Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)

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Accession:154096
"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
Reference:
1 . Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Long-term Synaptic Plasticity; Aging/Alzheimer`s; Deep brain stimulation; Homeostasis;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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RowanEtAl2014
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alz.hoc
alzinfo.m
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
batchcommon
checkirreg.hoc *
clusterrun.sh
col.dot *
col.hoc *
comppowspec.hoc *
condisconcellfig.hoc *
condisconpowfig.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
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e2hubsdisconpow.hoc *
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filtutils.hoc *
flexinput.hoc
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makepopspikenq.hoc *
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nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
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pyhoc.py
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runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
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vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: infot.hoc,v 1.43 2009/12/04 01:25:55 samn Exp $ 

print "Loading infot.hoc..."

if(!installed_infot) {install_infot()}

//* tentropsig(v1,v2,nshuf,nbins,twoway,[xpast,ypast,hval])
//significance test of transfer entropy using shuffling
//returns (te - tes) / sds , where te is transfer entropy, tes is transfer entropy
//of shuffled data, sds is std-dev of transfer entrop of shuffled data
//should only accept as significanat values > 4-6
func tentropsig () { local nshuf,i,xp,yp,hv,nbins,te localobj v1,v2,vo
  v1=new Vector() v2=new Vector() vo=new Vector(1)
  v1.copy($o1) v2.copy($o2) nshuf=$3 nbins=$4
  if(numarg()>4) xp=$5 else xp=1
  if(numarg()>5) yp=$6 else yp=2
  if(numarg()>6) hv=$7 else hv=0
  te=v1.tentrop(v2,nbins,xp,yp,nshuf,vo,hv)
  if(1||verbose_infot>2) printf("te=%g,sig=%g\n",te,vo.x(0))
  return vo.x(0)
}

//** mutinfbshufv(v1,v2,[nshuf,nbins])
//return vector with mutual information from shuffled v1,v2
//used for significance test , i.e. : ((miorig - mishufmean) / mishufstdev) > 2
obfunc mutinfbshufv () { local nshuf,nbins,i localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2)
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  for i=0,nshuf-1 {
    v1.shuffle() v2.shuffle()
    ve.append(v1.mutinfb(v2,nbins))
  }
  return ve
}

//** mutinfbsig(v1,v2,[nshuf,nbins])
//get significance of mutual information, should be at least > 2
func mutinfbsig () { local nshuf,nbins,st localobj ve
  if(numarg()>2) nshuf=$3 else nshuf=20
  if(numarg()>3) nbins=$4 else nbins=10
  ve=mutinfbshufv($o1,$o2,nshuf,nbins)
  st=ve.stdev
  if(st<=0) st=1
  return ($o1.mutinfb($o2,nbins) - ve.mean) / st
}

//** tentropspksig(v1,v2,nshuffles)
//get significance of tentropspks using shuffling
//returns (TE - AvgTEShuffle) / StdDevTEShuffle
func tentropspksig () { local nshuf,i,xp,yp,hv,nbins,te,sd localobj v1,v2,ve
  v1=new Vector() v2=new Vector() ve=new Vector()
  v1.copy($o1) v2.copy($o2) nshuf=$3
  te=$o1.tentropspks($o2)
  for i=0,nshuf-1 {
    v1.shuffle()     ve.append(v1.tentropspks(v2))
  }
  if(verbose_infot>2) printf("te=%g,ve.mean=%g,ve.stdev=%g\n",te,ve.mean,ve.stdev)
  if(verbose_infot>2) ve.printf
  sd=ve.stdev()
  if(sd<=0)sd=1
  return (te-ve.mean)/sd
}

