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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 (func_conn_fmri_mean_subj_visual.py) was created September 6, 2015.
#
# Based in part on Matlab scripts by Horwitz et al.
#
# Author: Antonio Ulloa
#
# Last updated by Antonio Ulloa on September 6, 2015
# **************************************************************************/
# func_conn_fmri_mean_subj_visual.py
#
# Calculate and plot functional connectivity (within-task time series correlation)
# of the mean BOLD (and synaptic) timeseries in IT with the BOLD (and synaptic) timeseries
# of all other simulated brain
# regions. It reads the BOLD (and synaptic) time-series from a single python data file (*.npy)
# and writes the correlation coefficients to an output data file (*.npy)
#
# For the visual model, the columns in input files are in the following order:
#
# V1, V4, IT, FS, D1, D2, FR
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math as m
from scipy.stats import poisson
# define the length of both each trial and the whole experiment
# in synaptic timesteps, as well as total number of trials
experiment_length = 3960
trial_length = 110
num_of_trials = 36 # number of trials
num_of_fmri_blocks = 11 # how many blocks of trials in the experiment
num_of_syn_blocks = 12 # fMRI blocks are less than synaptic blocks
# because blocks are removed from fmri time-series
scans_removed = 8 # number of scans removed from BOLD computation
num_of_modules = 7
synaptic_timesteps = experiment_length
# define the names of the input file where the synaptic and BOLD timeseries are contained
syn_file = 'avg_syn_across_subjs.npy'
BOLD_file = 'avg_BOLD_across_subjs_balloon.npy'
# define the name of the output file where the functional connectivity
# correlation coefficients will be stored
func_conn_dms_file = 'corr_fmri_IT_vs_all_dms.npy'
func_conn_ctl_file = 'corr_fmri_IT_vs_all_ctl.npy'
# define neural synaptic time interval in seconds. The simulation data is collected
# one data point at synaptic intervals (10 simulation timesteps). Every simulation
# timestep is equivalent to 5 ms.
Ti = 0.005 * 10
# Total time of scanning experiment in seconds (timesteps X 5)
T = 198
# Time for one complete trial in milliseconds
Ttrial = 5.5
# the scanning happened every Tr interval below (in milliseconds). This
# is the time needed to sample hemodynamic activity to produce
# each fMRI image.
Tr = 2
num_of_scans = T / Tr - scans_removed
# Construct a numpy array of time points
time_in_seconds = np.arange(0, T, Tr)
scanning_timescale = np.arange(0, synaptic_timesteps, synaptic_timesteps / (T/Tr))
synaptic_timescale = np.arange(0, synaptic_timesteps)
# open files containing synaptic activities and BOLD time-series
syn_ts = np.load(syn_file)
BOLD_ts = np.load(BOLD_file)
# now, split all time-series in blocks
syn_ts_v1=np.array_split(syn_ts[0], num_of_syn_blocks)
syn_ts_v4=np.array_split(syn_ts[1], num_of_syn_blocks)
syn_ts_it=np.array_split(syn_ts[2], num_of_syn_blocks)
syn_ts_fs=np.array_split(syn_ts[3], num_of_syn_blocks)
