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generate_paper_figures_supp_bioRxiv.m
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generate_paper_figures_supp_bioRxiv.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% generate_paper_figures_supp_bioRxiv.m
%%%
%%% MATLAB script to generate the Supplementary figures of the bioRxiv preprint
%%%
%%% NOTE : 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.
%%%
%%% Original: James Pang, Monash University, 2022
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Load relevant repository MATLAB functions
addpath(genpath('functions_matlab'));
%% MISCELLANEOUS VARIABLES
% NOTE: data provided is only for the below parameters, so please don't change them
hemisphere = 'lh';
num_modes = 200;
parc_name = 'Glasser360';
data_empirical_folder = 'data/empirical';
data_results_folder = 'data/results';
data_figures_folder = 'data/figures_bioRxiv';
data_template_surfaces_folder = 'data/template_surfaces_volumes';
data_template_eigenmodes_folder = 'data/template_eigenmodes';
%% LOAD SURFACES
mesh_interest = 'inflated';
[vertices, faces] = read_vtk(sprintf('data/template_surfaces_volumes/fsLR_32k_%s-%s.vtk', mesh_interest, hemisphere));
surface_inflated.vertices = vertices';
surface_inflated.faces = faces';
mesh_interest = 'midthickness';
[vertices, faces] = read_vtk(sprintf('data/template_surfaces_volumes/fsLR_32k_%s-%s.vtk', mesh_interest, hemisphere));
surface_midthickness.vertices = vertices';
surface_midthickness.faces = faces';
%% LOAD EMPIRICAL AND RESULTS DATA
% HCP subject list
subjectfile = sprintf('%s/subject_list_HCP.txt', data_empirical_folder);
subject_list = dlmread(subjectfile);
num_subjects = length(subject_list);
% cortex and medial wall mask
cortex = dlmread(sprintf('%s/fsLR_32k_cortex-%s_mask.txt', data_template_surfaces_folder, hemisphere));
cortex_ind = find(cortex);
num_vertices = length(cortex);
% subjects in training and test sets
subject_list_training = 1:200;
subject_list_test = 201:255;
subject_list_all = 1:255;
num_subjects_training = length(subject_list_training);
num_subjects_test = length(subject_list_test);
num_subjects_all = length(subject_list_all);
% parcellation
filename_common_parcellation = @(parc_name, hemisphere) sprintf('data/parcellations/fsLR_32k_%s-%s.txt', parc_name, hemisphere);
% EMPIRICAL: raw task-activation maps
data_task = load(sprintf('%s/S255_tfMRI_ALLTASKS_raw_%s.mat', data_empirical_folder, hemisphere));
% EMPIRICAL: sample resting-state fMRI
data_dtseries = ft_read_cifti(sprintf('%s/subject_rfMRI_REST.dtseries.nii', data_empirical_folder));
if strcmpi(hemisphere, 'lh')
data_resting = data_dtseries.dtseries(data_dtseries.brainstructure==1,:);
elseif strcmpi(hemisphere, 'rh')
data_resting = data_dtseries.dtseries(data_dtseries.brainstructure==2,:);
end
T = size(data_resting,2);
clear data_dtseries
% EMPIRICAL: sample neurovault maps
temp = matfile(sprintf('%s/fsLR_32k_neurovault_maps_N=10000_%s.mat', data_empirical_folder, hemisphere));
data_neurovault = temp.neurovault_maps(:,1:10);
% EMPIRICAL: myelin map in HCPMMP1 (Glasser360 in our naming convention) atlas
data_myelin = load(sprintf('%s/myelin_%s.mat', data_empirical_folder, parc_name));
% RESULTS: basis sets
basis_geometric = dlmread(sprintf('%s/basis_geometric_midthickness-%s_evec_%i.txt', data_results_folder, hemisphere, num_modes));
basis_connectome = load(sprintf('%s/basis_connectome_midthickness-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_connectome = basis_connectome.eig_vec;
basis_connectome_density_matched = load(sprintf('%s/basis_connectome_density_matched_midthickness-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_connectome_density_matched = basis_connectome_density_matched.eig_vec;
basis_EDR = load(sprintf('%s/basis_connectome_EDR_midthickness-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_EDR = basis_EDR.eig_vec;
basis_PCA_task = load(sprintf('%s/basis_PCA_task_S255_training-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_PCA_task = basis_PCA_task.eig_vec;
basis_PCA_resting = load(sprintf('%s/basis_PCA_resting_S255_training-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_PCA_resting = basis_PCA_resting.eig_vec;
basis_Fourier = load(sprintf('%s/basis_Fourier_sphere-%s_evec_%i.mat', data_results_folder, hemisphere, num_modes));
basis_Fourier = basis_Fourier.eig_vec;
% RESULTS: task recon accuracy
% eigenmodes
load(sprintf('%s/task_reconstruction_corr_modes=%i_%s_%s.mat', data_results_folder, num_modes, parc_name, hemisphere), ...
