Large-scale neural model of visual short-term memory (Ulloa, Horwitz 2016; Horwitz, et al. 2005,...)

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Accession:206337
Large-scale neural model of visual short term memory embedded into a 998-node connectome. The model simulates electrical activity across neuronal populations of a number of brain regions and converts that activity into fMRI and MEG time-series. The model uses a neural simulator developed at the Brain Imaging and Modeling Section of the National Institutes of Health.
References:
1 . Tagamets MA, Horwitz B (1998) Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb Cortex 8:310-20 [PubMed]
2 . Ulloa A, Horwitz B (2016) Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex. Front Neuroinform 10:32 [PubMed]
3 . Horwitz B, Warner B, Fitzer J, Tagamets MA, Husain FT, Long TW (2005) Investigating the neural basis for functional and effective connectivity. Application to fMRI. Philos Trans R Soc Lond B Biol Sci 360:1093-108 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Prefrontal cortex (PFC);
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Working memory;
Implementer(s): Ulloa, Antonio [antonio.ulloa at alum.bu.edu];
# ============================================================================
#
#                            PUBLIC DOMAIN NOTICE
#
#       National Institute on Deafness and Other Communication Disorders
#
# This software/database is a "United States Government Work" under the 
# terms of the United States Copyright Act. It was written as part of 
# the author's official duties as a United States Government employee and 
# thus cannot be copyrighted. This software/database is freely available 
# to the public for use. The NIDCD and the U.S. Government have not placed 
# any restriction on its use or reproduction. 
#
# Although all reasonable efforts have been taken to ensure the accuracy 
# and reliability of the software and data, the NIDCD and the U.S. Government 
# do not and cannot warrant the performance or results that may be obtained 
# by using this software or data. The NIDCD and the U.S. Government disclaim 
# all warranties, express or implied, including warranties of performance, 
# merchantability or fitness for any particular purpose.
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# Please cite the author in any work or product based on this material.
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# ==========================================================================



# ***************************************************************************
#
#   Large-Scale Neural Modeling software (LSNM)
#
#   Section on Brain Imaging and Modeling
#   Voice, Speech and Language Branch
#   National Institute on Deafness and Other Communication Disorders
#   National Institutes of Health
#
#   This file (display_Hagmanns_brain_connectivity.py) was created on July 18, 2015.
#
#
#   Author: Antonio Ulloa. Last updated by Antonio Ulloa on June 5, 2016
#   Based on: display_sensor_locations.py by Paula Sanz-Leon (TVB team)
# **************************************************************************/

# display_Hagmanns_brain_connectivity.py
#
# Displays Hagmann's brain's 998-nodes plus LSNM nodes, along with the connections
# between Hagmann's nodes and LSNM nodes.

from tvb.simulator.lab import *
from tvb.simulator.plot.tools import mlab

# build an array of TVB nodes that you want to look at closely to visualize what is
# connected to what
# Below are the node numbers for the TVB nodes where visual LSNM modules are embedded
nodes_to_be_examined = [345, 393, 413, 47, 74, 41, 125]
# Below are the node numbers for the TVB nodes where auditory LSNM modules are embedded
#nodes_to_be_examined =[474, 470, 477,44]

# Define the hypothetical Talairach locations of each LSNM visual modules
v1_lsnm = [18,-88,8]
v4_lsnm = [30,-72,-12]
it_lsnm = [28,-36,-8]
vpf_lsnm = [42,26,20]

# define the hypothetical Talairach locations of each LSNM auditory module
#a1_lsnm = [48,-26,10]
#a2_lsnm = [62,-32,10]
#st_lsnm = [59,-17,4]
#apf_lsnm= [56,21,5]

# now, define the TVB nodes that are closest to the visual LSNM module locations above
v1 = [14, -86, 7]
v4 = [33, -70, -7]
it = [31, -39, -6]
fs = [47, 19, 9]
d1 = [43, 29, 21]
d2 = [42, 39, 2]
fr = [29, 25, 40] 

