updated Resting State and Task based parameters to better match empirical data

Author: Antonio Ulloa

analysis/compare_TVB_998_vs_Emp_FC.py

@@ -244,8 +244,8 @@
 
 
 # now, convert back to from Z to R correlation coefficients
-empirical_fc_lowres = np.tanh(empirical_fc_lowres_Z)
-tvb_rs_fc_lowres = np.tanh(tvb_rs_fc_lowres_Z)
+empirical_fc_lowres = empirical_fc_lowres_Z
+tvb_rs_fc_lowres = tvb_rs_fc_lowres_Z
 
 # save the simulated FCs to a file 
 np.save(tvb_fc_file, tvb_rs_fc_lowres)

simulation/sim.py

@@ -390,11 +390,11 @@ def run(self):
         timesteps_to_time_constant = 5.0
 
         # define white matter transmission speed in mm/ms for TVB simulation
-        TVB_speed = 4.0
+        TVB_speed = 1.0                       # was 4.0 in original tvb sims
 
         # define global coupling strength as in Sanz-Leon et al (2015), figure 17,
         # 3rd column, 3rd row
-        TVB_global_coupling_strength = 0.0042
+        TVB_global_coupling_strength = 0.0542          # was 0.0042 in original tvb sims
 
         # declare a variable that describes number of nodes in TVB connectome
         TVB_number_of_nodes = 998

simulation/sim_without_LSNM.py

@@ -42,7 +42,7 @@
 #   This program makes use of The Virtual Brain library toolbox, downloaded
 #   from the TVB GitHub page.
 #
-#   Author: Antonio Ulloa. Last updated by Antonio Ulloa on December 6 2016 
+#   Author: Antonio Ulloa. Last updated by Antonio Ulloa on March 16 2017 
 # **************************************************************************/
 #
 # sim_without_LSNM.py
@@ -138,7 +138,7 @@ def dfun(self, state_variables, coupling, local_coupling=0.0):
 # define global coupling strength as in Sanz-Leon (2015) Neuroimage paper
 # figure 17 3rd column 3rd row
 #global_coupling_strength = 0.0042
-global_coupling_strength = 0.0042
+global_coupling_strength = 0.0542
 
 # Define connectivity to be used (998 ROI matrix from TVB demo set)
 white_matter = connectivity.Connectivity.from_file("connectivity_998.zip")