SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)

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" … 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."
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 [tuomomm at];
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;
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 * *
mutindexlist.sav *
from neuron import h
import matplotlib
import numpy
from pylab import *
import mytools
import pickle
import time
import sys
import random


v0 = -80
ca0 = 0.0001
proximalpoint = 400
distalpoint = 620
BACdt = 5.0
fs = 8
ITERS = 20
tstop = 11000.0

if len(sys.argv) > 1:
  upfromd = int(float(sys.argv[1]))
  upfromd = 300

unpicklefile = open('thresholddistalamp'+str(upfromd)+'_control.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
gmaxes = unpickledlist[0]
Nsyns = unpickledlist[1]
maxSynsPerSeg = unpickledlist[2]
maxLens = [1300,1185]

unpicklefile = open('synlocs'+str(upfromd)+'.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
synlocsAll = unpickledlist[3]

import mutation_stuff
MT = mutation_stuff.getMT()
defVals = mutation_stuff.getdefvals()
keyList = defVals.keys()
for idefval in range(0,len(keyList)):
  if type(defVals[keyList[idefval]]) is not list:
    defVals[keyList[idefval]] = [defVals[keyList[idefval]], defVals[keyList[idefval]]] #make the dictionary values [somatic, apical]
updatedVars = ['somatic','apical','basal'] # the possible classes of segments that defVals may apply to
whichDefVal = [0,1,0]                      # use the defVal[0] for somatic and basal segments and defVal[1] for apical segments
unpicklefile = open('scalings_cs.sav', 'r')
unpickledlist = pickle.load(unpicklefile)
theseCoeffsAllAll = unpickledlist[0]
theseMutValsAllAll = unpickledlist[2]

gsAllAll = []

for icell in range(0,1):
  synlocs = synlocsAll[icell]
  gsAll = []
  theseCoeffsAll = theseCoeffsAllAll[icell]

  counter = -1
  for igene in range(0,len(MT)):
   gsThisGene = []
   for imut in range(0,len(MT[igene])):
    gsThisMut = []
    nVals = len(MT[igene][imut])*[0]
    thesemutvars = []
    theseCoeffs = theseCoeffsAll[igene][imut]
    for imutvar in range(0,len(MT[igene][imut])):
      if type(MT[igene][imut][imutvar][1]) is int or type(MT[igene][imut][imutvar][1]) is float:
        MT[igene][imut][imutvar][1] = [MT[igene][imut][imutvar][1]]
      nVals[imutvar] = len(MT[igene][imut][imutvar][1])
    cumprodnVals = cumprod(nVals)
    allmutvars = cumprodnVals[len(MT[igene][imut])-1]*[thesemutvars]
    allmutvals = []
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
      for imutvar in range(0,len(MT[igene][imut])):
        if imutvar==0:
          allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][iallmutval%nVals[imutvar]]
          allmutvals[iallmutval][imutvar] = MT[igene][imut][imutvar][1][(iallmutval/cumprodnVals[imutvar-1])%nVals[imutvar]]
    for iallmutval in range(0,cumprodnVals[len(MT[igene][imut])-1]):
      counter = counter + 1                                                                                                                                                               
      unpicklefile = open('thresholddistalamp'+str(upfromd)+'_cs'+str(icell)+'_'+str(counter)+'.sav', 'r')
      unpickledlist = pickle.load(unpicklefile)
picklelist = [theseCoeffsAllAll,gsAllAll,MT]
file = open('thresholddistalamp'+str(upfromd)+'_cs.sav', 'w')

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