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;
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l5pc_scz
hay
models
morphologies
README.html
Ca_HVA.mod *
Ca_LVAst.mod *
CaDynamics_E2.mod *
epsp.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
ProbAMPANMDA2.mod *
ProbUDFsyn2.mod *
SK_E2.mod *
SKv3_1.mod *
calcapicalthresholds_control.py
calcapicalthresholds_epsp_control.py
calcifcurves.py
calcifcurves_comb.py
calcnspikesperburst2.py
calcsteadystate.py
calcupdown2responses.py
calcupdownresponses_noisydown.py
calcupdownresponses_noisyup.py
coding.py
coding_comb.py
coding_nonprop_comb_somaticI.py
coding_nonprop_somaticI.py
collectupdownresponses_noisy.py
control_cs.sav
controlamps_cs0.sav
controlamps_cs1.sav
controlamps_cs2.sav
controlamps_cs3.sav
controlamps_cs4.sav
controlamps_cs5.sav
controlamps_cs6.sav
drawfigcomb.py
drawnspikesperburst2.py
drawupdownresponses_noisy.py
findppicoeffs.py
findppicoeffs_comb.py
findppicoeffs_complement.py
findthresholdbasalamps_coding.py
findthresholddistalamps.py
findthresholddistalamps_coding.py
findthresholddistalamps_comb.py
mutation_stuff.py
mytools.py
protocol.py
runcontrols_cs.py
savebasalsynapselocations_coding.py
savesynapselocations.py
savesynapselocations_coding.py
scalemutations_cs.py
scalings_cs.sav
setparams.py
synlocs300.0.sav
                            
from neuron import h
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random
from setparams import *


random.seed(1)

v0 = -80
ca0 = 0.0001
proximalpoint = 400
distalpoint = 620
fs = 8
ITERS = 20
tstop = 11000.0
ISIs = [4*x for x in range(0,25)]
ISIs = [x for x in ISIs if mod(x,20) != 0]
currCoeff = 1.1

import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
keyList = defVals.keys()
for idefval in range(0,len(keyList)):
  if type(defVals[keyList[idefval]]) is not list:
    defVals[keyList[idefval]] = [defVals[keyList[idefval]], defVals[keyList[idefval]]] #make the dictionary values [somatic, apical]
updatedVars = ['somatic','apical','basal'] # the possible classes of segments that defVals may apply to
whichDefVal = [0,1,0]                      # use the defVal[0] for somatic and basal segments and defVal[1] for apical segments
unpicklefile = open('scalings_cs.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
theseCoeffsAllAll = unpickledlist[0]
theseMutValsAllAll = unpickledlist[2]

paramdicts = []
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.0, 'S_gCa_HVAbar_Ca_HVA': 1.0})   # 1 spike per burst, control
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 1.6})                               # 1-2 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2})                               # 2-3 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.9})   # 3-4 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.625}) # 3-5 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.5})   # 4-6 spikes per burst
paramdicts.append({'A_gNaTa_tbar_NaTa_t': 2.2, 'S_gCa_HVAbar_Ca_HVA': 0.3})   # 5-9 spikes per burst


#lensToStart = [100.0 + x*50 for x in range(0,16)]
lensToStart = [150.0, 300.0, 450.0, 600.0, 650.0]

gCoeffsAllAllAll = []

for istartdist in range(0,len(lensToStart)):
 startdist = lensToStart[istartdist]
 gCoeffsAllAll = []
 if len(sys.argv) > 2 and int(float(sys.argv[2])) != istartdist:
   continue
 unpicklefile = open('synlocs'+str(startdist)+'.sav', 'r')
 unpickledlist = pickle.load(unpicklefile)
 unpicklefile.close()
 Nsyns = unpickledlist[0]
 synlocsAll = unpickledlist[3]
 startdist = int(startdist)

 unpicklefile = open('thresholddistalamp'+str(startdist)+'.sav', 'r')
 unpickledlist = pickle.load(unpicklefile)
 unpicklefile.close()
 gsAllAll = unpickledlist[1]

 maxLens = [1300,1185]


 for icell in range(0,7):
  synlocs = synlocsAll[0]
  gCoeffsAll = []
  if len(sys.argv) > 3 and int(float(sys.argv[3])) != icell:
    continue
  morphology_file = "morphologies/cell1.asc"
  biophys_file = "models/L5PCbiophys3.hoc"
  template_file = "models/L5PCtemplate.hoc"