'task_recon_corr_geometric', 'task_recon_corr_connectome', ...
'task_recon_corr_connectome_density_matched', 'task_recon_corr_EDR')
% removing low frequency
load(sprintf('%s/task_reconstruction_corr_remove_lowf_modes=%i_%s_%s.mat', data_results_folder, num_modes, parc_name, hemisphere), ...
'task_recon_corr_remove_lowf_geometric', 'task_recon_corr_remove_lowf_connectome', ...
'task_recon_corr_remove_lowf_connectome_density_matched', 'task_recon_corr_remove_lowf_EDR')
% removing high frequency
load(sprintf('%s/task_reconstruction_corr_remove_highf_modes=%i_%s_%s.mat', data_results_folder, num_modes, parc_name, hemisphere), ...
'task_recon_corr_remove_highf_geometric', 'task_recon_corr_remove_highf_connectome', ...
'task_recon_corr_remove_highf_connectome_density_matched', 'task_recon_corr_remove_highf_EDR')
% RESULTS: resting recon accuracy
% eigenmodes
load(sprintf('%s/resting_reconstruction_corr_modes=%i_%s_%s.mat', data_results_folder, num_modes, parc_name, hemisphere), ...
'resting_recon_corr_geometric', 'resting_recon_corr_connectome', ...
'resting_recon_corr_connectome_density_matched', 'resting_recon_corr_EDR')
% RESULTS: power spectrum (HCP, NeuroVault, noise)
load(sprintf('%s/spectrum_modes=%i_%s.mat', data_results_folder, num_modes, hemisphere), ...
'spectrum_HCP', 'spectrum_neurovault', 'spectrum_noise')
% RESULTS: parcellated empirical resting-state time series
load(sprintf('%s/S255_resting_empirical_%s.mat', data_results_folder, parc_name), 'resting_emp')
% RESULTS: model
model_fit = load(sprintf('%s/model_results_Glasser360_%s.mat', data_results_folder, hemisphere), ...
'FC_emp', 'FCD_emp', 'FC_model_wave', 'FCD_model_wave', ...
'FC_model_mass', 'FCD_model_mass', 'KS');
% RESULTS: wave model visual simulation
model_wave_visual = load(sprintf('%s/model_wave_neural_visual_%s.mat', data_results_folder, hemisphere), ...
'tspan', 'simulated_neural_visual_vertex', 'simulated_neural_visual_parcel', ...
'ROIS', 'ROI_names');
% RESULTS: wave model optimization
model_wave_optim = load(sprintf('%s/model_wave_rest_optimization_%s.mat', data_results_folder, hemisphere), ...
'rs_vec', 'optim_edge_FC', 'optim_node_FC', 'optim_FCD');
% RESULTS: spin rotations
load(sprintf('%s/spin_perm_id_%s_10000_%s.mat', data_results_folder, parc_name, hemisphere), 'perm_id')
%% DEFINE HCP TASK CONTRAST NAMES
tasks = {'SOCIAL', 'MOTOR', 'GAMBLING', 'WM', 'LANGUAGE', 'EMOTION', 'RELATIONAL'};
task_contrasts = get_HCP_task_contrasts;
contrasts = {};
counter = 0;
for ii = 1:length(tasks)
task = tasks{ii};
for jj=1:length(task_contrasts.(task))
counter = counter+1;
contrasts{counter,1} = task_contrasts.(task){jj};
end
end
task_contrasts_ind = struct();
counter = 0;
for ii = 1:length(tasks)
task = tasks{ii};
task_contrasts_ind.(task) = counter + (1:length(task_contrasts.(task)));
counter = counter + length(task_contrasts.(task));
end
% representative contrasts per task
representative_contrasts = {'social_tom_random'; ...
