CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2019)

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Accession:256311
Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy. To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels.
Reference:
1 . Shuman T, Aharoni D, Cai DJ, Lee CR, Chavlis S, Page-Harley L, Vetere LM, Feng Y, Yi Yang C, Mollinedo-Gajate I, Chen L, Pennington ZT, Taxidis J, Flores SE, Cheng K, Javaherian M, Kaba CC, Rao N, La-Vu M, Pandi I, Shtrahman M, Bakhurin KI, Masmanidis SC, Khakh BS, Poirazi P, Silva AJ, Golshani P (2019) Breakdown of spatial coding and interneuron synchronization in epileptic mice Nature Neuroscience, in-press
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampal CA1 CR/VIP cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA1 basket cell; Hippocampus CA1 basket cell - CCK/VIP; Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron ; Hippocampus CA1 bistratified cell;
Channel(s): I A; I h; I K,Ca; I K; I CAN; I M; I Sodium; I_AHP; I Calcium;
Gap Junctions:
Receptor(s): AMPA; GabaA; GabaB; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Brian;
Model Concept(s): Spatial Navigation;
Implementer(s): Chavlis, Spyridon [schavlis at imbb.forth.gr]; Pandi, Ioanna ; Poirazi, Panayiota [poirazi at imbb.forth.gr];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I A; I K; I M; I h; I K,Ca; I CAN; I Sodium; I Calcium; I_AHP;
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Shuman_et_al_2019
mechanisms
ANsyn.mod *
bgka.mod *
burststim2.mod *
cad.mod
cadyn.mod *
cadyn_new.mod *
cagk.mod *
cal.mod *
calH.mod *
cancr.mod *
car.mod *
cat.mod *
ccanl.mod *
gskch.mod *
h.mod *
hha_old.mod *
hha2.mod *
hNa.mod *
IA.mod *
iccr.mod *
ichan2.mod *
ichan2aa.mod *
ichan2bc.mod *
ichan2bs.mod *
ichan2vip.mod *
Ih.mod *
Ihvip.mod *
ikscr.mod *
kad.mod *
kadistcr.mod *
kap.mod *
Kaxon.mod *
kca.mod *
Kdend.mod *
kdrcr.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
Naaxon.mod *
Nadend.mod *
nafcr.mod *
nap.mod *
Nasoma.mod *
nca.mod *
nmda.mod *
regn_stim.mod *
somacar.mod *
STDPE2Syn.mod *
vecstim.mod *
                            
TITLE HH channel that includes both a sodium and a delayed rectifier channel 
: and accounts for sodium conductance attenuation
: Bartlett Mel-modified Hodgkin - Huxley conductances (after Ojvind et al.)
: Terrence Brannon-added attenuation 
: Yiota Poirazi-modified Kdr and Na threshold and time constants
: to make it more stable, 2000, poirazi@LNC.usc.edu
: Used in all BUT somatic and axon sections. The spike threshold is about -50 mV

NEURON {
	SUFFIX hha_old
	USEION na READ ena WRITE ina 
	USEION k READ ek WRITE ik
	NONSPECIFIC_CURRENT il
	RANGE gnabar, gkbar, gl, el
	RANGE ar2, vhalfs
	RANGE inf, fac, tau
	RANGE taus
	RANGE W
	GLOBAL taumin
}

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

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

PARAMETER {   : parameters that can be entered when function is called in cell-setup
        a0r = 0.0003 (ms)
        b0r = 0.0003 (ms)
        zetar = 12    
	zetas = 12   
        gmr = 0.2   
	ar2 = 1.0               :initialized parameter for location-dependent
                                :Na-conductance attenuation, "s", (ar=1 -> zero attenuation)
	taumin = 3   (ms)       :min activation time for "s" attenuation system
        vvs  = 2     (mV)       :slope for "s" attenuation system
        vhalfr = -60 (mV)       :half potential for "s" attenuation system
	W = 0.016    (/mV)      :this 1/61.5 mV
:	gnabar = 0.2 (mho/cm2)  :suggested conductance values
:	gkbar = 0.12 (mho/cm2)
:	gl = 0.0001  (mho/cm2)
        gnabar = 0   (mho/cm2)  :initialized conductances
	gkbar = 0    (mho/cm2)  :actual values set in cell-setup.hoc
	gl = 0       (mho/cm2)
	ena = 60     (mV)       :Na reversal potential (also reset in
	ek = -77     (mV)       :K reversal potential  cell-setup.hoc)
	el = -70.0   (mV)       :steady state 
	celsius = 34 (degC)
	v            (mV)
        dt           (ms)
}

STATE {                         : the unknown parameters to be solved in the DEs
	m h n s
}

ASSIGNED {			: parameters needed to solve DE
	ina (mA/cm2)
	ik (mA/cm2)
	il (mA/cm2)
	inf[4]
	fac[4]
	tau[4]
}

BREAKPOINT {
	SOLVE states
	ina = gnabar*m*m*h*s*(v - ena) :Sodium current
	ik = gkbar*n*n*(v - ek)        :Potassium current
	il = gl*(v - el)               :leak current
}

INITIAL {                       : initialize the following parameter using states()
	states()
	s=1
	ina = gnabar*m*m*h*s*(v - ena)
	ik = gkbar*n*n*(v - ek)
	il = gl*(v - el)
}

PROCEDURE calcg() {
	mhn(v*1(/mV))
	m = m + fac[0]*(inf[0] - m)   :Na activation variable
	h = h + fac[1]*(inf[1] - h)   :Na inactivation variable
	n = n + fac[2]*(inf[2] - n)   :K activation variable
	s = s + fac[3]*(inf[3] - s)   :Na attenuation variable
}	

PROCEDURE states() {	: exact when v held constant
	calcg()
	VERBATIM
	return 0;
	ENDVERBATIM
}


FUNCTION varss(v, i) { :steady state values
	if (i==0) {
		varss = 1 / (1 + exp((v + 40)/(-3))) :Na activation
	}
	else if (i==1) {
		varss = 1 / (1 + exp((v + 45)/(3)))  :Na inactivation
	}
	else if (i==2) {	
		varss = 1 / (1 + exp((v + 42)/(-2))) :K activation

	} else {
                :"s" activation system for spike attenuation - Migliore 96 model
		varss = alpv(v,vhalfr)
       }
}


FUNCTION alpv(v(mV),vh) {    :used in "s" activation system infinity calculation
  alpv = (1+ar2*exp((v-vh)/vvs))/(1+exp((v-vh)/vvs))
}

FUNCTION alpr(v(mV)) {       :used in "s" activation system tau
  alpr = exp(1.e-3*zetar*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION betr(v(mV)) {       :used in "s" activation system tau
  betr = exp(1.e-3*zetar*gmr*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) 
}

FUNCTION vartau(v, i) { :estimate tau values
	LOCAL tmp
	if (i==0) {
	   vartau = 0.05      :Na activation tau
	}
	else if (i==1) {
           vartau = 0.5       :Na inactivation tau
        }
	else if (i==2) {
            vartau = 2.2      :K activation tau
       	} else {
	     tmp = betr(v)/(a0r+b0r*alpr(v)) 
	     if (tmp<taumin) {tmp=taumin}
	VERBATIM
	ENDVERBATIM
	     vartau = tmp      :s activation tau
       }
}	

PROCEDURE mhn(v) {
:       TABLE infinity, tau, fac DEPEND dt, celsius FROM -100 TO 100 WITH 200
	FROM i=0 TO 3 {
		tau[i] = vartau(v,i)
		inf[i] = varss(v,i)
		fac[i] = (1 - exp(-dt/tau[i]))
	}
}