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
Citations  Citation Browser
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 *
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nafcr.mod *
nap.mod *
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somacar.mod *
STDPE2Syn.mod *
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COMMENT

Ih current	 - hyperpolarization-activated nonspecific Na and K channel
		 - contributes to the resting membrane potential
		 - controls the afterhyperpolarization
Reference:

1.	Maccaferri, G. and McBain, C.J. The hyperpolarization-activated current
	(Ih) and its contribution to pacemaker activity in rat CA1 hippocampal
	stratum oriens-alveus interneurons, J. Physiol. 497.1:119-130,
	1996.

		V1/2 = -84.1 mV
		   k = 10.2
		reversal potential = -32.9 +/- 1.1 mV

at -70 mV, currents were fitted by a single exponetial of t = 2.8+/- 0.76 s
at -120 mV, two exponentials were required, t1 = 186.3+/-33.6 ms 
t2 = 1.04+/-0.16 s


2.	Maccaferri, G. et al. Properties of the
	Hyperpoarization-activated current in rat hippocampal CA1 Pyramidal
	cells. J. Neurophysiol. Vol. 69 No. 6:2129-2136, 1993.

		V1/2 = -97.9 mV
		   k = 13.4
		reversal potential = -18.3 mV

3.	Pape, H.C.  Queer current and pacemaker: The
	hyperpolarization-activated cation current in neurons, Annu. Rev. 
	Physiol. 58:299-327, 1996.

		single channel conductance is around 1 pS
		average channel density is below 0.5 um-2
		0.5 pS/um2 = 0.00005 mho/cm2 = 0.05 umho/cm2		
4.	Magee, J.C. Dendritic Hyperpolarization-Activated Currents Modify
	the Integrative Properties of Hippocampal CA1 Pyramidal Neurons, J.
	Neurosci., 18(19):7613-7624, 1998

Deals with Ih in CA1 pyramidal cells.  Finds that conductance density
increases with distance from the soma.

soma g = 0.0013846 mho/cm2
dendrite g (300-350 um away) = 0.0125 mho/cm2
see Table 1 in th paper

ENDCOMMENT

 UNITS {
        (mA) = (milliamp)
        (mV) = (millivolt)
}
 
NEURON {
        SUFFIX Ih
        USEION h READ eh WRITE ih VALENCE 1
        RANGE gkhbar,ih
        GLOBAL rinf, rexp, tau_r
}
 
INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}
 
PARAMETER {
        v (mV)
        p = 5 (degC)
        dt (ms)
        gkhbar = 0.001385 (mho/cm2)			
        eh = -32.9 (mV)
}
 
STATE {
        r
}
 
ASSIGNED {
        ih (mA/cm2)
	rinf rexp
	tau_r
}
 
BREAKPOINT {
        SOLVE deriv METHOD derivimplicit
        ih = gkhbar*r*(v - eh)
}
 
INITIAL {
	rates(v)
	r = rinf
}

DERIVATIVE deriv { :Computes state variable h at current v and dt.
	rates(v)
	r' = (rinf - r)/tau_r
}

PROCEDURE rates(v) {  :Computes rate and other constants at current v.
                      :Call once from HOC to initialize inf at resting v.
        TABLE rinf, rexp, tau_r DEPEND dt, p FROM -200
TO 100 WITH 300
	rinf = 1/(1 + exp((v+84.1)/10.2))
	rexp = 1 - exp(-dt/(tau_r))
	tau_r = 100 + 1/(exp(-17.9-0.116*v)+exp(-1.84+0.09*v))
}
 
UNITSON