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, Yang CY, 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 (2020) Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nat Neurosci 23:229-238 [PubMed]
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
cells
axoaxonic_cell17S.hoc
basket_cell17S.hoc *
bistratified_cell13S.hoc *
burst_cell.hoc *
olm_cell2.hoc *
pyramidal_cell_14VbTest.hoc
ranstream.hoc *
stim_cell.hoc
stim_cell_ca3.hoc *
stim_cell_ec.hoc *
stim_cell_noise.hoc *
vipcck_cell17S.hoc *
vipcr_cell17S.hoc *
                            
// Data from Saraga et al. (2003) paper
// changed morphology and some channel densities (BPG 12-1-09)
// OLM_Cell

begintemplate OLMCell
public is_art
public init, topol, basic_shape, subsets, geom, biophys, geom_nseg
public pre_list, connect2target

public soma, dend1, dend2, axon
public all

objref pre_list

proc init() {
  topol()
  subsets()
  geom()
  biophys()
  geom_nseg()
  pre_list = new List()
  synapses()
}

create soma, dend1, dend2, axon

proc topol() { local i
  connect dend1(0), soma(1)
  connect dend2(0), soma(0)
  connect axon(0), soma(1)
  //basic_shape()
}

proc basic_shape() {
  soma  { pt3dclear() pt3dadd(0, 0, 0, 10)   pt3dadd(15, 0, 0, 10)    }
  dend1 { pt3dclear() pt3dadd(15, 0, 0, 3)   pt3dadd(90, 0, 0, 3)     }
  dend2 { pt3dclear() pt3dadd(0, 0, 0, 3)    pt3dadd(-74, 0, 0, 3)    }
  axon  { pt3dclear() pt3dadd(15, 0, 0, 1.5) pt3dadd(15, 120, 0, 1.5) }
}

objref all
proc subsets() { local i
  objref all
  all = new SectionList()
  soma all.append()
  dend1 all.append()
  dend2 all.append()
  axon all.append()
}

proc geom() {
  forsec all {  }
  soma  {  L = 20  diam = 10   }
  dend1 {  L = 250  diam = 3   }
  dend2 {  L = 250  diam = 3   }
  axon  {  L = 150  diam = 1.5 }
}

external lambda_f
proc geom_nseg() {
  forsec all { nseg = int((L/(0.1*lambda_f(100))+.9)/2)*2 + 1  }
}

proc biophys() {

  Rm = 20000*2
  //Rm = 1/5e-05      // original
  
  forsec all {
    Ra = 150
    cm = 1.6
  }

  soma {
    insert IA
    gkAbar_IA = 0.0165
    insert Ih
    gkhbar_Ih = 0.00035*0.1
    //gkhbar_Ih = 0.0005
    //gkhbar_Ih = 0.001385
    insert Ksoma
    gksoma_Ksoma = 0.0319*1.5
    insert Nasoma
    gnasoma_Nasoma = 0.0107*1.2
    gl_Nasoma = 1/Rm
    el_Nasoma = -67     
  }

  dend1 {
    insert IA
    gkAbar_IA = 0.004*1.2
    //gkAbar_IA = 0.013
    //insert Ih
    //gkhbar_Ih = 0.001385
    insert Kdend
    gkdend_Kdend = 20*0.023
    insert Nadend
    gnadend_Nadend = 2*0.0117
    gl_Nadend = 1/Rm
    el_Nadend = -65
  }

  dend2 {
    insert IA
    gkAbar_IA = 0.004*1.2
    //gkAbar_IA = 0.013
    //insert Ih
    //gkhbar_Ih = 0.001385
    insert Kdend
    gkdend_Kdend = 20*0.023
    insert Nadend
    gnadend_Nadend = 2*0.0117
    gl_Nadend = 1/Rm
    el_Nadend = -65
  }

  axon {
    insert Kaxon
    gkaxon_Kaxon = 0.05104
    insert Naaxon
    gnaaxon_Naaxon = 0.01712
    gl_Naaxon = 1/Rm
    el_Naaxon = -67
    }
}

obfunc connect2target() { localobj nc //$o1 target point process, optional $o2 returned NetCon
  soma nc = new NetCon(&v(1), $o1)
  nc.threshold = -10
  if (numarg() == 2) { $o2 = nc } // for backward compatibility
  return nc
}

objref syn_
proc synapses_PC() {
  /* E0 */   dend1 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)     // AMPA     PC
  syn_.tau1 = 0.3
  syn_.tau2 = 0.6
  syn_.e    = 0
  /* E1 */   dend2 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)     // AMPA     PC
  syn_.tau1 = 0.3
  syn_.tau2 = 0.6
  syn_.e    = 0
}

proc synapses_CA3() {
  /* E2 */   dend1 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)   // AMPA   CA3
  syn_.tau1 = 2.0
  syn_.tau2 = 6.3
  syn_.e    = 0
  /* E3 */   dend2 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)   // AMPA   CA3
  syn_.tau1 = 2.0
  syn_.tau2 = 6.3
  syn_.e    = 0
}

proc synapses_IN() {
  /* I4 */   dend1 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)    // GABA-A Bistratified cells
  syn_.tau1 = 1.0
  syn_.tau2 = 8.0
  syn_.e    = -75
  /* I5 */   dend2 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)    // GABA-A Bistratified cells
  syn_.tau1 = 1.0
  syn_.tau2 = 8.0
  syn_.e    = -75      
  /* I6 */   dend1 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)    // GABA-A OLM
  syn_.tau1 = 0.25
  syn_.tau2 = 7.50
  syn_.e    = -75
  /* I7 */   dend2 syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)    // GABA-A OLM
  syn_.tau1 = 0.25
  syn_.tau2 = 7.50
  syn_.e    = -75      
  /* I8 */   soma syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)    // GABA-A VIP/CR
  syn_.tau1 = 1.0
  syn_.tau2 = 8.0
  syn_.e    = -75           
}

proc synapses_SEP() {
  /* I9 */   soma syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)      // GABA-A   Septum
  syn_.tau1 = 1
  syn_.tau2 = 8
  syn_.e    = -75
  /* I10 */   soma syn_ = new Exp2Syn(0.5)  pre_list.append(syn_)      // GABA-B   Septum
  syn_.tau1 = 35
  syn_.tau2 = 100
  syn_.e    = -75   
}

proc synapses() {
  synapses_PC()
  synapses_CA3()
  synapses_IN()
  synapses_SEP()
}

func is_art() { return 0 }

endtemplate OLMCell

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