'motor_cue_avg'; ...
'gambling_punish_reward'; ...
'wm_2bk_0bk'; ...
'language_math_story'; ...
'emotion_faces_shapes'; ...
'relational_match_rel'};
%% DEFINE FIGURE-RELATED PROPERTIES
fontsize_axis = 10;
fontsize_label = 12;
%% SUPPLEMENTARY FIGURE 1
surface_to_plot = surface_midthickness;
medial_wall = find(~cortex);
mode_list = [1:5, 10,25,50,100,200];
num_modes_to_plot = length(mode_list);
factor_x_small = 1.05;
factor_x_big = 2.3;
factor_y = 1.1;
init_x = 0.05;
init_y = 0.01;
length_x = (0.99-init_x)/(factor_x_small + factor_x_big*(4-1) + 1);
length_y = (0.95-init_y)/(factor_y*(num_modes_to_plot-1) + 1);
fig = figure('Position', [200 200 1000 900]);
for basis_ind=1:4
if basis_ind==1
data_to_plot = basis_geometric;
basis_name = 'geometric';
elseif basis_ind==2
data_to_plot = basis_connectome;
basis_name = 'connectome';
elseif basis_ind==3
data_to_plot = basis_connectome_density_matched;
basis_name = {'connectome'; '(density matched)'};
elseif basis_ind==4
data_to_plot = basis_EDR;
basis_name = 'EDR';
end
for mode_ind=1:num_modes_to_plot
mode = mode_list(mode_ind);
data_to_plot(medial_wall,mode) = min(data_to_plot(:,mode))*1.1;
clims = [min(data_to_plot(:,mode)), max(data_to_plot(:,mode))];
if clims(2)<=0
clims(2) = 0.01;
end
ax1 = axes('Position', [init_x+factor_x_big*length_x*(basis_ind-1) init_y+factor_y*length_y*(-[mode_ind-num_modes_to_plot]) length_x length_y]);
obj1 = patch(ax1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot(:,mode), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
caxis(clims)
camlight('headlight')
material dull
colormap(ax1,[0.5,0.5,0.5; bluewhitered])
axis off
axis image
if basis_ind==1
annotation(fig, 'textbox', [ax1.Position(1)-0.03, ax1.Position(2)+ax1.Position(4)*0.45, 0.01, 0.01], 'string', {'mode'; sprintf('%i',mode)}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
ax2 = axes('Position', [init_x+factor_x_small*length_x+factor_x_big*length_x*(basis_ind-1) init_y+factor_y*length_y*(-[mode_ind-num_modes_to_plot]) length_x length_y]);
obj2 = patch(ax2, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot(:,mode), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([90 0]);
elseif strcmpi(hemisphere, 'rh')
view([-90 0]);
end
caxis(clims)
camlight('headlight')
material dull
colormap(ax2,[0.5,0.5,0.5; bluewhitered])
axis off
axis image
if mode_ind==1
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*1, ax2.Position(1)-ax1.Position(1)+ax2.Position(3), 0.01], 'string', basis_name, 'edgecolor', 'none', ...
