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generate_paper_figures.m
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% generate_paper_figures.m
%
% Matlab code to generate the main figures of the paper
%
% Note that the configuration of your computer (e.g., screen resolution)
% affects how the figures created will look. Hence, they will not 100%
% visually match the figures in the paper, but the scientific contents
% are replicated.
%% LOAD CONNECTOME MATRICES, REGION LOCATIONS/NAMES, CORTICAL SURFACES, PARCELLATION, MULTIPLE COMPARISONS P-VALUE
load('data/connectome_human.mat')
load('data/connectome_chimp.mat')
load('data/connectome_macaque.mat')
load('data/connectome_marmoset.mat')
load('data/connectome_human_specific.mat')
load('data/connectome_chimp_specific.mat')
load('data/connectome_human_chimp_shared.mat')
load('data/region_props_chimp.mat', 'region_centroids', 'region_names')
load('data/RSN.mat', 'RSN_indices', 'RSN_names')
load('data/macaque_networks.mat', 'networks_indices', 'networks_names')
load('data/data_figures/multiple_comparisons.mat', 'multiple_comparisons', 'pvals_adj')
load('data/data_figures/bct_stats.mat', 'bct_stats')
human_surface = @(wb_hemi, surface_interest) sprintf('data/S1200.%s.%s_MSMAll.32k_fs_LR.surf.gii', wb_hemi, surface_interest);
chimp_surface = @(wb_hemi, surface_interest) sprintf('data/ChimpYerkes29_v1.2.%s.%s.32k_fs_LR.surf.gii', wb_hemi, surface_interest);
surface_interest = 'very_inflated';
surface_file{1}.lh = human_surface('L', surface_interest);
surface_file{1}.rh = human_surface('R', surface_interest);
surface_file{2}.lh = chimp_surface('L', surface_interest);
surface_file{2}.rh = chimp_surface('R', surface_interest);
parc_filename = @(hemisphere) sprintf('data/fsLR_32k_DK114-%s.txt', hemisphere);
%% DEFINE FIGURE-RELATED PROPERTIES
Yeo_7net_colormap = [120, 18, 134;
70, 130, 180;
0, 118, 14;
196, 68, 250;
220, 248, 164;
230, 148, 34;
205, 62, 78;
200, 200, 200]/255;
Yeo_names = {'VIS', 'SM', 'DA', 'VA', 'LIM', 'FP', 'DM'};
macaque_network_names = {'FRONT', 'TEMP', 'PAR', 'OCC', 'LIM'};
cb = cbrewer('qual', 'Set1', 8, 'pchip');
color_brown = cb(7,:);
color_green = cb(3,:);
color_blue = cb(2,:);
color_purple = cb(4,:);
color_orange = cb(5,:);
cb = cbrewer('qual', 'Accent', 8, 'pchip');
color_brown_light = cb(7,:);
cb = cbrewer('qual', 'Dark2', 8, 'pchip');
color_green_light = cb(1,:);
color_brown_lighter = cb(7,:);
cb = cbrewer('qual', 'Set2', 8, 'pchip');
color_green_lighter = cb(1,:);
% artworks
human_female = imread('artworks/human_female_2.png');
human_male = imread('artworks/human_male_2.png');
chimpanzee = imread('artworks/chimpanzee.png');
macaque = imread('artworks/macaque.png');
marmoset = imread('artworks/marmoset.png');
fontsize_axis = 10;
fontsize_label = 12;
show_padj = 0;
data_foldername = 'data/data_figures';
%% FIGURE 1
types = {'human', 'chimp'};
titles = {'human', 'chimpanzee'};
cmap = [color_brown; color_green];
parc_lh = dlmread(parc_filename('lh'));
parcels_lh = unique(parc_lh(parc_lh>0));
num_parcels_lh = length(parcels_lh);
parc_rh = dlmread(parc_filename('rh'));
parcels_rh = unique(parc_rh(parc_rh>0));
num_parcels_rh = length(parcels_rh);
N = 114;
normalization = 'maximum';
slice = 'axial';
markersize = 80;
node_cmap = [1, 1, 1];
node_cval = ones(N,1);
nodeLocations = region_centroids;
nodeSizes = (markersize/10)*ones(N,1);
edge_width = 1;
pvals_adj_ind = find(strcmpi(multiple_comparisons(:,1), 'Figure1'));
image_width = 0.04;
fig = figure('Position', [200 200 800 800]);
ax0 = axes('Position', [0.05 0.71 0.45 0.3]);
ax0_position = ax0.Position;
set(ax0,'visible','off')
% =========================================================================
% A-B: parcellation and connectome matrices
% =========================================================================
length_x = ax0_position(3);
length_y = ax0_position(4);
for type_ind=1:length(types)
type = types{type_ind};
if type_ind==1
image_to_plot = human_female;
elseif type_ind==2
image_to_plot = chimpanzee;
end
bw = image_to_plot>0;
W = utils.norm_matrix(eval(sprintf('connectome_%s', type)));
surface_to_plot_lh = gifti(surface_file{type_ind}.lh);
surface_to_plot_rh = gifti(surface_file{type_ind}.rh);
boundary_method = 'midpoint';
BOUNDARY_lh = findROIboundaries(surface_to_plot_lh.vertices,double(surface_to_plot_lh.faces),parc_lh,boundary_method);
BOUNDARY_rh = findROIboundaries(surface_to_plot_rh.vertices,double(surface_to_plot_rh.faces),parc_rh,boundary_method);
%%% parcellation (left lateral view)
parcellation_cmap = cbrewer('qual', 'Set1', num_parcels_lh+1*round(num_parcels_lh/15) , 'pchip');
parcellation_cmap = [0.5 0.5 0.5; parcellation_cmap((1:num_parcels_lh),:)];
clims = [0 max(parc_lh)];
ax1_1 = axes('Position', [ax0_position(1)+(ax0_position(3)+0.05)*(type_ind-1) ax0_position(2)+length_y*0.38 length_x*0.38 length_y*0.38]);
patch(ax1_1, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', parc_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{ii}(:,1), BOUNDARY_lh{ii}(:,2), BOUNDARY_lh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([-90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax1_1, parcellation_cmap)
axis off
axis image
%%% parcellation (left medial view)
parcellation_cmap = cbrewer('qual', 'Paired', num_parcels_lh+1*round(num_parcels_lh/15) , 'pchip');
parcellation_cmap = [0.5 0.5 0.5; parcellation_cmap((1:num_parcels_lh),:)];
clims = [0 max(parc_lh)];
ax1_2 = axes('Position', [ax0_position(1)+(ax0_position(3)+0.05)*(type_ind-1) ax0_position(2)*0.99 ax1_1.Position(3) ax1_1.Position(4)]);
patch(ax1_2, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', parc_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{ii}(:,1), BOUNDARY_lh{ii}(:,2), BOUNDARY_lh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax1_2, parcellation_cmap)
axis off
axis image
%%% connectome matrix
ax1_3 = axes('Position', [ax0_position(1)+length_x*0.4+(ax0_position(3)+0.05)*(type_ind-1) ax0_position(2)*0.95 length_x*0.55 length_y]);
imagesc(log10(W))
colormap(ax1_3, flipud(hot))
hold on;
xline(57.5, 'k-', 'linewidth', 1);
yline(57.5, 'k-', 'linewidth', 1);
hold off;
set(ax1_3, 'fontsize', fontsize_axis, 'fontweight', 'b', 'xtick', [29, 29+57], 'xticklabel', {'LH', 'RH'}, 'ytick', [])
% cbar = colorbar;
% ylabel(cbar, '$\log_{10}$(weight)', 'fontsize', fontsize_label, 'interpreter', 'latex')
% set(cbar, 'fontsize', fontsize_axis, 'ticklength', 0.01)
% axis off
axis image
ax1_image = axes('Position', [ax1_1.Position(1)-0.01-0.01*(type_ind-1) ax1_1.Position(2)+ax1_1.Position(4)*0.85 image_width image_width]);
imshow(bw);
colormap(ax1_image, flipud(gray))
annotation('textbox', [ax1_1.Position(1)-0.03, ax1_1.Position(2)-0.055, 0.1, 0.1], 'string', 'LH', ...
