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generate_paper_figures_bioRxiv.m
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% generate_paper_figures_bioRxiv.m
% code to generate the figures and movies in the preprint version of the paper
%
% If you use part or all of this code in your research, please cite us as follows:
% J.C. Pang, L.L. Gollo, and J.A. Roberts, Stochastic synchronization of dynamics on the human connectome, bioRxiv (2020)
% (DOI: https://doi.org/10.1101/2020.02.09.940817)
%% load relevant files and simulation parameters
% IT IS IMPORTANT THAT YOU RUN THIS SECTION FIRST
load('data/initial_conditions/initial_theta.mat', 'initial_theta')
load('data/initial_conditions/initial_frequency_gaussian.mat', 'w_gaussian')
load('data/initial_conditions/initial_frequency_uniform.mat', 'w_uniform')
load('data/initial_conditions/initial_frequency_lorentzian.mat', 'w_lorentzian')
load data/normW.mat
load data/fiberdist.mat
load data/513AALstrings.mat
load data/networks/FuncNetworks.mat
loc = load('data/513COG.mat');
loc = loc.COG;
load('data/random_permutation_points.mat')
load('data/modules.mat')
param = utils.loadParameters; % using default parameters
network_indices = utils.extract_networks; % extract various subnetworks of connectivity matrix used
correct_hemisphere_indices = [network_indices.left, network_indices.right];
figure_folder_name = 'figures'; % folder directory for saved figures
if ~isfolder(figure_folder_name)
mkdir(figure_folder_name)
end
figure_issave = 0; % shall we save generated figure?
figure_isclose = 0; % shall we close generated figure?
%% FIGURE 1: WHOLE-BRAIN NETWORK MODEL AND DYNAMICS
figure_filename = 'Figure1';
fontsize_axis = 10;
fontsize_label = 12;
network = 'brain';
distribution = 'hierarchical_2';
param = utils.extract_network_parameters(param, network, distribution);
param.N = size(param.A, 1); % defining number of nodes parameter
s = sum(param.A, 2); % defining node_strengths
coupling_vec = [0.0001:0.0001:0.01, 0.01+0.001:0.001:0.02];
param.c = 0.0027; % defining coupling strength parameter
param.noise = 0; % defining noise strength parameter
t1 = 8500;
t2 = 9000;
% LOAD DATA
data_snapshots = load(sprintf('data/paper_simulations/%s_%s/sample_dynamics.mat', network, distribution));
data_tuning = load(sprintf('data/paper_simulations/%s_%s/TuningCurve.mat', network, distribution));
data_phase = load(sprintf('data/paper_simulations/%s_%s/Y_coupling=%.4f_noise=%.3f.mat', network, distribution, param.c, param.noise));
fig = figure('Position', [100 100 600 800]);
%%%%% PANEL A
ax1 = axes('Position', [0.1 0.69 0.3 0.35]);
imagesc(log10(param.A(Ci_sorted_ind,Ci_sorted_ind)))
hold on
for i=1:length(cum_nodes_per_community)
if i==1
pos = [0.5 0.5 nodes_per_community(i) nodes_per_community(i)];
else
pos = [0.5+cum_nodes_per_community(i-1) 0.5+cum_nodes_per_community(i-1) nodes_per_community(i) nodes_per_community(i)];
end
rectangle('Position', pos, 'EdgeColor','k', 'linewidth',2)
end
hold off
colormap(ax1, parula)
cbar = colorbar;
ylabel(cbar, '${\rm log_{10} (weight)}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
set(cbar, 'Position', [0.415, 0.75125, 0.02, 0.22625])
axis square
set(ax1,'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], ...
'ytick', 100:100:500, 'xtick', 100:100:500);
xlabel('${\rm region}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('${\rm region}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%%%% PANEL B
ax2_main = axes('Position', [0.63 0.755 0.25 0.19]);
plot(s, param.w, 'ko', 'markersize', 5)
set(ax2_main, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [1 17], 'ylim', [0 0.105], 'ytick', 0:0.02:0.1);
xlabel('connectivity strength, $s$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('frequency, $\omega^0$ (Hz)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ax2_top = axes('Position', [0.63 0.95 0.25 0.03]);
hist = histogram(s, 30, 'facecolor', [0.4 0.4 0.4]);
set(ax2_top, 'xlim', [1 17], 'xtick', [], 'ytick', []);
axis off
ax2_right = axes('Position', [0.888 0.755 0.05 0.19]);
hist = histogram(param.w, 30, 'facecolor', [0.4 0.4 0.4]);
set(ax2_right, 'ylim', [0 0.105], 'xtick', [], 'ytick', []);
set(hist, 'orientation', 'horizontal')
axis off
%%%%% PANEL C
time_interest = linspace(200,250,4);
time_ind = dsearchn(param.T', time_interest');
hold on;
for j=1:length(time_interest)
ax3 = axes('Position', [0.04+0.8*(j-1)/length(time_interest) 0.44 1/length(time_interest) 0.16]);
[ax3, ~] = utils.draw_scatterBrain(ax3, loc, data_snapshots.Y(:, time_ind(j)), 30, 'axial');
set(findall(ax3, 'type', 'text'), 'visible', 'on')
caxis([0 2*pi])
if j==length(time_interest)
cbar = colorbar(ax3, 'eastoutside');
ylabel(cbar, 'phase, $\theta$', 'fontsize', fontsize_label, 'interpreter', 'latex')
set(cbar, 'Position', [0.88, 0.44, 0.02, 0.16], 'ytick', [])
end
colormap(ax3, hsv)
end
hold off
%%%%% PANEL D
ax4 = axes('Position', [0.1 0.06 0.35 0.27]);
yyaxis(ax4, 'left')
plot(coupling_vec, mean(data_tuning.sync_global), 'color', 'k', 'linewidth', 2)
hold on;
plot([param.c param.c], [0 1], 'r--', 'linewidth', 2)
hold off;
set(ax4, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 1], ...
'xlim', [coupling_vec(1), coupling_vec(end)], 'xtick', [1e-4 3e-4 1e-3 3e-3 1e-2], ...