syn_ts_d1=np.array_split(syn_ts[4], num_of_syn_blocks)
syn_ts_d2=np.array_split(syn_ts[5], num_of_syn_blocks)
syn_ts_fr=np.array_split(syn_ts[6], num_of_syn_blocks)
BOLD_ts_v1=np.array_split(BOLD_ts[0], num_of_fmri_blocks)
BOLD_ts_v4=np.array_split(BOLD_ts[1], num_of_fmri_blocks)
BOLD_ts_it=np.array_split(BOLD_ts[2], num_of_fmri_blocks)
BOLD_ts_fs=np.array_split(BOLD_ts[3], num_of_fmri_blocks)
BOLD_ts_d1=np.array_split(BOLD_ts[4], num_of_fmri_blocks)
BOLD_ts_d2=np.array_split(BOLD_ts[5], num_of_fmri_blocks)
BOLD_ts_fr=np.array_split(BOLD_ts[6], num_of_fmri_blocks)
# define an array with location of control blocks, and another array
# with location of task (DMS) blocks, relative to
# an array that contains all blocks (task-related blocks included)
syn_control_block_ids = np.arange(1, num_of_syn_blocks, 2)
syn_dms_block_ids = np.arange(0, num_of_syn_blocks, 2)
fmri_control_block_ids = np.arange(0, num_of_fmri_blocks, 2)
fmri_dms_block_ids = np.arange(1, num_of_fmri_blocks, 2)
# now, create an array of synaptic time-series containing DMS trials only:
syn_v1_dms_blocks = np.delete(np.asarray(syn_ts_v1), syn_control_block_ids, axis=0)
syn_v4_dms_blocks = np.delete(np.asarray(syn_ts_v4), syn_control_block_ids, axis=0)
syn_it_dms_blocks = np.delete(np.asarray(syn_ts_it), syn_control_block_ids, axis=0)
syn_fs_dms_blocks = np.delete(np.asarray(syn_ts_fs), syn_control_block_ids, axis=0)
syn_d1_dms_blocks = np.delete(np.asarray(syn_ts_d1), syn_control_block_ids, axis=0)
syn_d2_dms_blocks = np.delete(np.asarray(syn_ts_d2), syn_control_block_ids, axis=0)
syn_fr_dms_blocks = np.delete(np.asarray(syn_ts_fr), syn_control_block_ids, axis=0)
# now, create an array of BOLD time-series containing DMS trials only:
BOLD_v1_dms_blocks = np.delete(np.asarray(BOLD_ts_v1), fmri_control_block_ids, axis=0)
BOLD_v4_dms_blocks = np.delete(np.asarray(BOLD_ts_v4), fmri_control_block_ids, axis=0)
BOLD_it_dms_blocks = np.delete(np.asarray(BOLD_ts_it), fmri_control_block_ids, axis=0)
BOLD_fs_dms_blocks = np.delete(np.asarray(BOLD_ts_fs), fmri_control_block_ids, axis=0)
BOLD_d1_dms_blocks = np.delete(np.asarray(BOLD_ts_d1), fmri_control_block_ids, axis=0)
BOLD_d2_dms_blocks = np.delete(np.asarray(BOLD_ts_d2), fmri_control_block_ids, axis=0)
BOLD_fr_dms_blocks = np.delete(np.asarray(BOLD_ts_fr), fmri_control_block_ids, axis=0)
# ... and concatenate those DMS timeseries together
syn_v1_dms_ts = np.concatenate(syn_v1_dms_blocks)
syn_v4_dms_ts = np.concatenate(syn_v4_dms_blocks)
syn_it_dms_ts = np.concatenate(syn_it_dms_blocks)
syn_fs_dms_ts = np.concatenate(syn_fs_dms_blocks)
syn_d1_dms_ts = np.concatenate(syn_d1_dms_blocks)
syn_d2_dms_ts = np.concatenate(syn_d2_dms_blocks)
syn_fr_dms_ts = np.concatenate(syn_fr_dms_blocks)
BOLD_v1_dms_ts = np.concatenate(BOLD_v1_dms_blocks)
BOLD_v4_dms_ts = np.concatenate(BOLD_v4_dms_blocks)
BOLD_it_dms_ts = np.concatenate(BOLD_it_dms_blocks)
BOLD_fs_dms_ts = np.concatenate(BOLD_fs_dms_blocks)
BOLD_d1_dms_ts = np.concatenate(BOLD_d1_dms_blocks)
BOLD_d2_dms_ts = np.concatenate(BOLD_d2_dms_blocks)