'fontsize', fontsize_axis, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
annotation('textbox', [ax1_3.Position(1)-0.02, ax1_3.Position(2)+ax1_3.Position(4)*0.82, 0.1, 0.1], 'string', titles{type_ind}, ...
'fontsize', fontsize_label, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
end
% =========================================================================
% C: human-specific, chimp-specific, human-chimp shared connectomes
% =========================================================================
for ii=1:3
if ii==1
W = connectome_human_specific;
title_name = {'human-'; 'specific'};
edge_color = cmap(1,:);
elseif ii==2
W = connectome_chimp_specific;
title_name = {'chimpanzee-'; 'specific'};
edge_color = cmap(2,:);
elseif ii==3
W = connectome_human_chimp_shared;
title_name = {'shared'};
edge_color = 'k';
end
ax2_1 = axes('Position', [ax0_position(1)-0.03+0.22*(ii-1) ax0_position(2)-0.39 0.2 ax0_position(4)]);
[edge_X, edge_Y, edge_Z] = adjacency_plot_und(threshold_proportional(W, 1), nodeLocations); % get all the edges
[nodeLocations, edges] = utils.extract_scatterBrain_locs_edges(nodeLocations, edge_X, edge_Y, edge_Z, slice);
hold on;
if strcmpi(slice, 'axial')
scatter3(ax2_1, nodeLocations(:,1), nodeLocations(:,2), max(nodeLocations(:,3))*ones(N,1), ...
nodeSizes, node_cval, 'filled', 'markeredgecolor', 'k');
else
scatter3(ax2_1, nodeLocations(:,1), nodeLocations(:,2), nodeLocations(:,3), ...
nodeSizes, node_cval, 'filled', 'markeredgecolor', 'k');
end
plot3(edges(:,1), edges(:,2), edges(:,3), 'color', edge_color, 'linewidth', edge_width);
hold off;
text(0, 65, title_name, 'horizontalalignment', 'center', 'fontsize', fontsize_label, ...
'fontweight', 'b')
text(-40, -45, 'LH', 'horizontalalignment', 'center', 'fontsize', fontsize_axis, ...
'fontweight', 'b')
text(40, -45, 'RH', 'horizontalalignment', 'center', 'fontsize', fontsize_axis, ...
'fontweight', 'b')
colormap(ax2_1, node_cmap)
view(ax2_1, 2)
axis(ax2_1, 'equal')
set(ax2_1, 'visible', 'off')
end
% =========================================================================
% D: human vs chimp shared weights
% =========================================================================
ax3 = axes('Position', [ax2_1.Position(1)+ax2_1.Position(3)*1.45 ax2_1.Position(2)+0.03 ax2_1.Position(3)*1.1 ax2_1.Position(4)]);
triu_ind = find(triu(ones(N),1));
W_human = utils.norm_matrix(eval(sprintf('connectome_%s', 'human')),normalization);
W_human = W_human.*connectome_human_chimp_shared;
W_chimp = utils.norm_matrix(eval(sprintf('connectome_%s', 'chimp')),normalization);
W_chimp = W_chimp.*connectome_human_chimp_shared;
data_to_plot_x = W_human(triu_ind);
data_to_plot_y = W_chimp(triu_ind);
hold on;
plot(data_to_plot_x, data_to_plot_y, '.', 'color', 0.5*ones(1,3), 'markersize', 15)
plot(data_to_plot_x, polyval(polyfit(data_to_plot_x,data_to_plot_y,1), data_to_plot_x), ...
'k-', 'linewidth', 2);
hold off;
[rho,pval] = corr(data_to_plot_x, data_to_plot_y, 'type', 'pearson');
text(max(get(ax3,'xlim')), 1.05, sprintf('r = %.2f', rho), ...
'fontsize', fontsize_label-2, 'fontweight', 'bold', 'verticalalignment', 'top', 'horizontalalignment', 'right');
if ~show_padj
text(max(get(ax3,'xlim')), 0.95, utils.extract_pvalue_text(pval), ...
'fontsize', fontsize_label-2, 'fontweight', 'bold', 'verticalalignment', 'top', 'horizontalalignment', 'right');
else
text(max(get(ax3,'xlim')), 0.95, utils.extract_pvalue_text(pvals_adj(pvals_adj_ind(1)), 1, ''), ...
