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# National Institute on Deafness and Other Communication Disorders
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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 (plot_weights_among_given_nodes.py) was created on May 12, 2016.
#
#
# Author: Antonio Ulloa. Last updated by Antonio Ulloa on May 12, 2016
# Based on: display_sensor_locations.py by Paula Sanz-Leon (TVB team)
# **************************************************************************/
# plot_weights_among_given_nodes.py
#
# Displays Hagmann's brain's 998-nodes plus the weight among given nodes
# It also prints out what the connection weights are among those given
# connectome 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
# the following contains all nodes grouped by ROI
#nodes_list = [range(344, 354), # rPCAL, where V1/V2 is embedded
# range(390, 412), # rFUS, where V4 is embedded
# range(412, 418), # rPARH, where IT is embedded
# range(47, 57), # rPOPE, where FS is embedded
# range(57, 79), # rRMF, where D1 is embedded
# range(39, 47), # rPTRI, where D2 is embedded
# range(125, 138)] # rCMF, whenre FR is embedded
nodes_list = [range(344, 350), # V1/V2 ROI
range(390, 396), # V4 ROI
range(412, 418), # IT ROI
range(47, 53), # FS ROI
range(73, 79), # D1 ROI
range(39, 45), # D2 ROI
range(125, 131)] # FR ROI
#nodes_list = [range(345, 346), # V1/V2 ROI
# range(393, 394), # V4 ROI
# range(413, 414), # IT ROI
# range(47, 48), # FS ROI
# range(74, 75), # D1 ROI
# range(41, 42), # D2 ROI
# range(125, 126)] # FR ROI
nodes_grouped_by_region = np.array(nodes_list)
# the following contains all nodes with no grouping
nodes_to_be_examined = np.hstack(nodes_grouped_by_region)
print 'Nodes grouped by region:', nodes_grouped_by_region
print 'All nodes with no grouping:', nodes_to_be_examined
nodes = np.array([345, # V1/V2
393, # V4
413, # IT
47, # FS
74, # D1
41, # D2
125]) # FR
# Load connectivity from Hagmann's brain
white_matter = connectivity.Connectivity.from_file("connectivity_998.zip")
centres = white_matter.centres
# 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 host node is yellow
v1_module = mlab.points3d(centres[nodes_grouped_by_region[0], 0],
centres[nodes_grouped_by_region[0], 1],
centres[nodes_grouped_by_region[0], 2],
color=(1, 1, 0),scale_factor = 4.)
# V4 node is green
v4_module = mlab.points3d(centres[nodes_grouped_by_region[1], 0],
centres[nodes_grouped_by_region[1], 1],
centres[nodes_grouped_by_region[1], 2],
color=(0, 1, 0),scale_factor = 4.)
# IT node is blue
it_module = mlab.points3d(centres[nodes_grouped_by_region[2], 0],
centres[nodes_grouped_by_region[2], 1],
centres[nodes_grouped_by_region[2], 2],
color=(0, 0, 1),scale_factor = 4.)
# FS node is orange
fs_module = mlab.points3d(centres[nodes_grouped_by_region[3], 0],
centres[nodes_grouped_by_region[3], 1],
centres[nodes_grouped_by_region[3], 2],
color=(1, 0.5, 0),scale_factor = 4.)
# D1 node is red
d1_module = mlab.points3d(centres[nodes_grouped_by_region[4], 0],
centres[nodes_grouped_by_region[4], 1],
centres[nodes_grouped_by_region[4], 2],
color=(1, 0, 0),scale_factor = 4.)
# D2 node is magenta (or is it pink?)
d2_module = mlab.points3d(centres[nodes_grouped_by_region[5], 0],
centres[nodes_grouped_by_region[5], 1],
centres[nodes_grouped_by_region[5], 2],
color=(1, 0, 1),scale_factor = 4.)
# FR node is purple
fr_module = mlab.points3d(centres[nodes_grouped_by_region[6], 0],
centres[nodes_grouped_by_region[6], 1],
centres[nodes_grouped_by_region[6], 2],
color=(0.5, 0, 0.5),scale_factor = 4.)
# ... now Plot the connections weights among the nodes
for tvb_node in nodes_to_be_examined:
# extract TVB node numbers that are connected to TVB node above
tvb_conn = np.nonzero(white_matter.weights[tvb_node])
# extract the numpy array from it
tvb_conn = tvb_conn[0]
# now get rid of connected nodes that are not within the group of nodes that we are
# interested in examining
tvb_conn = np.intersect1d(tvb_conn, nodes_to_be_examined)
for connected_node in tvb_conn:
# first, check whether the two nodes that we are connecting belong to same
# ROI:
tvb_node_region = next(((i, node.index(tvb_node)) for i, node in enumerate(nodes_list) if tvb_node in node), None)[0]
tvb_conn_region = next(((i, node.index(connected_node)) for i, node in enumerate(nodes_list) if connected_node in node), None)[0]
#tvb_node_region = np.where(nodes_list==tvb_node)[0][0]
#tvb_conn_region = np.where(nodes_list==connected_node)[0][0]
# Display the current connection ONLY IF connected nodes do not belong to
# the same region:
if tvb_node_region != tvb_conn_region:
print 'Node ', tvb_node, ' is connected to node ', connected_node
cxn = numpy.array([centres[connected_node],
centres[tvb_node]])
connections = mlab.plot3d(cxn[:, 0], cxn[:, 1], cxn[:, 2],
color = (0, 0, 0),
tube_radius=0.3)
# Finally, show everything on screen
mlab.show(stop=True)