  theseCoeffsAll = theseCoeffsAllAll[icell]
  h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
objref st1
st1 = new IClamp(0.5)
L5PC.soma st1
forsec L5PC.somatic {
}
forsec L5PC.apical {
}
L5PC.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,1.0*2.0870,0.0002)
L5PC.distribute_channels("apic","gCa_HVAbar_Ca_HVA",3,1.0,0.1,685.0,885.0,1.0*0.000555)
L5PC.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.0,0.01,685.0,885.0,1.0*0.0187)
objref vsoma, vdend, recSite, vdend2, isoma, cadend, cadend2, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
objref sl,st2,ns,syn1,con1,isyn, tvec, syns["""+str(2*Nsyns)+"""]
isyn = new Vector()
tvec = new Vector()
sl = new List()
double siteVec[2]
sl = L5PC.locateSites("apic","""+str(distalpoint)+""")
maxdiam = 0
for(i=0;i<sl.count();i+=1){
  dd1 = sl.o[i].x[1]
  dd = L5PC.apic[sl.o[i].x[0]].diam(dd1)
  if (dd > maxdiam) {
    j = i
    maxdiam = dd
  }
}
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
print "distalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "distalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
access L5PC.apic[siteVec[0]]
st2 = new IClamp(siteVec[1])
st2.amp = 0
L5PC.apic[siteVec[0]] {
  st2
  syn1 = new epsp(siteVec[1])
  syn1.tau0 = 0.5
  syn1.imax = 0
  syn1.tau1 = 5
  syn1.onset = 145 
  cvode.record(&syn1.i,isyn,tvec)
}
access L5PC.soma
cvode.record(&v(0.5),vsoma,tvec)
cvode.record(&cai(0.5),casoma,tvec)
access L5PC.apic[siteVec[0]]
cvode.record(&v(siteVec[1]),vdend,tvec)
cvode.record(&cai(siteVec[1]),cadend,tvec)
""")
  for istim in range(0,Nsyns):
    h("""
siteVec[0] = """+str(synlocs[istim][0])+"""
siteVec[1] = """+str(synlocs[istim][1])+"""
access L5PC.apic[siteVec[0]]
L5PC.apic[siteVec[0]] {
  syns["""+str(istim)+"""] = new AlphaSynapse(siteVec[1])
  syns["""+str(istim)+"""].e = 0
  syns["""+str(istim)+"""].tau = 5
  syns["""+str(istim)+"""].onset = 10000
  syns["""+str(Nsyns+istim)+"""] = new AlphaSynapse(siteVec[1])
  syns["""+str(Nsyns+istim)+"""].e = 0
  syns["""+str(Nsyns+istim)+"""].tau = 5
  syns["""+str(Nsyns+istim)+"""].onset = 10000
}
""")

  paramdict = paramdicts[icell]
  setparams(paramdict)

  styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
  #cols = ['#aaffaa','#aaffaa','#66ff66','#66aaaa','#00aaaa','#00aaaa']
  #cols = ['#00aaaa','#00bb77','#11cc44','#11dd11','#55ee00','#99dd00']
  cols = ['#00aaaa','#11cc44','#55ee00','#bbaa00','#ee6600','#ff0000', '#aa00aa','#772277','#333333']
  #yplus = [1, 2, 3, 4, 5, 6, -1, -2, -3]
  #yplus = [x+3 for x in yplus]
  yplus = [0, 0, 0, 0, 0, 0, 0, 0, 0]
  coeffCoeffs = [[0.25,0],[0.125,0],[0.5,0],[0.5,1.0/3],[0.5,2.0/3],[0.5,1.0],[-0.25,0],[-0.125,0],[-0.5,0]]

  counter = -1
  
  for igene in range(0,len(MT)):
   gCoeffsThisGene = []
   for imut in range(0,len(MT[igene])):
    gCoeffsThisMut = []
    nVals = len(MT[igene][imut])*[0]
    thesemutvars = []
    theseCoeffs = theseCoeffsAll[igene][imut]
    for imutvar in range(0,len(MT[igene][imut])):
      thesemutvars.append(MT[igene][imut][imutvar][0])
      if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
        MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
      nVals[imutvar] = len(MT[igene][imut][imutvar][1])
    cumprodnVals = cumprod(nVals)
    allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars]
    allmutvals = []
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
      allmutvals.append([0]*len(thesemutvars))
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
      for imutvar in range(0,len(MT[igene][imut])):
        if imutvar==0:
          allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
        else:
          allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
      
