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 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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# plotntes.py
# Mark Rowan, School of Computer Science, University of Birmingham, UK
# Aug 2013

# For a given directory containing experiments, containing multiple runs,
# and a supplied list of data segments, obtain and plot the nTE per population
# for each of the data segments (averaged over all runs)

# E.g. for experiment 'proswt3', containing runs '1', '2'... ,
# and for the requested segments '3', '12', and '50',
# plot the nTE of each population for each segment across all runs

# Usage: python plotntes.py <path_to_experiment_variable> <data_segments>

# ----------------------------------------------------------------------
# loadspks(filepath)
# takes name of file to be read and calls grvec read procedure to load file
def loadspks(filepath):
    filename = filepath + "/spks"

    if os.path.exists(filename):
        h.grv_.read_vfile(filename)  # open file using grvec
        print "There are %d segments in this file" % numsegs()
    else:
        print "ERROR: No spks file found at path %s!" % filename
        sys.exit()


# numsegs()
# returns number of data segments in the file
def numsegs():
    return int(h.grv_.llist.count())


# read(index)
# takes index of file segment to be read (1 to numsegs)
# returns vec and tvec spiking data in a 2-D array
# data[0] is the spike time vector
# data[1] is the vector of cell IDs which spike at times in data[0]
def read(index):
    print "Reading grvec data"
    data =  np.zeros( (1, 1) ) # Preassign zero-length data variable

    # Check we're not requesting an index beyond the extent of the file
    if index > numsegs():
        print "Index must be <= %d" % numsegs()

    else:
        h.grv_.rv_readvec(index, h.tvec, h.vec) # Read segment 'index' into vec/tvec

        # Convert h.tvec and h.vec into Python / numpy arrays.
        data = np.zeros( (2, h.vec.size()) ) # Preallocate for speed

        for i in range(int(h.vec.size())):
            data[0,i] = h.tvec.x[i]
            data[1,i] = h.vec.x[i]

        #print data.shape
        return data

# 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
        f.close()
    else:
        print "ERROR: No vars file found at path %s! Ignoring this run.\n" % filename
        #sys.exit()
        auxvars = 0
    return auxvars

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

# 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
import copy


# Check filename was supplied
if len(sys.argv) < 3:
    print "Usage:\npython plotntes.py <data dir path> <data segment>"
    sys.exit()


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

# Load grvec / intfzip simulation
h('xopen("setup.hoc")')
h('xopen("nrnoc.hoc")')
h('load_file("init.hoc")')
h('load_file("grvec.hoc")')
h('load_file("labels.hoc")')
h('load_file("infot.hoc")')

# 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
CELLID = 0
global TYPE
TYPE = 1
global FIRINGRATE
FIRINGRATE = 3
global ACTIVITY
ACTIVITY = 2
global TARGET
TARGET = 4
global SCALE
SCALE = 5
global DEAD 
DEAD = 6

# MAIN LOOP
filepath = sys.argv[1] # Describes the top-level experiment directory
segnum = int(sys.argv[2]) # Data segment to be plotted

h.usetable_infot = 0 # Turn off log lookup tables
binsize = 10.0 # ms for bin size
poplabels = np.array(['E6', 'I6', 'I6L', 'E5B', 'E5R', 'I5', 'I5L', 'E4', 'I4', 'I4L', 'E2', 'I2', 'I2L'])
base_popsizes = np.array([59, 25, 13, 17, 65, 25, 13, 30, 20, 14, 150, 25, 13]) # First element was 60, but cell 0 always seems to be missing

