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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scznet
approxhaynet
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README.html
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Ca_LVAst.mod *
CaDynamics_E2.mod *
Ih.mod *
Im.mod *
K_Pst.mod *
K_Tst.mod *
Nap_Et2.mod *
NaTa_t.mod *
ProbAMPANMDA2.mod
ProbAMPANMDA2group.mod
ProbAMPANMDA2groupdet.mod
ProbUDFsyn2.mod
ProbUDFsyn2group.mod
ProbUDFsyn2groupdet.mod
SK_E2.mod *
SKv3_1.mod *
approxhaynetstuff.py
calcEEG.py
calcEEG_uncombined.py
calcmutgains.py
calcmutgains_comb.py
calcmutoscs.py
calcmutoscs_comb.py
calcspectra.py
calcspectra_comb.py
combinemutgains_parallel.py
combinemutgains_parallel_comb.py
combinemutgains_parallel_withLFP.py
combinemutoscs_parallel.py
combinemutoscs_parallel_comb.py
drawfig1ab.py
drawfig1c.py
drawfig2ab.py
drawfig2c.py
drawstationary_EEG.py
drawstationary_EEG_pop.py
mutation_stuff.py *
mytools.py
pars_withmids_combfs_final.sav *
runmanycellsLFP.py
runsinglecellLFP.py
saveisidists.py
savespikesshufflephases.py
scalings_cs.sav
simosc_parallel.py
simosc_parallel_comb_varconn.py
simseedburst_func.py
simseedburst_func_comb_varconn.py
simseedburst_func_withLFP.py
simseedburst_func_withLFP.pyc
                            
#Copied from calcspikes_conn_more_more.py 10.5.2016
import mytools
import simseedburst_func_comb_varconn
from pylab import *
from neuron import h
import pickle
from os.path import exists
import time

Nmc = 150
rates = [0.4,0.5,0.6,0.7,0.8,0.9,1.0,1.1,1.2,1.3,1.4,1.5,1.6]
#gSynCoeffs = [0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4]
seeds = range(1,1001)
cols = ['#0000FF','#400090','#900040','#FF0000','#904000','#409000','#00FF00','#30A030','#606060']

extensions_final = ['_final', '_extended_final', '_withisi_final', '_withisi_extended_final', '_final_alternative', '_extended_final_alternative']
mutcombID = int(sys.argv[4])
igsyn = 0
irate = int(sys.argv[5])
iseed = int(sys.argv[6])

gSynCoeff = 1.07
gNoiseCoeff = 1.07
rateCoeff = rates[irate]
myseed = seeds[iseed]

maxIDtab = array([[126, 190, 294, 314, nan, 334, nan, nan, 370, nan, nan, nan, 422, 442, nan]])
IDtab = r_[maxIDtab, maxIDtab-1, maxIDtab+1, maxIDtab+2] #epsilon=1/2, epsilon=1/4, epsilon=-1/4, epsilon=-1/2


if exists('spikes_parallel'+str(Nmc)+'_mutcombID'+str(mutcombID)+'_'+str(rateCoeff)+'_gNoise'+str(gNoiseCoeff)+'_gsyn'+str(gSynCoeff)+'_seed'+str(myseed)+'.sav'):
  print 'spikes_parallel'+str(Nmc)+'_mutcombID'+str(mutcombID)+'_'+str(rateCoeff)+'_gNoise'+str(gNoiseCoeff)+'_gsyn'+str(gSynCoeff)+'_seed'+str(myseed)+'.sav exists'
else:
  Q=simseedburst_func_comb_varconn.simseedburst_func(Nmc,11000,IDtab[mutcombID],mutcombID,myseed,0.00039,0.0006,5,rateCoeff,gNoiseCoeff,gSynCoeff,1)

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