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]
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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 *
import math

def spike_times(time,vrec,V_min_peak=-20,V_max_valley=0):
  valley_reached = 1
  sptime = []  
  for j in range(1,len(time)-1):
    if valley_reached and vrec[j] >= V_min_peak and vrec[j] > vrec[j-1] and vrec[j] >= vrec[j+1]:
      valley_reached = 0
    elif valley_reached==False and vrec[j] <= V_max_valley:
      valley_reached = 1
  return sptime  

#membpotderivs(time,vrec): Given the membrane potentials (vrec) at time points time[0],time[1],...,time[N],
#return the derivatives at time points time[1],time[2],...,time[N-1]
def membpotderivs(time,vrec):
  N = len(time)
  tdiff = [x-y for x,y in zip(time[1:N-1],time[0:N-2])]
  vdiff = [x-y for x,y in zip(vrec[1:N-1],vrec[0:N-2])]
  mderiv = [x/y for x,y in zip(vdiff,tdiff)]
  return [0.5*(x+y) for x,y in zip(mderiv[1:N-2],mderiv[0:N-3])]

#limitcyclescaledv(v1,dv1,v2,dv2): Give the coefficient for memb. pot. derivative that one has to use in order to make
#the difference on the derivative axis as significant as the difference on the memb. pot. axis
def limitcyclescaledv(v1,dv1,v2,dv2):
  maxv = max(max(v1),max(v2))
  minv = min(min(v1),min(v2))
  maxdv = max(max(dv1),max(dv2))
  mindv = min(min(dv1),min(dv2))
  return 1.0*(maxv-minv)/(maxdv-mindv)

def limitcyclediff(v1,dv1,v2,dv2,dvcoeff=0.1):
  N1 = len(v1)
  N2 = len(v2)
  dv1 = [dvcoeff*x for x in dv1]
  dv2 = [dvcoeff*x for x in dv2]
  Dmin = N1*[0]
  for i in range(0,N1):
    Dmin[i] = math.sqrt(min([(x-v1[i])**2+(y-dv1[i])**2 for x,y in zip(v2,dv2)]))
  vdiff = [x-y for x,y in zip(v1[1:N1],v1[0:N1-1])] 
  dvdiff = [x-y for x,y in zip(dv1[1:N1],dv1[0:N1-1])] 
  h = [math.sqrt(x**2+y**2) for x,y in zip(vdiff,dvdiff)]
  #use the trapezoid rule for integration:
  Dminmean = [(x+y)/2.0 for x,y in zip(Dmin[1:N1],Dmin[0:N1-1])]
  print "hsum="+str(sum(h))
  return sum([x*y for x,y in zip(Dminmean,h)])

def interpolate(tref,vref,tint): #Assumes that the trefs come sorted!
  vint = len(tint)*[0.0]
  addedOne = False
  #print tref
  #print tint
  #if tref[len(tref)-1] == tint[len(tint)-1]:
  #  tref.append(tref[len(tref)-1]+0.0001)
  #  vref.append(vref[len(tref)-1])
  #  addedOne = True
  if tref[0] > tint[0] or tref[len(tref)-1] < tint[len(tint)-1]:
    print "Extrapolation needed!"
    return len(tint)*[-1]
  indvrecnow = 0  
  for j in range(0,len(tint)):
    while tref[indvrecnow+1] <= tint[j]:
      indvrecnow = indvrecnow + 1
      if indvrecnow == len(tref)-1: # It must be the last index if this happens
        vint[j:len(tint)] = [vref[indvrecnow]]*(len(tint)-j)
        return vint
    vint[j] = vref[indvrecnow] + 1.0*(tint[j]-tref[indvrecnow])/(tref[indvrecnow+1]-tref[indvrecnow])*(vref[indvrecnow+1]-vref[indvrecnow])
  return vint

#kronecker product of list A and list B
def kron(A,B):
  C = []
  if type(B[0]) is int or type(B[0]) is float:
    for i in range(0,len(A)):
      for j in range(0,len(B)):
        print "asdf"
        print B[j]
  elif type(B[0][0]) is int or type(B[0][0]) is float:
    for i in range(0,len(A)):
      for j in range(0,len(B)):
        C.append([x*A[i] for x in B[j]])
  return C
def cumprod(A):
  B = len(A)*[0]; B[0]=A[0]
  for j in range(1,len(A)):
    B[j] = B[j-1]*A[j]
  return B