In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)

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Accession:258738
"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability"
Reference:
1 . Sherif MA, Neymotin SA, Lytton WW (2020) In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ Schizophr 6:25 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; Hippocampus CA3 stratum oriens lacunosum-moleculare interneuron;
Channel(s): I h;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s): NR2A GRIN2A;
Transmitter(s): Glutamate; Gaba;
Simulation Environment: NEURON;
Model Concept(s): Schizophrenia;
Implementer(s): Sherif, Mohamed [mohamed.sherif.md at gmail.com];
Search NeuronDB for information about:  Hippocampus CA3 pyramidal GLU cell; Hippocampus CA3 interneuron basket GABA cell; AMPA; NMDA; I h; Gaba; Glutamate;
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CA3modelCode_npjSchizophrenia_September2020--main
data
README.md
CA1ih.mod
CA1ika.mod *
CA1ikdr.mod *
CA1ina.mod *
cagk.mod *
caolmw.mod *
capr.mod *
expsynstdp.mod
Gfluctp.mod *
HCN1.mod *
HCN2.mod
IA.mod
icaolmw.mod *
icapr.mod *
iholmkop.mod *
iholmw.mod *
ihpyrkop.mod *
ihstatic.mod *
infot.mod *
kahppr.mod *
kaolmkop.mod *
kapyrkop.mod *
kcaolmw.mod *
kcpr.mod *
kdrbwb.mod *
kdrolmkop.mod *
kdrpr.mod *
kdrpyrkop.mod *
km.mod
misc.mod *
MyExp2Syn.mod *
MyExp2SynAlpha.mod *
MyExp2SynBB.mod *
MyExp2SynNMDA.mod *
MyExp2SynNMDABB.mod *
nafbwb.mod *
nafolmkop.mod *
nafpr.mod *
nafpyrkop.mod *
samnutils.mod
sampen.mod
stats.mod
updown.mod *
vecst.mod *
wrap.mod *
analysisPlottingCode.py
aux_fun.inc *
batch.py
conf.py
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
fig1sample.png
fig1simulationConfig.cfg
geom.py
grvec.hoc *
init.hoc
labels.hoc *
local.hoc *
misc.h
network.py
nqs.hoc *
nqs_utils.hoc *
nrnoc.hoc *
params.py
psd.py
pyinit.py
pywrap.hoc *
run.py
runone.py
simctrl.hoc *
stats.hoc *
syncode.hoc *
updown.hoc
xgetargs.hoc *
                            
// $Id: updown.hoc,v 1.121 2013/02/15 22:42:34 samn Exp $

print "Loading updown.hoc..."

declare("printStep",0.1)
CREEP_UPDOWN=1
NOV_UPDOWN=1

declare("test_do_graphs", 1)
declare("drawlr", 1, "drawth", 1,"drawscale",0,"shapesz",15,"flipit",0)
declare("minthreshlx", 0, "maxthreshlx",9e3*printStep)
declare("black",1,"red",2,"blue",3,"green",4)
declare("vthg",new Vector()) // global, stores threshold lines

// EXAMPLE USAGE
// {gvnew(-2) panobj.read_vfile("/u/billl/nrniv/sync/data/05nov02.501/v05nov02.1000")}
// panobj.rv(7)
// vec.copy(panobj.vrtmp)
// vec.mul(-1)
// gg(vec,printStep)
// objref output
// output=updown(vec)
// output.pr(10)

// updown(vec[,nq,#CUTS,LOGCUT,MIN]) -- use updown() to find spikes
// LOC(0) PEAK(1) WIDTH(2) BASE(3) HEIGHT(4) START(5) SLICES(6) SHARP(7) INDEX(8) FILE(9)
// other options
declare("pos_updown",1) // set to 1 to move whole curve up above 0
declare("maxp_updown",0.95) // draw top sample at 95% of max 
declare("minp_updown",0.05) // draw bottom sample at 5% of max
declare("over_updown",1)    // turn over and try again if nothing found
declare("allover_updown",0) // turn over and add these locs (not debugged)
declare("verbose_updown",0) // give messages, can also turn on DEBUG_VECST for messages from updn()
declare("dynamic_th",0)
declare("padsz_updown",5000) // default padsize for updown function
declare("bumpcap_updown",2e4) // default capacity for number of bumps

