SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)

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Accession:237469
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
Reference:
1 . Mäki-Marttunen T, Krull F, Bettella F, Hagen E, Næss S, Ness TV, Moberget T, Elvsåshagen T, Metzner C, Devor A, Edwards AG, Fyhn M, Djurovic S, Dale AM, Andreassen OA, Einevoll GT (2019) Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations. Cereb Cortex 29:875-891 [PubMed]
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): Neocortex L5/6 pyramidal GLU cell;
Channel(s): Ca pump; I A, slow; I h; I K; I K,Ca; I K,leak; I L high threshold; I M; I Na,p; I Na,t; I T low threshold;
Gap Junctions: Gap junctions;
Receptor(s): AMPA; NMDA; Gaba;
Gene(s): Cav1.2 CACNA1C; Cav1.3 CACNA1D; Cav3.3 CACNA1I; HCN1; Kv2.1 KCNB1; Nav1.1 SCN1A; PMCA ATP2B2;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON; Python; LFPy;
Model Concept(s): Schizophrenia; Oscillations;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; AMPA; NMDA; Gaba; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I K,leak; I M; I h; I K,Ca; I A, slow; Ca pump; Gaba; Glutamate;
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drawfig1ab.py
drawfig1c.py
drawfig2ab.py
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mutation_stuff.py *
mytools.py
pars_withmids_combfs_final.sav *
runmanycellsLFP.py
runsinglecellLFP.py
saveisidists.py
savespikesshufflephases.py
scalings_cs.sav
simosc_parallel.py
simosc_parallel_comb_varconn.py
simseedburst_func.py
simseedburst_func_comb_varconn.py
simseedburst_func_withLFP.py
simseedburst_func_withLFP.pyc
                            
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import sys
import time
from os.path import exists

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]]
import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
geneNames = mutation_stuff.getgenenames()
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

variants = [[0,5,1],[2,4,7],[1,2,13],[3,1,0],[5,0,0],[8,3,0],[12,2,0],[13,4,0]] #from drawallmeangains (maxCountersAll) #Removed KCNN3!

rates = [0.1*x for x in range(4,17)]


icell = 0
gsyn = 1.07
gNoise = 1.07

cols = ['#666666','#012345','#cc00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
col_control = '#2222ff'

if exists('nSpikes_control.sav'):
  unpicklefile = open('nSpikes_control.sav', 'r')
  unpickledlist = pickle.load(unpicklefile)
  unpicklefile.close()
  nSpikes_control = unpickledlist[0]
else:
  nSpikes_control = []
  for irate in range(0,len(rates)):
    myrate = rates[irate]
    nSpikesThisRate = []
    print "loading control rate="+str(myrate)
    for myseed in range(1,10):
      if exists('spikes_parallel150_mutID0_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav'):
        unpicklefile = open('spikes_parallel150_mutID0_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav', 'r')
        unpickledlist = pickle.load(unpicklefile)
        unpicklefile.close()
        Nplaced = 0
        spikedCells_all = []
        for j in range(0,len(unpickledlist[1])):
          spikedCells = unpickledlist[1][j]
          spikedCellsUnique = unique(spikedCells)
          spikedCells2 = zeros(spikedCells.shape)
          for i in range(0,len(spikedCellsUnique)):
            spikedCells2[spikedCells == spikedCellsUnique[i]] = Nplaced + i
          Nplaced = Nplaced + len(spikedCellsUnique)
          spikedCells_all = hstack([spikedCells_all, spikedCells2])
        spikes = [hstack(unpickledlist[0]),spikedCells_all]
        nSpikesThisRate.append(len(spikes[0]))
      else:
        print 'spikes_parallel150_mutID0_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav not found'
    nSpikes_control.append(nSpikesThisRate[:])
  file = open('nSpikes_control.sav', 'w')
  pickle.dump([nSpikes_control],file)
  file.close()

for counter in range(0,461):
  if not exists('nSpikes'+str(counter)+'.sav'):
    nSpikesThisIter = []
    print "loading mutID="+str(counter)
    for irate in range(0,len(rates)):
      myrate = rates[irate]
      nSpikesThisRate = []
      print "loading rate="+str(myrate)
      for myseed in range(1,10):
        if exists('spikes_parallel150_mutID'+str(counter)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav'):
          unpicklefile = open('spikes_parallel150_mutID'+str(counter)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav', 'r')
          unpickledlist = pickle.load(unpicklefile)
          unpicklefile.close()
        else:
          continue
        Nplaced = 0
        spikedCells_all = []
        for j in range(0,len(unpickledlist[1])):
          spikedCells = unpickledlist[1][j]
          spikedCellsUnique = unique(spikedCells)
          spikedCells2 = zeros(spikedCells.shape)
          for i in range(0,len(spikedCellsUnique)):
            spikedCells2[spikedCells == spikedCellsUnique[i]] = Nplaced + i
          Nplaced = Nplaced + len(spikedCellsUnique)
          spikedCells_all = hstack([spikedCells_all, spikedCells2])
        spikes = [hstack(unpickledlist[0]),spikedCells_all]