'xscale', 'log');
xlabel(ax4, 'coupling strength, $c$ (in log scale)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel(ax4, 'synchronization, $S$', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(param.c*0.9, 1.04, '$c^{*}$', 'fontsize', 16, 'interpreter', 'latex', 'color', 'r')
yyaxis(ax4, 'right')
plot(coupling_vec, mean(data_tuning.meta_global), 'color', 'b', 'linewidth', 2)
set(ax4, 'ticklength', [0.02, 0.02], 'ylim', [0, max(mean(data_tuning.meta_global))*1.1], ...
'xlim', [coupling_vec(1), coupling_vec(end)], 'xtick', [1e-4 3e-4 1e-3 3e-3 1e-2], ...
'xscale', 'log');
ylabel(ax4, 'metastability, $M$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ax4.YAxis(1).Color = 'k';
ax4.YAxis(2).Color = 'b';
%%%%% PANEL E
ax5 = axes('Position', [0.67 0.23 0.25 0.1]);
hold on;
plot(param.T, utils.calc_orderParameter(data_phase.Y), 'k-', 'linewidth', 2)
fill([t1 t2 t2 t1], [0 0 1 1], [1 1 1], 'EdgeColor','r', 'facecolor', 'none', 'linewidth', 2);
hold off;
set(ax5, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 1], 'xlim', [param.time_start, param.T(end)], 'xtick', linspace(param.time_start, param.T(end), 3));
xlabel('time, $t$ (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('coherence, $R$', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(5200, 0.8, 'at $c^{*}$', 'fontsize', 12, 'interpreter', 'latex', 'color', 'r')
%%%%% PANEL F
ax6 = axes('position', [0.67, 0.06, 0.22, 0.1]);
imagesc(param.T, 1:param.N, data_phase.Y(correct_hemisphere_indices,:));
colormap(ax6, hsv)
caxis([0, 2*pi])
set(ax6, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], ...
'xlim', [t1 t2], 'xtick', linspace(t1, t2, 3));
xlabel('time, $t$ (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('${\rm region}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
cbar = colorbar(ax6);
ylabel(cbar, 'phase, $\theta$', 'fontsize', fontsize_label-2, 'interpreter', 'latex')
set(cbar, 'fontsize', fontsize_axis-2, 'ticklength', 0.05, 'YTick', [0, pi, 2*pi], ...
'YTickLabel', {'$0$', '$\pi$', '$2\pi$'}, ...
'TickLabelInterpreter', 'latex', 'location', 'eastoutside', 'position', [0.9 0.06 0.015, 0.1])
%%% PANEL LETTERS
annotation(fig, 'textbox', [0.04, 1, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.56, 1, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.04, 0.64, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.04, 0.35, 0.01, 0.01], 'string', 'D', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.57, 0.35, 0.01, 0.01], 'string', 'E', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.57, 0.18, 0.01, 0.01], 'string', 'F', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%%% TEXTS AND ARROWS
annotation(fig, 'arrow', [0.29 0.36], [0.69 0.63], 'linewidth', 1, 'headwidth', 15)
annotation(fig, 'arrow', [0.70 0.63], [0.69 0.63], 'linewidth', 1, 'headwidth', 15)
annotation(fig, 'arrow', [0.08 0.85], [0.42 0.42], 'linewidth', 1, 'linestyle', '--', 'headstyle', 'plain')
annotation(fig, 'textbox', [0.37, 0.41, 0.2, 0.01], 'string', 'time', 'edgecolor', 'none', ...
'fontsize', 12, 'horizontalalignment', 'center', 'interpreter', 'latex')
annotation(fig, 'arrow', [0.865 0.865], [0.22 0.17], 'linewidth', 1, 'headwidth', 10, 'color', 'r')
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 2: NODE PHASES AND NETWORK COHERENCE FOR VARIOUS NOISE STRENGTHS
figure_filename = 'Figure2';
fontsize_axis = 12;
fontsize_label = 15;
network = 'brain';
distribution = 'hierarchical_2';
param = utils.extract_network_parameters(param, network, distribution);
param.N = size(param.A, 1); % defining number of nodes parameter
s = sum(param.A, 2); % defining node_strengths
param.c = 0.0027; % defining coupling strength parameter
noise_types = {'without'; 'intermediate'; 'high'};
time_interest = [6600, 7900, 9200];
time_interest_ind = dsearchn(param.T', time_interest');
% LOAD DATA
for noise_ind = 1:length(noise_types)
data_raw{noise_ind} = load(sprintf('data/paper_simulations/%s_%s/Y_noise=%s.mat', network, distribution, noise_types{noise_ind}));
end
colors = lines(3);
fig = figure('Position', [100, 100, 500, 500]);
ax1 = axes('Position', [0.11 0.12 0.75 0.45]);
hold on;
for noise_ind = 1:length(noise_types)
plot(param.T, utils.calc_orderParameter(data_raw{noise_ind}.Y), '-', 'color', colors(noise_ind,:), 'linewidth', 2)
end
for i = 1:length(time_interest)
plot([time_interest(i) time_interest(i)], [0 0.6], 'k:')
end
hold off;
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [param.time_start, param.T(end)], 'ylim', [0 0.6]);
xlabel('time, $t$ (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('coherence, $R$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ax1_right = axes('Position', [0.89 0.12 0.1 0.45]);
hold on;
for noise_ind = 1:length(noise_types)
R = utils.calc_orderParameter(data_raw{noise_ind}.Y(:,param.time_start_ind:end));
[f, Ri] = ksdensity(R, 'support', 'positive');
plot(f, Ri, 'color', colors(noise_ind,:), 'linewidth', 1.5)
plot([0 15], mean(R)*[1,1], ':', 'color', colors(noise_ind,:), 'linewidth', 2)
end
hold off;
set(ax1_right, 'xlim', get(ax1_right, 'xlim')+[-0.02 0.02], 'ylim', [0 0.6], 'xtick', [], 'ytick', []);
axis off
init_x = 0.285; init_y = 0.86; spacing_x = 0.194; spacing_y = 0.148;
for noise_ind = 1:length(noise_types)
for time_ind = 1:length(time_interest)
ax2 = axes('Position', [init_x+spacing_x*(time_ind-1), init_y-spacing_y*(noise_ind-1), 0.13, 0.13]);
[~, obj] = utils.draw_scatterBrain(ax2, loc, data_raw{noise_ind}.Y(:,time_interest_ind(time_ind)), 15, 'axial');
if noise_ind==2 && time_ind==length(time_interest)
caxis([0 2*pi])
cbar = colorbar(ax2);
ylabel(cbar, 'phase, $\theta$', 'fontsize', fontsize_label-2, 'interpreter', 'latex')
set(cbar, 'fontsize', fontsize_axis-2, 'ticklength', 0.05, 'YTick', [0, pi, 2*pi], ...
