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
                            
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import sys
import scipy.io
from os.path import exists

f,axarr = subplots(1,3)
for i in range(0,3):
  axarr[i].set_position([0.1+0.3*i,0.5,0.23,0.45])
  f.text(0.065+0.3*i, 0.94, chr(ord('A')+i), fontsize=22)

Is = unique([0.34+0.0025*x for x in range(0,11)]+[0.35+0.05*x for x in range(0,22)])
styles = ['g-','g-','g-','g-','g-','g-','g-','g-','g-']
cols = ['#666666','#012345','#aa00aa','#bbaa00','#ee6600','#ff0000', '#00aaaa','#772277','#00cc00']
col_control = '#2222ff'
ispDef = 0 # Consider a local maximum above -35mV a spike only if after last spike the membrane potential came below -45mV 

icell = 0
icomb = 0
combs_all = [ [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]],        
              [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,3,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,0], [5,0,0], [8,5,0], [13,2,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,1,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,4,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,1,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,5,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,3,0]],
              [[0,5,1], [1,2,15], [2,4,7], [3,1,1], [5,0,0], [8,5,0], [13,0,0]],         
              [[0,5,0], [1,3,0], [2,5,1], [3,0,1], [6,3,0], [8,3,0], [12,1,1], [13,5,0]] ]
lensToStart = [150.0, 300.0, 450.0, 600.0, 650.0]
startdist = int(lensToStart[1])
currCoeff = 1.1

unpicklefile = open('ifcurvesmut_cs'+str(icell)+'_comb'+str(icomb)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
ISIsThisMutVal = unpickledlist[0]
spTimesThisMutVal = unpickledlist[1+ispDef]

Is_control = [0.35+0.05*x for x in range(0,22)]
unpicklefile = open('ifcurvesmut_cs'+str(icell)+'_0_0_0.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
spTimesThisMutVal0 = unpickledlist[1+ispDef]
nSpikes_control = [sum([1 for x in spTimesThisMutVal0[5][j] if x >= 500]) for j in range(0,len(Is_control))]

somaticIs = [-0.1, -0.09, -0.08, -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01, 0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
synconductances = unique([0.000005, 0.00001, 0.000015, 0.00002, 0.000025, 0.00003, 0.000035, 0.00004, 0.000045, 0.00005, 0.000055, 0.0000025, 0.0000075, 0.0000125, 0.0000175, 0.0000225, 0.0000275, 0.0000325, 0.0000375, 0.0000425, 0.0000475, 0.0000525])
thrs = [[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0.00011, 0.00012, inf],[0,1,2,3,4,inf]]


iters = [0, 2, 5, 6, 8]
for iiter in range(0,len(iters)):
  iter = iters[iiter]
  if iter==5:
    continue
  nSpikes = [sum([1 for x in spTimesThisMutVal[iiter][j] if x >= 500]) for j in range(0,len(Is))]
  axarr[0].plot(Is, [x/7.5 for x in nSpikes], styles[iter],color=cols[iter],linewidth=1)
axarr[0].plot(Is_control, [x/7.5 for x in nSpikes_control], styles[iter],color=col_control,linewidth=1)



if exists('PPIcoeffs300_cs'+str(icell)+'_0.sav'):
  print ' opening PPIcoeffs300_cs'+str(icell)+'_0.sav and PPIcoeffs_complement_300_cs'+str(icell)+'_0.sav'
  unpicklefile = open('PPIcoeffs300_cs'+str(icell)+'_0.sav', 'r')
  unpickledlist = pickle.load(unpicklefile)
  unpicklefile.close()
  unpicklefile = open('PPIcoeffs_complement_300_cs'+str(icell)+'_0.sav', 'r')
  unpickledlist2 = pickle.load(unpicklefile)
  unpicklefile.close()
  PPIcoeffs_control_Hay = unpickledlist[1][4]
  print str(unpickledlist[1][4])
  ISIs_control_Hay = unpickledlist[2]
  for iISI in range(0,len(unpickledlist2[2])):
    ind = next((i for i,x in enumerate(ISIs_control_Hay) if unpickledlist2[2][iISI] < x ))
    #print "unpickledlist2[2][iISI]="+str(unpickledlist2[2][iISI])+", ISIs_Hay="+str(ISIs_Hay)+", ind="+str(ind)                                                                                                                       
    ISIs_control_Hay.insert(ind,unpickledlist2[2][iISI])
    PPIcoeffs_control_Hay.insert(ind,unpickledlist2[1][4][iISI])
else:
  print ' PPIcoeffs300_cs'+str(icell)+'_0.sav not found'