'fontsize', fontsize_label-2, 'fontweight', 'bold', 'verticalalignment', 'top', 'horizontalalignment', 'right');
end
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'box', 'off', 'xlim', [0 1], 'ylim', [0 1])
xlabel('shared (human)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel('shared (chimpanzee)', 'fontsize', fontsize_label, 'interpreter', 'latex')
axis square
% =========================================================================
% E: topological metrics
% =========================================================================
factor_x = 1.3;
factor_y = 1.01;
init_x = ax0_position(1)+0.05;
init_y = 0.03;
length_x = (ax3.Position(1)+ax3.Position(3)*0.65-init_x)/(factor_x*(2-1) + 1);
length_y = (ax3.Position(2)-0.03-2*init_y)/(factor_y*(1-1) + 1);
for ii=1:2
if ii==1
stats_to_plot = 'clustering_coeff';
stats_title = {'regional'; 'clustering coefficient'};
elseif ii==2
stats_to_plot = 'path_length';
stats_title = {'regional'; 'path length'};
end
%%% violin plot
ax4_1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
data_violin = utils.padconcatenation(bct_stats{1}.(stats_to_plot), bct_stats{2}.(stats_to_plot), 2);
violins = utils.violinplot(data_violin, {});
for type_ind_2 = 1:length(types)
violins(type_ind_2).MedianPlot.SizeData = 30;
violins(type_ind_2).ViolinColor = cmap(type_ind_2,:);
violins(type_ind_2).ViolinAlpha = 0.2;
violins(type_ind_2).ScatterPlot.SizeData = 10;
violins(type_ind_2).ScatterPlot.MarkerFaceAlpha = 1;
violins(type_ind_2).BoxColor = [0 0 0];
violins(type_ind_2).BoxWidth = 0.01;
violins(type_ind_2).WhiskerPlot.LineStyle = 'none';
end
set(ax4_1, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xticklabel', titles)
ylabel(stats_title, 'fontsize', fontsize_label, 'interpreter', 'latex')
[~,pval] = ttest2(bct_stats{1}.(stats_to_plot), bct_stats{2}.(stats_to_plot));
if ~show_padj
text(min(get(gca, 'xlim'))*1.2, max(get(gca, 'ylim'))*1, utils.extract_pvalue_text(pval), ...
'fontsize', fontsize_label-2, 'fontweight', 'bold', 'verticalalignment', 'top', 'horizontalalignment', 'left');
else
text(min(get(gca, 'xlim'))*1.2, max(get(gca, 'ylim'))*1, utils.extract_pvalue_text(pvals_adj(pvals_adj_ind(1+ii)), 1, ''), ...
'fontsize', fontsize_label-2, 'fontweight', 'bold', 'verticalalignment', 'top', 'horizontalalignment', 'left');
end
for type_ind_2 = 1:length(types)
if type_ind_2==1
image_to_plot = human_female;
yloc = 0.38;
elseif type_ind_2==2
image_to_plot = chimpanzee;
yloc = 0.81;
end
bw = image_to_plot>0;
ax4_1_image = axes('Position', [ax4_1.Position(1)+0.01+(type_ind_2-1)*0.14 ax4_1.Position(2)+ax4_1.Position(4)*yloc image_width image_width]);
imshow(bw);
colormap(ax4_1_image, flipud(gray))
end
if ii==2
%%% human - chimpanzee surface plot
factor_y_2 = 0.95;
init_x_2 = ax4_1.Position(1) + ax4_1.Position(3)*1.05;
init_y_2 = ax4_1.Position(2)*1.9;
length_y_2 = (ax4_1.Position(4)*0.95)/(factor_y_2*(4-1) + 1);
length_x_2 = length_y_2*1.2;
parc_lh = dlmread(parc_filename('lh'));
parcels_lh = unique(parc_lh(parc_lh>0));
num_parcels_lh = length(parcels_lh);
parc_rh = dlmread(parc_filename('rh'));
parcels_rh = unique(parc_rh(parc_rh>0));
num_parcels_rh = length(parcels_rh);
surface_to_plot_lh = gifti(surface_file{1}.lh);
surface_to_plot_rh = gifti(surface_file{1}.rh);
boundary_method = 'midpoint';
BOUNDARY_lh = findROIboundaries(surface_to_plot_lh.vertices,double(surface_to_plot_lh.faces),parc_lh,boundary_method);
BOUNDARY_rh = findROIboundaries(surface_to_plot_rh.vertices,double(surface_to_plot_rh.faces),parc_rh,boundary_method);
data_parcel = bct_stats{1}.(stats_to_plot) - bct_stats{2}.(stats_to_plot);
data_to_plot_lh = zeros(size(surface_to_plot_lh.vertices,1),1);
data_to_plot_rh = zeros(size(surface_to_plot_rh.vertices,1),1);
for jj=1:num_parcels_lh
parcel = parcels_lh(jj);
data_to_plot_lh(find(parc_lh==parcel)) = data_parcel(jj);
end
for jj=1:num_parcels_rh
parcel = parcels_rh(jj);
data_to_plot_rh(find(parc_rh==parcel)) = data_parcel(num_parcels_lh+jj);
end
clims = [min(data_parcel), max(data_parcel)];
ind_zeros = find(parc_lh==0);
data_to_plot_lh(data_to_plot_lh<clims(1)) = clims(1);
data_to_plot_lh(data_to_plot_lh>clims(2)) = clims(2);
data_to_plot_lh(ind_zeros) = clims(1)*1.1;
ind_zeros = find(parc_rh==0);
data_to_plot_rh(data_to_plot_rh<clims(1)) = clims(1);
data_to_plot_rh(data_to_plot_rh>clims(2)) = clims(2);
data_to_plot_rh(ind_zeros) = clims(1)*1.1;
clims(1) = clims(1)*1.1;
%%% left lateral view
ax5_1 = axes('Position', [init_x_2 init_y_2+factor_y_2*length_y_2*(4-1) length_x_2 length_y_2]);
patch(ax5_1, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', data_to_plot_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for jj = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{jj}(:,1), BOUNDARY_lh{jj}(:,2), BOUNDARY_lh{jj}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([-90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax5_1, [0.5 0.5 0.5; utils.bluewhitered])
axis off
axis image
%%% left medial view
ax5_2 = axes('Position', [init_x_2 init_y_2+factor_y_2*length_y_2*(3-1) length_x_2 length_y_2]);
patch(ax5_2, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', data_to_plot_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for jj = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{jj}(:,1), BOUNDARY_lh{jj}(:,2), BOUNDARY_lh{jj}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax5_2, [0.5 0.5 0.5; utils.bluewhitered])
axis off
axis image
%%% right lateral view
ax5_3 = axes('Position', [init_x_2 init_y_2+factor_y_2*length_y_2*(2-1) length_x_2 length_y_2]);
patch(ax5_3, 'Vertices', surface_to_plot_rh.vertices, 'Faces', surface_to_plot_rh.faces, 'FaceVertexCData', data_to_plot_rh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for jj = 1:length(BOUNDARY_rh)
plot3(BOUNDARY_rh{jj}(:,1), BOUNDARY_rh{jj}(:,2), BOUNDARY_rh{jj}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax5_3, [0.5 0.5 0.5; utils.bluewhitered])
axis off
axis image
%%% right medial view
ax5_4 = axes('Position', [init_x_2 init_y_2+factor_y_2*length_y_2*(1-1) length_x_2 length_y_2]);
patch(ax5_4, 'Vertices', surface_to_plot_rh.vertices, 'Faces', surface_to_plot_rh.faces, 'FaceVertexCData', data_to_plot_rh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for jj = 1:length(BOUNDARY_rh)
plot3(BOUNDARY_rh{jj}(:,1), BOUNDARY_rh{jj}(:,2), BOUNDARY_rh{jj}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([-90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax5_4, [0.5 0.5 0.5; utils.bluewhitered])
axis off
axis image
cbar = colorbar(ax5_4,'southoutside');
ylabel(cbar, 'h -- c', 'fontsize', fontsize_label, 'interpreter', 'latex')
set(cbar, 'fontsize', fontsize_axis, 'ticklength', 0.0, ...