'YTickLabel', {'$0$', '$\pi$', '$2\pi$'}, ...
'TickLabelInterpreter', 'latex', 'position', [init_x+spacing_x*(time_ind-1)+0.14, init_y-spacing_y*(noise_ind-1)+0.005, 0.02, 0.12])
end
colormap(hsv)
end
end
%%% BOX
annotation('rectangle', [0.886 0.12 0.11 0.45], 'color', 'k', 'linewidth', 1.5)
%%% TEXTS
annotation('textbox', [0.11, init_y-spacing_y*(1-1)+0.03, 0.1, 0.1], 'string', {'without'; 'noise'}, 'edgecolor', 'none', ...
'fontsize', 15, 'fontweight', 'b', 'color', colors(1,:), 'horizontalalignment', 'center')
annotation('textbox', [0.11, init_y-spacing_y*(2-1)+0.03, 0.1, 0.1], 'string', {'intermediate'; 'noise'}, 'edgecolor', 'none', ...
'fontsize', 15, 'fontweight', 'b', 'color', colors(2,:), 'horizontalalignment', 'center')
annotation('textbox', [0.11, init_y-spacing_y*(3-1)+0.03, 0.1, 0.1], 'string', {'high'; 'noise'}, 'edgecolor', 'none', ...
'fontsize', 15, 'fontweight', 'b', 'color', colors(3,:), 'horizontalalignment', 'center')
annotation('textbox', [0.89 0.46 0.1 0.1], 'string', 'pdf of $R$', 'edgecolor', 'none', ...
'fontsize', 9, 'fontweight', 'b', 'color', 'k', 'horizontalalignment', 'center', ...
'interpreter', 'latex')
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 3: STOCHASTIC SYNCHRONIZATION
figure_filename = 'Figure3';
fontsize_axis = 12;
fontsize_label = 15;
network = 'brain';
distribution = 'hierarchical_2';
param = utils.extract_network_parameters(param, network, distribution);
param.c = 0.0027; % defining coupling strength parameter
noise_vec = 0:0.001:0.3;
% LOAD DATA
data_SR = load(sprintf('data/paper_simulations/%s_%s/statistics_varyingNoise.mat', network, distribution));
sync_mean = mean(data_SR.statistics.sync.global,2);
sync_std = std(data_SR.statistics.sync.global,0,2)/sqrt(param.noise_trials);
fig = figure('Position', [100 100 700 500]);
utils.shadedErrorBar(noise_vec, (sync_mean/sync_mean(1) - 1)*100, (sync_std/sync_mean(1))*100, 'lineprops', {'k', 'markerfacecolor','k', 'linewidth', 2});
hold on;
plot(linspace(0,0.06,100), zeros(1,100), 'k:', 'linewidth', 1)
hold off;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0 0.2]);
xlabel('noise strength, $\sigma$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('\% change in synchronization, $\Delta S$', 'fontsize', fontsize_label, 'interpreter', 'latex')
box off
axes('Position', [0.54 0.53 0.35 0.35])
cmap = lines(5);
hold on;
baseline = plot(linspace(0,0.06,100), zeros(1,100), 'k:', 'linewidth', 1);
for group=1:5
group_data = eval(sprintf('data_SR.statistics.sync.strength_%i', group));
group_data_mean = mean(group_data,2);
group_data_std = std(group_data,0,2)/sqrt(param.noise_trials);
plot(noise_vec, (group_data_mean/group_data_mean(1) - 1)*100, 'color', cmap(group,:), 'linewidth', 2);
end
hold off;
text(0.09, 18, {'connectivity'; 'strength'}, 'fontweight', 'b', 'fontsize', 10, 'rotation', 90, 'horizontalalignment', 'center')
set(get(get(baseline,'Annotation'),'LegendInformation'),'IconDisplayStyle','off');
leg = legend('HUBS', '', '', '', 'NONHUBS');
set(leg, 'box', 'off', 'fontsize', fontsize_axis-2, 'position', [0.73 0.77 0.19 0.1])
set(gca, 'fontsize', fontsize_axis-2, 'ticklength', [0.02, 0.02], 'xlim', [0 0.2], 'ylim', [-80 40]);
box off
xlabel('$\sigma$', 'fontsize', fontsize_label-2, 'interpreter', 'latex')
ylabel('$\Delta S$ (\%)', 'fontsize', fontsize_label-2, 'interpreter', 'latex')
annotation(fig, 'arrow', [0.73, 0.73], [0.74 0.90])
hold on;
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 4: ROLE OF DISTRIBUTION OF NATURAL FREQUENCIES ON SYNCHRONIZATION DYNAMICS
figure_filename = 'Figure4';
fontsize_axis = 10;
fontsize_label = 12;
network = 'brain';
distributions = {'hierarchical_2', 'constant', 'uniform', 'gaussian', 'lorentzian'};
distribution_labels = {'hierarchical', 'homogeneous', 'rand-uniform', 'rand-gaussian', 'rand-lorentzian'};
param.c = 0.0027; % defining coupling strength parameter
param.noise = 0; % defining noise strength parameter
colors = lines(length(distributions));
fig = figure('Position', [100 100 700 700]);
%%%%% PANEL A
init_x = 0.06; init_y = 0.84; diff_x = 0.16; diff_y = 0.14;
locs_x = [init_x, init_x+diff_x, init_x, init_x+diff_x, init_x+diff_x/2];
locs_y = [init_y, init_y, init_y-diff_y, init_y-diff_y, init_y-2*diff_y];
length_x = 0.15; length_y = 0.11;
for dist=1:length(distributions)
distribution = distributions{dist};
param = utils.extract_network_parameters(param, network, distribution);
if strcmpi(distribution, 'uniform')
param.w = w_uniform(:,1);
elseif strcmpi(distribution, 'gaussian')
param.w = w_gaussian(:,1);
elseif strcmpi(distribution, 'lorentzian')
param.w = w_lorentzian(:,1);
end
ax1 = axes('position', [locs_x(dist), locs_y(dist), length_x, length_y]);
histogram(param.w, 'facecolor', colors(dist,:), 'binwidth', 0.003,'normalization','probability')
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], ...