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
      counter = counter + 1                                                                                                                                                               
      if len(sys.argv) > 1 and int(float(sys.argv[1])) != counter:
        continue
      gCoeffsThisMutVal = []
      close("all")
      f, axarr = plt.subplots(2, 2)
      maxCac = 0
      maxCadc = 0
      #for iter in [2, 5, 8, -1]:
      iters = [0, 2, 5, 6, 8, -1]
      nspsThisVal = []
      for iiter in range(0,len(iters)):
        nspsThisIter = []
        iter = iters[iiter]
        gCoeffsThisIter = []
        if iter >= 0:
          thisCoeff = coeffCoeffs[iter][0]*theseCoeffs[iallmutval] + coeffCoeffs[iter][1]*(1.0 - 0.5*theseCoeffs[iallmutval])
        else:
          thisCoeff = 0
        if iter == -1 and (igene > 0 or imut > 0 or iallmutval > 0):
          continue # do the control only once!
        if iter == 5:
          continue
        thisg = gsAllAll[icell][igene][imut][iallmutval][iiter]
        print "iter="+str(iter)+", thisCoeff="+str(thisCoeff)+", thisg="+str(thisg)
        
        mutText = ""
        for imutvar in range(0,len(MT[igene][imut])):
          if imutvar > 0 and imutvar%2==0:
            mutText = mutText+"\n"
          mutvars = allmutvars[iallmutval][imutvar]
          mutvals = allmutvals[iallmutval][imutvar]
          if type(mutvars) is str:
            mutvars = [mutvars]
          mutText = mutText + str(mutvars) + ": "
          for kmutvar in range(0,len(mutvars)):
            mutvar = mutvars[kmutvar]
            if mutvar.find('offm') > -1 or mutvar.find('offh') > -1 or mutvar.find('ehcn') > -1:
              newVal =  [x+mutvals*thisCoeff for x in defVals[mutvar]]
              if mutvals >= 0 and kmutvar==0:
                mutText = mutText + "+" + str(mutvals) +" mV"
              elif kmutvar==0:
                mutText = mutText  + str(mutvals) +" mV"
            else:
              newVal = [x*(mutvals**thisCoeff) for x in defVals[mutvar]]
              if kmutvar==0:
                mutText = mutText + "*" + str(mutvals)
            if kmutvar < len(mutvars)-1:
              mutText = mutText + ", "
            if mutvar.find('_Ih') > -1:
              updateThese = [1,1,1]
            elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
              updateThese = [1,1,0]
            elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
              updateThese = [1,0,0]
            elif mutvar.find('_Im') > -1:
              updateThese = [0,1,0]
            else:
              print "Error: str=" + str(mutvar)
              updatedThese = [0,0,0]
            for iupdated in range(0,3):
              if updateThese[iupdated]:
                print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
                h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")
        print mutText
        thisCa = h.L5PC.soma[0].minCai_CaDynamics_E2

        for iISI in range(0,len(ISIs)):
          gCoeffsThisISI = []
          PPIdt = ISIs[iISI]
          nextCoeffs = [0,15.0,4.0]
          hasSpiked = 0
          for iterI in range(0,ITERS+2):
            for istim in range(0,Nsyns):
              h("syns["+str(istim)+"].gmax = "+str(thisg*currCoeff))
              h("syns["+str(Nsyns+istim)+"].gmax = "+str(thisg*currCoeff*nextCoeffs[min(iterI,2)]))
              h("syns["+str(Nsyns+istim)+"].onset = "+str(10000+PPIdt))
            h("""
tstop = """+str(tstop)+"""
cai0_ca_ion = """+str(thisCa)+"""
v_init = """+str(v0)+"""
st1.amp = 0
st1.del = 200
st1.dur = 10
""")
            timenow = time.time()
            h.init()
            try:
              h.run()
            except RuntimeError:
              hasErred = 1
              print "Too large g!"
  