        nSpikesThisRate.append(len(spikes[0]))
        if len(spikes[0]) < 20:
          print str(spikes[0])
      nSpikesThisIter.append(nSpikesThisRate[:])
      file = open('nSpikes'+str(counter)+'.sav', 'w')
      pickle.dump([nSpikesThisIter],file)
      file.close()

print "Loading control f-I curves..."
ispDef = 0
Is = [0.35+0.05*x for x in range(0,22)]
unpicklefile = open('../haymod/ifcurvesmut_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spTimesThisMutVal = unpickledlist[1+ispDef]
nSpikes_control_if = [sum([1 for x in spTimesThisMutVal[5][j] if x >= 500]) for j in range(0,len(Is))]

counter = 0

close("all")                               
f, axarr = plt.subplots(2, len(variants)+1)              
lenvarper2 = 4 # len(variants)/2
for ix in range(0,lenvarper2):
  for iy in range(0,2):
    axarr[0,ix+lenvarper2*iy].set_position([0.1+0.176*ix, 0.1+0.44*(1-iy), 0.176, 0.37])
    axarr[1,ix+lenvarper2*iy].set_position([0.121+0.176*ix, 0.33+0.44*(1-iy), 0.075, 0.11])

axarr[0,8].set_position([0.1+0.176*4, 0.1+0.44*(1-1), 0.176, 0.37])
axarr[1,8].set_position([0.121+0.176*4, 0.33+0.44*(1-1), 0.075, 0.11])

timesAll = []
VsomaAll = []
spikeFreqsAll = []
for igene in range(0,len(MT)):
  for imut in range(0,len(MT[igene])):
    nVals = len(MT[igene][imut])*[0]
    thesemutvars = []
    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]):
      mutval = allmutvals[iallmutval]

      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 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 for x in defVals[mutvar]]
            if kmutvar==0:
              mutText = mutText + "*" + str(mutvals)
          if kmutvar < len(mutvars)-1:
            mutText = mutText + ", "

      iters = [0, 2, 6, 8]
      iters_if = [0, 2, 5, 6, 8, -1]
      doSkip = True
      ivar = -1
      for iiter in range(0,len(iters)):
        iter = iters[iiter]
        counter = counter+1

        for ivar2 in range(0,len(variants)):
          if igene == variants[ivar2][0] and imut == variants[ivar2][1] and iallmutval == variants[ivar2][2]:
            ivar = ivar2
            doSkip = False
            break
        if doSkip:
          continue
        if exists('nSpikes'+str(counter)+'.sav'):
          unpicklefile = open('nSpikes'+str(counter)+'.sav', 'r')
          unpickledlist = pickle.load(unpicklefile)
          unpicklefile.close()
          nSpikesThisIter = unpickledlist[0]
        else:
          print 'nSpikes'+str(counter)+'.sav not found!'
          continue
        axarr[0,ivar].plot(rates, [mean(x)/150./11.0 for x in nSpikesThisIter], 'b-',color=cols[iter])
        print "igene="+str(igene)+", imut="+str(imut)+", iallmutval="+str(iallmutval)+", ivar="+str(ivar)+", counter="+str(counter)+", iter="+str(iter)
      if doSkip:
        continue
      print mutText

      print "Loading f-I curves..."
      unpicklefile = open('../haymod/ifcurvesmut_cs'+str(icell)+'_'+str(igene)+'_'+str(imut)+'_'+str(iallmutval)+'.sav', 'r')
      unpickledlist = pickle.load(unpicklefile)
      unpicklefile.close()
      spTimesThisMutVal = unpickledlist[1+ispDef]
      for iiter in range(0,len(iters_if)):
        iter = iters_if[iiter]
        if iter == 5 or iter == -1:
          continue
        nSpikes_if = [sum([1 for x in spTimesThisMutVal[iiter][j] if x >= 500]) for j in range(0,len(Is))]
        axarr[1,ivar].plot(Is, [x/7.5 for x in nSpikes_if], 'b-', color=cols[iter])
      axarr[1,ivar].plot(Is, [x/7.5 for x in nSpikes_control_if], 'b-', color=col_control)
      
      axarr[0,ivar].plot(rates, [mean(x)/150.0/11. for x in nSpikes_control], 'b-',color=col_control)
      axarr[0,ivar].set_title(geneNames[igene])

      axarr[0,ivar].set_xlim([0.4,1.6])
      axarr[0,ivar].set_ylim([0,12])
      axarr[0,ivar].set_xticks([0.6, 1.0, 1.4])
      axarr[0,ivar].set_yticks([0, 3, 6, 9, 12])
      if ivar < lenvarper2:
        axarr[0,ivar].set_xticklabels(['', '', ''])
      elif ivar==6:
        #axarr[0,ivar].set_xlabel('rate factor r                                ')
        axarr[0,ivar].set_xlabel('rate factor $r$')
        #axarr[0,ivar].set_xlabel('rate factor $c_{\mathrm{rate}}$                            ')
      if ivar % lenvarper2 > 0:
        axarr[0,ivar].set_yticklabels(['', '', '', '', ''])        
      else:
        axarr[0,ivar].set_ylabel('$f$ (Hz)')

