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 L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA 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 [bill.lytton at downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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RowanEtAl2014
batchscripts
mod
README
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 *
decvec.hoc *
default.hoc *
drline.hoc *
e2hubsdisconpow.hoc *
e2incconpow.hoc *
filtutils.hoc *
flexinput.hoc
geom.hoc *
graphplug.hoc *
grvec.hoc *
infot.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
mosinit.hoc
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotavg.py
plotbatch.sh
plotbatchcluster.sh
plotdeletions.py
plotntes.py
powchgtest.hoc *
pyhoc.py
python.hoc *
pywrap.hoc *
ratlfp.dat *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: comppowspec.hoc,v 1.11 2011/02/22 21:02:34 samn Exp $ 


// performs comparisons of experimental to simulation power spectra

{colW=colH=3 mytstop=1e3}

//strdef strrcs
//strrcs="nqsnet.hoc,65,network.hoc,125,params.hoc,112,run.hoc,53,nload.hoc,182"
//rcsopen(strrcs) // load sim from RCS

mytstop=htmax=tstop=20e3

rcsopen("load.hoc",88)

if(g==nil)gg()

objref nqe // experimental data power spectra
nqe=new NQS("/u/samn/ibohk/data/10sep24_sal-j6-0611-2.bpf_matfftpow_smooth.nqs")
//nqe=new NQS("/u/samn/ibohk/data/10sep24_sal-j6-0611-2.bpf_matfftpow_raw.nqs")

declare("sixcut",0) // do a six Hz cutoff

objref nqe2
nqe2=new NQS()
nqe.select("f","<=",100) // 209716
nqe2.cp(nqe.out) 

objref nqe2rs // resample power spectra to have same size as nqf200 (or nqpmtm)
strdef strpow
strpow="nqf200"
proc mknqe2rs () {
  {nqsdel(nqe2rs) nqe2rs=new NQS("f","pow")}
  if(!strcmp(strpow,"nqf200")) nsz=2048 else nsz=2049
  for i=0,1 { // do the resampling
    nqe2rs.v[i].copy(nqe2.v[i])
    resample(nqe2rs.v[i],nsz)
  }  
  mx=nqe2rs.v[1].max // peak amplitude in experimental data
}

objref nqn,vfctr
{vfctr=new Vector() vfctr.indgen(0.05,1,0.05)}

fctr=1

//* mknqn - make an nqs with error to find optimal matches
proc mknqn () { local sc
  {mknqe2rs() nqsdel(nqn) nqn=new NQS("sidx","SIMTYP","col","err","fctr","DISCONCOL")}
  for i=0,nqbatch.v.size-1 {
    print i
    nq=nqbatch.get(strpow,i).o
    sidx=nqbatch.get("sidx",i).x
    SIMTYP=nqbatch.get("SIMTYP",i).x
    DISCONCOL=nqbatch.get("DISCONCOL",i).x
    for j=0,numcols-1 {
      {sprint(tstr,"C%dintraE",j) vec.resize(0) vec.copy(nq.v[nq.fi(tstr)])}

      if(sixcut) vec.fill(0,0,62*2)
      if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)

      for vtr(&fctr,vfctr) {
        vec0.copy(vec)
        if(sixcut) vec0.fill(0,0,62)
        if(mx>vec0.max) sc=fctr*mx/vec0.max else sc=fctr*vec0.max/mx
        vec0.mul(sc)
        nqn.append(sidx,SIMTYP,j,vec0.meansqerr(nqe2rs.v[1]),fctr,DISCONCOL)
      }
    }
  }
}

objref myv[5]
//* drit(exclude disconcol) - draw the best matches
proc drit () { local skipdiscon
  if(numarg()>0)skipdiscon=$1 else skipdiscon=1
  nqe2.gr("pow","f",0,1,1)  
  for case(&SIMTYP,0,18,20,-18,&i) {
    myv[i]=new Vector()
    if(skipdiscon) nqn.select("SIMTYP",SIMTYP) else nqn.select("SIMTYP",SIMTYP,"DISCONCOL",0)
    err=nqn.getcol("err").min
    nqn.select("SIMTYP",SIMTYP,"err",err)
    nq=nqbatch.get(strpow,nqn.fetch("sidx")).o
    vec.resize(0)
    sprint(tstr,"C%dintraE",nqn.fetch("col"))
    vec.copy(nq.v[nq.fi(tstr)])
    fctr=nqn.fetch("fctr")
    if(sixcut) vec.fill(0,0,62*2)
    if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)
    {myv[i].copy(vec) myv[i].mul(fctr*mx/myv[i].max)}  
    myv[i].plot(g,nq.v,i+2,1)
    print SIMTYP,err
  }
}

//* drbad(exclude disconcol) - draw the worst matches
proc drbad () { local skipdiscon,idx
  if(numarg()>0)skipdiscon=$1 else skipdiscon=1
  nqe2.gr("pow","f",0,1,1)  
  for case(&SIMTYP,0,18,20,-18,&i) {
    myv[i]=new Vector()
    if(skipdiscon) nqn.select("SIMTYP",SIMTYP) else nqn.select("SIMTYP",SIMTYP,"DISCONCOL",0)
    err=nqn.getcol("err").max
    
    nqn.select("SIMTYP",SIMTYP,"err",err)
//    print nqn.select("SIMTYP",SIMTYP,"err",">=",err*.9)
//    for vtr(&idx,nq
    nq=nqbatch.get(strpow,nqn.fetch("sidx")).o
    vec.resize(0)
    sprint(tstr,"C%dintraE",nqn.fetch("col"))
    vec.copy(nq.v[nq.fi(tstr)])
    fctr=nqn.fetch("fctr")
    if(sixcut) vec.fill(0,0,62*2)
    if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)
    {myv[i].copy(vec) myv[i].mul(fctr*mx/myv[i].max)}  
    myv[i].plot(g,nq.v,i+2,1)
    print SIMTYP,err
  }
}


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