'position', [ax5_4.Position(1)+ax5_4.Position(3)*0.1, ax5_4.Position(2)*0.4, ax5_4.Position(3)*0.8, 0.01], ...
'ytick', [ceil(min(clims)), floor(max(clims))])
annotation('textbox', [ax5_1.Position(1)-0.045, ax5_1.Position(2)-0.048, 0.1, 0.1], 'string', 'LH', ...
'fontsize', fontsize_axis, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
annotation('textbox', [ax5_1.Position(1)-0.045, ax5_3.Position(2)-0.048, 0.1, 0.1], 'string', 'RH', ...
'fontsize', fontsize_axis, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
end
end
%%% panel letters
annotation(fig, 'textbox', [0.02, 0.98, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.51, 0.98, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.02, 0.65, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.68, 0.65, 0.01, 0.01], 'string', 'D', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.02, 0.31, 0.01, 0.01], 'string', 'E', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.49, 0.31, 0.01, 0.01], 'string', 'F', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%% FIGURE 2
% load all data relevant to Figure 2
data_Figure2 = load(sprintf('%s/Figure2.mat', data_foldername));
types = {'human', 'chimp'};
titles = {'human', 'chimpanzee'};
cmap = [color_brown; color_green];
matrix_cmap = [1,1,1; cbrewer('seq', 'Blues', 100, 'pchip')];
edge_alpha = 0.8;
edge_width = 1;
edge_colors = [cmap(1,:), edge_alpha;
cmap(2,:), edge_alpha];
N = 114;
slice = 'axial';
node_interests = [86,84];
frac = 0.1;
markersize = 40;
node_interests_colors = brighten(lines(3),0.3);
node_interests_colors = node_interests_colors(2:end,:);
node_cmap = [1, 1, 1; node_interests_colors];
node_cval = ones(N,1);
node_cval(node_interests(1)) = 2;
node_cval(node_interests(2)) = 3;
nodeLocations = region_centroids;
nodeSizes = (markersize/10)*ones(N,1);
nodeSizes(node_interests) = markersize;
image_width = 0.07;
fig = figure('Position', [200 200 850 600]);
% =========================================================================
% A: connectomes
% =========================================================================
for type_ind=1:length(types)
type = types{type_ind};
W = eval(sprintf('connectome_%s', type));
[edge_X, edge_Y, edge_Z] = adjacency_plot_und(threshold_proportional(W, frac), nodeLocations); % get all the edges
[nodeLocations, edges] = utils.extract_scatterBrain_locs_edges(nodeLocations, edge_X, edge_Y, edge_Z, slice);
if type_ind==1
image_to_plot = human_female;
elseif type_ind==2
image_to_plot = chimpanzee;
end
bw = image_to_plot>0;
ax1 = axes('Position', [-0.04+0.25*(type_ind-1) 0.55 0.3 0.33]);
hold on;
if strcmpi(slice, 'axial')
scatter3(ax1, nodeLocations(:,1), nodeLocations(:,2), max(nodeLocations(:,3))*ones(N,1), ...
nodeSizes, node_cval, 'filled', 'markeredgecolor', 'k');
else
scatter3(ax1, nodeLocations(:,1), nodeLocations(:,2), nodeLocations(:,3), ...
nodeSizes, node_cval, 'filled', 'markeredgecolor', 'k');
end
plot3(edges(:,1), edges(:,2), edges(:,3), 'color', edge_colors(type_ind,:), 'linewidth', edge_width);
hold off;
text(0, 60, titles{type_ind}, 'horizontalalignment', 'center', 'fontsize', fontsize_label, ...
'fontweight', 'b')
text(nodeLocations(node_interests(1),1)+6, nodeLocations(node_interests(1),2), max(nodeLocations(:,3)), 'region $i$', ...
'horizontalalignment', 'left', 'fontsize', fontsize_axis, 'interpreter', 'latex')
text(nodeLocations(node_interests(2),1)+6, nodeLocations(node_interests(2),2), max(nodeLocations(:,3)), 'region $j$', ...
'horizontalalignment', 'left', 'fontsize', fontsize_axis, 'interpreter', 'latex')
text(-40, -41, 'LH', 'horizontalalignment', 'center', 'fontsize', fontsize_axis, ...
'fontweight', 'b')
text(40, -41, 'RH', 'horizontalalignment', 'center', 'fontsize', fontsize_axis, ...