'xlim', [0.008 0.102], 'xtick', [0.01, 0.05, 0.1], ...
'yticklabel', {});
title(distribution_labels{dist}, 'fontsize', fontsize_label-3, 'fontweight', 'b')
if dist==5
xlabel('$\omega^0$ (Hz)', 'fontsize', fontsize_label, 'interpreter', 'latex')
else
set(ax1, 'xticklabel', {})
end
if dist==1 || dist==3 || dist==5
ylabel('${\rm pdf}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
end
end
%%%%%% PANEL B
ax2 = axes('position', [0.5, 0.56, 0.35, 0.39]);
hold on;
for dist=1:length(distributions)
distribution = distributions{dist};
data_phase = load(sprintf('data/paper_simulations/%s_%s/Y_coupling=%.4f_noise=%.3f.mat', network, distribution, param.c, param.noise));
data_coherence = utils.calc_orderParameter(data_phase.Y);
plot(param.T, data_coherence, 'color', colors(dist,:), 'linewidth', 2)
text(param.T(end)*1.01, data_coherence(end-500), distribution_labels{dist}, 'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(dist,:))
end
hold off
set(ax2, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 1], 'xlim', [param.time_start, param.T(end)]);
xlabel('time, $t$ (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('coherence, $R$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%%%%% PANEL C
ax3 = axes('position', [0.27, 0.06, 0.45, 0.37]);
hold on;
for dist=1:length(distributions)
distribution = distributions{dist};
param = utils.extract_network_parameters(param, network, distribution);
data_SR = load(sprintf('data/paper_simulations/%s_%s/statistics_varyingNoise.mat', network, distribution));
if strcmpi(distribution, 'hierarchical_2')
sync_mean = mean(data_SR.statistics.sync.global,2);
sync_std = std(data_SR.statistics.sync.global,0,2)/sqrt(param.noise_trials);
else
sync_mean = mean(data_SR.statistics.sync,2);
sync_std = std(data_SR.statistics.sync,0,2)/sqrt(param.noise_trials);
end
if strcmpi(distribution, 'hierarchical_2') || strcmpi(distribution, 'constant')
noise_vec = 0:0.001:0.3;
else
noise_vec = 0:0.001:0.2;
end
utils.shadedErrorBar(noise_vec, (sync_mean/sync_mean(1) - 1)*100, (sync_std/sync_mean(1))*100, ...
'lineprops', {'color', colors(dist,:), 'markerfacecolor','k', 'linewidth', 2});
if dist==length(distributions)
plot(noise_vec, zeros(size(noise_vec)), 'k:', 'linewidth', 1)
end
if dist==1 || dist==5
text(0.202, (sync_mean(end)/sync_mean(1) - 1)*100*1, distribution_labels{dist}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(dist,:));
end
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0 0.2], 'ylim', [-100, 20], ...
'xtick', 0:0.05:0.3);
xlabel('noise strength, $\sigma$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('$\Delta S$ (\%)', 'fontsize', fontsize_label, 'interpreter', 'latex')
end
%%% PANEL LETTERS
annotation(fig, 'textbox', [0.02, 0.99, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.44, 0.99, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.19, 0.46, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 5: ROLE OF NETWORK'S TOPOLOGY ON SYNCHRONIZATION DYNAMICS
figure_filename = 'Figure5';
fontsize_axis = 10;
fontsize_label = 12;
distribution = 'hierarchical_2';
networks = {'brain', 'full_connected', 'weight_50', 'weight_100', 'strength', 'strengthsequence'};
network_labels{1} = 'human connectome';
network_labels{2} = 'fully connected';
network_labels{3} = {'weight preserving'; '(50% randomized)'};
network_labels{4} = {'weight preserving'; '(100% randomized)'};
network_labels{5} = {'geometry & strength'; 'preserving'};
network_labels{6} = {'geometry & strength'; 'sequence preserving'};
topology_numbers = {'(0)', '(i)', '(ii)', '(iii)', '(iv)', '(v)'};
param.c = 0.0027; % defining coupling strength parameter
param.noise = 0; % defining noise strength parameter
colors = lines(length(networks));
fig = figure('Position', [100 100 700 700]);
%%%%% PANEL A
init_x = 0.08; init_y = 0.86; diff_x = 0.185; diff_y = 0.15;
locs_x = [init_x, init_x+diff_x, init_x, init_x+diff_x, init_x, init_x+diff_x];
locs_y = [init_y, init_y, init_y-diff_y, init_y-diff_y, init_y-2*diff_y, init_y-2*diff_y];
length_x = 0.12; length_y = 0.09;
for net=1:length(networks)
network = networks{net};
param = utils.extract_network_parameters(param, network, distribution);
mfile = matfile(sprintf('data/network_surrogates/%s.mat', network));
connectivity = mfile.(network)(:,:,1);
ax1 = axes('position', [locs_x(net), locs_y(net), length_x, length_y]);
semilogy(triu(fiberdist(chosen_points,chosen_points),1), triu(connectivity(chosen_points,chosen_points),1), '.', 'markersize', 0.001, 'markerfacecolor', colors(net,:), 'markeredgecolor', colors(net,:))
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0 300], 'ylim', [1e-7, 5e0], 'ytick', [1e-6, 1e-4, 1e-2, 1]);
title(network_labels{net}, 'fontsize', fontsize_label-3, 'fontweight', 'b')
if net==5 || net==6
xlabel('${\rm fiber\ length\ (mm)}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
else
set(ax1, 'xticklabel', {})
end
if net==3
ylabel('${\rm weight\ (in\ log\ scale)}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
elseif net==2 || net==4 || net==6
set(ax1, 'yticklabel', {})
end
if net~=1
text(285, 1e-6, ['topology ', topology_numbers{net}], 'fontsize', 7, 'color', 'k', 'horizontalalignment', 'right')
end
end
%%%%%% PANEL B
ax2 = axes('position', [0.5, 0.56, 0.32, 0.39]);
hold on;
for net=1:length(networks)
network = networks{net};
data_phase = load(sprintf('data/paper_simulations/%s_%s/Y_coupling=%.4f_noise=%.3f.mat', network, distribution, param.c, param.noise));
data_coherence = utils.calc_orderParameter(data_phase.Y);
plot(param.T, data_coherence, 'color', colors(net,:), 'linewidth', 2)
if net==1 || net==6
text(param.T(end)*1.01, data_coherence(end-500), network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:))
elseif net==2
text(param.T(end)*1.01, 0.83, network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:))
elseif net==3
text(param.T(end)*1.01, 0.53, network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:))
elseif net==4
text(param.T(end)*1.01, 0.76, network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:))
elseif net==5
text(param.T(end)*1.01, 0.64, network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:))
end
end
hold off
set(ax2, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 1], 'xlim', [param.time_start, param.T(end)]);
xlabel('time, $t$ (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('coherence, $R$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%%%%% PANEL C
ax3 = axes('position', [0.27, 0.06, 0.45, 0.37]);
hold on;
for net=1:length(networks)
network = networks{net};
param = utils.extract_network_parameters(param, network, distribution);
data_SR = load(sprintf('data/paper_simulations/%s_%s/statistics_varyingNoise.mat', network, distribution));
if strcmpi(network, 'brain')
sync_mean = mean(data_SR.statistics.sync.global,2);
sync_std = std(data_SR.statistics.sync.global,0,2)/sqrt(param.noise_trials);
noise_vec = 0:0.001:0.3;
else
sync_mean = mean(data_SR.statistics.sync,2);
sync_std = std(data_SR.statistics.sync,0,2)/sqrt(param.noise_trials);
noise_vec = 0:0.001:0.2;
end
utils.shadedErrorBar(noise_vec, (sync_mean/sync_mean(1) - 1)*100, (sync_std/sync_mean(1))*100, ...
