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]
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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 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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 V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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RowanEtAl2014
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#!/bin/sh
# Common logic to take a selection of variables and values and spawn a load of relevant cluster jobs
# Data will be saved at $datadir/$var$val/$seed by default using this script

datadir="$1" # Base save path
var="$2" # Parameter to vary
vals="$3" # Array of values to assign to var
initargs="$4" # Array of non-varying parameters (e.g. scaling=0)
jobname="$5" # Job name
seeds="$6" # Array of random seeds

if [ "$seeds" = "" ]; then
  #seeds="1 2 3 4 5 6 7 8 9 11 13 14 15 16 17 18 20 21 22 23" # Default set of 20 (excluding 10,12,19 due to bad wiring)
  #seeds="31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60" # additional 30 runs
  seeds="1 2 3 4 5 6 7 8 9 11 13 14 15 16 17 18 20 21 22 23 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60" # all 50 runs
fi

##################################

for seed in $seeds; do
  for val in $vals; do

    # Make savedir name
    savepath="$datadir/$var$val/$seed"

    # Create savedir
    echo
    echo "mkdir -p $savepath"
    mkdir -p "$savepath"
    
    # Append seeds to 'args'
    # randsy needs to be negative for normal-distribution randomisation
    seedstring=" -c {inputseed=$seed} -c {pseed=$seed} -c {dvseed=$seed} -c {stimseed=$seed} -c {randsy=$seed*-1}"
    args=$initargs$seedstring

    export var
    export val
    export args
    export savepath
    export jobname

    # Run sim
    echo "msub -v var,val,args,savepath -o $savepath/$jobname.out -e $savepath/$jobname.err -N $jobname clusterrun.sh"
    msub -v var,val,args,savepath -o $savepath/$jobname.out -e $savepath/$jobname.err -N $jobname clusterrun.sh
  done
done