BOLD_fr_dms_ts = np.concatenate(BOLD_fr_dms_blocks)
# but also, get rid of the DMS blocks, to create arrays with only control trials
syn_v1_control_blocks = np.delete(np.asarray(syn_ts_v1), syn_dms_block_ids, axis=0)
syn_v4_control_blocks = np.delete(np.asarray(syn_ts_v4), syn_dms_block_ids, axis=0)
syn_it_control_blocks = np.delete(np.asarray(syn_ts_it), syn_dms_block_ids, axis=0)
syn_fs_control_blocks = np.delete(np.asarray(syn_ts_fs), syn_dms_block_ids, axis=0)
syn_d1_control_blocks = np.delete(np.asarray(syn_ts_d1), syn_dms_block_ids, axis=0)
syn_d2_control_blocks = np.delete(np.asarray(syn_ts_d2), syn_dms_block_ids, axis=0)
syn_fr_control_blocks = np.delete(np.asarray(syn_ts_fr), syn_dms_block_ids, axis=0)
BOLD_v1_control_blocks = np.delete(np.asarray(BOLD_ts_v1), fmri_dms_block_ids, axis=0)
BOLD_v4_control_blocks = np.delete(np.asarray(BOLD_ts_v4), fmri_dms_block_ids, axis=0)
BOLD_it_control_blocks = np.delete(np.asarray(BOLD_ts_it), fmri_dms_block_ids, axis=0)
BOLD_fs_control_blocks = np.delete(np.asarray(BOLD_ts_fs), fmri_dms_block_ids, axis=0)
BOLD_d1_control_blocks = np.delete(np.asarray(BOLD_ts_d1), fmri_dms_block_ids, axis=0)
BOLD_d2_control_blocks = np.delete(np.asarray(BOLD_ts_d2), fmri_dms_block_ids, axis=0)
BOLD_fr_control_blocks = np.delete(np.asarray(BOLD_ts_fr), fmri_dms_block_ids, axis=0)
# ... and concatenate the control blocks together
syn_v1_ctl_ts = np.concatenate(syn_v1_control_blocks)
syn_v4_ctl_ts = np.concatenate(syn_v4_control_blocks)
syn_it_ctl_ts = np.concatenate(syn_it_control_blocks)
syn_fs_ctl_ts = np.concatenate(syn_fs_control_blocks)
syn_d1_ctl_ts = np.concatenate(syn_d1_control_blocks)
syn_d2_ctl_ts = np.concatenate(syn_d2_control_blocks)
syn_fr_ctl_ts = np.concatenate(syn_fr_control_blocks)
BOLD_v1_ctl_ts = np.concatenate(BOLD_v1_control_blocks)
BOLD_v4_ctl_ts = np.concatenate(BOLD_v4_control_blocks)
BOLD_it_ctl_ts = np.concatenate(BOLD_it_control_blocks)
BOLD_fs_ctl_ts = np.concatenate(BOLD_fs_control_blocks)
BOLD_d1_ctl_ts = np.concatenate(BOLD_d1_control_blocks)
BOLD_d2_ctl_ts = np.concatenate(BOLD_d2_control_blocks)
BOLD_fr_ctl_ts = np.concatenate(BOLD_fr_control_blocks)
# now, convert DMS and control timeseries into pandas timeseries, so we can analyze it
syn_V1_dms_ts = pd.Series(syn_v1_dms_ts)
syn_V4_dms_ts = pd.Series(syn_v4_dms_ts)
syn_IT_dms_ts = pd.Series(syn_it_dms_ts)
syn_FS_dms_ts = pd.Series(syn_fs_dms_ts)
syn_D1_dms_ts = pd.Series(syn_d1_dms_ts)
syn_D2_dms_ts = pd.Series(syn_d2_dms_ts)
syn_FR_dms_ts = pd.Series(syn_fr_dms_ts)
BOLD_V1_dms_ts = pd.Series(BOLD_v1_dms_ts)
BOLD_V4_dms_ts = pd.Series(BOLD_v4_dms_ts)
BOLD_IT_dms_ts = pd.Series(BOLD_it_dms_ts)
BOLD_FS_dms_ts = pd.Series(BOLD_fs_dms_ts)
BOLD_D1_dms_ts = pd.Series(BOLD_d1_dms_ts)
BOLD_D2_dms_ts = pd.Series(BOLD_d2_dms_ts)
BOLD_FR_dms_ts = pd.Series(BOLD_fr_dms_ts)
syn_V1_ctl_ts = pd.Series(syn_v1_ctl_ts)
syn_V4_ctl_ts = pd.Series(syn_v4_ctl_ts)
syn_IT_ctl_ts = pd.Series(syn_it_ctl_ts)
syn_FS_ctl_ts = pd.Series(syn_fs_ctl_ts)
syn_D1_ctl_ts = pd.Series(syn_d1_ctl_ts)
syn_D2_ctl_ts = pd.Series(syn_d2_ctl_ts)
