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

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"... 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. ... "
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 cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
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 L6 pyramidal corticothalamic cell; Neocortex V1 L2/6 pyramidal intratelencephalic cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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# plotdeletions.py
# Mark Rowan, School of Computer Science, University of Birmingham, UK
# May 2013

# For a given directory containing experiments, each containing multiple runs,
# obtain the proportion of deleted cells (I or E) and plot an error graph

# E.g. for experiment 'ADproswt', containing directories 'proswt0.5', 'proswt2',
# each of which contains runs 1-20: find the proportion of dead E/I cells from 'aux' file
# and plot against the neurostimulation weight value, obtained from 'vars' file.

# Usage: python plotdeletions.py <path_to_experiment>

# ----------------------------------------------------------------------

# loadvars(filepath)
# Reads variables saved in the vars file into memory
def loadvars(filepath):
    filename = filepath + "/vars"

    if os.path.exists(filename):
        # Read the file into 'auxvars'
        import imp
        f = open(filename)
        auxvars = imp.load_source('auxvars', '', f) # Read parameters (e.g. numcells) from aux file
        print "ERROR: No vars file found at path %s! Ignoring this run.\n" % filename
        auxvars = 0
    return auxvars

# readlastauxdata(filepath)
# Reads the last "t = ..." segment from a given aux file and returns it as a list of lists
def readlastauxdata(filepath):
    filename = filepath + "/aux"
    datalist = [] # Create empty list

    if os.path.exists(filename):
        # Search to end of file to find line number (nasty hack, but easy to code)
        with open(filename) as f:
            for numlines, l in enumerate(f):
        numlines +=2 # Account for EOF (1 line) and "t = ..." (1 line)
        #print "Number of lines in aux file = %d" % numlines

        curlineno = numlines - auxvars.numcells # Number of lines to backup
        while curlineno < numlines:
            curline = linecache.getline(filename, curlineno)
            splitline = curline.split(",") # Read this line from the file
            datalist.append(splitline) # datalist contains cells 0->numcells
            curlineno += 1
        print "ERROR: No aux file found at path %s!\n" % filename

    #print datalist
    return datalist
    # There is no sanity-checking here. Ideally we should at least ensure that each
    # line we read from the file is actually a data-line with the correct format,
    # rather than the EOF or "t = ..." markers (in case of a truncated file, or similar)

# getIcellIDs(auxdata)
# Returns a list of all cell IDs for I-cells (so they can be explicitly plotted / ignored)
def getIcellIDs(auxdata):
    cell_list = []
    for i in range(auxvars.numcells):
        celltype = int(auxdata[i][TYPE])
        if (h.strm(h.CTYP.o(celltype).s,"^I")):
            # Cell is I type
    return cell_list

# getEcellIDs(auxdata)
# Returns a list of all cell IDs for E-cells (so they can be explicitly plotted / ignored)
def getEcellIDs(auxdata):
    cell_list = []
    for i in range(auxvars.numcells):
        celltype = int(auxdata[i][TYPE])
        if (h.strm(h.CTYP.o(celltype).s,"^I")) == False:
            # Cell is E type
    return cell_list

# ----------------------------------------------------------------------

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

# Check filename was supplied
if len(sys.argv) < 2:
    print "Usage:\npython plotdeletions.py <data dir path>"

# Handle NEURON imports
print "Loading NEURON/grvec routines... \n\n"
from neuron import h

# Load grvec / intfzip simulation

# Load file
global auxvars # Vars from the vars file are held here, to be accessible by all methods
# Set index numbers for the aux file data lines (format: CELL,TYPE,ACTIVITY(firing rate),POSTSYNACTIVITY,TARGET,SCALE,DEAD)
global CELLID
global TYPE
TYPE = 1
global TARGET
global SCALE
global DEAD 
DEAD = 6

filepath = sys.argv[1] # Describes the top-level experiment directory
# Determine whether we are plotting frequency or weight
plotfreq = 0
plotwt = 0
if "freq" in filepath:
    plotfreq = 1
    if "wt" in filepath:
        plotwt = 1
    # Otherwise, we are plotting stimulation start time

# Get list of sub-directories for each experimental parameter value
dirlist = [o for o in os.listdir(filepath) if os.path.isdir(os.path.join(filepath,o))]
print dirlist

if "baseline" in dirlist:
    print "'baseline' dir not found. Please add a symlink to a baseline comparison dir'"

# Pre-allocate global graph x/y axes
yaxisI = np.zeros([2,len(dirlist)]) # Dimension 1 is mean, dimension 2 is std
yaxisE = np.zeros([2,len(dirlist)]) # Dimension 1 is mean, dimension 2 is std
xaxis = np.zeros([1,len(dirlist)])

# For each experiment's sub-directory (i.e. wt/freq value on the x-axis)
for exptdir in dirlist:
    exptdirpath = "%s/%s" % (filepath, exptdir)

    rundirlist = [o for o in os.listdir(exptdirpath) if os.path.isdir(os.path.join(exptdirpath,o))]
    print rundirlist
    exptnum = dirlist.index(exptdir) # Find indexof(exptdir)
    # Pre-allocate y axis arrays for *this run*
    thisyaxisI = np.array([])
    thisyaxisE = np.array([])

