Added tables that describe location of LSNM modules within TVB connectome

Author: Antonio Ulloa

analysis/compute_BOLD_balloon_66_regions.py

@@ -82,7 +82,7 @@
 from matplotlib import cm as CM
 
 # define the name of the input file where the synaptic activities are stored
-SYN_file  = 'synaptic_in_TVB_ROI.npy'
+SYN_file  = 'synaptic_in_66_ROIs.npy'
 
 # define the name of the output file where the BOLD timeseries will be stored
 BOLD_file = 'bold_balloon_66_regions.npy'

analysis/compute_syn_visual_66_regions.py

@@ -0,0 +1,191 @@
+# ============================================================================
+#
+#                            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.
+#
+# Please cite the author in any work or product based on this material.
+# 
+# ==========================================================================
+
+
+
+# ***************************************************************************
+#
+#   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 (compute_syn_visual_66_regions.py) was created on October 11, 2016.
+#
+#
+#   Author: Antonio Ulloa
+#
+#   Last updated by Antonio Ulloa on October 11, 2016
+#
+#   Based on computer code originally developed by Barry Horwitz et al
+# **************************************************************************/
+
+# compute_syn_visual_66_regions.py
+#
+# Calculate and plot simulated synaptic activities in 33 ROIs (right hemisphere)
+# as defined by Haggman et al
+# 
+# ... using data from visual delay-match-to-sample simulation (or resting state simulation
+# of the same duration as the DMS).
+# It also saves the synaptic activities for 33 ROIs (right hemisphere) in a python data file
+# (*.npy)
+# The data is saved in a numpy array where the columns are the 33 ROIs:
+#
+
+import numpy as np
+
+import matplotlib.pyplot as plt
+
+import matplotlib as mpl
+
+# set matplot lib parameters to produce visually appealing plots
+mpl.style.use('ggplot')
+
+# define the name of the output file where the integrated synaptic activity will be stored
+syn_file = 'synaptic_in_66_ROIs.npy'
+
+# the following ranges define the location of the nodes within a given ROI in Hagmann's brain.
+# They were taken from the excel document:
+#       "Hagmann's Talairach Coordinates (obtained from TVB).xlsx"
+# Extracted from The Virtual Brain Demo Data Sets
+# Please note that arrays in Python start from zero so one does need to account for that and shift
+# indices given by the above document by one location.
+# Use 6 nodes within rPCAL including host node 345
+v1_loc = range(344, 350)     # Hagmann's brain nodes included within V1 ROI
+
+# Use 6 nodes within rFUS including host node 393
+v4_loc = range(390, 396)     # Hagmann's brain nodes included within V4 ROI       
+
+# Use 6 nodes within rPARH including host node 413
+it_loc = range(412, 418)     # Hagmann's brain nodes included within IT ROI
+
+# Use 6 nodes within rRMF including host node 74
+d1_loc = range(73, 79)       # Hagmann's brain nodes included within D1 ROI
+
+# Use 6 nodes within rPTRI including host node 41
+d2_loc = range(39, 45)       # Hagmann's brain nodes included within D2 ROI
+
+# Use 6 nodes within rPOPE including host node 47
+fs_loc = range(47, 53)       # Hagmann's brain nodes included within FS ROI
+
+# Use 6 nodes within rCMF including host node 125
+fr_loc = range(125, 131)     # Hagmann's brain nodes included within FR ROI
+
+# Use 6 nodes within lPARH
+lit_loc= range(911, 917)     # Hagmann's brain nodes included within left IT ROI
+
+# Load TVB nodes synaptic activity
+tvb_synaptic = np.load("tvb_abs_syn.npy")
+
+# Load TVB host node synaptic activities into separate numpy arrays
+tvb_ev1 = tvb_synaptic[:, 0, v1_loc[0]:v1_loc[-1]+1, 0]
+tvb_ev4 = tvb_synaptic[:, 0, v4_loc[0]:v4_loc[-1]+1, 0]
+tvb_eit = tvb_synaptic[:, 0, it_loc[0]:it_loc[-1]+1, 0]
+tvb_ed1 = tvb_synaptic[:, 0, d1_loc[0]:d1_loc[-1]+1, 0]
+tvb_ed2 = tvb_synaptic[:, 0, d2_loc[0]:d2_loc[-1]+1, 0]
+tvb_efs = tvb_synaptic[:, 0, fs_loc[0]:fs_loc[-1]+1, 0]
+tvb_efr = tvb_synaptic[:, 0, fr_loc[0]:fr_loc[-1]+1, 0]
+tvb_iv1 = tvb_synaptic[:, 1, v1_loc[0]:v1_loc[-1]+1, 0]
+tvb_iv4 = tvb_synaptic[:, 1, v4_loc[0]:v4_loc[-1]+1, 0]
+tvb_iit = tvb_synaptic[:, 1, it_loc[0]:it_loc[-1]+1, 0]
+tvb_id1 = tvb_synaptic[:, 1, d1_loc[0]:d1_loc[-1]+1, 0]
+tvb_id2 = tvb_synaptic[:, 1, d2_loc[0]:d2_loc[-1]+1, 0]
+tvb_ifs = tvb_synaptic[:, 1, fs_loc[0]:fs_loc[-1]+1, 0]
+tvb_ifr = tvb_synaptic[:, 1, fr_loc[0]:fr_loc[-1]+1, 0]
+
+# now extract synaptic activity in the contralateral IT
+tvb_elit = tvb_synaptic[:, 0, lit_loc[0]:lit_loc[-1]+1, 0]
+tvb_ilit = tvb_synaptic[:, 1, lit_loc[0]:lit_loc[-1]+1, 0]
+
+# add all units WITHIN each region together across space to calculate
+# synaptic activity in EACH brain region
+v1_syn = np.sum(tvb_ev1+tvb_iv1, axis=1)
+v4_syn = np.sum(tvb_ev4+tvb_iv4, axis=1)
+it_syn = np.sum(tvb_eit+tvb_iit, axis=1)
+d1_syn = np.sum(tvb_ed1+tvb_id1, axis=1)
+d2_syn = np.sum(tvb_ed2+tvb_id2, axis=1)
+fs_syn = np.sum(tvb_efs+tvb_ifs, axis=1)
+fr_syn = np.sum(tvb_efr+tvb_ifr, axis=1)
+
+# now, add unit across space in the contralateral IT
+lit_syn = np.sum(tvb_elit + tvb_ilit, axis=1)
+
+# create a numpy array of timeseries
+synaptic = np.array([v1_syn, v4_syn, it_syn, fs_syn, d1_syn, d2_syn, fr_syn, lit_syn])
+
+# now, save all synaptic timeseries to a single file 
+np.save(syn_file, synaptic)
+
+# Extract number of timesteps from one of the matrices
+timesteps = v1_syn.shape[0]
+print 'Timesteps = ', timesteps
+
+# Construct a numpy array of timesteps (data points provided in data file)
+# to convert from timesteps to time in seconds we do the following:
+# Each simulation time-step equals 5 milliseconds
+# However, we are recording only once every 10 time-steps
+# Therefore, each data point in the output files represents 50 milliseconds.
+# Thus, we need to multiply the datapoint times 50 ms...
+# ... and divide by 1000 to convert to seconds
+#t = np.linspace(0, 659*50./1000., num=660)
+t = np.linspace(0, timesteps * 50.0 / 1000., num=timesteps)
+
+
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN V1')
+plt.plot(t, v1_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN V4')
+plt.plot(v4_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN IT')
+plt.plot(it_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN FS')
+plt.plot(fs_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN D1')
+plt.plot(d1_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN D2')
+plt.plot(d2_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN FR')
+plt.plot(fr_syn)
+# Set up figures to plot synaptic activity
+plt.figure()
+plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN LEFT IT')
+plt.plot(lit_syn)
+
+# Show the plots on the screen
+plt.show()

