Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)

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Accession:169457
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
Reference:
1 . Mäki-Marttunen T, Halnes G, Devor A, Witoelar A, Bettella F, Djurovic S, Wang Y, Einevoll GT, Andreassen OA, Dale AM (2016) Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study. Biol Psychiatry Cogn Neurosci Neuroimaging 1:49-59 [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:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): 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;
Gap Junctions:
Receptor(s):
Gene(s): Nav1.1 SCN1A; Nav1.7 SCN9A; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.2 CACNA1C; Kv2.1 KCNB1; HCN1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Coincidence Detection; Active Dendrites; Detailed Neuronal Models; Schizophrenia;
Implementer(s): Maki-Marttunen, Tuomo [tuomo.maki-marttunen at tut.fi];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; 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;
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Maki-MarttunenEtAl2015
models
morphologies
readme.txt
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
NaTs2_t.mod
SK_E2.mod *
SKv3_1.mod *
collectscalings.py
collectthresholddistalamps.py
drawfig1.py
drawfig2.py
drawfig3.py
drawfig4.py
drawfig5.py
findthresholddistalamps.py
mutation_stuff.py
mytools.py
runcontrols.py
savesynapselocations.py
scalemutations.py
scalings.sav
                            
# savesynapselocations.py
# A script for randomly picking locations along the apical dendrite (from 300um on)
# A maximum of 50 synapses per segment are allowed, while the total number of synapses
# is 3000.
#
# The input code for the hoc-interface is based on BAC_firing.hoc by Etay Hay (2011)
#
# Tuomo Maki-Marttunen, Jan 2015
# (CC BY)                                     
from neuron import h
import matplotlib
matplotlib.use('Agg')
from pylab import *
import mytools
import pickle
import time
import sys
import random

random.seed(1)

proximalpoint = 400
distalpoint = 620
BACdt = 5.0
fs = 8
maxLens = [1300,1185]

maxSynsPerSeg = 50
Nsyns = 3000

synlocsAll = []

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

  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+"""\")
access L5PC.soma
objref sl,syn1,con1,isyn, tvec, syns["""+str(Nsyns)+"""]
tvec = new Vector()
sl = new List()
double siteVec[2]
""")
  synsInSegs = [0]*len(h.L5PC.apic)
  for istim in range(0,Nsyns):
    myiseg = -1
    while myiseg == -1:
      x = 300.0+(maxLens[icell]-300)*random.random()
      h("""sl = L5PC.locateSites("apic","""+str(x)+""")
Nsegs_x = sl.count()
""")
      iseg = random.randint(0,h.Nsegs_x-1)
      h("iseg = sl.o["+str(iseg)+"].x[0]")
      if synsInSegs[int(h.iseg)] < maxSynsPerSeg:
        myiseg = int(h.iseg)
        break
      print "istim = "+str(istim)+", x = "+str(x)+", continue searching for iseg..."
    synsInSegs[myiseg] = synsInSegs[myiseg] + 1
    h("""
siteVec[0] = sl.o[j].x[0]
siteVec[1] = sl.o[j].x[1]
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)+""" 
}
""")
    synlocs.append([h.siteVec[0],h.siteVec[1]])
  
  synlocsAll.append(synlocs[:])
picklelist = [Nsyns,maxSynsPerSeg,maxLens,synlocsAll]
file = open('synlocs.sav', 'w')
pickle.dump(picklelist,file)
file.close()

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