'fontweight', 'b')
colormap(ax1, node_cmap)
view(ax1, 2)
axis(ax1, 'equal')
set(ax1, 'visible', 'off')
ax1_image = axes('Position', [ax1.Position(1)+0.04 ax1.Position(2)+ax1.Position(4)*0.8 image_width image_width]);
imshow(bw);
colormap(ax1_image, flipud(gray))
end
% =========================================================================
% B left: model schematic
% =========================================================================
ax2 = axes('Position', [ax1.Position(1)+ax1.Position(3)*1.08 ax1.Position(2)*1.02 ax1.Position(3)*0.8 ax1.Position(4)]);
circle_locs_x = [0.25, 0.7];
circle_locs_y = [0.55, 0.55];
hold on
plot(circle_locs_x, circle_locs_y, 'k-', 'linewidth', 3)
plot(circle_locs_x(1), circle_locs_y(1)+0.17, 'ko', 'markersize', 25, 'linewidth', 3, 'markerfacecolor', 'none')
plot(circle_locs_x(2), circle_locs_y(2)+0.17, 'ko', 'markersize', 25, 'linewidth', 3, 'markerfacecolor', 'none')
plot(circle_locs_x(1), circle_locs_y(1), 'ko', 'markersize', 40, 'markerfacecolor', node_interests_colors(1,:))
plot(circle_locs_x(2), circle_locs_y(2), 'ko', 'markersize', 40, 'markerfacecolor', node_interests_colors(2,:))
hold off;
set(ax2, 'xtick', [], 'ytick', [], 'xlim', [0 1], 'ylim', [0 1])
% annotation lines
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*0.322 ax2.Position(1)+ax2.Position(3)*0.318], [ax2.Position(1)+ax2.Position(4)*0.76 ax2.Position(1)+ax2.Position(4)*0.74], ...
'linestyle', 'none', 'headlength', 10, 'headwidth', 12)
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*0.770 ax2.Position(1)+ax2.Position(3)*0.766], [ax2.Position(1)+ax2.Position(4)*0.76 ax2.Position(1)+ax2.Position(4)*0.74], ...
'linestyle', 'none', 'headlength', 10, 'headwidth', 12)
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*(circle_locs_x(1)+0.10) ax2.Position(1)+ax2.Position(3)*(circle_locs_x(1)+0.04)], [ax2.Position(2)+ax2.Position(4)*0.25 ax2.Position(2)+ax2.Position(4)*0.42], ...
'linewidth', 3, 'headlength', 10, 'headwidth', 12)
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*(circle_locs_x(2)+0.10) ax2.Position(1)+ax2.Position(3)*(circle_locs_x(2)+0.04)], [ax2.Position(2)+ax2.Position(4)*0.25 ax2.Position(2)+ax2.Position(4)*0.42], ...
'linewidth', 3, 'headlength', 10, 'headwidth', 12)
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*(circle_locs_x(1)-0.10) ax2.Position(1)+ax2.Position(3)*(circle_locs_x(1)-0.04)], [ax2.Position(2)+ax2.Position(4)*0.25 ax2.Position(2)+ax2.Position(4)*0.42], ...
'linewidth', 3, 'headlength', 10, 'headwidth', 12)
annotation('arrow', [ax2.Position(1)+ax2.Position(3)*(circle_locs_x(2)-0.10) ax2.Position(1)+ax2.Position(3)*(circle_locs_x(2)-0.04)], [ax2.Position(2)+ax2.Position(4)*0.25 ax2.Position(2)+ax2.Position(4)*0.42], ...
'linewidth', 3, 'headlength', 10, 'headwidth', 12)
% texts
text(circle_locs_x(1), circle_locs_y(1), {'region'; '$i$'}, 'horizontalalignment', 'center', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(circle_locs_x(2), circle_locs_y(2), {'region'; '$j$'}, 'horizontalalignment', 'center', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(circle_locs_x(1), circle_locs_y(1)+0.32, '$w$', 'horizontalalignment', 'center', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(circle_locs_x(2), circle_locs_y(1)+0.32, '$w$', 'horizontalalignment', 'center', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(mean(circle_locs_x), circle_locs_y(1), '$A_{ij}$', 'horizontalalignment', 'center', 'verticalalignment', 'top', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(circle_locs_x(1)+0.08, circle_locs_y(1)-0.3, '$D$', 'horizontalalignment', 'left', 'verticalalignment', 'top', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(circle_locs_x(2)+0.08, circle_locs_y(1)-0.3, '$D$', 'horizontalalignment', 'left', 'verticalalignment', 'top', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(circle_locs_x(1)-0.08, circle_locs_y(1)-0.3, '$I_0$', 'horizontalalignment', 'right', 'verticalalignment', 'top', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
text(circle_locs_x(2)-0.08, circle_locs_y(1)-0.3, '$I_0$', 'horizontalalignment', 'right', 'verticalalignment', 'top', 'fontsize', fontsize_label*1.2, 'interpreter', 'latex')
axis off
% =========================================================================
% B right: time series
% =========================================================================
ax3 = axes('Position', [ax2.Position(1)+ax2.Position(3)*1.2 ax2.Position(2)*1.13 ax2.Position(3)*0.5 ax2.Position(4)*0.7]);
hold on;
plot(data_Figure2.timeseries.time, data_Figure2.timeseries.S(node_interests(1),:), 'color', node_interests_colors(1,:), 'linewidth', 1.5)
plot(data_Figure2.timeseries.time, data_Figure2.timeseries.S(node_interests(2),:), 'color', node_interests_colors(2,:), 'linewidth', 1.5)
hold off;
text(max(data_Figure2.timeseries.time), mean(data_Figure2.timeseries.S(node_interests(1),:)), 'region $i$', ...
'horizontalalignment', 'left', 'fontsize', fontsize_axis, 'interpreter', 'latex')
text(max(data_Figure2.timeseries.time), mean(data_Figure2.timeseries.S(node_interests(2),:)), 'region $j$', ...
'horizontalalignment', 'left', 'fontsize', fontsize_axis, 'interpreter', 'latex')
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ytick', []);
xlabel('time (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel({'regional synaptic'; 'response, $S$'}, 'fontsize', fontsize_label, 'interpreter', 'latex')
% =========================================================================
% C: dynamic range calculation
% =========================================================================
node = 53;
ax4 = axes('Position', [0.09 0.07 ax1.Position(3)*1.2 ax1.Position(4)*1.15]);
hold on;
plot(data_Figure2.tuningcurve.w, data_Figure2.tuningcurve.S(node,:), 'k.');
plot(data_Figure2.tuningcurve.stats.xval_10(node)*ones(1,2), [0, 1], 'k:');
plot(data_Figure2.tuningcurve.stats.xval_90(node)*ones(1,2), [0, 1], 'k:');
plot(data_Figure2.tuningcurve.w, data_Figure2.tuningcurve.stats.Fval_10(node)*(ones(size(data_Figure2.tuningcurve.w))), 'k:');
plot(data_Figure2.tuningcurve.w, data_Figure2.tuningcurve.stats.Fval_90(node)*(ones(size(data_Figure2.tuningcurve.w))), 'k:');
plot(data_Figure2.tuningcurve.stats.xval_10(node), data_Figure2.tuningcurve.stats.Fval_10(node), 'k.', 'markersize', 20)
plot(data_Figure2.tuningcurve.stats.xval_90(node), data_Figure2.tuningcurve.stats.Fval_90(node), 'k.', 'markersize', 20)
hold off;
text(data_Figure2.tuningcurve.stats.xval_10(node), data_Figure2.tuningcurve.stats.Fval_10(node)-0.04, '($w_{10}$, $\overline{S}_{10}$)', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(data_Figure2.tuningcurve.stats.xval_90(node), data_Figure2.tuningcurve.stats.Fval_90(node)-0.04, '($w_{90}$, $\overline{S}_{90}$)', 'fontsize', fontsize_label, 'interpreter', 'latex')
text(1.3, 0.4, {'dynamic range ='; '$10\log_{10}(w_{90}/w_{10})$'}, 'fontsize', fontsize_label, ...