analysis/compute_syn_visual_TVB.py

@@ -105,7 +105,7 @@
 lit_loc= range(911, 917)     # Hagmann's brain nodes included within left IT ROI
 
 # Load TVB nodes synaptic activity
-tvb_synaptic = np.load("tvb_synaptic.npy")
+tvb_synaptic = np.load("tvb_abs_syn.npy")
 
 # Load TVB host node synaptic activities into separate numpy arrays
 tvb_ev1 = tvb_synaptic[:, 0, v1_loc[0]:v1_loc[-1]+1, 0]

auditory_model/Location of Auditory LSNM modules within Connectome.xlsx

@@ -1,9 +1,9 @@
-* Simulation Start Time: Tue Oct  4 14:46:21 2016
-* Simulation End Time: Tue Oct  4 19:21:31 2016
+* Simulation Start Time: Wed Oct  5 14:17:40 2016
+* Simulation End Time: Thu Oct  6 14:03:51 2016
 * Simulation duration (timesteps): 39600
 * Model description used: /Users/Antonio/Documents/NEURALBYTES/NIHPROJECT2/lsnm_in_python/visual_model/model.txt
 * Weights list used: /Users/Antonio/Documents/NEURALBYTES/NIHPROJECT2/lsnm_in_python/visual_model/subject_12/weightslist.txt
 * Neural net used: 
 * Simulation script used: /Users/Antonio/Documents/NEURALBYTES/NIHPROJECT2/lsnm_in_python/visual_model/script_resting_state_198_seconds.py
 * Were weights changed to generate a new subject? NO
-* Was TVB Connectome used in the simulation? NO
+* Was TVB Connectome used in the simulation? YES