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 mytools
from pylab import *
from neuron import h
import pickle
from os.path import exists
import numpy
import scipy.io
import time
from matplotlib.collections import PatchCollection

seeds = range(1,6)
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]
iosc_chosen = 6

cols = ['#0000FF','#400090','#900040','#FF0000','#904000','#409000','#00FF00','#30A030','#606060']
col_control = '#2222ff'  

f,axarr = subplots(2,1)
for iosc in range(0,len(oscfreqs)):
  oscfreq = oscfreqs[iosc]
  foundOne = False
  for iseed in range(0,len(seeds)):
    myseed = seeds[iseed]
    mutIDs = [0]
    if iosc == iosc_chosen:
      mutIDs = [0,70]
    for imut in range(0,len(mutIDs)):
      mutID = mutIDs[imut]
      mycolor = col_control
      mystyle = 'b-'
      if imut == 1:
        mycolor = cols[2]
        mystyle = 'b--'
      if exists('spikes_parallel_osc'+str(oscfreq)+'_150_mutID'+str(mutID)+'_1.0_gNoise1.07_gsyn1.07_seed'+str(myseed)+'.sav'):
        unpicklefile = open('spikes_parallel_osc'+str(oscfreq)+'_150_mutID'+str(mutID)+'_1.0_gNoise1.07_gsyn1.07_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]
        for ipol in range(0,int(11*oscfreq)+1):
          polygon = Polygon(array([[ipol*1000/oscfreq + x for x in [0,500/oscfreq,500/oscfreq,0,0]],[150*iosc + x for x in [0,0,150,150,0]]]).T, True)
          p = PatchCollection([polygon], cmap=matplotlib.cm.jet)
          p.set_facecolor('#D0D0FF')
          p.set_edgecolor('none')
          axarr[0].add_collection(p)
        if iosc == iosc_chosen:
          axarr[0].plot(spikes[0][::10],[x+iosc*150 for x in spikes[1]][::10],'b.',color='#000000',markersize=0.5)
        else:
          axarr[0].plot(spikes[0][::10],[x+iosc*150 for x in spikes[1]][::10],'b.',markersize=0.5)
        foundOne = True
      else:
        print 'spikes_parallel_osc'+str(oscfreq)+'_150_mutID'+str(mutID)+'_1.0_gNoise1.07_gsyn1.07_seed'+str(myseed)+'.sav not found'
      if iosc == iosc_chosen:
        unpicklefile = open('spectrum_freq'+str(oscfreq)+'_'+str(mutID)+'.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))]

        unpicklefile = open('spectrum_freq'+str(oscfreq)+'_0.sav','r')
        unpickledlist = pickle.load(unpicklefile)
        unpicklefile.close()
        fs_control = unpickledlist[0]
        ft_fs_control = unpickledlist[1]
        fts_control = [mean([abs(ft_fs_control[isamp][ifreq])**2 for isamp in range(0,len(ft_fs_control))]) for ifreq in range(0,len(fs_control))]
        axarr[1].plot([1000*x for x in fs_control], fts_control, color=col_control)
        axarr[1].plot([1000*x for x in fs], fts, 'b--',color=cols[2])#,linewidth=0.5)
        axnew = f.add_axes([0.27, 0.17, 0.2, 0.25],axisbg='w')
        axnew.plot([1000*x for x in fs_control], fts_control, color=col_control)
        axnew.plot([1000*x for x in fs], fts, 'b--',color=cols[2])#,linewidth=0.5)
    if foundOne:
      break
            
axarr[0].set_xlim([0,11000])
axarr[0].set_xticks([0,2000,4000,6000,8000,10000])
axarr[0].set_xlabel('$t$ (ms)')
axarr[0].set_yticks([])
axarr[0].set_ylabel('Neurons')
for iosc in range(0,len(oscfreqs)):
  axarr[0].plot([0,11000],[iosc*150,iosc*150],'b-',linewidth=0.25,color='#777777')
  axarr[0].text(-1000+10017,iosc*150+23,str(oscfreqs[iosc])+' Hz',fontsize=8,color='#ffffff',fontweight='bold')
  axarr[0].text(-1000+10017,iosc*150+37,str(oscfreqs[iosc])+' Hz',fontsize=8,color='#ffffff',fontweight='bold')
  axarr[0].text(-1000+10000-17,iosc*150+23,str(oscfreqs[iosc])+' Hz',fontsize=8,color='#ffffff',fontweight='bold')
  axarr[0].text(-1000+10000-17,iosc*150+37,str(oscfreqs[iosc])+' Hz',fontsize=8,color='#ffffff',fontweight='bold')
  axarr[0].text(-1000+10000,iosc*150+30,str(oscfreqs[iosc])+' Hz',fontsize=8,color='#ff0000',fontweight='bold')
axarr[0].set_ylim([0,150*17])

axarr[1].set_xlim([0,20])
axarr[1].set_ylim([0,8e7])
axarr[1].plot([1,2,2,1,1],[0,0,5e7,5e7,0],'k-',linewidth=0.25)
axarr[1].set_xlabel('$f$ (Hz)')
axarr[1].set_ylabel('Power')
axarr[1].set_yticks([0,2e7,4e7,6e7,8e7])
          
axnew.set_xlim([1,2])
axnew.set_ylim([0,5e7])
axnew.set_xlabel('$f$ (Hz)',fontsize = 8)
axnew.set_ylabel('Power',fontsize = 8)
for tick in axnew.xaxis.get_major_ticks()+axnew.yaxis.get_major_ticks():
  tick.label.set_fontsize(6)
t = axnew.yaxis.get_offset_text()
t.set_size(6)

axarr[0].set_position([0.125, 0.536363636364, 0.375, 0.9-0.536363636364])
axarr[1].set_position([0.125, 0.1, 0.375, 0.463636363636-0.1])

f.text(0.07, 0.86, 'A', fontsize=28)
f.text(0.07, 0.43, 'B', fontsize=28)
f.savefig('fig2ab.eps')

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