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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kaprox.mod
kdrca1.mod
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naxn.mod *
zcaquant.mod
aBeta.hoc
add_ca.hoc
bAP_peak_vecs.hoc
c91662.ses
C91662_Link.txt
cond_report.hoc
control_boxes.hoc
distribute_currents.hoc
fig1.jpg
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fig2A_c91662.hoc
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fig3.ses
fig4.jpg
fig4.ses
fig5.jpg
fig6b.jpg
figs.hoc
find_averages.hoc
fixnseg.hoc
GaspiriniEtAl2007Fig1Stimulation.ses
generate_conc_graph.hoc
gka_averager.hoc
graph_na3_kinetics.hoc
init_and_run_and_graph.hoc
leaky_distal.hoc
maxica.hoc
maxica.ses.20100525
mosinit.hoc
na3_shifter.hoc
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
                            
/*
The RangeVarPlots of imax_caquant are treated specially:
1.  they are appended to rvplist so that their x and y values can be saved to pairs of vectors
2.  their paths are captured to a List of SectionLists to assist with future tasks, e.g.
drawing dots at locations of particular features

Revised so that black--the path that runs through the tuft--is on top.
Then come the paths into prox, intermed, and distal obliques, in that order.
*/

objref caquant_graphs[5] // used for referencing 5 relevant graphs to fig 6

// objref rvplist, list_of_seclists
objref rvplist
rvplist = new List()
// list_of_seclists = new List()
objref proxsl, intermedsl, distsl, tuftsl // mnemonic names for SectionLists

proc append_to_lists() {
  rvplist.append($o1)
  $o2 = new SectionList()
  $o1.list($o2)
}


objectvar save_window_, rvp_
objectvar scene_vector_[15]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{
save_window_ = new Graph(0)
save_window_.size(0,900,-0.004,2.32831e-10)
scene_vector_[10] = save_window_
{save_window_.view(0, -0.004, 900, 0.004, 766, 25, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")

/*
objectvar rvp_
rvp_ = new RangeVarPlot("imax_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.611502, 0.559744)
append_to_lists(rvp_, tuftsl)
*/

objectvar rvp_
rvp_ = new RangeVarPlot("imax_caquant")
soma rvp_.begin(1)
apic[53] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 7, 1, 0.611502, 0.516613)
append_to_lists(rvp_, distsl)

objectvar rvp_
rvp_ = new RangeVarPlot("imax_caquant")
soma rvp_.begin(1)
apic[67] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 3, 1, 0.598722, 0.478275)
append_to_lists(rvp_, intermedsl)

objectvar rvp_
rvp_ = new RangeVarPlot("imax_caquant")
soma rvp_.begin(1)
apic[97] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.582748, 0.435144)
append_to_lists(rvp_, proxsl)

objectvar rvp_
rvp_ = new RangeVarPlot("imax_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.611502, 0.559744)
append_to_lists(rvp_, tuftsl)
caquant_graphs[0]=save_window_
}

{
save_window_ = new Graph(0)
save_window_.size(0,900,0,5)
scene_vector_[11] = save_window_
{save_window_.view(0, 0, 900, 5, 766, 289, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")

/*
objectvar rvp_
rvp_ = new RangeVarPlot("hwi_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.173802, 0.976678)
*/

objectvar rvp_
rvp_ = new RangeVarPlot("hwi_caquant")
soma rvp_.begin(1)
apic[53] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 7, 1, 0.176997, 0.981469)

objectvar rvp_
rvp_ = new RangeVarPlot("hwi_caquant")
soma rvp_.begin(1)
apic[67] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 3, 1, 0.170607, 0.995847)

objectvar rvp_
rvp_ = new RangeVarPlot("hwi_caquant")
soma rvp_.begin(1)
apic[97] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.192972, 1.01502)

objectvar rvp_
rvp_ = new RangeVarPlot("hwi_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.173802, 0.976678)
caquant_graphs[1]=save_window_
}
{
save_window_ = new Graph(0)
save_window_.size(0,900,0,0.0005)
scene_vector_[12] = save_window_
{save_window_.view(0, 0, 900, 0.0005, 439, 553, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")

