# ============================================================================
#
# 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
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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)