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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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)

oscamp = 0.25
## These are the frequencies used in the article figure 2:
#oscfreqs = [0.0625, 0.125, 0.1875, 0.25, 0.3125, 0.375, 0.4375, 0.5, 0.5625, 0.625, 0.6875, 0.75, 0.875, 1.0, 1.125, 1.25, 1.375, 1.5, 1.625, 1.75, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 7.5, 10.0, 15.0]
# This is a smaller set of frequencies that captures most of the shape of the response curve:
oscfreqs = [0.5, 0.625, 0.75, 0.875, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 7.5, 10.0, 15.0]

gsyn = 1.07
gNoise = 1.07

fts_control = []
ft_df = 0.00001

if exists('spectrum_freq1.0_0.sav'): #Do this just to get the fs_control
  unpicklefile = open('spectrum_freq1.0_0.sav','r')
  unpickledlist = pickle.load(unpicklefile)
  unpicklefile.close()
  fs_control = unpickledlist[0] 

print "Loading control..."
amps_control = []
amps_control_std = []
if exists('spectra_fig2_0.sav'):
  unpicklefile = open('spectra_fig2_0.sav','r')
  unpickledlist = pickle.load(unpicklefile)
  unpicklefile.close()
  fts = unpickledlist[0]
  fts_std = unpickledlist[1]
  amps_control = unpickledlist[2]
  amps_control_std = unpickledlist[3]
else:
  for iosc in range(0,len(oscfreqs)):
    oscfreq = oscfreqs[iosc]
    if exists('spectrum_freq'+str(oscfreq)+'_0.sav'):
      unpicklefile = open('spectrum_freq'+str(oscfreq)+'_0.sav','r')
      unpickledlist = pickle.load(unpicklefile)
      unpicklefile.close()
      fs_control = unpickledlist[0]
      ft_fs = unpickledlist[1]
      fts = [mean([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs_control))]
      fts_std = [std([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs_control))]
      iosc_fs = next(i for i,x in enumerate(fs_control) if x>oscfreq/1000)
      amps_control.append(fts[iosc_fs])
      amps_control_std.append(fts_std[iosc_fs])
    else:
      print 'spectrum_freq'+str(oscfreq)+'_0.sav not found!'
  print "Loading done"
  picklelist = [fts, fts_std, amps_control, amps_control_std]
  file = open('spectra_fig2_0.sav','w')
  pickle.dump(picklelist,file)
  file.close()

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

counter = -1

close("all")                               
f, axarr = plt.subplots(1, len(variants)+1)
lenvarper2 = 4
for ix in range(0,lenvarper2):
  for iy in range(0,2):
    axarr[ix+lenvarper2*iy].set_position([0.08+0.176*ix, 0.1+0.44*(1-iy), 0.176, 0.37])
axarr[8].set_position([0.08+0.176*4, 0.1+0.44*(1-1), 0.176, 0.37])

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]

      thisCoeff = 1
      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 igene == 0 and imut == 0 and iallmutval == 0:
        iters = [-1, 0, 2, 6, 8]
      else:
        iters = [0, 2, 6, 8]
      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('spectra_fig2_'+str(counter)+'.sav'):
          unpicklefile = open('spectra_fig2_'+str(counter)+'.sav','r')
          unpickledlist = pickle.load(unpicklefile)
          unpicklefile.close()
          fts = unpickledlist[0]
          fts_std = unpickledlist[1]
          amps = unpickledlist[2]
          amps_std = unpickledlist[3]
        else:
          amps = []
          amps_std = []
          for iosc in range(0,len(oscfreqs)):
            oscfreq = oscfreqs[iosc]
            iosc_fs = next(i for i,x in enumerate(fs_control) if x>oscfreq/1000)
            if exists('spectrum_freq'+str(oscfreq)+'_'+str(counter)+'.sav'):
              unpicklefile = open('spectrum_freq'+str(oscfreq)+'_'+str(counter)+'.sav','r')
              unpickledlist = pickle.load(unpicklefile)
              unpicklefile.close()
              fs = unpickledlist[0]
              ft_fs = unpickledlist[1]
              fts = [mean([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs))]
              fts_std = [std([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs))]
              amps.append(fts[iosc_fs])
              amps_std.append(fts_std[iosc_fs])
            else:
              print 'spectrum_freq'+str(oscfreq)+'_'+str(counter)+'.sav does not exist' 
              amps.append([])
              amps_std.append([])
          picklelist = [fts, fts_std, amps, amps_std]
          file = open('spectra_fig2_'+str(counter)+'.sav','w')
          pickle.dump(picklelist,file)
          file.close()
        if len(amps) > 0:
          if not any([type(amps[iosc]) is list for iosc in range(0,len(amps))]):
            axarr[ivar].semilogx(oscfreqs, [amps[iosc] for iosc in range(0,len(amps))], 'b-', color=cols[iter])
          else:
            myvec = zeros([len(amps),])
            for iosc in range(0,len(amps)):
              if type(amps[iosc]) is not list:
                myvec[iosc] = amps[iosc]
              else: 
                myvec[iosc] = nan
            axarr[ivar].semilogx(oscfreqs, myvec, 'b.-', color=cols[iter])
            print "Some frequencies not found in counter="+str(counter)+", iter="+str(iter)+"!"
        else:
          print "No frequencies not found in counter="+str(counter)+", iter="+str(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