'fontsize', fontsize_label, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
end
end
end
%% SUPPLEMENTARY FIGURE 2
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
fig = figure;
hold on;
for ii=1:length(tasks)
task = tasks{ii};
num_contrasts = length(task_contrasts_ind.(task));
for jj=1:num_contrasts
contrast = contrasts{task_contrasts_ind.(task)(jj)};
if jj==1
plot(nanmean(task_recon_corr_geometric.(contrast),1), 'color', colors(ii,:), 'linewidth', 2, 'displayname', lower(tasks{ii}))
else
plot(nanmean(task_recon_corr_geometric.(contrast),1), 'color', colors(ii,:), 'linewidth', 2, 'HandleVisibility', 'off')
end
end
end
hold off;
leg = legend('fontsize', fontsize_axis, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1], 'xtick', 50:50:200)
xlabel('number of modes', 'fontsize', fontsize_label)
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
%% SUPPLEMENTARY FIGURE 3
% load all data relevant to Supplementary Figure 3
data_SuppFigure3 = load(sprintf('%s/SuppFigure3.mat', data_figures_folder));
parc_name_list = {'Schaefer100', 'Schaefer200', 'Glasser360', 'Schaefer400', 'Schaefer600', 'Schaefer800', 'Schaefer1000'};
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
color_shade_cmap = repmat(colors(ii,:),length(parc_name_list),1);
brighten_vec = linspace(0,0.85,length(parc_name_list));
for parc_name_ind=1:length(parc_name_list)
color_shade_cmap(parc_name_ind,:) = brighten(color_shade_cmap(parc_name_ind,:), brighten_vec(parc_name_ind));
end
color_shade_cmap = flipud(color_shade_cmap);
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
plot(data_SuppFigure3.task_recon_corr_parcellations.(parc_name_interest).(contrast), 'color', color_shade_cmap(parc_name_ind,:), 'linewidth', 2, 'displayname', parc_name_interest)
end
hold off;
leg = legend('fontsize', fontsize_axis, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
if ii>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(task_name, 'fontsize', fontsize_axis, 'fontweight', 'normal')
if ii==1
annotation('textarrow', ax1.Position(1)+ax1.Position(3).*[0.7, 0.05], ...
ax1.Position(2)+ax1.Position(4).*[0.72, 0.92], 'String', {'decreasing'; 'resolution'}, ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center');
else
annotation('textarrow', ax1.Position(1)+ax1.Position(3).*[0.7, 0.05], ...
ax1.Position(2)+ax1.Position(4).*[0.72, 0.92], 'String', {''}, ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center');
end
end
% rest
ii = 8;
brighten_vec = linspace(0,0.8,length(parc_name_list));
color_shade_cmap = repmat(brighten_vec', 1, 3);
color_shade_cmap = flipud(color_shade_cmap);
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
plot(data_SuppFigure3.resting_recon_corr_parcellations.(parc_name_interest), 'color', color_shade_cmap(parc_name_ind,:), 'linewidth', 2, 'displayname', parc_name_interest)
end
hold off;
leg = legend('fontsize', fontsize_axis, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
xlabel('number of modes', 'fontsize', fontsize_label)
if ii==1
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title('rest', 'fontsize', fontsize_axis, 'fontweight', 'normal')
annotation('textarrow', ax1.Position(1)+ax1.Position(3).*[0.7, 0.05], ...
ax1.Position(2)+ax1.Position(4).*[0.735, 0.9], 'String', {''}, ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center');
%% SUPPLEMENTARY FIGURE 4
% load all data relevant to Supplementary Figure 4
data_SuppFigure4 = load(sprintf('%s/SuppFigure4.mat', data_figures_folder));
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:(length(representative_contrasts)+1)
if ii>length(representative_contrasts)
titlename = 'rest';
data_to_plot_template = nanmean(resting_recon_corr_geometric,1);
data_to_plot_individual = data_SuppFigure4.resting_recon_corr_individual;
else
contrast = representative_contrasts{ii};
task = lower(tasks{ii});
titlename = task;
data_to_plot_template = nanmean(task_recon_corr_geometric.(contrast),1);
data_to_plot_individual = data_SuppFigure4.task_recon_corr_individual.(contrast);
end
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
plot(1:num_modes, data_to_plot_template, 'color', colors(ii,:), 'linewidth', 2, 'displayname', 'template')
plot(1:num_modes, data_to_plot_individual, '--', 'color', colors(ii,:), 'linewidth', 2, 'displayname', 'individual-specific')
hold off;
if ii==length(representative_contrasts)+1
leg = legend('fontsize', fontsize_axis-1, 'interpreter', 'none', 'box', 'off', 'numcolumns', 1, ...
