.mod
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kdrca1.mod
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naxn.mod *
zcaquant.mod
aBeta.hoc
add_ca.hoc
bAP_peak_vecs.hoc
c91662.ses
C91662_Link.txt
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control_boxes.hoc
distribute_currents.hoc
fig1.jpg
fig2.jpg
fig2A_c91662.hoc
fig3.jpg
fig3.ses
fig4.jpg
fig4.ses
fig5.jpg
fig6b.jpg
figs.hoc
find_averages.hoc
fixnseg.hoc
GaspiriniEtAl2007Fig1Stimulation.ses
generate_conc_graph.hoc
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leaky_distal.hoc
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maxica.ses.20100525
mosinit.hoc
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ntc_additions.hoc
oblique_application.hoc
oblique_scaled_ka.hoc
obliques_primary_tuft.hoc
paper_fig_buttons.hoc
sectiontest.hoc
shrink_obliques.hoc
SubBranch.hoc
trigger_and_start.hoc
wait_for_go.hoc
                            
// find_averages.hoc
// To create concentration dependent graph:
// I want to graph the average voltages in the dendrites
// (do I want to break it up into the obliques, primary, tuft)?
// graph the averages in bins of 50 microns to get started

objref bin_vecx, bin_vecy, bin_vecca
objref tmp_vecy, bin_std, bin_stderr
objref tmp_vecca, bin_stdca, bin_stderrca

tmp_vecy = new Vector()
tmp_vecca = new Vector()
bin_vecx = new Vector()
bin_vecy = new Vector()
bin_std = new Vector()
bin_stderr = new Vector()
bin_stdca = new Vector()
bin_stderrca = new Vector()

interval=50 // 50 um bin size
proc graph_avg() {  // pass the graph object as $o1, the ca graph object as $o2, color as $3
// global vectors distrx contains the distance from the soma, distry the voltages, distrca max calcium
  // reset vectors
  bin_vecy=new Vector()
  bin_vecca=new Vector()
  bin_vecx=new Vector()
  bin_std=new Vector()
  bin_stderr=new Vector()
  bin_stdca=new Vector()
  bin_stderrca=new Vector()
  // xv is x value
//  for (xv=0;xv<850;xv += interval) {
  for j=0, distrx.size()-1 {
    xv=distrx.x[j]-interval/2  // start of bin interval under consideration
    
    bin_vecx.append(distrx.x[j])  // each point on apical dendrites is a bin center
    tmp_vecy = new Vector()  // first store all the y values for a bin here
                             // then calculate the statistics on them
    tmp_vecca = new Vector()  // first store all the calcium values for a bin here
                             // then calculate the statistics on them
    // iterate over distrx, distry the distributions over the apical dends.
    for i=0, distrx.size()-1 {
      dist = distrx.x[i]
      if ((dist>=xv)&& (dist<xv+interval)) {
        // the point is in the interval under consideration
        tmp_vecy.append(distry.x[i])
        tmp_vecca.append(distrca.x[i])
      }
    }
    bin_vecy.append(tmp_vecy.mean())
    bin_std.append(tmp_vecy.stdev())
    bin_stderr.append(tmp_vecy.stderr())
    bin_vecca.append(tmp_vecca.mean())
    bin_stdca.append(tmp_vecca.stdev())
    bin_stderrca.append(tmp_vecca.stderr())
  }
/**/
//  bin_vecy.mark($o1, bin_vecx, "o", 4, $3, 1) // default size 12 pts, color $3, brush 1
//  bin_vecy.mark($o1, bin_vecx, "|", 12, $3, 1) // default size 12 pts, color $3, brush 1
  for i=0, bin_vecx.size()-1 {
     x = bin_vecx.x[i]
     deltay = bin_stderr.x[i]
     tmp_vecx = new Vector()
     tmp_vecy = new Vector()
     tmp_vecx.append(x,x)
     tmp_vecy.append(bin_vecy.x[i]+deltay,bin_vecy.x[i]-deltay)
     tmp_vecy.line($o1, tmp_vecx, $3, 1)
     // calcium
     deltaca = bin_stderrca.x[i]
     tmp_vecca = new Vector()
     tmp_vecca.append(bin_vecca.x[i]+deltaca,bin_vecca.x[i]-deltaca)
     tmp_vecca.line($o2, tmp_vecx, $3, 1)
  }
  bin_vecy.c.add(bin_stderr).mark($o1, bin_vecx, "-", 4, $3, 1) // default size 12 pts, color $3, brush 1
  bin_vecy.c.sub(bin_stderr).mark($o1, bin_vecx, "-", 4, $3, 1) // default size 12 pts, color $3, brush 1
  bin_vecca.c.add(bin_stderrca).mark($o2, bin_vecx, "-", 4, $3, 1) // default size 12 pts, color $3, brush 1
  bin_vecca.c.sub(bin_stderrca).mark($o2, bin_vecx, "-", 4, $3, 1) // default size 12 pts, color $3, brush 1
/**/
/*
  bin_vecy.line($o1, bin_vecx, $3, 1)
//  bin_vecy.c.add(bin_std).line($o1, bin_vecx, $3, 5)
//  bin_vecy.c.sub(bin_std).line($o1, bin_vecx, $3, 5)
  bin_vecy.c.add(bin_stderr).line($o1, bin_vecx, $3, 5)
  bin_vecy.c.sub(bin_stderr).line($o1, bin_vecx, $3, 5)
  //calcium
  bin_vecca.line($o2, bin_vecx, $3, 1)
  bin_vecca.c.add(bin_stderrca).line($o2, bin_vecx, $3, 5)
  bin_vecca.c.sub(bin_stderrca).line($o2, bin_vecx, $3, 5)
*/
}

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