# 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
allnormtes = np.zeros( (len(dirlist), len(base_popsizes)) ) # Pre-allocate x-axis array

for rundir in dirlist:
    rundirpath = "%s/%s" % (filepath, rundir)

    print "\nrundir %s" % rundir
    print "rundirpath %s" % rundirpath
    runnum = dirlist.index(rundir) # Find indexof(rundir)
    print "Loading data for run %s of %s from dir '%s'" % (runnum, len(dirlist), rundir)
    auxvars = loadvars(rundirpath) # Get vars (for wt/freq)
    netscale = round(auxvars.numcells / 470)
    popsizes = base_popsizes * netscale # Scale up if we have > 470 cells
    # Plot normalised transfer entropy (nTE)
    # Uses a time-binned MUA vector over two given segments of data, for comparison
    print "\nLoading data from %s" % rundirpath
    loadspks(rundirpath)
    print "Reading segment %d" % segnum
    data = read(segnum) # Read NEURON data for segment i

    # Create empty numpy array for number of spikes-per-bin
    firstspk = data[0,0]
    lastspk = data[0,len(data[1])-1]
    timecovered = lastspk-firstspk
    numbins = int(timecovered / binsize)

    spikesperbin = np.zeros( [auxvars.numcells, numbins] ) # x,y matrix (== cellid, spikes per bin)

    # Make MUA time series vector by counting all population spikes during each 'binsize' ms
    print "Binning file segment %d into %d bins (window size = %f ms)" % (segnum, numbins, binsize)
    for j in range(len(data[1])):
        spiketime = data[0,j]
        cellid = data[1,j]
        bin = int(float((spiketime % (numbins * binsize)) / binsize)) # Find bin number into which this spike should go
        spikesperbin[cellid,bin] += 1

    # Combine cells from each population together
    allpopMUAs = []
    currentpopcell = 0
    for popnum in range(len(popsizes)):
        finalpopcell = currentpopcell + popsizes[popnum] - 1
        popMUA = np.sum(spikesperbin[currentpopcell:finalpopcell, :], axis=0)
        currentpopcell += popsizes[popnum]
        allpopMUAs.append(popMUA) # append all popMUAs into one structure

    #print allpopMUAs
    #print "\n"
    normte = np.zeros(len(popsizes))
    # For each population, obtain its MUA against all other populations
    for popnum in range(len(allpopMUAs)):
        allotherpops = copy.deepcopy(allpopMUAs)
        thispop = allotherpops.pop(popnum) # Gives us this population, AND removes it from 'remaining' list
        thispopvec = h.Vector(thispop) # Convert thispop to a hoc Vector

        # Now, for each 'other' population, compare it to 'thispop'
        for otherpop in allotherpops:
            # Convert otherpop to a hoc Vector
            otherpopvec = h.Vector(otherpop)
            normtevec = h.normte(thispopvec, otherpopvec, 30) # Try 30 shuffles
            #normtevec.printf("%8.4f\n")
            #print "using %f, popnum %d" % (normtevec.x[2], popnum)
            normte[popnum] += normtevec.x[2]
            #print normte
            #print "\n"

    allnormtes[runnum] = normte
    print "allnormtes"
    print allnormtes

xaxis = range(len(base_popsizes))
meanperpop = np.mean(allnormtes, axis=0)
stdperpop = np.std(allnormtes, axis=0)
print meanperpop
print stdperpop
pyplot.errorbar(xaxis, meanperpop, yerr=stdperpop, linestyle='-', marker='x', markersize='4')
# Draw labels
xlocations=range(len(popsizes)) # Allow plot to be spaced equally on x-axis, independent of the value     
pyplot.xticks(xlocations,poplabels) # Display frequency/weight values over the x tick locations 
pyplot.xlabel("Population")
pyplot.ylabel("nTE")

# Save at 300 DPI as 'filepath/deletionscale.pdf'
matplotlib.rcParams.update({'font.size': 16})
pyplot.savefig("%s/nte%s.pdf" % (filepath, segnum), dpi=300, format="pdf")
# Save processed plot data to a file for later analysis
np.savez("%s/nte%s" % (filepath, segnum), x=xaxis, y=meanperpop, yerr=stdperpop, xlabels=poplabels, xlocations=xlocations)
print "Done"

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