'Position', [ax1.Position(1)+ax1.Position(3)*0.5 ax1.Position(2)+ax1.Position(4)*0.61 ax1.Position(3)*0.5 ax1.Position(3)*0.2]);
leg.ItemTokenSize = leg.ItemTokenSize/1.3;
end
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1], 'xtick', 50:50:200)
if ii>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if ii==1 ||ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(titlename, 'fontsize', fontsize_axis, 'fontweight', 'normal', 'interpreter', 'none')
ax2 = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.42 ax1.Position(2)+ax1.Position(4)*0.18 ax1.Position(3)*0.5 ax1.Position(4)*0.3]);
plot(1:num_modes, data_to_plot_template-data_to_plot_individual, 'k-', 'linewidth', 2)
yline(0, 'k:');
set(ax2, 'fontsize', fontsize_axis-2, 'ticklength', [0.02 0.02], 'xlim', [2 num_modes], 'ylim', 0.05*[-1 1], 'xtick', 100:100:200)
xlabel('number of modes', 'fontsize', fontsize_label-2)
ylabel('difference', 'fontsize', fontsize_label-2)
end
%% SUPPLEMENTARY FIGURE 5
% load all data relevant to Supplementary Figure 5
data_SuppFigure5 = load(sprintf('%s/SuppFigure5.mat', data_figures_folder));
n = 200;
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:(length(representative_contrasts)+1)
if ii>length(representative_contrasts)
titlename = 'rest';
data_to_plot_template = resting_recon_corr_geometric(:,n);
data_to_plot_individual = data_SuppFigure5.resting_recon_corr_individual;
else
contrast = representative_contrasts{ii};
task = lower(tasks{ii});
titlename = task;
data_to_plot_template = task_recon_corr_geometric.(contrast)(:,n);
data_to_plot_individual = data_SuppFigure5.task_recon_corr_individual.(contrast);
end
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
plot([0 1], [0 1], 'k:')
plot(data_to_plot_individual, data_to_plot_template, '.', 'color', colors(ii,:), 'markersize', 10)
hold off;
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [0.8 1], 'ylim', [0.8 1], 'xtick', 0.8:0.1:1, 'ytick', 0.8:0.1:1)
if ii>4
xlabel('individual-specific accuracy', 'fontsize', fontsize_label)
end
if ii==1 ||ii==5
ylabel('template accuracy', 'fontsize', fontsize_label)
end
title(titlename, 'fontsize', fontsize_axis, 'fontweight', 'normal', 'interpreter', 'none')
ax2 = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.7 ax1.Position(2)+ax1.Position(4)*0.15 ax1.Position(3)*0.3 ax1.Position(4)*0.22]);
data_to_plot_diff = data_to_plot_template-data_to_plot_individual;
limits = [min(data_to_plot_diff), max(data_to_plot_diff)];
histogram(data_to_plot_diff, 12, 'facecolor', 0.7*ones(1,3))
set(ax2, 'fontsize', fontsize_axis-2, 'ticklength', [0.02 0.02], 'xlim', max(abs(limits))*[-1 1], ...
'xtick', [-max(abs(limits)), 0, max(abs(limits))], 'xticklabel', {sprintf('%.2f', -max(abs(limits))), '0', sprintf('%.2f', max(abs(limits)))})
xlabel('difference', 'fontsize', fontsize_label-2)
ylabel('count', 'fontsize', fontsize_label-2)
box on
end
%% SUPPLEMENTARY FIGURE 6
% load all data relevant to Supplementary Figure 6
data_SuppFigure6 = load(sprintf('%s/SuppFigure6.mat', data_figures_folder));
num_modes_temp = 500;
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:(length(representative_contrasts)+1)
if ii>length(representative_contrasts)
titlename = 'rest';
data_to_plot_template = data_SuppFigure6.resting_recon_corr_geometric_500;
data_to_plot_individual = data_SuppFigure6.resting_recon_corr_individual_500;
else
contrast = representative_contrasts{ii};
task = lower(tasks{ii});
titlename = task;
data_to_plot_template = data_SuppFigure6.task_recon_corr_geometric_500.(contrast);
data_to_plot_individual = data_SuppFigure6.task_recon_corr_individual_500.(contrast);
end
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
plot(1:num_modes_temp, data_to_plot_template, 'color', colors(ii,:), 'linewidth', 2, 'displayname', 'template')
plot(1:num_modes_temp, data_to_plot_individual, '--', 'color', colors(ii,:), 'linewidth', 2, 'displayname', 'individual-specific')
hold off;
if ii==length(representative_contrasts)+1
leg = legend('fontsize', fontsize_axis-1, 'interpreter', 'none', 'box', 'off', 'numcolumns', 1, ...
