Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)

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Accession:249463
Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells
Reference:
1 . Mäki-Marttunen T, Devor A, Phillips WA, Dale AM, Andreassen OA, Einevoll GT (2019) Computational modeling of genetic contributions to excitability and neural coding in layer V pyramidal cells: applications to schizophrenia pathology Front. Comput. Neurosci. 13:66
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: Neocortex;
Cell Type(s):
Channel(s): I A; I M; I h; I K,Ca; I Calcium; I A, slow; I Na,t; I Na,p; I L high threshold; I T low threshold;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python;
Model Concept(s): Schizophrenia; Dendritic Action Potentials; Action Potential Initiation; Synaptic Integration;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  AMPA; NMDA; Gaba; I Na,p; I Na,t; I L high threshold; I T low threshold; I A; I M; I h; I K,Ca; I Calcium; I A, slow; Gaba; Glutamate;
// Author: Etay Hay, 2011
//    Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of
//    Dendritic and Perisomatic Active Properties
//    (Hay et al., PLoS Computational Biology, 2011) 
//
// Modified by Tuomo Maki-Marttunen, 2015, basing on TTC.hoc by Etay Hay, Dendritic excitability and gain control in recurrent cortical microcircuits (Hay and Segev, 2014, Cerebral Cortex)
// Template for models of L5 Pyramidal Cell

begintemplate L5PCtemplate
  public init
  public synlist
  public locateSites, getLongestBranch, distributeSyn, setparameters, initPreSynTrain, queuePreTrains, setpretrains, initRand
  public soma, dend, apic, axon, getAbsSecIndex
  public all, somatic, apical, axonal, basal, nSecSoma, nSecApical, nSecBasal, nSecAxonal, nSecAll, nSecAxonalOrig, SecSyn, distribute_channels
  objref SecSyn, this
  objref all, somatic, apical, axonal, basal
  strdef tstr
  objref preconlist,synLocList, rList, preTrainList
  objref rd1
  objref sref, fih
  objref synlist

//$s1 - morphology file name
proc init() {localobj nl,import
	all = new SectionList()
	somatic = new SectionList()
	basal = new SectionList()
	apical = new SectionList()
	axonal = new SectionList()
	forall delete_section()

  nl = new Import3d_Neurolucida3()
  nl.quiet = 0
  nl.input($s1)
  import = new Import3d_GUI(nl, 0)
  import.instantiate(this)
  geom_nseg()
  biophys()
	forsec this.all {
		if(diam == 0){
	    diam =  1
	    printf("Error : Morphology problem with section [%s] 0 diam \n", secname())
		}
  }
  synlist = new List()
  preconlist = new List()
  preTrainList = new List()

  lengthA = 0
  lengthB = 0
  forsec "apic" {
          lengthA = lengthA + L
  }
  forsec "dend" {
          lengthB = lengthB + L
  }
  pA = lengthA/(lengthA + lengthB)
}

create soma[1], dend[1], apic[1], axon[1]

proc geom() {
}

proc geom_nseg() {local nSec, L1, L2, D1, D2, nSeg1, nSeg2
  soma area(.5) // make sure diam reflects 3d points
  nSec = 0
  forsec all {
    nseg = 1 + 2*int(L/40)
    nSec = nSec + 1
  }

  nSecAll = nSec
  print nSecAll
  nSec = 0
  forsec somatic { nSec = nSec + 1}
  nSecSoma	= 	nSec
  print nSecSoma
  nSec = 0
  forsec apical { nSec = nSec + 1}
  nSecApical= 	nSec
  print nSecApical
  nSec = 0
  forsec basal { nSec = nSec + 1}
  nSecBasal	= 	nSec
  print nSecBasal
  nSec = 0
  forsec axonal { nSec = nSec + 1}
  nSecAxonalOrig = nSecAxonal	= 	nSec
  print nSecAxonal
}

proc biophys() {localobj bp
	delete_axon()
	area(0.5)
	distance()
	access soma

  bp = new L5PCbiophys()
  bp.biophys(this)
}

// deleting axon, keeping only first 60 micrometers
proc delete_axon(){
    forsec axonal{delete_section()}
    create axon[2]
    access axon[0]{
      L= 30
      diam = 1
      nseg = 1+2*int(L/40)
      all.append()
      axonal.append()
    }
    access axon[1]{
      L= 30
      diam = 1
      nseg = 1+2*int(L/40)
      all.append()
      axonal.append()
    }

