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

 Download zip file   Auto-launch 
Help downloading and running models
" … 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]
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];
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 *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
ProbUDFsyn2.mod *
ProbUDFsyn2group.mod *
ProbUDFsyn2groupdet.mod *
SK_E2.mod *
SKv3_1.mod * *
pars_withmids_combfs_final.sav *
import mytools
from pylab import *
from neuron import h
import pickle
from os.path import exists
import numpy
import time

Nmc = 150
seeds = range(1,1000)

oscamp = 0.25

gsyn = 1.07
gNoise = 1.07
myrate = 1.0

import mutation_stuff
MT = mutation_stuff.getMT()
geneNames = mutation_stuff.getgenenames()
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                                                                                

ft_df = 0.00001      # kHz
ft_maxf = 0.02       # kHz
ft_fs = [ft_df*x for x in range(0,int(round(ft_maxf/ft_df))+1)]

counter = -1
combmutIDs = [1, 0, 2, 3]
for counter in range(0,4):
  oscfreq = float(sys.argv[1])
  combmutID = combmutIDs[counter]
  if not exists('spectrum_freq'+str(oscfreq)+'_comb'+str(counter)+'.sav'):
    FRft = []
    for iseed in range(0,8):
      myseed = seeds[iseed]
      if exists('spikes_parallel_osc'+str(oscfreq)+'_'+str(Nmc)+'_combmutID'+str(combmutID)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav'):
        unpicklefile = open('spikes_parallel_osc'+str(oscfreq)+'_'+str(Nmc)+'_combmutID'+str(combmutID)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav','r')
        unpickledlist = pickle.load(unpicklefile)
        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]
        spikes = spikes[0]
        sps = array([spikes[i] for i in range(0,len(spikes)) if spikes[i] >= 2000 and spikes[i] < 11000])
        FRftthis = [0.0 for x in ft_fs]
        for ifreq in range(0,len(ft_fs)):
          FRftthis[ifreq] = sum(exp(-2*pi*1j*sps*ft_df*(ifreq-1)))
        print 'spikes_parallel_osc'+str(oscfreq)+'_'+str(Nmc)+'_combmutID'+str(combmutID)+'_'+str(myrate)+'_gNoise'+str(gNoise)+'_gsyn'+str(gsyn)+'_seed'+str(myseed)+'.sav not found'
    if len(FRft) > 0:
      picklelist = [ft_fs, FRft]
      file = open('spectrum_freq'+str(oscfreq)+'_comb'+str(counter)+'.sav','w')