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| 1 | +# ============================================================================ |
| 2 | +# |
| 3 | +# PUBLIC DOMAIN NOTICE |
| 4 | +# |
| 5 | +# National Institute on Deafness and Other Communication Disorders |
| 6 | +# |
| 7 | +# This software/database is a "United States Government Work" under the |
| 8 | +# terms of the United States Copyright Act. It was written as part of |
| 9 | +# the author's official duties as a United States Government employee and |
| 10 | +# thus cannot be copyrighted. This software/database is freely available |
| 11 | +# to the public for use. The NIDCD and the U.S. Government have not placed |
| 12 | +# any restriction on its use or reproduction. |
| 13 | +# |
| 14 | +# Although all reasonable efforts have been taken to ensure the accuracy |
| 15 | +# and reliability of the software and data, the NIDCD and the U.S. Government |
| 16 | +# do not and cannot warrant the performance or results that may be obtained |
| 17 | +# by using this software or data. The NIDCD and the U.S. Government disclaim |
| 18 | +# all warranties, express or implied, including warranties of performance, |
| 19 | +# merchantability or fitness for any particular purpose. |
| 20 | +# |
| 21 | +# Please cite the author in any work or product based on this material. |
| 22 | +# |
| 23 | +# ========================================================================== |
| 24 | + |
| 25 | + |
| 26 | + |
| 27 | +# *************************************************************************** |
| 28 | +# |
| 29 | +# Large-Scale Neural Modeling software (LSNM) |
| 30 | +# |
| 31 | +# Section on Brain Imaging and Modeling |
| 32 | +# Voice, Speech and Language Branch |
| 33 | +# National Institute on Deafness and Other Communication Disorders |
| 34 | +# National Institutes of Health |
| 35 | +# |
| 36 | +# This file (compute_syn_visual_TVB.py) was created on June 4, 2016. |
| 37 | +# |
| 38 | +# |
| 39 | +# Author: Antonio Ulloa |
| 40 | +# |
| 41 | +# Last updated by Antonio Ulloa on June 4, 2016 |
| 42 | +# |
| 43 | +# Based on computer code originally developed by Barry Horwitz et al |
| 44 | +# **************************************************************************/ |
| 45 | + |
| 46 | +# compute_syn_visual_TVB.py |
| 47 | +# |
| 48 | +# Calculate and plot simulated synaptic activities in given ROIs that include |
| 49 | +# only TVB connectome nodes |
| 50 | +# |
| 51 | +# ... using data from visual delay-match-to-sample simulation. |
| 52 | +# It also saves the synaptic activities for each and all modules in a python data file |
| 53 | +# (*.npy) |
| 54 | +# The data is saved in a numpy array with columns in the following order: |
| 55 | +# |
| 56 | +# V1 ROI (right hemisphere, contains only TVB nodes) |
| 57 | +# V4 ROI (right hemisphere, contains only TVB nodes) |
| 58 | +# IT ROI (right hemisphere, contains only TVB nodes) |
| 59 | +# FS ROI (right hemisphere, contains only TVB nodes) |
| 60 | +# D1 ROI (right hemisphere, contains only TVB nodes) |
| 61 | +# D2 ROI (right hemisphere, contains only TVB nodes) |
| 62 | +# FR ROI (right hemisphere, contains only TVB nodes) |
| 63 | +# IT ROI (left hemisphere, contains only TVB nodes) |
| 64 | + |
| 65 | +import numpy as np |
| 66 | + |
