Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017)

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Accession:223955
Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy
Reference:
1 . Ben-Shalom R, Keeshen CM,Berrios KN, An JY, Sanders SJ, Bender KJ (2017) Opposing effects on NaV1.2 function underlie differences between SCN2A variants observed in individuals with autism spectrum disorder or infantile seizures Biological Psychiatry, epub before print
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell;
Channel(s): I Na,t; I Sodium; I K;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.2 SCN2A;
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s):
Implementer(s): Ben-Shalom, Roy [bens.roy at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; I Na,t; I K; I Sodium;
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SCN2A_ASD
Excitability
YoungI1473M
Cad.mod *
CaH.mod *
CaT.mod *
charge.mod *
h.mod *
Kca.mod *
Kv.mod *
Kv1_axonal.mod *
Kv7.mod *
na8st.mod *
na8st1.mod *
nax8st.mod *
nax8st1.mod
28_04_10_num19.hoc *
Cell parameters.hoc *
charge.hoc *
mosinit.hoc *
scn2aExps.hoc
                            
load_file("nrngui.hoc")
xopen("$(NEURONHOME)/lib/hoc/noload.hoc")
load_proc("nrnmainmenu")

xopen("28_04_10_num19.hoc")

xopen("Cell parameters.hoc")
xopen("charge.hoc")

parameters()
geom_nseg()
init_channels()

objref zz

zz = new Impedance()

func rn() { local rn
  init()  // make sure all changes to g, c, ri etc. have taken effect
  soma zz.loc(0.5)  // sets origin for impedance calculations to middle of soma
  zz.compute(0)  // DC input R
  soma { rn = zz.input(0.5) }  // rn is input R at middle of the soma
  return rn
}


t=5
tstop=580
steps_per_ms=40
dt=0.010		


proc init() {local saveDt, i

	finitialize(v_init)
	fcurrent()
	saveDt = dt
	dt = .5
	for i=1,500/dt fadvance()
	dt = saveDt


	init_channels()

	if (cvode.active()) {
		cvode.re_init()
	} else {
		fcurrent()
	}
	frecord_init()
}


load_file("all_28_04_10_num19.ses")



objref sl
sl = new SectionList()
sl.wholetree()

objref spbox
spbox = new VBox()
spbox.intercept(1)

objref sp
sp = new PlotShape(sl)
sp.show(0)

ncmap=13
vstep=0.0833
vlow=0
vhigh=vlow+(ncmap-1)*vstep
sp.colormap(ncmap,1)

i1=int(3*(ncmap-1)/8)
//print i1
for (i=0; i<=i1; i=i+1) {
	f=i/(3*(ncmap-1)/8)
	print i,f,0,255*f,255
	sp.colormap(i,0,255*f,255)
}
print " "
i2=int((ncmap-1)/2)
for (i=i1+1; i<=i2; i=i+1) {
	f=(i-3*(ncmap-1)/8)/((ncmap-1)/8)
	print i,f,0,255,255*(1-f)
	sp.colormap(i,0,255,255*(1-f))
}
print " "
i3=int(5*(ncmap-1)/8)
for (i=i2+1; i<=i3; i=i+1) {
	f=(i-(ncmap-1)/2)/((ncmap-1)/8)
	print i,f,255*f,255,0
	sp.colormap(i,255*f,255,0)
}
print " "
for (i=i3+1; i<=ncmap-1; i=i+1) {
	f=(i-5*(ncmap-1)/8)/(3*(ncmap-1)/8)
	print i,f,255,255*(1-f),0
	sp.colormap(i,255,255*(1-f),0)
} 

sp.variable("overl_charge_")
sp.exec_menu("Shape Plot")
//sp.view(-608.167, -740.999, 1548.33, 1812, 509, 84, 276, 323)
sp.scale(vlow,vhigh)

fast_flush_list.append(sp)
sp.save_name("fast_flush_list.")

spbox.intercept(0)
spbox.map("Shape plot",500,20,300,400)



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