      axarr[ivar].semilogx(oscfreqs, amps_control, 'b-', color=col_control)
      axarr[ivar].set_title(geneNames[igene])
      axarr[ivar].set_xlim([0.4,15])
      axarr[ivar].set_ylim([0,7.5e7])
      axarr[ivar].set_yticks([0, 3e7, 6e7])
      if ivar < lenvarper2:
        axarr[ivar].set_xticklabels(['']*len(axarr[ivar].get_xticks()))
      elif ivar == 6:
        axarr[ivar].set_xlabel('Input frequency $f$ (Hz)')
      if ivar % lenvarper2 > 0:
        axarr[ivar].set_yticklabels(['', '', ''])        
      elif ivar == 0:
        axarr[ivar].set_ylabel('Power of the frequency component corresponding $f$                                            ')
  f.savefig("fig2c.eps")

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

amps_thismut = []
for iiter in range(0,4):
  iter = iters[iiter]
  if exists('spectra_fig2_comb'+str(combmutIDnums[iiter])+'.sav'):
    unpicklefile = open('spectra_fig2_comb'+str(combmutIDnums[iiter])+'.sav','r')
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()
    fts = unpickledlist[0]
    fts_std = unpickledlist[1]
    amps = unpickledlist[2]
    amps_std = unpickledlist[3]
  else:
    amps = []
    amps_std = []
    for iosc in range(0,len(oscfreqs)):
      oscfreq = oscfreqs[iosc]
      iosc_fs = next(i for i,x in enumerate(fs_control) if x>oscfreq/1000)
      if exists('spectrum_freq'+str(oscfreq)+'_comb'+str(combmutIDnums[iiter])+'.sav'):
        unpicklefile = open('spectrum_freq'+str(oscfreq)+'_comb'+str(combmutIDnums[iiter])+'.sav','r')
        unpickledlist = pickle.load(unpicklefile)
        unpicklefile.close()
        fs = unpickledlist[0]
        ft_fs = unpickledlist[1]
        fts = [mean([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs))]
        fts_std = [std([abs(ft_fs[isamp][ifreq])**2 for isamp in range(0,len(ft_fs))]) for ifreq in range(0,len(fs))]
        amps.append(fts[iosc_fs])
        amps_std.append(fts_std[iosc_fs])
      else:
        print 'spectrum_freq'+str(oscfreq)+'_comb'+str(combmutIDnums[iiter])+'.sav does not exist'
        amps.append(nan)
        amps_std.append(nan)
    picklelist = [fts, fts_std, amps, amps_std]
    file = open('spectra_fig2_comb'+str(combmutIDnums[iiter])+'.sav','w')
    pickle.dump(picklelist,file)
    file.close()

  if len(amps) > 0:
    if not any([type(amps[iosc]) is list for iosc in range(0,len(amps))]):
      axarr[ivar].semilogx(oscfreqs, [amps[iosc] for iosc in range(0,len(amps))], 'b-', color=cols[iter])
    else:
      myvec = zeros([len(amps),])
      for iosc in range(0,len(amps)):
        if type(amps[iosc]) is not list:
          myvec[iosc] = amps[iosc]
        else: 
          myvec[iosc] = nan
      axarr[ivar].semilogx(oscfreqs, myvec, 'b.-', color=cols[iter])
      print "Some frequencies not found in counter="+str(counter)+", iter="+str(iter)+"!"
  else:
    print "No frequencies not found in counter="+str(counter)+", iter="+str(iter)+"!"

  amps_thismut.append(amps[:])
axarr[ivar].semilogx(oscfreqs, amps_control, 'b-', color=col_control)
axarr[ivar].set_ylim([0,8e7])

axarr[ivar].set_title("Combination")
axarr[ivar].set_xlim([0.4,15])
axarr[ivar].set_ylim([0,7.5e7])
axarr[ivar].set_yticks([0, 3e7, 6e7])
axarr[ivar].set_yticklabels(['', '', ''])        

for ivar in range(0,9):
  t = axarr[ivar].yaxis.get_offset_text()
  if type(t) is matplotlib.text.Text:
    if t.get_text()[0:2] == '1e':
      t.set_text('$\\times 10^{'+t.get_text()[2:]+'}$')
    t.set_position((t.get_position()[0]-0.15,t.get_position()[1]))
f.text(0.01, 0.9, 'C', fontsize=31)
f.savefig("fig2c.eps")

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