            times=np.array(h.tvec)
            Vsoma=np.array(h.vsoma)
            spikes = mytools.spike_times(times,Vsoma,-20,-45)
            nSpikes1 = len(spikes)
            print "nextCoeffs="+str(nextCoeffs)+", "+str(nSpikes1)+" spikes, simulation done in "+str(time.time()-timenow)+" seconds"
            nSpikes_normal = 1
            if icell > 0 or startdist <= 200: # For icell=0, 1 spike normally generated (except for the inputs nearest to soma), while for icell=1,2,3,4,5,6, two spikes normally generated
              nSpikes_normal = 2
            hasSpiked = hasSpiked or (nSpikes1 > nSpikes_normal)
            if iterI == 0 and hasSpiked:
              print "istartdist="+str(istartdist)+", icell="+str(icell)+", igene="+str(igene)+", imut="+str(imut)+", iallmuval="+str(iallmutval)+", iiter="+str(iiter)+", iISI="+str(iISI)+": extra spikes elicited for iterI=0!"
            if iterI > 0 and not hasSpiked:
              print "istartdist="+str(istartdist)+", icell="+str(icell)+", igene="+str(igene)+", imut="+str(imut)+", iallmuval="+str(iallmutval)+", iiter="+str(iiter)+", iISI="+str(iISI)+": no extra spikes for iterI>0!"
              nextCoeffs = [nextCoeffs[1],2*nextCoeffs[1],1.5*nextCoeffs[1]]
              continue
            if iterI > 1 and nSpikes1 > nSpikes_normal:
              nextCoeffs = [nextCoeffs[0],nextCoeffs[2],0.5*(nextCoeffs[0]+nextCoeffs[2])]
            if iterI > 1 and nSpikes1 <= nSpikes_normal:
              nextCoeffs = [nextCoeffs[2],nextCoeffs[1],0.5*(nextCoeffs[2]+nextCoeffs[1])]
            #print str(nSpikes1)+", nextCoeffs="+str(nextCoeffs)
          gCoeffsThisISI = nextCoeffs[:]
          gCoeffsThisIter.append(gCoeffsThisISI[:])
  
        #Print the parameters and their default values:
        for idefval in range(0,len(defVals.keys())):
          thisdefval = defVals.keys()[idefval]
          if thisdefval.find('_Im') > -1:
            h('print "L5PC.apic[0].'+thisdefval+' = ", L5PC.apic[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][1]))
            #) #+" (def="+str(defVals[thisdefval])+")"
          else:
            h('print "L5PC.soma[0].'+thisdefval+' = ", L5PC.soma[0].'+thisdefval+', "Default = ", '+str(defVals[thisdefval][0]))
            #h('print L5PC.soma[0]."+thisdefval) #+" (def="+str(defVals[thisdefval])+")"                       
  
        #Restore default values:
        for imutvar in range(0,len(MT[igene][imut])):
          mutvars = allmutvars[iallmutval][imutvar]
          mutvals = allmutvals[iallmutval][imutvar]
          if type(mutvars) is str:
            mutvars = [mutvars]
          for kmutvar in range(0,len(mutvars)):
            mutvar = mutvars[kmutvar]
            newVal = defVals[mutvar]
            if mutvar.find('_Ih') > -1:
              updateThese = [1,1,1]
            elif mutvar.find('_Ca_HVA') > -1 or mutvar.find('_Ca_LVAst') > -1 or mutvar.find('_SKv3.1') > -1 or mutvar.find('_Ca_HVA') > -1 or mutvar.find('_SK_E2') > -1 or mutvar.find('_NaTa_t') > -1 or mutvar.find('_CaDynamics_E2') > -1:
              updateThese = [1,1,0]
            elif mutvar.find('_K_Pst') > -1 or mutvar.find('_K_Tst') > -1 or mutvar.find('_Nap_Et2') > -1:
              updateThese = [1,0,0]
            elif mutvar.find('_Im') > -1:
              updateThese = [0,1,0]
            else:
              print "Error: str=" + str(mutvar)
              updatedThese = [0,0,0]
            for iupdated in range(0,3):
              if updateThese[iupdated]:
                print """forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}"""
                h("""forsec L5PC."""+str(updatedVars[iupdated])+""" {
"""+mutvar+""" = """+str(newVal[whichDefVal[iupdated]])+"""
}""")
        gCoeffsThisMutVal.append(gCoeffsThisIter[:])
        picklelist = [theseCoeffsAll,gCoeffsThisMutVal,ISIs,MT]
        file = open('PPIcoeffs_complement_'+str(startdist)+'_cs'+str(icell)+'_'+str(counter)+'.sav', 'w')
        pickle.dump(picklelist,file)
        file.close()
      gCoeffsThisMut.append(gCoeffsThisMutVal[:])
    gCoeffsThisGene.append(gCoeffsThisMut[:])
   gCoeffsAll.append(gCoeffsThisGene[:])
  gCoeffsAllAll.append(gCoeffsAll[:])
 gCoeffsAllAllAll.append(gCoeffsAllAll[:])

#picklelist = [theseCoeffsAllAll,gsThisAllAll,MT]
#file = open('thresholddistalamp'+str(startdist)+'_cs'+str(icell)+'_'+str(counter)+'.sav', 'w')
#pickle.dump(picklelist,file)
#file.close()
  

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