| 67 | +import matplotlib.pyplot as plt |
| 68 | + |
| 69 | +import matplotlib as mpl |
| 70 | + |
| 71 | +# set matplot lib parameters to produce visually appealing plots |
| 72 | +mpl.style.use('ggplot') |
| 73 | + |
| 74 | +# define the name of the output file where the integrated synaptic activity will be stored |
| 75 | +syn_file = 'synaptic_in_TVB_ROI.npy' |
| 76 | + |
| 77 | +# the following ranges define the location of the nodes within a given ROI in Hagmann's brain. |
| 78 | +# They were taken from the excel document: |
| 79 | +# "Hagmann's Talairach Coordinates (obtained from TVB).xlsx" |
| 80 | +# Extracted from The Virtual Brain Demo Data Sets |
| 81 | +# Please note that arrays in Python start from zero so one does need to account for that and shift |
| 82 | +# indices given by the above document by one location. |
| 83 | +# Use 6 nodes within rPCAL including host node 345 |
| 84 | +v1_loc = range(344, 350) # Hagmann's brain nodes included within V1 ROI |
| 85 | + |
| 86 | +# Use 6 nodes within rFUS including host node 393 |
| 87 | +v4_loc = range(390, 396) # Hagmann's brain nodes included within V4 ROI |
| 88 | + |
| 89 | +# Use 6 nodes within rPARH including host node 413 |
| 90 | +it_loc = range(412, 418) # Hagmann's brain nodes included within IT ROI |
| 91 | + |
| 92 | +# Use 6 nodes within rRMF including host node 74 |
| 93 | +d1_loc = range(73, 79) # Hagmann's brain nodes included within D1 ROI |
| 94 | + |
| 95 | +# Use 6 nodes within rPTRI including host node 41 |
| 96 | +d2_loc = range(39, 45) # Hagmann's brain nodes included within D2 ROI |
| 97 | + |
| 98 | +# Use 6 nodes within rPOPE including host node 47 |
| 99 | +fs_loc = range(47, 53) # Hagmann's brain nodes included within FS ROI |
| 100 | + |
| 101 | +# Use 6 nodes within rCMF including host node 125 |
| 102 | +fr_loc = range(125, 131) # Hagmann's brain nodes included within FR ROI |
| 103 | + |
| 104 | +# Use 6 nodes within lPARH |
| 105 | +lit_loc= range(911, 917) # Hagmann's brain nodes included within left IT ROI |
| 106 | + |
| 107 | +# Load TVB nodes synaptic activity |
| 108 | +tvb_synaptic = np.load("tvb_synaptic.npy") |
| 109 | + |
| 110 | +# Load TVB host node synaptic activities into separate numpy arrays |
| 111 | +tvb_ev1 = tvb_synaptic[:, 0, v1_loc[0]:v1_loc[-1]+1, 0] |
| 112 | +tvb_ev4 = tvb_synaptic[:, 0, v4_loc[0]:v4_loc[-1]+1, 0] |
| 113 | +tvb_eit = tvb_synaptic[:, 0, it_loc[0]:it_loc[-1]+1, 0] |
| 114 | +tvb_ed1 = tvb_synaptic[:, 0, d1_loc[0]:d1_loc[-1]+1, 0] |
| 115 | +tvb_ed2 = tvb_synaptic[:, 0, d2_loc[0]:d2_loc[-1]+1, 0] |
| 116 | +tvb_efs = tvb_synaptic[:, 0, fs_loc[0]:fs_loc[-1]+1, 0] |
| 117 | +tvb_efr = tvb_synaptic[:, 0, fr_loc[0]:fr_loc[-1]+1, 0] |
| 118 | +tvb_iv1 = tvb_synaptic[:, 1, v1_loc[0]:v1_loc[-1]+1, 0] |
| 119 | +tvb_iv4 = tvb_synaptic[:, 1, v4_loc[0]:v4_loc[-1]+1, 0] |
| 120 | +tvb_iit = tvb_synaptic[:, 1, it_loc[0]:it_loc[-1]+1, 0] |
| 121 | +tvb_id1 = tvb_synaptic[:, 1, d1_loc[0]:d1_loc[-1]+1, 0] |
| 122 | +tvb_id2 = tvb_synaptic[:, 1, d2_loc[0]:d2_loc[-1]+1, 0] |
| 123 | +tvb_ifs = tvb_synaptic[:, 1, fs_loc[0]:fs_loc[-1]+1, 0] |
| 124 | +tvb_ifr = tvb_synaptic[:, 1, fr_loc[0]:fr_loc[-1]+1, 0] |
| 125 | + |
| 126 | +# now extract synaptic activity in the contralateral IT |
| 127 | +tvb_elit = tvb_synaptic[:, 0, lit_loc[0]:lit_loc[-1]+1, 0] |