  nSecAxonal = 2
  connect axon(0), soma(0.5)
  connect axon[1](0), axon[0](1) 
  access soma
}

proc distribute_channels()	{local dist,val,base,maxLength
	base = $8
	soma distance()
	maxLength = getLongestBranch($s1)

	forsec $s1		{
		if(0==strcmp($s2,"Ra")){
			Ra = $8
		} else {
			for(x) {
				if ($3==3) {
					dist = distance(x)
				} else {
					dist = distance(x)/maxLength
				}
				val = calculate_distribution($3,dist,$4,$5,$6,$7,$8)
				sprint(tstr,"%s(%-5.10f) = %-5.10f",$s2,x,val)
				execute(tstr)
			}
		}
	}
}

// $1 is the distribution type:
//     0 linear, 1 sigmoid, 2 exponential
//     3 step for absolute distance (in microns)
func calculate_distribution()	{local value
	if ($1==0)	{value = $3 + $2*$4}
	if ($1==1) {value = $3 + ($4/(1+exp(($2-$5)/$6)))}
  	if ($1==2) {value = $3 + $6*exp($4*($2-$5))}
	if ($1==3) {
		if (($2 > $5) && ($2 < $6)) {
			value = $3
		} else {
			value = $4
		}
	}
	value = value*$7
	return value
}

// $s1 section
func getLongestBranch(){local maxL,d localobj distallist,sref
    sprint(tstr,"%s distance()",$s1)
    execute(tstr,this)    
    
  	if(0==strcmp($s1,"axon")){
      sprint(tstr,"%s[0] distance(1)",$s1)
      execute(tstr,this)    
  	}

		maxL = 0
		d = 0
		distallist = new SectionList()
		forsec $s1 {
			sref = new SectionRef()
			if (sref.nchild==0) distallist.append()
		}
		forsec distallist{
			d = distance(1)
			if(maxL<d) maxL = d
		}
		// for the soma case
		if (maxL == 0) {
      $s1 {
        maxL = L
      }
    }
		return maxL
	}

// $s1 section
// $2 distance x in micrometers
// return list of [1,2] vectors  - of the appropriate section and the location in each vector
obfunc locateSites() {local maxL,site,d0,d1,siteX,i localobj vv,ll
	ll = new List()

  sprint(tstr,"%s distance()",$s1)
  execute(tstr,this)    
    
	if(0==strcmp($s1,"axon")){
    sprint(tstr,"%s[0] distance(1)",$s1)
    execute(tstr,this)    
	}

	maxL = getLongestBranch($s1)
	site = $2
	i = 0
	forsec $s1 {
    if (distance(0) < distance(1)) {
  		d0 = distance(0)
  		d1 = distance(1)
  	} else {
  		d1 = distance(0)
  		d0 = distance(1)
  	}

    if (site <= d1 && site >= d0) {
      siteX = (site-d0)/(d1-d0)
      secNum = i
      vv = new Vector()
      ll.append(vv.append(secNum,siteX))
		}
		i = i+1
	}
  return ll
}

func getAbsSecIndex(){ local nAbsInd, index  localobj str,strObj
    strObj  =  new StringFunctions()
    str     =  new String()
    nAbsInd = 0
    index   = 0

    if(strObj.substr($s1, "soma") > 0) {
        strObj.tail($s1, "soma", str.s)
        if(sscanf(str.s, "%*c%d", &index) < 0) {
            index = 0
        }
        nAbsInd = index
    }else if (strObj.substr($s1, "axon") >0) {
        strObj.tail($s1, "axon", str.s)
        if(sscanf(str.s, "%*c%d", &index) < 0) {
            index = 0
        }
        nAbsInd = nSecSoma + index
    }else if (strObj.substr($s1, "dend") >0) {
        strObj.tail($s1, "dend", str.s)
        if(sscanf(str.s, "%*c%d", &index) < 0) {
            index = 0
        }
        nAbsInd = nSecSoma + nSecAxonalOrig + index
    }else if (strObj.substr($s1, "apic") > 0) {
        strObj.tail($s1, "apic", str.s)
        if(sscanf(str.s, "%*c%d", &index) < 0) {
            index = 0
        }
        nAbsInd = nSecSoma + nSecAxonalOrig + nSecBasal + index
    }
    return nAbsInd
}