//* normte() get normalized transfer entropy using tentropspks in output vector vo
//vo.x(0)=transfer entropy of $o1->$o2
//vo.x(1)=H($o2Future|$o2Past)
//vo.x(2)=normalized transfer entropy in 0,1 range
//$3==number of shuffles
//$o1,$o2 should both have same size and non-negative values. this func is meant for time-binned spike train data
obfunc normte () { local a localobj ve,vo
  a=allocvecs(ve) vo=new Vector()
  nshuf=0
  nshuf=$3 vrsz(3+nshuf,vo) 
  te=$o1.tentropspks($o2,vo,nshuf)
  if(verbose_infot>2) vo.printf
  if(vo.x(1)<=0 && verbose_infot>0){printf("WARNING H(X2F|X2P)==%g<=0\n",vo.x(1)) vo.x(1)=1 }
  if (nshuf>0) {
    ve.copy(vo,3,vo.size-1)
    vo.resize(4)
    if (ve.mean!=vo.x[2]) printf("normte ERRA\n")
    vo.append(ve.stdev)
  } 
  vo.x[2]=te
  dealloc(a)
  return vo
}

//* GetTENQ() get an nqs with useful transfer entropy info
obfunc GetTENQ () { local te01,te10,pf01,pf10 localobj nqte,vo1,vo2
  if(numarg()>3) nqte=$o4
  if(nqte==nil) {
    nqte=new NQS("from","to","TE","NTE","HX2|X2P","prefdir","TEshufavg","TEshufstd","sig")
  } else nqte.clear()
  vo1=normte($o1,$o2,$3)
  vo2=normte($o2,$o1,$3)
  te01=vo1.x(2)
  te10=vo2.x(2)
  if(vo1.x(4)<=0)vo1.x(4)=1
  if(vo2.x(4)<=0)vo2.x(4)=1
  if(te01>0 || te10>0) {
    pf01=(te01-te10)/(te01+te10)
    pf10=(te10-te01)/(te01+te10)
  } else {
    pf01=pf10=0
  }
nqte.append(0,1,vo1.x(0),te01,vo1.x(1),pf01,vo1.x(3),vo1.x(4),(vo1.x(0)-vo1.x(3))/vo1.x(4))
nqte.append(1,0,vo2.x(0),te10,vo2.x(1),pf10,vo2.x(3),vo2.x(4),(vo2.x(0)-vo2.x(3))/vo2.x(4))
  return nqte
}

//** prefdte() get preferred direction of transfer entropy
//$o1=vec 1, $o2=vec 2, $3 = # of times to shuffle
func prefdte () { local nshuf,a,te01,te10,pfd localobj v1,v2,vtmp
  a=allocvecs(v1,v2,vtmp)
  v1.copy($o1) v2.copy($o2) nshuf=$3 vtmp.resize(3)
  v1=normte($o1,$o2,nshuf)
  v2=normte($o2,$o1,nshuf)
  te01=v1.x(2)
  te10=v2.x(2)
  pfd=(te01-te10)/(te01+te10)
  dealloc(a)
  return pfd
}

//** mkchist() averages entries in window into disc values and returns in new output vec
//$o1=input vec,$2=win size
obfunc mkchist () { local idx,eidx,wsz localobj vin,vout
  vin=$o1 wsz=$2 vout=new Vector() 
  vout.resize(1+vin.size/wsz) vout.resize(0)
  for(idx=0;idx<=vin.size;idx+=wsz) {
    eidx=idx+wsz-1
    if(eidx>=vin.size)eidx=vin.size-1
    if(eidx>idx) vout.append( int(vin.mean(idx,eidx)) )
  }
  return vout
}

//get magnitude of difference in a preferred direction - just abs of diff, but if theyre both neg, return 0
//$1 = nTE_X->Y
//$2 = nTE_Y->X
func prefdmag () { local n1,n2,s
  n1=$1 n2=$2
  if(n1>0 && n2<=0) return n1-n2 //n1 is relatively strong
  if(n2>0 && n1<=0) return n2-n1 //n2 is relatively strong
  if(n2<0 && n1<0) return 0      //both are weak
  return abs(n1-n2)              //both are weak positive
}


//** simple test for nte
{declare("vb","o[2]","vs","o[2]")}
for i=0,1 {
  vb[i]=new Vector()
  vs[i]=new Vector()
}

//mkspktrain(Random,rate,tmax) -- make a spike train with specified rate,tmax
//Random obj must be initialized
obfunc mkspktrain () { local tmax,rate,t,dt,intt localobj rdp,vs
  rdp=$o1 rate=$2 tmax=$3
  intt=1e3/rate
  t = 0
  vs=new Vector()
  while(t<=tmax) {
    dt = rdp.poisson(intt)
    t += dt
    vs.append(t)
  }
  return vs
}