      axarr[1,ivar].set_xlim([0.35,1.4])
      axarr[1,ivar].set_ylim([0,20])
      axarr[1,ivar].set_xticks([0.5,1.0])
      axarr[1,ivar].set_yticks([0,10,20])
      axarr[1,ivar].set_xlabel('$I$ (nA)',fontsize=8)
      for tick in axarr[1,ivar].xaxis.get_major_ticks()+axarr[1,ivar].yaxis.get_major_ticks():
        tick.label.set_fontsize(6)
      axarr[1,ivar].yaxis.set_tick_params(pad=3)      

      f.savefig("fig1c.eps")

iters = [0, 2, 6, 8]
iters_if = [0, 2, 6, 8]
combmutIDnums = [1, 0, 2, 3]
ivar = 8

for iiter in range(0,len(iters)):
  iter = iters[iiter]
  combmutIDnum = combmutIDnums[iiter]
  if not exists('nSpikes_comb_'+str(iiter)+'.sav'):
    nSpikesThisIter = []
    print "loading mutID="+str(combmutIDnum)
    for irate in range(0,len(rates)):
      myrate = rates[irate]
      nSpikesThisRate = []
      print "loading rate="+str(myrate)
      for myseed in range(1,12):
        if exists('spikes_parallel150_mutcombID'+str(combmutIDnum)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav'):
          unpicklefile = open('spikes_parallel150_mutcombID'+str(combmutIDnum)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav', 'r')
          unpickledlist = pickle.load(unpicklefile)
          unpicklefile.close()
          Nplaced = 0
          spikedCells_all = []
          for j in range(0,len(unpickledlist[1])):
            spikedCells = unpickledlist[1][j]
            spikedCellsUnique = unique(spikedCells)
            spikedCells2 = zeros(spikedCells.shape)
            for i in range(0,len(spikedCellsUnique)):
              spikedCells2[spikedCells == spikedCellsUnique[i]] = Nplaced + i
            Nplaced = Nplaced + len(spikedCellsUnique)
            spikedCells_all = hstack([spikedCells_all, spikedCells2])
          spikes = [hstack(unpickledlist[0]),spikedCells_all]
          nSpikesThisRate.append(len(spikes[0]))
          if len(spikes[0]) < 20:
            print str(spikes[0])
        else:
          print 'spikes_parallel150_mutcombID'+str(combmutIDnum)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav not found'
      nSpikesThisIter.append(nSpikesThisRate[:])
      file = open('nSpikes_comb_'+str(iiter)+'.sav', 'w')
      pickle.dump([nSpikesThisIter],file)
      file.close()

  if exists('nSpikes_comb_'+str(iiter)+'.sav'):
    unpicklefile = open('nSpikes_comb_'+str(iiter)+'.sav', 'r')
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()
    nSpikesThisIter = unpickledlist[0]
  else:
    print 'nSpikes_comb_'+str(iiter)+'.sav not found!'
    continue
  axarr[0,ivar].plot(rates, [mean(x) for x in nSpikesThisIter], 'b-',color=cols[iter])

unpicklefile = open('../haymod/ifcurves_comb_cs'+str(icell)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spTimesThisMutVal = unpickledlist[1+ispDef]

for iiter in range(0,len(iters_if)):
  iter = iters_if[iiter]
  if iter == 5 or iter == -1:
    continue
  nSpikes_if = [sum([1 for x in spTimesThisMutVal[iiter][j] if x >= 500]) for j in range(0,len(Is))]
  axarr[1,ivar].plot(Is, [x/7.5 for x in nSpikes_if], 'b-', color=cols[iter])
  print "Comb nSpikes="+str(nSpikes_if)

axarr[1,ivar].plot(Is, [x/7.5 for x in nSpikes_control_if], 'b-', color=col_control)

axarr[0,ivar].plot(rates, [mean(x) for x in nSpikes_control], 'b-',color=col_control)
axarr[0,ivar].set_title("Combination")
axarr[0,ivar].set_xlim([0.4,1.6])
axarr[0,ivar].set_ylim([0,20000])
axarr[0,ivar].set_xticks([0.6, 1.0, 1.4])
axarr[0,ivar].set_yticks([0, 5000, 10000, 15000, 20000])
axarr[0,ivar].set_yticklabels(['', '', '', '', ''])        

axarr[1,ivar].set_xlim([0,1.4])
axarr[1,ivar].set_ylim([0,20])
axarr[1,ivar].set_xticks([0,0.5,1.0])
axarr[1,ivar].set_yticks([0,10,20])
axarr[1,ivar].set_xlabel('$I$ (nA)',fontsize=8)
for tick in axarr[1,ivar].xaxis.get_major_ticks()+axarr[1,ivar].yaxis.get_major_ticks():
  tick.label.set_fontsize(6)
      
f.text(0.025, 0.88, 'C', fontsize=36)
f.savefig("fig1c.eps")

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