syn_FR_ctl_ts = pd.Series(syn_fr_ctl_ts)
BOLD_V1_ctl_ts = pd.Series(BOLD_v1_ctl_ts)
BOLD_V4_ctl_ts = pd.Series(BOLD_v4_ctl_ts)
BOLD_IT_ctl_ts = pd.Series(BOLD_it_ctl_ts)
BOLD_FS_ctl_ts = pd.Series(BOLD_fs_ctl_ts)
BOLD_D1_ctl_ts = pd.Series(BOLD_d1_ctl_ts)
BOLD_D2_ctl_ts = pd.Series(BOLD_d2_ctl_ts)
BOLD_FR_ctl_ts = pd.Series(BOLD_fr_ctl_ts)
# ... and calculate the functional connectivity of IT with the other modules
funct_conn_it_v1_dms_SYN = syn_IT_dms_ts.corr(syn_V1_dms_ts)
funct_conn_it_v4_dms_SYN = syn_IT_dms_ts.corr(syn_V4_dms_ts)
funct_conn_it_d1_dms_SYN = syn_IT_dms_ts.corr(syn_D1_dms_ts)
funct_conn_it_d2_dms_SYN = syn_IT_dms_ts.corr(syn_D2_dms_ts)
funct_conn_it_fs_dms_SYN = syn_IT_dms_ts.corr(syn_FS_dms_ts)
funct_conn_it_fr_dms_SYN = syn_IT_dms_ts.corr(syn_FR_dms_ts)
funct_conn_it_v1_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_V1_dms_ts)
funct_conn_it_v4_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_V4_dms_ts)
funct_conn_it_d1_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_D1_dms_ts)
funct_conn_it_d2_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_D2_dms_ts)
funct_conn_it_fs_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_FS_dms_ts)
funct_conn_it_fr_dms_BOLD = BOLD_IT_dms_ts.corr(BOLD_FR_dms_ts)
funct_conn_it_v1_ctl_SYN = syn_IT_ctl_ts.corr(syn_V1_ctl_ts)
funct_conn_it_v4_ctl_SYN = syn_IT_ctl_ts.corr(syn_V4_ctl_ts)
funct_conn_it_d1_ctl_SYN = syn_IT_ctl_ts.corr(syn_D1_ctl_ts)
funct_conn_it_d2_ctl_SYN = syn_IT_ctl_ts.corr(syn_D2_ctl_ts)
funct_conn_it_fs_ctl_SYN = syn_IT_ctl_ts.corr(syn_FS_ctl_ts)
funct_conn_it_fr_ctl_SYN = syn_IT_ctl_ts.corr(syn_FR_ctl_ts)
funct_conn_it_v1_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_V1_ctl_ts)
funct_conn_it_v4_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_V4_ctl_ts)
funct_conn_it_d1_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_D1_ctl_ts)
funct_conn_it_d2_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_D2_ctl_ts)
funct_conn_it_fs_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_FS_ctl_ts)
funct_conn_it_fr_ctl_BOLD = BOLD_IT_ctl_ts.corr(BOLD_FR_ctl_ts)
#func_conn_dms_syn = np.array([funct_conn_it_v1_dms_SYN,funct_conn_it_v4_dms_SYN,
# funct_conn_it_d1_dms_SYN,funct_conn_it_d2_dms_SYN,
# funct_conn_it_fs_dms_SYN,funct_conn_it_fr_dms_SYN])
#func_conn_ctl_syn = np.array([funct_conn_it_v1_ctl_SYN,funct_conn_it_v4_ctl_SYN,
# funct_conn_it_d1_ctl_SYN,funct_conn_it_d2_ctl_SYN,
# funct_conn_it_fs_ctl_SYN,funct_conn_it_fr_ctl_SYN])
#
#func_conn_dms_fmri = np.array([funct_conn_it_v1_dms_BOLD,funct_conn_it_v4_dms_BOLD,
# funct_conn_it_d1_dms_BOLD,funct_conn_it_d2_dms_BOLD,
# funct_conn_it_fs_dms_BOLD,funct_conn_it_fr_dms_BOLD])
#func_conn_ctl_fmri = np.array([funct_conn_it_v1_ctl_BOLD,funct_conn_it_v4_ctl_BOLD,
# funct_conn_it_d1_ctl_BOLD,funct_conn_it_d2_ctl_BOLD,
# funct_conn_it_fs_ctl_BOLD,funct_conn_it_fr_ctl_BOLD])
#
# now, save all correlation coefficients to a output files
#np.save(func_conn_dms_file, func_conn_dms)
#np.save(func_conn_ctl_file, func_conn_ctl)