//make random spikes with frequency $1, tmax=$2, offset for spikes=$3, alpha=$4 -- ratio of spikes from
//vs[0] that get placed in vs[1] 
//spikes in vs[0] are randomly picked, spikes in vs[1] are same as in vs[0] but shifted forward by $3 offset
//so vs[0] 'drives' vs[1], or can be used to predict it, but vs[1] cant be used to predict vs[0]
proc mkspks () { local tmax,rate,t,dt,intt,off,i,alpha localobj rdp
  rate=$1 tmax=$2 off=$3 
  if(numarg()>3)alpha=$4 else alpha=1
  intt=1e3/rate
  rdp=new Random()
  rdp.ACG(1234)
  rdp.poisson(intt)
  for i=0,1 vs[i].resize(0)
  vs[0]=mkspktrain(rdp,rate,tmax)
  if(alpha < 1.0) {
    for vtr(&t,vs[0]) if(rdp.uniform(0,1) <= alpha) vs[1].append(t+off)
  } else {
    vs[1].copy(vs[0])
    vs[1].add(off)
  }
}
//test nTE : nTE of X0 -> X1 should be much higher than nTE of X1 -> X0
//optional $1=offset == offset to shift spikes by, in ms
//optional $2=rate == rate of spikes, in Hz
//optional $3=bin size , in ms
//optional $4=alpha == ratio of spikes of X0 that get placed in X1 with offset
//optional $5=max time, in ms
func testnte () { local a,i,bisv,maxt,alpha,off,rate,binsz,dur  localobj nqt,nqout
  if(numarg()>0)off=$1 else off=10
  if(numarg()>1)rate=$2 else rate=50
  if(numarg()>2)binsz=$3 else binsz=10
  if(numarg()>3)alpha=$4 else alpha=1
  if(numarg()>4)dur=$5 else dur=10000
  bisv=binmin_infot binmin_infot=0
  print "output should be close to:\n\t0 1 0.6707 0.9975 0.6707 0.9407 0.003435 0.001672 399.1"
  print "\t1 0 0.02183 0.03049 0.6685 -0.9407 0.002828 0.001442 13.17"
  mkspks(rate,dur,off,alpha)
  maxt=vs[1].max
  printf("maxt=%g\n",maxt)
  if(vs[1].max>maxt)maxt=vs[1].max
  for i=0,1 vb[i].hist(vs[i],0,(maxt+binsz-1)/binsz,binsz)
  nqt=GetTENQ(vb[0],vb[1],200) 
nqout=new NQS("X1","X2")
nqout.odec("X1")
nqout.odec("X2")
batch_flag=1
nqout.append(vb[0],vb[1])
nqout.sv("/u/samn/bpftest/data/09dec17.func.testnte.nqs")
batch_flag=0
  nqt.pr
  nqsdel(nqt)
  binmin_infot=bisv
  return 1
}

//get kernel smoothed prob distrib in an nqs
//$o1=input vector
//$2=increment in x , smaller values mean finer resolution
//$3=bandwidth - higher means smoother output
// $4=min value in output, $5=max value in output
obfunc khist () { local min,max,inc,h,x,i,s localobj vx,vy,nq,vin
  vin=$o1 
  if(numarg()>1)inc=$2 else inc=0.1
  if(numarg()>2)h=$3 else h=vin.getbandwidth()
  if(numarg()>3)min=$4 else min=vin.min()
  if(numarg()>4)max=$5 else max=vin.max()
  {vx=new Vector() vy=new Vector()}
  vx.indgen(min,max,inc)
  vy.copy(vx)
  for vtr(&x,vx,&i) vy.x(i) = vin.kprob1D(h,x)
  s=vy.sum 
  if(s!=0) vy.div(vy.sum)
  nq=new NQS("x","y")
  nq.v[0]=vx
  nq.v[1]=vy
  return nq
}

Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39[PubMed]

References and models cited by this paper

References and models that cite this paper

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