'lineprops', {'color', colors(net,:), 'markerfacecolor','k', 'linewidth', 2});
if net==length(networks)
plot(noise_vec, zeros(size(noise_vec)), 'k:', 'linewidth', 1)
end
if net==1
text(0.202, (sync_mean(end)/sync_mean(1) - 1)*100, network_labels{net}, ...
'fontsize', fontsize_label-3, 'fontweight', 'b', 'color', colors(net,:));
end
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [-100, 20], 'xlim', [0 0.2], ...
'xtick', 0:0.05:0.3);
xlabel('noise strength, $\sigma$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('$\Delta S$ (\%)', 'fontsize', fontsize_label, 'interpreter', 'latex')
end
%%% PANEL LETTERS
annotation(fig, 'textbox', [0.02, 0.99, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.44, 0.99, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.19, 0.46, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 6: SYNCHRONIZATION AND PHASE-CLUSTERING STATISTICS
figure_filename = 'Figure6';
fontsize_axis = 10;
fontsize_label = 12;
network = 'brain';
distribution = 'hierarchical_2';
param = utils.extract_network_parameters(param, network, distribution);
data_type = {'sync', 'ave_cluster', 'ave_largest_cluster'};
cb = cbrewer('qual', 'Set3', 12, 'pchip');
color_ind = [1, 4, 7];
colors = [cb(color_ind,:); 0.5 0.5 0.5; 0.5 0.5 0.5];
xticklabels = {'without noise', 'intermediate noise', 'high noise', '{\bf uncoupled}', '{\bf random}'};
sig_combinations = [1,2; 2,3; 1,3; 3,4; 3,5;];
% LOAD DATA
data_coupled = load(sprintf('data/paper_simulations/%s_%s/ClusterData_Coupled.mat', network, distribution));
data_uncoupled = load(sprintf('data/paper_simulations/%s_%s/ClusterData_Uncoupled.mat', network, distribution));
data_random = load(sprintf('data/paper_simulations/%s_%s/ClusterData_Random.mat', network, distribution));
for data_type_ind = 1:length(data_type)
data_pvals{data_type_ind} = load(sprintf('data/paper_simulations/%s_%s/ClusterData_Coupled_pvals_%s.mat', network, distribution, data_type{data_type_ind}));
end
fig = figure('Position', [100 100 650 600]);
init_x = 0.06; init_y = 0.7; diff_x = 0.52; diff_y = 0.29;
length_x = 0.37; length_y = 0.26; diff_x_cutpanel = length_x*1.12;
%%%%% PANEL A
ax1 = axes('Position', [init_x 0.63 0.32 0.32]);
polarplot([0 deg2rad(45)], [0 1], 'k', 'linewidth', 2);
hold on;
polarscatter(deg2rad(45), 1, 200, 'filled', 'markerfacecolor', 'y', 'markeredgecolor', 'k');
hold off;
set(gca, 'fontsize', fontsize_axis, 'rtick', 1, 'rticklabel', {}, 'rlim', [0 1], ...
'thetatick', 0:90:360, 'thetaticklabel', {}, 'thetaaxisunits', 'radians', ...
'gridalpha', 1, ...
'TickLabelInterpreter', 'latex', 'ticklength', [0.02, 0.02])
text(0.43, 0.25, '$\theta_j$', 'fontsize', 12, 'fontweight', 'b', 'color', 'k', 'interpreter', 'latex')
text(cosd(45)*1.5, sind(45)*1.55, '$(x_j, y_j)$', 'fontsize', 12, 'fontweight', 'b', 'color', 'k', 'interpreter', 'latex')
%%%%% PANEL B
ax2 = axes('Position', [init_x 0.18 0.32 0.32]);
groups = 7;
cluster_cartoon{1} = linspace(-10,12,5); cluster_cartoon{2} = linspace(30,50,6);
cluster_cartoon{3} = linspace(80,120,8); cluster_cartoon{4} = linspace(150,200,12);
cluster_cartoon{5} = linspace(240,280,8); cluster_cartoon{6} = linspace(300,320,6);
cluster_cartoon{7} = [65, 135, 220, 335];
cluster_colors = hsv(6);
polarscatter(deg2rad(cluster_cartoon{7}), ones(1,length(cluster_cartoon{7})), 200, 'filled', 'markerfacecolor', 'w', ...
'markeredgecolor', 'k');
hold on;
for j = 1:groups-1
polarscatter(deg2rad(cluster_cartoon{j}), ones(1,length(cluster_cartoon{j})), 200, 'filled', 'markerfacecolor', cluster_colors(j,:), ...
'markeredgecolor', 'k');
end
hold off
set(gca, 'fontsize', fontsize_axis, 'rtick', 1, 'rticklabel', {}, 'rlim', [0 1], ...
'thetatick', 0:90:360, 'thetaticklabel', {}, 'thetaaxisunits', 'radians', ...
'gridalpha', 1, ...