# now, group the values to be plotted by brain module
it_v1_corr_syn = (funct_conn_it_v1_dms_SYN, funct_conn_it_v1_ctl_SYN)
it_v4_corr_syn = (funct_conn_it_v4_dms_SYN, funct_conn_it_v4_ctl_SYN)
it_d1_corr_syn = (funct_conn_it_d1_dms_SYN, funct_conn_it_d1_ctl_SYN)
it_d2_corr_syn = (funct_conn_it_d2_dms_SYN, funct_conn_it_d2_ctl_SYN)
it_fs_corr_syn = (funct_conn_it_fs_dms_SYN, funct_conn_it_fs_ctl_SYN)
it_fr_corr_syn = (funct_conn_it_fr_dms_SYN, funct_conn_it_fr_ctl_SYN)
it_v1_corr_fmri = (funct_conn_it_v1_dms_BOLD, funct_conn_it_v1_ctl_BOLD)
it_v4_corr_fmri = (funct_conn_it_v4_dms_BOLD, funct_conn_it_v4_ctl_BOLD)
it_d1_corr_fmri = (funct_conn_it_d1_dms_BOLD, funct_conn_it_d1_ctl_BOLD)
it_d2_corr_fmri = (funct_conn_it_d2_dms_BOLD, funct_conn_it_d2_ctl_BOLD)
it_fs_corr_fmri = (funct_conn_it_fs_dms_BOLD, funct_conn_it_fs_ctl_BOLD)
it_fr_corr_fmri = (funct_conn_it_fr_dms_BOLD, funct_conn_it_fr_ctl_BOLD)
# define number of groups to plot
N = 2
# create a list of x locations for each group
index = np.arange(N)
width = 0.1 # width of the bars
fig, ax = plt.subplots()
ax.set_ylim([0,1])
rects_v1 = ax.bar(index, it_v1_corr_syn, width, color='purple', label='V1')
rects_v4 = ax.bar(index + width, it_v4_corr_syn, width, color='darkred', label='V4')
rects_fs = ax.bar(index + width*2, it_fs_corr_syn, width, color='lightyellow', label='FS')
rects_d1 = ax.bar(index + width*3, it_d1_corr_syn, width, color='lightblue', label='D1')
rects_d2 = ax.bar(index + width*4, it_d2_corr_syn, width, color='yellow', label='D2')
rects_fr = ax.bar(index + width*5, it_fr_corr_syn, width, color='red', label='FR')
ax.set_title('FUNCTIONAL CONNECTIVITY OF IT WITH OTHER BRAIN REGIONS (SYNAPTIC)')
# get rid of x axis ticks and labels
ax.set_xticks([])
ax.set_xlabel('DMS TASK CONTROL TASK')
ax.xaxis.set_label_coords(0.5, -0.025)
# Shrink current axis by 10% to make space for legend
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
# place a legend to the right of the figure
plt.legend(loc='center left', bbox_to_anchor=(1.02, .5))
# create a new figure
fig, ax = plt.subplots()
ax.set_ylim([0,1])
rects_v1 = ax.bar(index, it_v1_corr_fmri, width, color='purple', label='V1')
rects_v4 = ax.bar(index + width, it_v4_corr_fmri, width, color='darkred', label='V4')
rects_fs = ax.bar(index + width*2, it_fs_corr_fmri, width, color='lightyellow', label='FS')
rects_d1 = ax.bar(index + width*3, it_d1_corr_fmri, width, color='lightblue', label='D1')
rects_d2 = ax.bar(index + width*4, it_d2_corr_fmri, width, color='yellow', label='D2')
rects_fr = ax.bar(index + width*5, it_fr_corr_fmri, width, color='red', label='FR')
ax.set_title('FUNCTIONAL CONNECTIVITY OF IT WITH OTHER BRAIN REGIONS (fMRI)')
# get rid of x axis ticks and labels
ax.set_xticks([])
ax.set_xlabel('DMS TASK CONTROL TASK')
ax.xaxis.set_label_coords(0.5, -0.025)
# Shrink current axis by 10% to make space for legend
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
# place a legend to the right of the figure
plt.legend(loc='center left', bbox_to_anchor=(1.02, .5))
# Show the plots on the screen
plt.show()
