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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testsubthppi300_comb_fixed.py
                            
# runcontrols
# A script for determining the control neuron F-I curve and limit cycle.
#
# The input code for the hoc-interface is based on BAC_firing.hoc by Etay Hay (2011)
#
# Tuomo Maki-Marttunen, Oct 2014
# (CC BY)

from neuron import h
import mytools
import pickle
import numpy as np

spikfreqsAll = []
timescAll = []
VsomacAll = []
VDerivcAll = []
VDcoeffAll = []
VdendcAll = []
VdDcoeffAll = []
VdDerivcAll = []
CasomacAll = []
CaDerivcAll = []
CaDcoeffAll = []
CadendcAll = []
CadDerivcAll = []
CadDcoeffAll = []
times_controlAll = []
Vsoma_controlAll = []
Vdend_controlAll = []
Casoma_controlAll = []
Cadend_controlAll = []

for icell in range(0,2):
  morphology_file = "morphologies/cell"+str(icell+1)+".asc"
  biophys_file = "models/L5PCbiophys3.hoc"
  template_file = "models/L5PCtemplate.hoc"
  v0 = -80
  ca0 = 0.0001

  proximalpoint = 400
  distalpoint = 620
  BACdt = 5.0

  h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
cvode.atol(0.0002)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
access L5PC.soma
objref st1
st1 = new IClamp(0.5)
L5PC.soma st1

objref vsoma, vdend, recSite, vdend2, isoma, cadend, cadend2, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
vdend2 = new Vector()
cadend2 = new Vector()
objref sl,ns,tvec
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
L5PC.apic[siteVec[0]] cvode.record(&v(siteVec[1]),vdend,tvec)
L5PC.apic[siteVec[0]] cvode.record(&cai(siteVec[1]),cadend,tvec)
L5PC.soma cvode.record(&v(0.5),vsoma,tvec)
L5PC.soma cvode.record(&cai(0.5),casoma,tvec)
""")

  Is = [0.1*x for x in range(0,16)]
  spikfreqs = len(Is)*[0]
  for iI in range(0,len(Is)):
    squareAmp = Is[iI]
    squareDur = 3800
    tstop = 4000
    h("""
tstop = """+str(tstop)+"""
v_init = """+str(v0)+"""
cai0_ca_ion = """+str(ca0)+"""
st1.amp = """+str(squareAmp)+"""
st1.dur = """+str(squareDur)+"""
st1.del = 200
""")
    h.init()
    h.run()

    times=np.array(h.tvec)
    Vsoma=np.array(h.vsoma)
    Vdend=np.array(h.vdend)
    Casoma=np.array(h.casoma)
    Cadend=np.array(h.cadend)
    spikes = mytools.spike_times(times,Vsoma,-35,100)
    spikfreqs[iI] = sum([1 for x in spikes if x >= 500.0])/3.5

    if abs(Is[iI]-1.0) < 0.0001:
      Vsoma_control = Vsoma
      Casoma_control = Casoma
      Vdend_control = Vdend
      Cadend_control = Cadend
      times_control = times
      spikes_control = spikes