| 128 | +tvb_ilit = tvb_synaptic[:, 1, lit_loc[0]:lit_loc[-1]+1, 0] |
| 129 | + |
| 130 | +# add all units WITHIN each region together across space to calculate |
| 131 | +# synaptic activity in EACH brain region |
| 132 | +v1_syn = np.sum(tvb_ev1+tvb_iv1, axis=1) |
| 133 | +v4_syn = np.sum(tvb_ev4+tvb_iv4, axis=1) |
| 134 | +it_syn = np.sum(tvb_eit+tvb_iit, axis=1) |
| 135 | +d1_syn = np.sum(tvb_ed1+tvb_id1, axis=1) |
| 136 | +d2_syn = np.sum(tvb_ed2+tvb_id2, axis=1) |
| 137 | +fs_syn = np.sum(tvb_efs+tvb_ifs, axis=1) |
| 138 | +fr_syn = np.sum(tvb_efr+tvb_ifr, axis=1) |
| 139 | + |
| 140 | +# now, add unit across space in the contralateral IT |
| 141 | +lit_syn = np.sum(tvb_elit + tvb_ilit, axis=1) |
| 142 | + |
| 143 | +# create a numpy array of timeseries |
| 144 | +synaptic = np.array([v1_syn, v4_syn, it_syn, fs_syn, d1_syn, d2_syn, fr_syn, lit_syn]) |
| 145 | + |
| 146 | +# now, save all synaptic timeseries to a single file |
| 147 | +np.save(syn_file, synaptic) |
| 148 | + |
| 149 | +# Extract number of timesteps from one of the matrices |
| 150 | +timesteps = v1_syn.shape[0] |
| 151 | +print 'Timesteps = ', timesteps |
| 152 | + |
| 153 | +# Construct a numpy array of timesteps (data points provided in data file) |
| 154 | +# to convert from timesteps to time in seconds we do the following: |
| 155 | +# Each simulation time-step equals 5 milliseconds |
| 156 | +# However, we are recording only once every 10 time-steps |
| 157 | +# Therefore, each data point in the output files represents 50 milliseconds. |
| 158 | +# Thus, we need to multiply the datapoint times 50 ms... |
| 159 | +# ... and divide by 1000 to convert to seconds |
| 160 | +#t = np.linspace(0, 659*50./1000., num=660) |
| 161 | +t = np.linspace(0, timesteps * 50.0 / 1000., num=timesteps) |
| 162 | + |
| 163 | + |
| 164 | +# Set up figures to plot synaptic activity |
| 165 | +plt.figure() |
| 166 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN V1') |
| 167 | +plt.plot(t, v1_syn) |
| 168 | +# Set up figures to plot synaptic activity |
| 169 | +plt.figure() |
| 170 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN V4') |
| 171 | +plt.plot(v4_syn) |
| 172 | +# Set up figures to plot synaptic activity |
| 173 | +plt.figure() |
| 174 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN IT') |
| 175 | +plt.plot(it_syn) |
| 176 | +# Set up figures to plot synaptic activity |
| 177 | +plt.figure() |
| 178 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN FS') |
| 179 | +plt.plot(fs_syn) |
| 180 | +# Set up figures to plot synaptic activity |
| 181 | +plt.figure() |
| 182 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN D1') |
| 183 | +plt.plot(d1_syn) |
| 184 | +# Set up figures to plot synaptic activity |
| 185 | +plt.figure() |
| 186 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN D2') |
| 187 | +plt.plot(d2_syn) |
| 188 | +# Set up figures to plot synaptic activity |
| 189 | +plt.figure() |
| 190 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN FR') |
| 191 | +plt.plot(fr_syn) |
| 192 | +# Set up figures to plot synaptic activity |
| 193 | +plt.figure() |
| 194 | +plt.suptitle('SIMULATED SYNAPTIC ACTIVITY IN LEFT IT') |
| 195 | +plt.plot(lit_syn) |
| 196 | + |
| 197 | +# Show the plots on the screen |
| 198 | +plt.show() |
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