'TickLabelInterpreter', 'latex', 'ticklength', [0.02, 0.02])
%%%%% PANEL C
data_sync = {squeeze(data_coupled.(data_type{1})(:,:,1)); ...
squeeze(data_coupled.(data_type{1})(:,:,2)); ...
squeeze(data_coupled.(data_type{1})(:,:,3)); ...
data_uncoupled.(data_type{1}); ...
data_random.(data_type{1})};
ax3 = axes('Position', [init_x+diff_x init_y length_x length_y]);
siglines_init_x = 0.28;
h = rm_raincloud(data_sync, colors(1,:), 0, 'ks', 0.01, 4.5);
for i=1:size(data_sync,1)-1
h.l(i).LineStyle = 'none';
end
for i=1:size(data_sync,1)
color = colors(i,:);
h.p{i}.FaceColor = color;
h.p{i}.Visible = 'off';
h.s{i}.MarkerFaceColor = color;
h.m(i).MarkerFaceColor = color;
h.s{i}.SizeData = 30;
h.m(i).SizeData = 30;
if i==size(data_sync,1) || i==size(data_sync,1)-1
h.s{i}.Marker = 'd';
h.m(i).Marker = 'd';
end
end
hold on;
for i=1:size(sig_combinations,1)
pval = data_pvals{1}.p_mat_corrected(sig_combinations(i,1), sig_combinations(i,2));
[sig_symbol, sig_text] = utils.get_sig_star(pval);
siglines_loc_x = siglines_init_x + siglines_init_x*0.12*(i-1);
siglines_loc_y = [h.m(sig_combinations(i,1)).YData, h.m(sig_combinations(i,2)).YData];
plot([siglines_loc_x*0.98, siglines_loc_x, siglines_loc_x, siglines_loc_x*0.98], ...
[siglines_loc_y(1), siglines_loc_y(1), siglines_loc_y(2), siglines_loc_y(2)], 'k-', 'linewidth', 1)
if strcmpi(sig_text, 'ns')
text(siglines_loc_x*1.025, mean(siglines_loc_y), sig_text, 'fontweight', 'b', ...
'fontsize', 8, 'horizontalalignment', 'center')
else
text(siglines_loc_x*1.02, mean(siglines_loc_y), sig_symbol, 'fontweight', 'b', ...
'fontsize', 10, 'horizontalalignment', 'center')
end
end
for i=1:size(data_sync,1)
plot([h.m(i).XData-std(h.s{i}.XData),h.m(i).XData+std(h.s{i}.XData)], ...
[h.m(i).YData, h.m(i).YData], 'k-', 'linewidth', 2)
end
hold off;
child = get(ax3,'Children');
set(ax3, 'Children', [child(size(data_sync,1)+1:end); child(1:size(data_sync,1))], ...
'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0, 0.44], 'yticklabel', [], ...
'ylim', get(ax3, 'ylim').*[0.4, 1.151]);
xlabel('synchronization, $S$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%%%% PANEL D
data_clusters = {squeeze(data_coupled.(data_type{2})(:,:,1)); ...
squeeze(data_coupled.(data_type{2})(:,:,2)); ...
squeeze(data_coupled.(data_type{2})(:,:,3)); ...
data_uncoupled.(data_type{2}); ...
data_random.(data_type{2})};
ax4 = axes('Position', [init_x+diff_x init_y-diff_y length_x length_y]);
siglines_init_x = 20;
h = rm_raincloud(data_clusters, colors(1,:), 0, 'ks', 0.406, 4.5);
for i=1:size(data_clusters,1)-1
h.l(i).LineStyle = 'none';
end
for i=1:size(data_clusters,1)
color = colors(i,:);
h.p{i}.FaceColor = color;
h.p{i}.Visible = 'off';
h.s{i}.MarkerFaceColor = color;
h.m(i).MarkerFaceColor = color;
h.s{i}.SizeData = 30;
h.m(i).SizeData = 30;
if i==size(data_sync,1) || i==size(data_sync,1)-1
h.s{i}.Marker = 'd';
h.m(i).Marker = 'd';
end
end
hold on;
for i=1:size(sig_combinations,1)
pval = data_pvals{2}.p_mat_corrected(sig_combinations(i,1), sig_combinations(i,2));
[sig_symbol, sig_text] = utils.get_sig_star(pval);
siglines_loc_x = siglines_init_x + siglines_init_x*0.05*(i-1);
siglines_loc_y = [h.m(sig_combinations(i,1)).YData, h.m(sig_combinations(i,2)).YData];
plot([siglines_loc_x*0.99, siglines_loc_x, siglines_loc_x, siglines_loc_x*0.99], ...
[siglines_loc_y(1), siglines_loc_y(1), siglines_loc_y(2), siglines_loc_y(2)], 'k-', 'linewidth', 1)
if strcmpi(sig_text, 'ns')
text(siglines_loc_x*1.025, mean(siglines_loc_y), sig_text, 'fontweight', 'b', ...
'fontsize', 8, 'horizontalalignment', 'center')
else
text(siglines_loc_x*1.015, mean(siglines_loc_y), sig_symbol, 'fontweight', 'b', ...
'fontsize', 10, 'horizontalalignment', 'center')
end
end
for i=1:size(data_clusters,1)
plot([h.m(i).XData-std(h.s{i}.XData),h.m(i).XData+std(h.s{i}.XData)], ...
[h.m(i).YData, h.m(i).YData], 'k-', 'linewidth', 2)
end
hold off;
child = get(ax4,'Children');
set(ax4, 'Children', [child(size(data_clusters,1)+1:end); child(1:size(data_clusters,1))], ...
'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [12, 25], 'yticklabel', [], ...