//** updown - calls Vector updown function and returns NQS with spikes/bumps
obfunc updown () { local a,ii,npts,logflag,min,x,sz,midp localobj bq,cq,v1,v2,v3,bb,tl,vtmp
  if (verbose_updown) printf("MAXTIME appears to be %g (printStep=%g)\n",$o1.size*printStep,printStep)
  npts=10 // n sample locations
  tl=new List()
  bq=new NQS("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") 
  if (numarg()>1) cq=$o2 else cq=new NQS()
  if (cq.m!=10) {
    cq.verbose = 0
    cq.resize(0) 
    cq.resize("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") }
    cq.verbose = 1
  if (numarg()>2) npts=$3
  if (numarg()>3) logflag=$4 else logflag=0
  if (numarg()>4) {
    if (npts!=$o5.size) printf("Correcting npts from %d to %d\n",npts,npts=$o5.size)
    if ($o5.ismono(1)) $o5.reverse
    if (! $o5.ismono(-1)) {printf("updown: Arg 5 (%s) must be monotonic\n",$o5) return}
  }

  bq.listvecs(bb)
  bq.pad(padsz_updown)
  a=allocvecs(npts+3,bumpcap_updown)
  v1=mso[a+0] v2=mso[a+1] v3=mso[a+2]
  for ii=0,npts-1 tl.append(mso[ii+a+3])
  v1.copy($o1)
  if (pos_updown) {
    min=v1.min
    v1.sub(min) // make it uniformly positive
  } else min=0
  if (numarg()>4) v2.copy($o5) else {
    if(logflag==2){ //"double-log" spacing

      midp = v1.max * 0.5 //50% of max is center of log axis in vertical direction

      // 1/2 of lines btwn max and midpoint
      vtmp=new Vector()
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(maxp_updown*v1.max,midp)

      v2.append(vtmp)

      // 1/2 of lines btwn min and midpoint
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(minp_updown*v1.max,midp)

      v2.append(vtmp)

      //make sure they're monotonically increasing
      v2.sort()
      v2.reverse()

    } else {
      v2.indgen(2,2+npts-1,1)   // sampling at npts points, start at 2 to avoid log(1)=0
      if (logflag) v2.log() // log sampling
      v2.scale(-maxp_updown*v1.max,-minp_updown*v1.max) v2.mul(-1)
    }
  }

  if(dynamic_th) { //allocate memory so updown can use th
    v2.resize(v1.max()+1)
    v2.resize(0)
    printf("dynamic_th\n")
  }

  vthg.copy(v2) // <--------- whats this for? copies threshold lines to global vthg

  //v2 = thresh
  v1.updown(v2,tl,bb)

  if (pos_updown) { bq.v[1].add(min) bq.v[3].add(min) }
  cq.append(bq)
  sz=bq.size(1)
  if (allover_updown) { // do it upside down as well
    v1.mul(-1) // v2 will be upside-down
    if (pos_updown) {min=v1.min v1.sub(min)}
    v1.updown(v2,tl,bb)
    bq.v[8].add(sz) bq.v[4].mul(-1) // turn HEIGHT upside down
    cq.append(bq)
  } else if (over_updown && sz==0) { // turn it over an try again
    print "updown() checking upside-down"
    v1.mul(-1) // v2 will be upside-down
    v1.updown(v2,tl,bb)
  } 
  for case(&x,0,2,5) cq.v[x].mul(printStep)
  nqsdel(bq)
  dealloc(a)
  return cq
}

//** drupdown(voltage,vector with updown output,vth - vector of threshold slices) - draw output from updown
proc drupdown () { local idx,x,left,right localobj vec,output,vth, vslice
  vec=$o1 output=$o2 vth=$o3
  vslice = new Vector()
  if(g==nil) gg()
  gg(vec,printStep,black,3) //5 is for thick lines       
//  output.v[1].mark(g,output.v[0],"o",shapesz,red,1) //draw circles at top of peaks  // Sam's line
  output.v[1].add(vec.min()).mark(g,output.v[0],"o",shapesz,red,1) //draw circles at top of peaks

  if(drawlr) for idx=0,output.v.size-1 {
    left = output.v[5].x(idx)
    right = left+output.v[2].x(idx)
    g.mark(left, vec.x(left/printStep), "t",shapesz,blue ,1)
    g.mark(right,vec.x(right/printStep),"s",shapesz,green,1)
  }       
  // draw horizontal lines for slices
//  if(drawth) for vtr(&x,vth) drline(minthreshlx,x,maxthreshlx,x,red,1) // Sam's line
  vslice = vth.add(vec.min())
  if(drawth) for vtr(&x,vth) drline(minthreshlx,x,maxthreshlx,x,red,1)
}

//** testupdown($o1=input vector,$2=num slices,$3=logarithmic-spaced slices,$o4=thresholds slices locations)
// runs updown on input vector and returns an NQS with detected spikes/bumps
obfunc testupdown (){ local idx,left,right,x,a localobj vtmp,output,vec
  {a=allocvecs(vec) output = new NQS() vec.copy($o1)}
  if(flipit) vec.mul(-1)
  vec.sub(vec.min)
  if(numarg()>3){
    updown(vec,output,$2,$3,$o4)
  } else if(numarg()>2){
    updown(vec,output,$2,$3)
  } else{
    updown(vec,output)
  }
  if(test_do_graphs) drupdown(vec,output,vthg)
  dealloc(a)
  return output
}

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