unpicklefile = open('PPIcoeffs'+str(startdist)+'_cs'+str(icell)+'_comb'+str(icomb)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
PPIcoeffs_Hay_thismutval = [x[:] for x in unpickledlist[1]]
#ISIs_Hay = unpickledlist[2][:]
ISIs_Hay = unique([4*x for x in range(0,25)]+[20*x for x in range(0,26)])
iters = [0, 2, 6, 8]
for iiter in range(0,len(iters)):
  iter = iters[iiter]
  if iter==5:
    continue
  axarr[1].plot(ISIs_Hay,[x[2]*currCoeff for x in PPIcoeffs_Hay_thismutval[iiter]],color=cols[iter],linewidth=1.0)
axarr[1].plot(ISIs_control_Hay,[x[2]*currCoeff for x in PPIcoeffs_control_Hay],color=col_control,linewidth=1.0)



iters = [0, 2, 5, 6, 8, -1]
coding_outputs_thismut = []
coding_outputs_control = []
for isynconductance in range(0,len(synconductances)):
  coding_outputs_thiscond = []
  coding_outputs_control_thiscond = []
  synconductance = synconductances[isynconductance]
  for iI in range(0,len(somaticIs)):
    unpicklefile = open('codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'_somaticI'+str(somaticIs[iI])+'.sav', 'r') #maybe codings/codings_nonprop'+str(synconductance)+'_cs'+str(icell)+'_comb'+str(icomb)+'.sav'
    unpickledlist = pickle.load(unpicklefile)
    unpicklefile.close()
    coding_outputs = unpickledlist[2]
    myfigs = [[],[],[],[],[]]
    myfigs_control = []
    for iplot in range(0,7):
      for iiter in range(0,len(iters)):
        iter = iters[iiter]
        if iter == -1:
          myfigs_control.append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)])
          continue # don't add the control anyway to myfigs                                                                                                                                                                          
        else:
          myfigs[iiter].append([next((i for i,x in enumerate(thrs[iplot]) if x >= coding_outputs[iiter][j][iplot])) for j in range(0,128)])
    coding_outputs_thiscond.append(myfigs[:])
    if iter == -1:
      coding_outputs_control_thiscond.append(myfigs_control[:])
  coding_outputs_thismut.append(coding_outputs_thiscond[:])
  if iter == -1:
    coding_outputs_control.append(coding_outputs_control_thiscond[:])

print "Analyzing controls..."
Npatterns_control = []
for icond in range(0,len(synconductances)):
  Npatterns_control.append(mean([len(unique([sum([(4**iplot)*coding_outputs_control[icond][iI][iplot][j] for iplot in range(0,7)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))]))

for iiter in range(0,len(iters)-1):
  iter = iters[iiter]
  if iter == 5:
    continue
  Npatterns = []
  for icond in range(0,len(synconductances)):
    Npatterns.append(mean([len(unique([sum([(4**iplot)*coding_outputs_thismut[icond][iI][iiter][iplot][j] for iplot in range(0,7)]) for j in range(0,128)])) for iI in range(0,len(somaticIs))]))
  #print str(Npatterns)                                                                                                                                                                                                                  
  print "iiter="+str(iiter)+", Npatterns="+str(mean(Npatterns))+" +- "+str(std(Npatterns))
  axarr[2].plot(1e6*synconductances,Npatterns,'b-',color=cols[iter])
axarr[2].plot(1e6*synconductances,Npatterns_control,'b-',color=col_control)

axarr[0].set_xlabel('$I$ (nA)',fontsize=9)
axarr[0].set_xticks([0.4,0.8,1.2])
axarr[0].set_ylabel('$f$ (Hz)',fontsize=9)
axarr[0].set_yticks([0,10,20])

axarr[1].set_xlabel('ISI (ms)',fontsize=9)
axarr[1].set_xticks([0,100,200,300,400,500])
axarr[1].set_ylabel('PPI factor',fontsize=9)
axarr[1].set_yticks([0,5,10])

axarr[2].set_xlabel('Single-synapse conductance (pS)',fontsize=9)
axarr[2].set_xticks([0,20,40,60])
axarr[2].set_ylabel('output diversity',fontsize=9)
axarr[2].set_yticks([0,10,20])

for i in range(0,3):
  for tick in axarr[i].yaxis.get_major_ticks()+axarr[i].xaxis.get_major_ticks():
    tick.label.set_fontsize(6)

f.savefig("figcomb.eps")

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