/*
objectvar rvp_
rvp_ = new RangeVarPlot("cmax_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.585943, 0.943131)
*/

objectvar rvp_
rvp_ = new RangeVarPlot("cmax_caquant")
soma rvp_.begin(1)
apic[53] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 7, 1, 0.589138, 0.943131)

objectvar rvp_
rvp_ = new RangeVarPlot("cmax_caquant")
soma rvp_.begin(1)
apic[67] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 3, 1, 0.589137, 0.943131)

objectvar rvp_
rvp_ = new RangeVarPlot("cmax_caquant")
soma rvp_.begin(1)
apic[97] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.589137, 0.938338)

objectvar rvp_
rvp_ = new RangeVarPlot("cmax_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.585943, 0.943131)
caquant_graphs[2]=save_window_}
{
save_window_ = new Graph(0)
save_window_.size(0,900,0,8)
scene_vector_[13] = save_window_
{save_window_.view(0, 0, 900, 8, 766, 553, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")

/*
objectvar rvp_
rvp_ = new RangeVarPlot("svr_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.64345, 0.588498)
*/

objectvar rvp_
rvp_ = new RangeVarPlot("svr_caquant")
soma rvp_.begin(1)
apic[53] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 7, 1, 0.643451, 0.583706)

objectvar rvp_
rvp_ = new RangeVarPlot("svr_caquant")
soma rvp_.begin(1)
apic[67] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 3, 1, 0.64345, 0.578914)

objectvar rvp_
rvp_ = new RangeVarPlot("svr_caquant")
soma rvp_.begin(1)
apic[97] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.637061, 0.569329)

objectvar rvp_
rvp_ = new RangeVarPlot("svr_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.64345, 0.588498)
caquant_graphs[3]=save_window_
}
{
save_window_ = new Graph(0)
save_window_.size(0,900,0,0.0005)
scene_vector_[14] = save_window_
{save_window_.view(0, 0, 900, 0.0005, 1093, 553, 300.48, 200.32)}
flush_list.append(save_window_)
save_window_.save_name("flush_list.")

/*
objectvar rvp_
rvp_ = new RangeVarPlot("cmaxp_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.410224, 0.919169)
*/

objectvar rvp_
rvp_ = new RangeVarPlot("cmaxp_caquant")
soma rvp_.begin(1)
apic[53] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 7, 1, 0.407029, 0.904792)

objectvar rvp_
rvp_ = new RangeVarPlot("cmaxp_caquant")
soma rvp_.begin(1)
apic[67] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 3, 1, 0.400639, 0.885623)

objectvar rvp_
rvp_ = new RangeVarPlot("cmaxp_caquant")
soma rvp_.begin(1)
apic[97] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 2, 1, 0.400639, 0.871246)

objectvar rvp_
rvp_ = new RangeVarPlot("cmaxp_caquant")
soma rvp_.begin(1)
apic[42] rvp_.end(1)
rvp_.origin(0)
save_window_.addobject(rvp_, 1, 1, 0.410224, 0.919169)
caquant_graphs[4]=save_window_
}
objectvar scene_vector_[1]
{doNotify()}


objref shlist // list of Shape plots that show the paths

/*
generate Shape plots that show the paths that correspond to the RangeVarPlots
requires maxica.hoc to have been loaded so that these SectionLists exist:
proxsl, intermedsl, distsl, tuftsl
*/
proc pathshapes() { local ii  localobj tobj, tsl
  shlist = new List()
  for ii=0,rvplist.count()-1 { // iterate over the list of RVPs
    tsl = new SectionList()
    rvplist.o(ii).list(tsl) // get section list
    tobj = new Shape(0) // create and size a new Shape
    tobj.size(-10,740,-429.402,319.402)
    tobj.view(-10, -429.402, 750, 748.804, 7+100*(ii+1), 105+100*(ii+1), 200.64, 200.32)
    tobj.observe(tsl) // display only what's in the RVP's SectionList
    shlist.append(tobj) // save for future use
  }
}

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