'fontweight', 'b', 'horizontalalignment', 'center', 'interpreter', 'latex')
set(ax4, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], ...
'ylim', [0 1]);
xlabel('global recurrent strength, $w$', 'fontsize', fontsize_label, 'interpreter', 'latex')
ylabel({'mean regional synaptic'; 'response, $\overline{S}$'}, 'fontsize', fontsize_label, 'interpreter', 'latex')
% =========================================================================
% D: response to varying w
% =========================================================================
node_list = [5, 11, 74, 113];
w_list = [0.6, 0.8];
factor_y = 1.1;
init_x = ax4.Position(1)+ax4.Position(3)*1.3;
init_y = ax4.Position(2);
length_x = (0.95 - 1.*init_x);
length_y = (ax4.Position(2)+ax4.Position(4) - init_y)/(factor_y*(2-1) + 1);
ax5_1 = axes('Position', [init_x init_y+factor_y*length_y*(2-1) length_x length_y]);
hold on;
plot(data_Figure2.timeseries_varyingw.time, data_Figure2.timeseries_varyingw.S(node_list(1),:), '-', 'color', 'b', 'linewidth', 1.5)
plot(data_Figure2.timeseries_varyingw.time, data_Figure2.timeseries_varyingw.S(node_list(4),:), '-', 'color', 'r', 'linewidth', 1.5)
annotation('rectangle', [ax5_1.Position(1) ax5_1.Position(2) ax5_1.Position(3)*0.5 ax5_1.Position(4)], ...
'FaceColor', 'k', 'FaceAlpha', 0.1, 'edgecolor', 'none')
hold off;
leg = legend('region w/ low dynamic range', 'region w/ high dynamic range', 'fontsize', fontsize_axis-2, 'location', 'southeast', 'box', 'off');
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(leg, 'Position', get(leg, 'Position')+[0.05 0 0 0])
set(ax5_1, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0 max(data_Figure2.timeseries_varyingw.time)], ...
'xticklabel', {}, 'ylim', [0 0.9], 'ytick', [0 0.4 0.8]);
ax5_2 = axes('Position', [init_x init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
plot(data_Figure2.timeseries_varyingw.time, data_Figure2.timeseries_varyingw.S(node_list(1),:), '-', 'color', 'b', 'linewidth', 1.5)
plot(data_Figure2.timeseries_varyingw.time, data_Figure2.timeseries_varyingw.S(node_list(2),:), '-', 'color', 'k', 'linewidth', 1.5)
annotation('rectangle', [ax5_2.Position(1) ax5_2.Position(2) ax5_2.Position(3)*0.5 ax5_2.Position(4)], ...
'FaceColor', 'k', 'FaceAlpha', 0.1, 'edgecolor', 'none')
hold off;
leg = legend('region w/ low dynamic range', 'region w/ low dynamic range', 'fontsize', fontsize_axis-2, 'location', 'southeast', 'box', 'off');
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(leg, 'Position', get(leg, 'Position')+[0.05 0 0 0])
set(ax5_2, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xlim', [0 max(data_Figure2.timeseries_varyingw.time)], ...
'ylim', [0 0.9], 'ytick', [0 0.4 0.8]);
xlabel('time (s)', 'fontsize', fontsize_label, 'interpreter', 'latex')
y_label = ylabel('regional synaptic response, $S$', 'fontsize', fontsize_label, 'interpreter', 'latex');
set(y_label, 'Position', get(y_label, 'Position')+[0 0.5 0])
annotation(fig, 'textbox', [ax5_1.Position(1), ax5_1.Position(2)+ax5_1.Position(4)*1.2, ax5_1.Position(3)*0.5, 0.01], 'string', sprintf('$w = %.1f$', w_list(1)), 'edgecolor', 'none', ...
'fontsize', fontsize_label, 'horizontalalignment', 'center', 'interpreter', 'latex')
annotation(fig, 'textbox', [ax5_1.Position(1)+ax5_1.Position(3)*0.5, ax5_1.Position(2)+ax5_1.Position(4)*1.2, ax5_1.Position(3)*0.5, 0.01], 'string', sprintf('$w = %.1f$', w_list(2)), 'edgecolor', 'none', ...
'fontsize', fontsize_label, 'horizontalalignment', 'center', 'interpreter', 'latex')
%%% panel letters
annotation(fig, 'textbox', [0.02, 0.96, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.56, 0.96 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.02, 0.52, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.49, 0.52, 0.01, 0.01], 'string', 'D', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
%% FIGURE 3
% load all data relevant to Figure 3
data_Figure3 = load(sprintf('%s/Figure3.mat', data_foldername));
types = {'human', 'chimp'};
titles = {'human', 'chimpanzee'};
N = 114;
cmap = [color_brown; color_green];
S_transition = 0.3;
stats_to_plot = 'dynamic_range';
stats_to_plot_name = 'dynamic range';
parc_lh = dlmread(parc_filename('lh'));
parcels_lh = unique(parc_lh(parc_lh>0));
num_parcels_lh = length(parcels_lh);
parc_rh = dlmread(parc_filename('rh'));
parcels_rh = unique(parc_rh(parc_rh>0));
num_parcels_rh = length(parcels_rh);
ylim_min = min([data_Figure3.tuningcurve.stats{1}.(stats_to_plot); data_Figure3.tuningcurve.stats{2}.(stats_to_plot)]);
ylim_max = max([data_Figure3.tuningcurve.stats{1}.(stats_to_plot); data_Figure3.tuningcurve.stats{2}.(stats_to_plot)]);
image_width = 0.035;
fig = figure('Position', [400 100 700 1000]);
for type_ind = 1:length(types)
type = types{type_ind};
if type_ind==1
image_to_plot = human_female;
elseif type_ind==2
image_to_plot = chimpanzee;
end
gain_cmap = repmat(cmap(type_ind,:),N,1);
% brighten_vec = linspace(0.8,0,N);
brighten_vec = zeros(N,1);
for ii=1:N
gain_cmap(ii,:) = brighten(gain_cmap(ii,:), brighten_vec(ii));
end
bw = image_to_plot>0;
[~,xval_transition_ind] = sort(data_Figure3.tuningcurve.stats{type_ind}.xval_transition, 'ascend');
% =====================================================================
% A: tuning curve
% =====================================================================
ax1 = axes('Position', [0.15+0.43*(type_ind-1) 0.84 0.35 0.13]);
hold on;
for ii=1:N
plot(data_Figure3.tuningcurve.w, data_Figure3.tuningcurve.S{type_ind}(xval_transition_ind(ii),:), 'color', gain_cmap(ii,:))
end
hold off;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 1], ...