proc setparameters(){
  EsynConductance = $1
  IsynConductance = $2
  NsynE = $3
  NsynI = $4
}

proc initRand() {
  rList = new List() //for stochastic synapses

        rd1 = new Random($1) // unique to this TTC
        rd1.uniform(0,1)
}


double siteVec[2]

proc distributeSyn() {local sitenum,syni,preconi,i localobj sl,nilstim
        strdef treename,cmd2

        for(i=0;i<(NsynE+NsynI);i+=1){
		if (i==(NsynE+NsynI)/4-1) { print "25% of synapses done" }
		if (i==(NsynE+NsynI)/2-1) { print "50% of synapses done" }
		if (i==3*(NsynE+NsynI)/4-1) { print "75% of synapses done" }
		if (i==(NsynE+NsynI)-1) { print "synapses done" }
                if (rd1.repick()<pA){
                        treename = "apic"
                } else {
                        treename = "dend"
                }

                sl = locateSites(treename,rd1.repick()*getLongestBranch(treename))

                sitenum = int((sl.count()-1)*rd1.repick())
                siteVec[0] = sl.o[sitenum].x[0]
                siteVec[1] = sl.o[sitenum].x[1]

                sprint(cmd2,"access %s[siteVec[0]]",treename)
                execute(cmd2,this)

                sprint(cmd2,"%s[siteVec[0]] sref = new SectionRef()",treename)
                execute(cmd2,this)
                if (i<NsynE){
                        sref {
                                synlist.append(new ProbAMPANMDA2(siteVec[1]))
                                syni = synlist.count()-1 //synapse index
                                rList.append(new Random(int(1000000*rd1.repick())))
                                rList.o[syni].negexp(1)
                                synlist.o[syni].setRNG(rList.o[syni])
                                synlist.o[syni].tau_r_AMPA = 0.3
                                synlist.o[syni].tau_d_AMPA = 3
                                synlist.o[syni].tau_r_NMDA = 2
                                synlist.o[syni].tau_d_NMDA = 65
                                synlist.o[syni].e = 0
                                synlist.o[syni].Dep = 800
                                synlist.o[syni].Fac = 0
                                synlist.o[syni].Use = 0.6
                                synlist.o[syni].u0 = 0
                                synlist.o[syni].gmax = EsynConductance

                                preconlist.append(new NetCon(nilstim, synlist.o[syni]))
                                preconi = preconlist.count()-1 //connection index
                                preconlist.o[preconi].weight = 1
                                preconlist.o[preconi].delay = 0
                        }
                } else {
                        sref {
                                synlist.append(new ProbUDFsyn2(siteVec[1]))
                                syni = synlist.count()-1 //synapse index
                                rList.append(new Random(int(1000000*rd1.repick())))
                                rList.o[syni].negexp(1)
                                synlist.o[syni].setRNG(rList.o[syni])
                                synlist.o[syni].tau_r = 1
                                synlist.o[syni].tau_d = 20
                                synlist.o[syni].e = -80
                                synlist.o[syni].Dep = 800
                                synlist.o[syni].Fac = 0
                                synlist.o[syni].Use = 0.25
                                synlist.o[syni].u0 = 0
                                synlist.o[syni].gmax = IsynConductance

                                preconlist.append(new NetCon(nilstim, synlist.o[syni]))
                                preconi = preconlist.count()-1 //connection index
                                preconlist.o[preconi].weight = 1
                                preconlist.o[preconi].delay = 0
                        }
                }
        }
}

//$o1 list of vectors
proc setpretrains(){local j
  preTrainList = new List()
  for(j=0;j<(NsynE+NsynI);j+=1){
    preTrainList.append($o1.o[j])
  }
}

proc queuePreTrains(){
        fih = new FInitializeHandler("initPreSynTrain()",this)
}

// sets presynaptic spike events
proc initPreSynTrain(){local ti,si
        for(ti=0;ti<preTrainList.count();ti+=1){
                for(si=0;si<preTrainList.o[ti].size();si+=1){
                        preconlist.o[ti].event(preTrainList.o[ti].x[si])
                }
        }
}


endtemplate L5PCtemplate

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