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
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
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burststim2.mod *
cad.mod
cadyn.mod *
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cagk.mod *
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calH.mod *
cancr.mod *
car.mod *
cat.mod *
ccanl.mod *
gskch.mod *
h.mod *
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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 *
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kdrcr.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
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nafcr.mod *
nap.mod *
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regn_stim.mod *
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TITLE Slow Ca-dependent potassium current
:
:   Ca++ dependent K+ current IC responsible for slow AHP
:   Differential equations
:
:   Model based on a first order kinetic scheme
:
:       + n cai <->     (alpha,beta)
:
:   Following this model, the activation fct will be half-activated at 
:   a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter)
:
:   The mod file is here written for the case n=2 (2 binding sites)
:   ---------------------------------------------
:
:   This current models the "slow" IK[Ca] (IAHP): 
:      - potassium current
:      - activated by intracellular calcium
:      - NOT voltage dependent
:
:   A minimal value for the time constant has been added
:
:   Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994.
:   See also: http://www.cnl.salk.edu/~alain , http://cns.fmed.ulaval.ca
:   modifications by Yiota Poirazi 2001 (poirazi@LNC.usc.edu)
:   taumin = 0.5 ms instead of 0.1 ms	

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

NEURON {
    SUFFIX kca
    USEION k READ ek WRITE ik
    USEION ca READ cai
    RANGE  gk, gbar, m_inf, tau_m
    GLOBAL beta, cac
}


UNITS {
    (mA) = (milliamp)
    (mV) = (millivolt)
    (molar) = (1/liter)
    (mM) = (millimolar)
}


PARAMETER {
    v                (mV)
    celsius =  36    (degC)
    ek      = -80    (mV)
    cai     = 2.4e-5 (mM)            : initial [Ca]i
    gbar    = 0.01   (mho/cm2)
    beta    = 0.03   (1/ms)          : backward rate constant
    cac     = 0.025  (mM)            : middle point of activation fct
    taumin  = 0.5    (ms)            : minimal value of the time cst
    gk
}


STATE {
    m   : activation variable to be solved in the DEs
}               

ASSIGNED {: parameters needed to solve DE 
    ik      (mA/cm2)
    m_inf
    tau_m   (ms)
    tadj
}
BREAKPOINT { 
    SOLVE states METHOD derivimplicit
    gk = gbar*m*m*m     : maximum channel conductance
    ik = gk*(v - ek)    : potassium current induced by this channel
}

DERIVATIVE states { 
    evaluate_fct(v,cai)
    m' = (m_inf - m) / tau_m
}

UNITSOFF
INITIAL {
    :
    :  activation kinetics are assumed to be at 22 deg. C
    :  Q10 is assumed to be 3
    :
    tadj = 3 ^ ((celsius-22.0)/10) : temperature-dependent adjastment factor
    evaluate_fct(v,cai)
    m = m_inf
}

PROCEDURE evaluate_fct(v(mV),cai(mM)) {  LOCAL car
    car = (cai/cac)^2
    m_inf = car / ( 1 + car )      : activation steady state value
    tau_m =  1 / beta / (1 + car) / tadj
    if(tau_m < taumin) { tau_m = taumin }   : activation min value of time cst
}
UNITSON

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