'xtick', 0:0.5:2, 'xlim', [0 2]);
xlabel('global recurrent strength, $w$', 'fontsize', fontsize_label, 'interpreter', 'latex')
if type_ind==1
ylabel({'mean regional synaptic'; 'response, $\overline{S}$'}, 'fontsize', fontsize_label, 'interpreter', 'latex')
end
title(titles{type_ind}, 'fontsize', fontsize_label)
ax1_image= axes('Position', [ax1.Position(1)+ax1.Position(3)*0.02 ax1.Position(2)+ax1.Position(4)*0.7 image_width image_width]);
imshow(bw);
colormap(ax1_image, flipud(gray))
% =====================================================================
% B: dynamic range distribution
% =====================================================================
if type_ind==1
ax2 = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.6 ax1.Position(2)-ax1.Position(4)*1.65 ax1.Position(3) ax1.Position(4)]);
data_violin = utils.padconcatenation(data_Figure3.tuningcurve.stats{1}.(stats_to_plot), data_Figure3.tuningcurve.stats{2}.(stats_to_plot), 2);
violins = utils.violinplot(data_violin, {});
for type_ind_2 = 1:length(types)
violins(type_ind_2).MedianPlot.SizeData = 30;
violins(type_ind_2).ViolinColor = cmap(type_ind_2,:);
violins(type_ind_2).ViolinAlpha = 0.2;
violins(type_ind_2).ScatterPlot.SizeData = 10;
violins(type_ind_2).ScatterPlot.MarkerFaceAlpha = 1;
violins(type_ind_2).BoxColor = [0 0 0];
violins(type_ind_2).BoxWidth = 0.01;
violins(type_ind_2).WhiskerPlot.LineStyle = 'none';
text(type_ind_2, max(data_violin(:,type_ind_2))*1.01, ['\sigma = ', sprintf('%.2f', std(data_violin(:,type_ind_2)))], 'color', cmap(type_ind_2,:), ...
'fontsize', fontsize_label, 'fontweight', 'b', 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
end
set(ax2, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xticklabel', titles)
ylabel(stats_to_plot_name, 'fontsize', fontsize_label, 'interpreter', 'latex')
for type_ind_2 = 1:length(types)
if type_ind_2==1
image_to_plot = human_female;
yloc = 0.65;
elseif type_ind_2==2
image_to_plot = chimpanzee;
yloc = 0.9;
end
bw2 = image_to_plot>0;
ax2_image = axes('Position', [ax2.Position(1)+0.01+(type_ind_2-1)*0.17 ax2.Position(2)+ax2.Position(4)*yloc image_width image_width]);
imshow(bw2);
colormap(ax2_image, flipud(gray))
end
end
% =====================================================================
% C: dynamic range spatial arrangement
% =====================================================================
dynamic_range_cmap = cbrewer('seq', 'YlGnBu', 130, 'pchip');
dynamic_range_cmap = [0.5 0.5 0.5; flipud(dynamic_range_cmap(end-N+1:end,:))];
surface_to_plot_lh = gifti(surface_file{type_ind}.lh);
surface_to_plot_rh = gifti(surface_file{type_ind}.rh);
boundary_method = 'midpoint';
BOUNDARY_lh = findROIboundaries(surface_to_plot_lh.vertices,double(surface_to_plot_lh.faces),parc_lh,boundary_method);
BOUNDARY_rh = findROIboundaries(surface_to_plot_rh.vertices,double(surface_to_plot_rh.faces),parc_rh,boundary_method);
data_parcel = data_Figure3.tuningcurve.stats{type_ind}.(stats_to_plot);
data_to_plot_lh = zeros(size(surface_to_plot_lh.vertices,1),1);
data_to_plot_rh = zeros(size(surface_to_plot_rh.vertices,1),1);
for ii=1:num_parcels_lh
parcel = parcels_lh(ii);
data_to_plot_lh(find(parc_lh==parcel)) = data_parcel(ii);
end
for ii=1:num_parcels_rh
parcel = parcels_rh(ii);
data_to_plot_rh(find(parc_rh==parcel)) = data_parcel(num_parcels_lh+ii);
end
% clims = [min(data_parcel(data_parcel>0)), max(data_parcel(data_parcel>0))];
clims = prctile(data_parcel(data_parcel>0), [4 96]);
ind_zeros = find(parc_lh==0);
data_to_plot_lh(data_to_plot_lh<clims(1)) = clims(1);
data_to_plot_lh(data_to_plot_lh>clims(2)) = clims(2);
data_to_plot_lh(ind_zeros) = clims(1)-0.1;
ind_zeros = find(parc_rh==0);
data_to_plot_rh(data_to_plot_rh<clims(1)) = clims(1);
data_to_plot_rh(data_to_plot_rh>clims(2)) = clims(2);
data_to_plot_rh(ind_zeros) = clims(1)-0.1;
clims(1) = clims(1)-0.1;
ax3 = axes('Position', [ax1.Position(1) ax2.Position(2)-ax2.Position(4)*1.4 ax1.Position(3) ax1.Position(4)]);
ax3_positions = ax3.Position;
set(ax3, 'visible', 'off')
ax3_1 = axes('Position', [ax3_positions(1)+0.01 ax3_positions(2)-0.03 0.16 0.26]);
patch(ax3_1, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', data_to_plot_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{ii}(:,1), BOUNDARY_lh{ii}(:,2), BOUNDARY_lh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([-90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax3_1, dynamic_range_cmap)
axis off
axis image
ax3_1_image = axes('Position', [ax3_positions(1)-0.03 ax3_1.Position(2)+ax3_1.Position(4)*0.55 image_width image_width]);
imshow(bw);
colormap(ax3_1_image, flipud(gray))
ax3_2 = axes('Position', [ax3_1.Position(1) ax3_1.Position(2)-ax3_1.Position(4)/3.1 ax3_1.Position(3) ax3_1.Position(4)]);
patch(ax3_2, 'Vertices', surface_to_plot_lh.vertices, 'Faces', surface_to_plot_lh.faces, 'FaceVertexCData', data_to_plot_lh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_lh)
