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]
Citations  Citation Browser
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 [tuomomm at uio.no];
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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scznet
haymod
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 *
SK_E2.mod *
SKv3_1.mod *
calcifcurves.py
calcifcurves_comb.py
collectppispthrcoeff300_relthr.py
collectscalings_cs.py
collectthresholddistalamps300.py
copydata.sh
drawppiranges.py
drawsubthppiamps_comb.py
findsubthppi300_relthr.py
findsubthppi300_relthr_comb_one.py
findthresholddistalamp300_control.py
findthresholddistalamps300.py
findthresholddistalamps300_comb.py
mutation_stuff.py *
mutation_stuff.pyc
mutindexlist.sav
mytools.py *
mytools.pyc
ppi300_relthr_comb_recordSK.py
presaved.tar.gz
runcontrol.py
savesynapselocations300.py
scalemutations_cs.py
testsubthppi300_comb_fixed.py
                            
from neuron import h
import matplotlib
matplotlib.use('Agg')
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random

random.seed(1)

v0 = -80
ca0 = 0.0001
proximalpoint = 400
distalpoint = 620
#distalpoint = 960
BACdt = 5.0
fs = 8
maxSynsPerSeg = 100
Nsyns = 3000
maxLens = [1300,1185]

gmaxes = []

unpicklefile = open('synlocs300.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
unpicklefile.close()
Nsyns = unpickledlist[0]
maxSynsPerSeg = unpickledlist[1]
synlocsAll = unpickledlist[3]

for icell in range(0,2):
  synlocs = synlocsAll[icell]
  morphology_file = "morphologies/cell"+str(icell+1)+".asc"
  biophys_file = "models/L5PCbiophys3.hoc"
  template_file = "models/L5PCtemplate.hoc"

  h("""
load_file("stdlib.hoc")
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
load_file("import3d.hoc")
objref L5PC
load_file(\""""+biophys_file+"""\")
load_file(\""""+template_file+"""\")
L5PC = new L5PCtemplate(\""""+morphology_file+"""\")
objref st1, st2
st1 = new IClamp(0.5)
st2 = new IClamp(0.5)
L5PC.soma st1
L5PC.soma st2
forsec L5PC.somatic {
}
forsec L5PC.apical {
}
L5PC.distribute_channels("apic","gIhbar_Ih",2,-0.8696,3.6161,0.0,1.0*2.0870,0.0002)
L5PC.distribute_channels("apic","gCa_HVAbar_Ca_HVA",3,1.0,0.1,685.0,885.0,1.0*0.000555)
L5PC.distribute_channels("apic","gCa_LVAstbar_Ca_LVAst",3,1.0,0.01,685.0,885.0,1.0*0.0187)
objref sl,ns,syn1,con1,isyn, tvec, syns["""+str(Nsyns)+"""]
isyn = new Vector()
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
access L5PC.apic[siteVec[0]]
L5PC.apic[siteVec[0]] {
  syn1 = new AlphaSynapse(siteVec[1])
  syn1.e = 0
  syn1.tau = 5
  syn1.onset = 10000 + """+str(BACdt)+""" 
  cvode.record(&syn1.i,isyn,tvec)
}
""")
  for istim in range(0,Nsyns):
    h("""
siteVec[0] = """+str(synlocs[istim][0])+"""
siteVec[1] = """+str(synlocs[istim][1])+"""
access L5PC.apic[siteVec[0]]
L5PC.apic[siteVec[0]] {
  syns["""+str(istim)+"""] = new AlphaSynapse(siteVec[1])
  syns["""+str(istim)+"""].e = 0
  syns["""+str(istim)+"""].tau = 5
  syns["""+str(istim)+"""].onset = 10000 + """+str(BACdt)+""" 
}
""")
  
  h("""
objref vsoma, vdend, recSite, vdend2, isoma, cadend, casoma
vsoma = new Vector()
casoma = new Vector()
vdend = new Vector()
cadend = new Vector()
vdend2 = new Vector()
access L5PC.soma
cvode.record(&v(0.5),vsoma,tvec)
cvode.record(&cai(0.5),casoma,tvec)
sl = new List()
sl = L5PC.locateSites("apic","""+str(proximalpoint)+""")
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 "proximalpoint gCa_HVA: ", L5PC.apic[siteVec[0]].gCa_HVAbar_Ca_HVA
print "proximalpoint gCa_LVA: ", L5PC.apic[siteVec[0]].gCa_LVAstbar_Ca_LVAst
access L5PC.apic[siteVec[0]]
cvode.record(&v(siteVec[1]),vdend,tvec)
cvode.record(&cai(siteVec[1]),cadend,tvec)
recSite = new IClamp(siteVec[1])
recSite.amp = 0
L5PC.apic[siteVec[0]] {
        recSite
}
access L5PC.soma
isoma = new Vector()
cvode.record(&st1.i,isoma,tvec)
""")

  ITERS = 27
  if icell==0:
    nextgs = [0.000,0.001,0.0005]
  if icell==1:
    nextgs = [0.000,0.01,0.005]
  for iter in range(0,ITERS):
    tstop = 11000.0
    squareAmp = 0.0
    squareDur = 10.0
    epsp_gmax = nextgs[min(iter,2)]
    print epsp_gmax
    h("""
tstop = """+str(tstop)+"""
v_init = """+str(v0)+"""
""")
    for istim in range(0,Nsyns):
      h("syns["+str(istim)+"].gmax = "+str(epsp_gmax))
    h.init()
    h.run()

    times=np.array(h.tvec)
    Casoma=np.array(h.casoma)
    Cadend=np.array(h.cadend)
    Vsoma=np.array(h.vsoma)
    Vdend=np.array(h.vdend)
    nSpikes = len(mytools.spike_times(times,Vsoma,-35,100))
    if iter > 2 and nSpikes > 0:
      nextgs = [nextgs[0],nextgs[2],0.5*(nextgs[0]+nextgs[2])]
    if iter > 2 and nSpikes == 0:
      nextgs = [nextgs[2],nextgs[1],0.5*(nextgs[2]+nextgs[1])]
    print str(nSpikes)+", nextgs="+str(nextgs)

  gmaxes.append(nextgs[2])
picklelist = [gmaxes,Nsyns,maxSynsPerSeg]
file = open('thresholddistalamp300_control.sav', 'w')
pickle.dump(picklelist,file)
file.close()