'ylim', get(ax4, 'ylim').*[0.4, 0.9]);
xlabel('${\rm number\ of\ clusters}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%%%% PANEL E
data_largest_cluster = {squeeze(data_coupled.(data_type{3})(:,:,1)); ...
squeeze(data_coupled.(data_type{3})(:,:,2)); ...
squeeze(data_coupled.(data_type{3})(:,:,3)); ...
data_uncoupled.(data_type{3}); ...
data_random.(data_type{3})};
ax5 = axes('Position', [init_x+diff_x init_y-2*diff_y length_x length_y]);
siglines_init_x = 108;
h = rm_raincloud(data_largest_cluster, colors(1,:), 0, 'ks', 3.95, 4.5);
for i=1:size(data_largest_cluster,1)-1
h.l(i).LineStyle = 'none';
end
for i=1:size(data_largest_cluster,1)
color = colors(i,:);
h.p{i}.FaceColor = color;
h.p{i}.Visible = 'off';
h.s{i}.MarkerFaceColor = color;
h.m(i).MarkerFaceColor = color;
h.s{i}.SizeData = 30;
h.m(i).SizeData = 30;
if i==size(data_sync,1) || i==size(data_sync,1)-1
h.s{i}.Marker = 'd';
h.m(i).Marker = 'd';
end
end
hold on;
for i=1:size(sig_combinations,1)
pval = data_pvals{3}.p_mat_corrected(sig_combinations(i,1), sig_combinations(i,2));
[sig_symbol, sig_text] = utils.get_sig_star(pval);
siglines_loc_x = siglines_init_x + siglines_init_x*0.08*(i-1);
siglines_loc_y = [h.m(sig_combinations(i,1)).YData, h.m(sig_combinations(i,2)).YData];
plot([siglines_loc_x*0.98, siglines_loc_x, siglines_loc_x, siglines_loc_x*0.98], ...
[siglines_loc_y(1), siglines_loc_y(1), siglines_loc_y(2), siglines_loc_y(2)], 'k-', 'linewidth', 1)
if strcmpi(sig_text, 'ns')
text(siglines_loc_x*1.04, mean(siglines_loc_y), sig_text, 'fontweight', 'b', ...
'fontsize', 8, 'horizontalalignment', 'center')
else
text(siglines_loc_x*1.015, mean(siglines_loc_y), sig_symbol, 'fontweight', 'b', ...
'fontsize', 10, 'horizontalalignment', 'center')
end
end
for i=1:size(data_largest_cluster,1)
plot([h.m(i).XData-std(h.s{i}.XData),h.m(i).XData+std(h.s{i}.XData)], ...
[h.m(i).YData, h.m(i).YData], 'k-', 'linewidth', 2)
end
hold off;
child = get(ax5,'Children');
set(ax5, 'Children', [child(size(data_largest_cluster,1)+1:end); child(1:size(data_largest_cluster,1))], ...
'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [30, 150], 'yticklabel', ...
fliplr({'without noise', 'intermediate noise', 'high noise', '{\bf uncoupled}', '{\bf random}'}), 'YTickLabelRotation', 25, ...
'ylim', get(ax5, 'ylim').*[0.4, 0.9]);
xlabel('${\rm size\ of\ largest\ cluster}$', 'fontsize', fontsize_label, 'interpreter', 'latex')
%%% PANEL LETTERS
annotation(fig, 'textbox', [0.05, 0.98, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.05, 0.55, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.47, 0.98, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.47, 0.70, 0.01, 0.01], 'string', 'D', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.47, 0.41, 0.01, 0.01], 'string', 'E', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%%% SAVING FIGURE
if figure_issave
set(fig, 'PaperPositionMode','auto')
print(fig, '-painters', '-depsc', sprintf('%s/%s.eps', figure_folder_name, figure_filename))
print(fig, '-dpng', '-r600', sprintf('%s/%s.png', figure_folder_name, figure_filename))
end
if figure_isclose
close all
clear fig
end
%% FIGURE 7: FUNCTIONAL CONNECTIVITY OF SUBNETWORKS
figure_filename = 'Figure7';
fontsize_axis = 10;
fontsize_axis_small = 8;
fontsize_label = 12;
markersize = 3;
linewidth = 1.5;
network = 'brain';
distribution = 'hierarchical_2';
param = utils.extract_network_parameters(param, network, distribution);
network_type = 'funcnets';
combinations.positive = [5,2; 11,3];
combinations.negative = [6,4; 10,6];
combinations.neutral = [7,7; 8,1];
noise_names = {'without noise'; 'intermediate noise'; 'high noise'};
% LOAD DATA
data_FC = load(sprintf('data/paper_simulations/%s_%s/functional_connectivities.mat', network, distribution));
limits = [floor(min(data_FC.FC_mean.(network_type)(:))*10)/10, ...
ceil(max(data_FC.FC_mean.(network_type)(:))*10)/10];
limits_errorplot = [-0.05, 0.10];
labels = data_FC.labels.(network_type);
init_x = 0.07; init_y = 0.74; diff_x = 0.08; top_diff_y = 0.05; bottom_diff_y = 0.03;
top_length_x = 0.25; top_length_y = 0.25;
mid_length_x = 0.25*1.35; mid_length_y = 0.25*1.35;
bottom_length_x = 0.22; bottom_length_y = 0.07;
fig = figure('Position', [100 100 700 800]);
%%%%% PANELS A, B, C
for noise_ind = 1:3
ax1 = axes('position', [init_x+(top_length_x+diff_x)*(noise_ind-1), init_y, top_length_x, top_length_y]);
[ax1, ~, cbar1] = utils.draw_tricorrmat(ax1, data_FC.FC_mean.(network_type)(:,:,noise_ind), labels, fontsize_axis_small, fontsize_axis_small);
caxis(limits)
cmap = utils.bluewhitered;
colormap(ax1, cmap)
set(cbar1, 'ticks', [limits(1), 0, limits(2)])
cbar_pos = get(cbar1, 'position');
annotation('textbox', [cbar_pos(1)+cbar_pos(3)/2, cbar_pos(2)*0.99, 0.01, 0.01], 'string', '$FC$', 'edgecolor', 'none', ...
'fontsize', fontsize_axis_small, 'horizontalalignment', 'center', 'interpreter', 'latex')
title(noise_names{noise_ind}, 'fontsize', fontsize_axis, ...
'fontweight', 'bold', 'horizontalalignment', 'center', 'color', 'k')
end
%%%%% PANEL D
bubble_sizes = [2 11];
ax2 = axes('position', [init_x, init_y-0.02-(mid_length_y+top_diff_y), mid_length_x, mid_length_y]);
[ax2, ~, ~] = utils.draw_tricorrmat(ax2, data_FC.FC_mean.(network_type)(:,:,2)-data_FC.FC_mean.(network_type)(:,:,1), labels, fontsize_axis_small, fontsize_axis_small);
cmap = utils.bluewhitered;
[~, ~, cbar2] = utils.draw_tricorrmat_withbubbles(ax2, data_FC.FC_mean.(network_type)(:,:,2)-data_FC.FC_mean.(network_type)(:,:,1), labels, cmap, bubble_sizes, fontsize_axis_small, fontsize_axis_small);
hold on;
for i = 1:size(combinations.positive,1)
rectangle('Position', [combinations.positive(i,2)-0.5, combinations.positive(i,1)-0.5, 1, 1], ...