    # For each experimental run (seeds 1-20), to get error bars
    for rundir in rundirlist:
        print "\nrundir %s" % rundir
        rundirpath = "%s/%s" % (exptdirpath, rundir)
        print "rundirpath %s" % rundirpath
        runnum = rundirlist.index(rundir) # Find indexof(rundir)
        print "Loading data for run %s of %s from dir '%s'" % (runnum, len(rundirlist), rundir)
        auxvars = loadvars(rundirpath) # Get vars (for wt/freq)
        if auxvars:
            # Only if the 'vars' file was successfully loaded...
            # Obtain the wt/freq/time value from 'vars' -> xaxis
            if plotfreq:
                if plotwt:

            # Read auxdata
            auxdata = readlastauxdata(rundirpath)

            # Get list of cells
            listofIcells = getIcellIDs(auxdata)
            listofEcells = getEcellIDs(auxdata)

            # For each I cell
            deadIproportion = 0
            for Icell in listofIcells:
                # Obtain the 'dead' flag value from 'aux' at end of run
                if int(auxdata[Icell][DEAD]):
                    # If cell is dead, add to proportion of dead cells for this run
                    deadIproportion += 1 # float(1.0/len(listofIcells))
            # For each E cell
            deadEproportion = 0
            for Ecell in listofEcells:
                # Obtain the 'dead' flag value from 'aux' at end of run
                if int(auxdata[Ecell][DEAD]):
                    # If cell is dead, add to proportion of dead cells for this run
                    deadEproportion += 1 # float(1.0/len(listofEcells))

            # Compare to baseline for this particular run
            # Find baseline level of deletion for this particular run
            BLrundirpath = "%s/baseline/%s" % (filepath, rundir)
            print "Loading baseline data from %s" % BLrundirpath
            # Read auxdata
            BLauxdata = readlastauxdata(BLrundirpath)

            # For each I cell
            BLdeadIproportion = 0
            for Icell in listofIcells:
                # Obtain the 'dead' flag value from 'aux' at end of run
                if int(BLauxdata[Icell][DEAD]):
                    # If cell is dead, add to proportion of dead cells for this run
                    BLdeadIproportion += 1 # float(1.0/len(listofIcells))
            # For each E cell
            BLdeadEproportion = 0
            for Ecell in listofEcells:
                # Obtain the 'dead' flag value from 'aux' at end of run
                if int(BLauxdata[Ecell][DEAD]):
                    # If cell is dead, add to proportion of dead cells for this run
                    BLdeadEproportion += 1 # float(1.0/len(listofEcells))

            thisyaxisI = np.hstack([thisyaxisI,deadIproportion - BLdeadIproportion])
            thisyaxisE = np.hstack([thisyaxisE,deadEproportion - BLdeadEproportion])
            print thisyaxisE
            print thisyaxisI

    # Stack this run's y axis values onto the global graph axes
    yaxisI[0,exptnum] = np.mean(thisyaxisI)
    yaxisI[1,exptnum] = np.std(thisyaxisI)
    yaxisE[0,exptnum] = np.mean(thisyaxisE)
    yaxisE[1,exptnum] = np.std(thisyaxisE)

print xaxis
print yaxisI
print yaxisE

# Sort data
xsorted = xaxis[0][np.argsort(xaxis[0])]
yisorted = yaxisI[0][np.argsort(xaxis[0])]
yierrsorted = yaxisI[1][np.argsort(xaxis[0])]
yesorted = yaxisE[0][np.argsort(xaxis[0])]
yeerrsorted = yaxisE[1][np.argsort(xaxis[0])]

# Save processed plot data to a file for later analysis
#np.savez("%s/avgdeadnum.npz" % filepath, x=xsorted, yi=yisorted, ye=yesorted, yierr=yierrsorted, yeerr=yeerrsorted)

xlocations=np.arange(0,len(xsorted)) # Allow plot to be spaced equally on x-axis, independent of the value

pyplot.axhline(y=0, linewidth=2, color='0.5', linestyle='dashed') # Draw dashed line at y=0
pyplot.errorbar(xlocations, yisorted, yerr=yierrsorted, ecolor='b', elinewidth=2, linestyle='None', marker='x', markersize=10, markeredgewidth=3, markeredgecolor='b')
pyplot.errorbar(xlocations, yesorted, yerr=yeerrsorted, ecolor='r', elinewidth=2, linestyle='None', marker='x', markersize=10, markeredgewidth=3, markeredgecolor='r')

pyplot.xticks(xlocations,xsorted) # Display frequency/weight values over the x tick locations 
x1,x2,y1,y2 = pyplot.axis() 
pyplot.xlim(xmin = x1-1, xmax = x2+1) # Give a bit of space either side of the data

#pyplot.ylabel('Change in proportion of dead cells')
pyplot.ylabel('Change in # of dead cells')
if plotfreq:
    pyplot.xlabel('Stimulation frequency (Hz)')
    if plotwt:
        pyplot.xlabel('Stimulation weight')
        pyplot.xlabel('Stimulation start time (hours)')

matplotlib.rcParams.update({'font.size': 16})
pyplot.savefig("%s/avgdeadnum.pdf" % filepath, dpi=300, format="pdf")