Hyperexcitability from Nav1.2 channel loss in neocortical pyramidal cells (Spratt et al 2021)

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Accession:267067
Based on the Layer 5 thick-tufted pyramidal cell from the Blue Brain Project, we modify the distribution of the sodium channel Nav1.2 to recapitulate an increase in excitability observed in ex vivo slice experiments.
Reference:
1 . Spratt PWE, Alexander RPD, Ben-Shalom R, Sahagun A, Kyoung H, Keeshen CM, Sanders SJ, Bender KJ (2021) Paradoxical hyperexcitability from NaV1.2 sodium channel loss in neocortical pyramidal cells Cell Rep. 36(5):109483 [PubMed]
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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: Prefrontal cortex (PFC);
Cell Type(s): Neocortex layer 5 pyramidal cell;
Channel(s): I h; I M; I Potassium; I Sodium; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.2 SCN2A;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s):
Implementer(s): Ben-Shalom, Roy [rbenshalom at ucdavis.edu]; Kyoung, Henry [hkyoung at berkeley.edu];
Search NeuronDB for information about:  I L high threshold; I T low threshold; I M; I h; I Sodium; I Potassium;
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SprattEtAl2021
Ri Increase
mechanisms
branching.mod *
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
na12.mod
na12_mut.mod
na1216.mod *
na1216_mut.mod *
na16.mod
na8st.mod *
Nap_Et2.mod *
NaTa_t.mod *
NaTs2_t.mod *
nax8st.mod *
ProbAMPANMDA_EMS.mod *
ProbGABAAB_EMS.mod *
SK_E2.mod *
SKv3_1.mod *
vclmp_pl.mod *
26412.tmp *
                            
:Reference :Colbert and Pan 2002

NEURON	{
	SUFFIX NaTa_t
	USEION na READ ena WRITE ina
	RANGE gNaTa_tbar, gNaTa_t, ina
}

UNITS	{
	(S) = (siemens)
	(mV) = (millivolt)
	(mA) = (milliamp)
}

PARAMETER	{
	gNaTa_tbar = 0.00001 (S/cm2)
}

ASSIGNED	{
	v	(mV)
	ena	(mV)
	ina	(mA/cm2)
	gNaTa_t	(S/cm2)
	mInf
	mTau
	mAlpha
	mBeta
	hInf
	hTau
	hAlpha
	hBeta
}

STATE	{
	m
	h
}

BREAKPOINT	{
	SOLVE states METHOD cnexp
	gNaTa_t = gNaTa_tbar*m*m*m*h
	ina = gNaTa_t*(v-ena)
}

DERIVATIVE states	{
	rates()
	m' = (mInf-m)/mTau
	h' = (hInf-h)/hTau
}

INITIAL{
	rates()
	m = mInf
	h = hInf
}

PROCEDURE rates(){
  LOCAL qt
  qt = 2.3^((34-21)/10)
	
  UNITSOFF
    if(v == -38){
    	v = v+0.0001
    }
		mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))
		mBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))
		mTau = (1/(mAlpha + mBeta))/qt
		mInf = mAlpha/(mAlpha + mBeta)

    if(v == -66){
      v = v + 0.0001
    }

		hAlpha = (-0.015 * (v- -66))/(1-(exp((v- -66)/6)))
		hBeta  = (-0.015 * (-v -66))/(1-(exp((-v -66)/6)))
		hTau = (1/(hAlpha + hBeta))/qt
		hInf = hAlpha/(hAlpha + hBeta)
	UNITSON
}