'EdgeColor', 'k', 'linewidth', 2.5)
rectangle('Position', [combinations.negative(i,2)-0.5, combinations.negative(i,1)-0.5, 1, 1], ...
'EdgeColor', 'k', 'linewidth', 2.5)
rectangle('Position', [combinations.neutral(i,2)-0.5, combinations.neutral(i,1)-0.5, 1, 1], ...
'EdgeColor', 'k', 'linewidth', 2.5)
end
hold off;
cbar_pos = get(cbar2, 'position');
annotation('textbox', [cbar_pos(1)+cbar_pos(3)/2, cbar_pos(2)*0.98, 0.01, 0.01], 'string', '$\Delta FC$', 'edgecolor', 'none', ...
'fontsize', fontsize_axis_small, 'horizontalalignment', 'center', 'interpreter', 'latex')
annotation('textbox', [cbar_pos(1)+cbar_pos(3)/2, cbar_pos(2)*0.98, 0.01, 0.01], 'string', '$F$', 'edgecolor', 'none', ...
'fontsize', fontsize_axis_small, 'horizontalalignment', 'center', 'interpreter', 'latex')
%%%%% PANEL E TOP
for i = 1:size(combinations.positive,1)
ax3 = axes('position', [init_x+0.04+mid_length_x+diff_x+(bottom_length_x+0.03)*(i-1), init_y-0.05-(bottom_length_y+top_diff_y), bottom_length_x, bottom_length_y]);
hold on;
errorbar(1:3, squeeze(data_FC.FC_mean.global), squeeze(data_FC.FC_std.global), ...
'o--', 'color', 'm', 'markerfacecolor', 'm', 'markersize', markersize, 'linewidth', 1)
errorbar(1:3, squeeze(data_FC.FC_mean.(network_type)(combinations.positive(i,1),combinations.positive(i,2),:)), ...
squeeze(data_FC.FC_std.(network_type)(combinations.positive(i,1),combinations.positive(i,2),:)), ...
'o-', 'color', 'k', 'markerfacecolor', 'k', 'markersize', markersize, 'linewidth', linewidth)
hold off;
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xtick', 1:3, 'xticklabel', {}, ...
'xlim', [0.8, 3.2], 'ylim', limits_errorplot);
if i==1
ylabel('$FC$', 'fontsize', fontsize_label, 'interpreter', 'latex')
leg = legend('global', ['subnetwork_1', char(8212), 'subnetwork_2']);
set(leg, 'box', 'off', 'fontsize', fontsize_axis-2, 'NumColumns', 2, 'position', [0.8 0.68 0.01 0.01])
else
set(ax3, 'yticklabel', {})
end
title([data_FC.labels.(network_type){combinations.positive(i,1)}, char(8212), data_FC.labels.(network_type){combinations.positive(i,2)}], ...
'fontsize', 7, 'color', 'k');
end
%%%%% PANEL E MIDDLE
for i = 1:size(combinations.negative,1)
ax4 = axes('position', [init_x+0.04+mid_length_x+diff_x+(bottom_length_x+0.03)*(i-1), init_y-0.05-(bottom_length_y+top_diff_y)*1.8, bottom_length_x, bottom_length_y]);
hold on;
errorbar(1:3, squeeze(data_FC.FC_mean.global), squeeze(data_FC.FC_std.global), ...
'o--', 'color', 'm', 'markerfacecolor', 'm', 'markersize', markersize, 'linewidth', 1)
errorbar(1:3, squeeze(data_FC.FC_mean.(network_type)(combinations.negative(i,1),combinations.negative(i,2),:)), ...
squeeze(data_FC.FC_std.(network_type)(combinations.negative(i,1),combinations.negative(i,2),:)), ...
'o-', 'color', 'k', 'markerfacecolor', 'k', 'markersize', markersize, 'linewidth', linewidth)
hold off;
set(ax4, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xtick', 1:3, 'xticklabel', {}, ...
'xlim', [0.8, 3.2], 'ylim', limits_errorplot);
if i==1
ylabel('$FC$', 'fontsize', fontsize_label, 'interpreter', 'latex')
else
set(ax4, 'yticklabel', {})
end
title([data_FC.labels.(network_type){combinations.negative(i,1)}, char(8212), data_FC.labels.(network_type){combinations.negative(i,2)}], ...
'fontsize', 7, 'color', 'k');
end
%%%%% PANEL E BOTTOM
for i = 1:size(combinations.neutral,1)
ax5 = axes('position', [init_x+0.04+mid_length_x+diff_x+(bottom_length_x+0.03)*(i-1), init_y-0.05-(bottom_length_y+top_diff_y)*2.6, bottom_length_x, bottom_length_y]);
hold on;
errorbar(1:3, squeeze(data_FC.FC_mean.global), squeeze(data_FC.FC_std.global), ...
'o--', 'color', 'm', 'markerfacecolor', 'm', 'markersize', markersize, 'linewidth', 1)
errorbar(1:3, squeeze(data_FC.FC_mean.(network_type)(combinations.neutral(i,1),combinations.neutral(i,2),:)), ...
squeeze(data_FC.FC_std.(network_type)(combinations.neutral(i,1),combinations.neutral(i,2),:)), ...
'o-', 'color', 'k', 'markerfacecolor', 'k', 'markersize', markersize, 'linewidth', linewidth)
hold off;
set(ax5, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xtick', 1:3, 'xticklabel', {}, ...
'xlim', [0.8, 3.2], 'ylim', limits_errorplot, ...
'xticklabel', {'without \newline noise', 'intermediate \newline noise', ' high \newlinenoise'});
if i==1
ylabel('$FC$', 'fontsize', fontsize_label, 'interpreter', 'latex')
else
set(ax5, 'yticklabel', {})
end
title([data_FC.labels.(network_type){combinations.neutral(i,1)}, char(8212), data_FC.labels.(network_type){combinations.neutral(i,2)}], ...
'fontsize', 7, 'color', 'k');
end
%%% PANEL LETTERS
annotation('textbox', [0.02, 1, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation('textbox', [0.35, 1, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')