plot3(BOUNDARY_lh{ii}(:,1), BOUNDARY_lh{ii}(:,2), BOUNDARY_lh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax3_2, dynamic_range_cmap)
axis off
axis image
ax3_3 = axes('Position', [ax3_1.Position(1)+ax3_1.Position(3)*1.1 ax3_1.Position(2) ax3_1.Position(3) ax3_1.Position(4)]);
patch(ax3_3, 'Vertices', surface_to_plot_rh.vertices, 'Faces', surface_to_plot_rh.faces, 'FaceVertexCData', data_to_plot_rh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_rh)
plot3(BOUNDARY_rh{ii}(:,1), BOUNDARY_rh{ii}(:,2), BOUNDARY_rh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax3_3, dynamic_range_cmap)
axis off
axis image
ax3_4 = axes('Position', [ax3_3.Position(1) ax3_2.Position(2) ax3_1.Position(3) ax3_1.Position(4)]);
patch(ax3_4, 'Vertices', surface_to_plot_rh.vertices, 'Faces', surface_to_plot_rh.faces, 'FaceVertexCData', data_to_plot_rh, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
hold on;
for ii = 1:length(BOUNDARY_rh)
plot3(BOUNDARY_rh{ii}(:,1), BOUNDARY_rh{ii}(:,2), BOUNDARY_rh{ii}(:,3), 'Color', 'k', 'LineWidth',1, 'Clipping','off');
end
hold off;
view([-90 0]);
caxis(clims)
material dull
camlight('headlight');
colormap(ax3_4, dynamic_range_cmap)
axis off
axis image
cbar = colorbar(ax3_4,'southoutside');
ylabel(cbar, stats_to_plot_name, 'fontsize', fontsize_label, 'interpreter', 'latex')
set(cbar, 'fontsize', fontsize_axis, 'ticklength', 0.01, ...
'position', [ax3_positions(1)+ax3_positions(3)*0.32, ax3_3.Position(2)-0.012, ax3_3.Position(3)*0.8, 0.01], ...
'ytick', [])
annotation(fig, 'textbox', [cbar.Position(1)-0.04, cbar.Position(2)*1, 0.04, 0.01], 'string', 'low', 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'textbox', [cbar.Position(1)+cbar.Position(3)+0.005, cbar.Position(2)*1, 0.04, 0.01], 'string', 'high', 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation('textbox', [ax3_1.Position(1)-0.04, ax3_1.Position(2)+0.038, 0.1, 0.1], 'string', 'LH', ...
'fontsize', fontsize_axis, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
annotation('textbox', [ax3_1.Position(1)+ax3_1.Position(3)*1.73, ax3_1.Position(2)+0.038, 0.1, 0.1], 'string', 'RH', ...
'fontsize', fontsize_axis, 'LineStyle', 'none', 'horizontalalignment', 'center', ...
'verticalalignment', 'middle', 'fontweight', 'b')
% =====================================================================
% D: dynamic range distribution of different networks
% =====================================================================
ax4 = axes('Position', [ax1.Position(1) ax3.Position(2)-ax3.Position(4)*1.65 ax1.Position(3) ax1.Position(4)]);
data_violin = [];
for FN = 1:7
data_violin = cat(2, data_violin, data_Figure3.tuningcurve.stats{type_ind}.FN(:,FN));
end
violins = utils.violinplot(data_violin, {});
for FN = 1:7
violins(FN).MedianPlot.SizeData = 30;
violins(FN).ViolinColor = Yeo_7net_colormap(FN,:);
violins(FN).ViolinAlpha = 0.2;
violins(FN).ScatterPlot.SizeData = 10;
violins(FN).ScatterPlot.MarkerFaceAlpha = 1;
violins(FN).BoxColor = [0 0 0];
violins(FN).BoxWidth = 0.02;
violins(FN).WhiskerPlot.LineStyle = 'none';
end
set(ax4, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'xticklabel', Yeo_names, ...
'ylim', [ylim_min*0.95, ylim_max*1.05])
ylabel(stats_to_plot_name, 'fontsize', fontsize_label, 'interpreter', 'latex')
ax4_image= axes('Position', [ax4.Position(1)+ax4.Position(3)*0.02 ax4.Position(2)+ax4.Position(4)*0.7 image_width image_width]);
imshow(bw);
colormap(ax4_image, flipud(gray))
% =====================================================================
% E: FC of different networks
% =====================================================================
ax5 = axes('Position', [ax1.Position(1) ax4.Position(2)-ax4.Position(4)*1.35 ax1.Position(3) ax1.Position(4)]);
hold on;
for FN=1:7
plot(data_Figure3.tuningcurve.w, squeeze(data_Figure3.between_FC{type_ind}(FN,FN,:)), '-', 'color', Yeo_7net_colormap(FN,:), 'linewidth', 1)
end
plot(data_Figure3.tuningcurve.w, data_Figure3.global_FC{type_ind}, 'k-', 'linewidth', 2)
hold off;
if type_ind==2
leg = legend([Yeo_names, 'whole brain'], 'fontsize', fontsize_axis-2, 'location', 'northeast', 'box', 'off');
leg.ItemTokenSize = leg.ItemTokenSize/2;
end
set(ax5, 'fontsize', fontsize_axis, 'ticklength', [0.02, 0.02], 'ylim', [0 0.3])
xlabel('global recurrent strength, $w$', 'fontsize', fontsize_label, 'interpreter', 'latex')
if type_ind==1
ylabel('$FC$', 'fontsize', fontsize_label, 'interpreter', 'latex')
end
ax5_image= axes('Position', [ax5.Position(1)+ax5.Position(3)*0.02 ax5.Position(2)+ax5.Position(4)*0.7 image_width image_width]);
imshow(bw);
colormap(ax5_image, flipud(gray))
end
%%% panel letters
annotation(fig, 'textbox', [0.09, 0.99, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.3, 0.77, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.09, 0.58, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...