  spts = spikes_control[len(spikes_control)-3:len(spikes_control)]
  istart = next((i for i,x in enumerate(times_control) if x > spts[0]))
  iend = next((i for i,x in enumerate(times_control) if x > spts[1]))+4
  nsteps = iend-istart-1
  tdiff = [y-x for x,y in zip(times_control[istart:iend-1],times_control[istart+1:iend])]
  cadiff = [y-x for x,y in zip(Casoma_control[istart:iend-1],Casoma_control[istart+1:iend])]
  caddiff = [y-x for x,y in zip(Cadend_control[istart:iend-1],Cadend_control[istart+1:iend])]
  caderiv1 = [y/x for x,y in zip(tdiff[0:nsteps-1],cadiff[0:nsteps-1])]
  caderiv2 = [y/x for x,y in zip(tdiff[1:nsteps],cadiff[1:nsteps])]
  caderiv = [(x+y)/2.0 for x,y in zip(caderiv1,caderiv2)]
  cadderiv1 = [y/x for x,y in zip(tdiff[0:nsteps-1],caddiff[0:nsteps-1])]
  cadderiv2 = [y/x for x,y in zip(tdiff[1:nsteps],caddiff[1:nsteps])]
  cadderiv = [(x+y)/2.0 for x,y in zip(cadderiv1,cadderiv2)]
  vdiff = [y-x for x,y in zip(Vsoma_control[istart:iend-1],Vsoma_control[istart+1:iend])]
  vddiff = [y-x for x,y in zip(Vdend_control[istart:iend-1],Vdend_control[istart+1:iend])]
  vderiv1 = [y/x for x,y in zip(tdiff[0:nsteps-1],vdiff[0:nsteps-1])]
  vderiv2 = [y/x for x,y in zip(tdiff[1:nsteps],vdiff[1:nsteps])]
  vderiv = [(x+y)/2.0 for x,y in zip(vderiv1,vderiv2)]
  vdderiv1 = [y/x for x,y in zip(tdiff[0:nsteps-1],vddiff[0:nsteps-1])]
  vdderiv2 = [y/x for x,y in zip(tdiff[1:nsteps],vddiff[1:nsteps])]
  vdderiv = [(x+y)/2.0 for x,y in zip(vdderiv1,vdderiv2)]

  Vsomac = Vsoma_control[istart+1:iend-1]
  Vdendc = Vdend_control[istart+1:iend-1]
  Casomac = Casoma_control[istart+1:iend-1]
  Cadendc = Cadend_control[istart+1:iend-1]
  timesc = times_control[istart+1:iend-1]
  VDerivc = vderiv[:]
  VDcoeff =  mytools.limitcyclescaledv(Vsomac,VDerivc,Vsomac,VDerivc)
  VdDerivc = vdderiv[:]
  VdDcoeff =  mytools.limitcyclescaledv(Vdendc,VdDerivc,Vdendc,VdDerivc)
  CaDerivc = caderiv[:]
  CaDcoeff =  mytools.limitcyclescaledv(Casomac,CaDerivc,Casomac,CaDerivc)
  CadDerivc = cadderiv[:]
  CadDcoeff =  mytools.limitcyclescaledv(Cadendc,CadDerivc,Cadendc,CadDerivc)

  spikfreqsAll.append(spikfreqs[:])
  timescAll.append(timesc[:])
  VsomacAll.append(Vsomac[:])
  VDerivcAll.append(VDerivc[:])
  VDcoeffAll.append(VDcoeff)
  VdendcAll.append(Vdendc[:])
  VdDerivcAll.append(VdDerivc[:])
  VdDcoeffAll.append(VdDcoeff)
  CasomacAll.append(Casomac[:])
  CaDerivcAll.append(CaDerivc[:])
  CaDcoeffAll.append(CaDcoeff)
  CadendcAll.append(Cadendc[:])
  CadDerivcAll.append(CadDerivc[:])
  CadDcoeffAll.append(CadDcoeff)
  times_controlAll.append(times_control[:])
  Vsoma_controlAll.append(Vsoma_control[:])
  Vdend_controlAll.append(Vdend_control[:])
  Casoma_controlAll.append(Casoma_control[:])
  Cadend_controlAll.append(Cadend_control[:])

picklelist = [spikfreqsAll,timescAll,VsomacAll,VDerivcAll,VDcoeffAll,VdendcAll,VdDerivcAll,VdDcoeffAll,CasomacAll,CaDerivcAll,CaDcoeffAll,
              CadendcAll,CadDerivcAll,CadDcoeffAll,times_controlAll,Vsoma_controlAll,Vdend_controlAll,Casoma_controlAll,Cadend_controlAll,Is]
file = open('control_cs.sav', 'w')
pickle.dump(picklelist,file)
file.close()



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