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 [samn at neurosim.downstate.edu]; 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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# plotavg.py
# Mark Rowan, School of Computer Science, University of Birmingham, UK
# December 2012

# For plotting 'average of averages' of activity and scale when a number
# of runs with identical parameters, but different random seeds, have been
# done.

# Given a filepath, this simple script checks all subdirectories for any
# 'activity.npz' and 'scale.npz' files (which contain activity and scale
# factor data as plotted in the PDFs).

# It calculates the mean and std of the various runs' data and presents
# this on a similar-looking graph in the directory in which the script
# was called, as scale.pdf and activity.pdf


import sys
import os
import matplotlib
matplotlib.use('agg') # Prevent pyplot from trying to open a graphical front-end on headless clients
from matplotlib import pyplot
import numpy as np

filepath = sys.argv[1] # argv[0] is the name of the script, argv[1] is the filepath
print "\nLoading data from %s" % filepath


# Create list of sub-directories
#os.chdir(filepath) # Change to given directory
dirlist = [o for o in os.listdir(filepath)] # if os.path.isdir(o)]
print dirlist

# Initialise arrays for y-axes and one x-axis
scale = np.array([])
activity = np.array([])
x = np.array([])

# For each sub-directory
for dir in dirlist:
    # obtain activity.npz and scale.npz if present
    scalefile = "%s/%s/scale.npz" % (filepath, dir)
    activityfile = "%s/%s/activity.npz" % (filepath, dir)
    if not os.path.isfile(scalefile) or not os.path.isfile(activityfile):
        print "Missing scale.npz or activity.npz in %s" % dir
        # print error if not present, but continue
    else:
        # Load scale file
        print scalefile
        scaledata = np.load(scalefile)
        npscaledata = scaledata['y']
        print "Scale size: %d" % np.size(npscaledata)
        # Check if this data is shorter than it should be
        # Can't check scale.shape[1] if scale is currently only 1-D
        if np.size(scale.shape) > 1 and np.size(npscaledata) < scale.shape[1]:
            sizediff = scale.shape[1] - np.size(npscaledata)
            # Pad the array to bring it up to correct size
            npscaledata = np.hstack((npscaledata, np.zeros(sizediff)))
            print "Padded by %d" % sizediff

        # Append y-axis data to 'scale' (or assign scale=y if y is empty)
        if np.size(scale) < 1:
            scale = npscaledata
        else:
            scale = np.vstack((scale, npscaledata))
       
        # Grab this file's x-axis if it's bigger than the current one
        if np.size(x) < 1 or (np.size(x.shape) > 1 and x.shape[1] < np.size(scaledata['x'])):
            x = scaledata['x']


        # Load activity file
        print activityfile
        activitydata = np.load(activityfile)
        npactivitydata = activitydata['y']
        print "Activity size: %d" % np.size(npactivitydata)
        # Check if this data is shorter than it should be
        # Can't check activity.shape[1] if activity is currently only 1-D
        if np.size(activity.shape) > 1 and np.size(npactivitydata) < activity.shape[1]:
            sizediff = activity.shape[1] - np.size(npactivitydata)
            # Pad the array to bring it up to correct size
            npactivitydata = np.hstack((npactivitydata, np.zeros(sizediff)))
            print "Padded by %d" % sizediff

        # Append y-axis data to 'activity' (or assign activity=y if y is empty)
        if np.size(activity) < 1:
            activity = npactivitydata
        else:
            activity = np.vstack((activity, npactivitydata))

# Remove NaNs from data
print "Removing NaNs"
print "%d from scale" % np.sum(np.isnan(scale))
scale = np.nan_to_num(scale)
print "%d from activity" % np.sum(np.isnan(activity))
activity = np.nan_to_num(activity)

# Plot scale
pyplot.errorbar(x, np.mean(scale,0), np.std(scale,0), ecolor='grey', linestyle='-', marker='.', markersize=1.0)
# Draw labels
pyplot.xlabel("Time (days)")
pyplot.ylabel("Scale factor")
# Save at 300 DPI as 'filepath/scale.pdf'
pyplot.savefig("%s/scale.pdf" % filepath, dpi=300, format="pdf")
np.savez("%s/scale" % filepath, x=x, y=np.mean(scale,0), err=np.std(scale,0))

pyplot.clf() # Clear plot

# Plot activity
pyplot.errorbar(x, np.mean(activity,0), np.std(activity,0), ecolor='grey', linestyle='-', marker='.', markersize=1.0)
# Draw labels
pyplot.xlabel("Time (days)")
pyplot.ylabel("Activity (Hz)")
# Save at 300 DPI as 'filepath/activity.pdf'
pyplot.savefig("%s/activity.pdf" % filepath, dpi=300, format="pdf")
np.savez("%s/activity" % filepath, x=x, y=np.mean(activity,0), err=np.std(activity,0))