# now, define the TVB nodes that are closest to the auditory LSNM module locations above
#a1 = [51,-24,8]
#a2 = [61,-36,12]
#st = [59,-20,1]
#apf= [54,28,8]

# Load connectivity from Hagmann's brain
white_matter = connectivity.Connectivity.from_file("connectivity_998.zip")
centres = white_matter.centres

# Load one of the cortex 3d surface from TVB data files
CORTEX = surfaces.Cortex.from_file("cortex_80k/surface_80k.zip")

plot_surface(CORTEX, op=0.08)

# Threshold that will tell the visualization script whether to plot a given connection
# weight or ignore it
weight_threshold = 0.5

# Plot the 998 nodes of Hagmann's brain
#region_centres = mlab.points3d(centres[:, 0], 
#                               centres[:, 1], 
#                               centres[:, 2],
#                               color=(0.5, 0.5, 0.5),
#                               scale_factor = 1.)

# Now plot the hypothetical locations of LSNM visual modules

# V1 node is yellow
v1_module = mlab.points3d(v1[0],v1[1],v1[2],color=(1, 1, 0),scale_factor = 10.)

# V4 node is green
v4_module = mlab.points3d(v4[0],v4[1],v4[2],color=(0, 1, 0),scale_factor = 10.)

# IT node is blue
it_module = mlab.points3d(it[0],it[1],it[2],color=(0, 0, 1),scale_factor = 10.)

# FS node is orange
fs_module = mlab.points3d(fs[0],fs[1],fs[2],color=(1, 0.5, 0),scale_factor = 10.)

# D1 node is red
d1_module = mlab.points3d(d1[0],d1[1],d1[2],color=(1, 0, 0),scale_factor = 10.)

# D2 node is magenta (or is it pink?)
d2_module = mlab.points3d(d2[0],d2[1],d2[2],color=(1, 0, 1),scale_factor = 10.)

# FR node is purple
fr_module = mlab.points3d(fr[0],fr[1],fr[2],color=(0.5, 0, 0.5),scale_factor = 10.)

# ..., or plot the hypothetical locations of auditory LSNM modules
#a1_module = mlab.points3d(a1[0],a1[1],a1[2],color=(1, 1, 0),scale_factor = 8.)
#a2_module = mlab.points3d(a2[0],a2[1],a2[2],color=(0, 1, 0),scale_factor = 8.)
#st_module = mlab.points3d(st[0],st[1],st[2],color=(0, 0, 1),scale_factor = 8.)
#apf_module = mlab.points3d(apf[0],apf[1],apf[2],color=(1, 0, 0),scale_factor = 8.)

print ' '

# ... now Plot the connections among the nodes
for tvb_node in nodes_to_be_examined:

    print 'Node ', tvb_node, ' is connected to nodes: [', 
    
    # extract TVB node numbers that are connected to TVB node above by a value larger than
    # a given threshold
    #tvb_conn = (white_matter.weights[tvb_node] > weight_threshold).nonzero()
    # get the connection that has the strongest weight
    tvb_conn = np.argmax(white_matter.weights[tvb_node])
    # extract the numpy array from it
    #tvb_conn = tvb_conn[0]
    tvb_conn = [tvb_conn]
    
    for connected_node in tvb_conn:

        print connected_node, '(', white_matter.weights[tvb_node, connected_node], '),', 

        cxn = numpy.array([centres[connected_node],
                           centres[tvb_node]])

        connected = centres[connected_node]

        connections = mlab.plot3d(cxn[:, 0], cxn[:, 1], cxn[:, 2],
                                  color = (0, 0, 0),
                                  tube_radius=0.5)
        
        connected = mlab.points3d(connected[0], connected[1], connected[2],
                                color=(0.75, 0.75, 0.75),
                                scale_factor = 8.)

    print ']'

# Finally, show everything on screen
mlab.show(stop=True)

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