'Position', [ax1.Position(1)+ax1.Position(3)*0.5 ax1.Position(2)+ax1.Position(4)*0.61 ax1.Position(3)*0.5 ax1.Position(3)*0.2]);
leg.ItemTokenSize = leg.ItemTokenSize/1.35;
end
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [200 num_modes_temp], 'ylim', [0.8 1], 'xtick', 200:100:500, 'ytick', 0.8:0.1:1)
if ii>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if ii==1 ||ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(titlename, 'fontsize', fontsize_axis, 'fontweight', 'normal', 'interpreter', 'none')
ax2 = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.42 ax1.Position(2)+ax1.Position(4)*0.18 ax1.Position(3)*0.5 ax1.Position(4)*0.3]);
plot(1:num_modes_temp, data_to_plot_template-data_to_plot_individual, 'k-', 'linewidth', 2)
yline(0, 'k:');
set(ax2, 'fontsize', fontsize_axis-2, 'ticklength', [0.02 0.02], 'xlim', [200 num_modes_temp], 'ylim', 0.025*[-1 1], 'xtick', 200:100:500)
xlabel('number of modes', 'fontsize', fontsize_label-2)
ylabel('difference', 'fontsize', fontsize_label-2)
end
%% SUPPLEMENTARY FIGURE 7
% load all data relevant to Supplementary Figure 7
data_SuppFigure7 = load(sprintf('%s/SuppFigure7.mat', data_figures_folder));
parc_name_list = {'Schaefer100', 'Schaefer200', 'Glasser360', 'Schaefer400', 'Schaefer600', 'Schaefer800', 'Schaefer1000'};
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
if parc_name_ind<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(parc_name_ind-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(parc_name_ind-4-1)+0.12 init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
plot(data_SuppFigure7.resting_recon_corr_parcellations_geometric.(parc_name_interest), 'color', colors2(1,:), 'linewidth', 2, 'displayname', 'geometric')
plot(data_SuppFigure7.resting_recon_corr_parcellations_connectome.(parc_name_interest), 'color', colors2(2,:), 'linewidth', 2, 'displayname', 'connectome')
plot(data_SuppFigure7.resting_recon_corr_parcellations_connectome_density_matched.(parc_name_interest), 'color', colors2(3,:), 'linewidth', 2, 'displayname', 'connectome (density matched)')
plot(data_SuppFigure7.resting_recon_corr_parcellations_EDR.(parc_name_interest), 'color', colors2(4,:), 'linewidth', 2, 'displayname', 'EDR')
hold off;
if parc_name_ind==length(parc_name_list)
leg = legend('fontsize', fontsize_axis, 'interpreter', 'none', 'box', 'off', 'numcolumns', 1, ...
'Position', [ax1.Position(1)+ax1.Position(3)*0.7 ax1.Position(2)+ax1.Position(4)*0.1 ax1.Position(3)*0.5 ax1.Position(3)*0.5]);
leg.ItemTokenSize = leg.ItemTokenSize/2;
end
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
if parc_name_ind>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if parc_name_ind==1 || parc_name_ind==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(parc_name_interest, 'fontsize', fontsize_axis, 'fontweight', 'normal')
end
%% SUPPLEMENTARY FIGURE 8
% load all data relevant to Supplementary Figure 8
data_SuppFigure8 = load(sprintf('%s/SuppFigure8.mat', data_figures_folder));
parc_name_list = {'Schaefer100', 'Schaefer200', 'Glasser360', 'Schaefer400', 'Schaefer600', 'Schaefer800', 'Schaefer1000'};
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.08;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
yticks = [];
for ii=1:length(tasks)
yticks = [yticks, mean(task_contrasts_ind.(tasks{ii}))];
end
recon_corr_combined = zeros(length(contrasts), num_modes, length(parc_name_list));
clims_combined = zeros(length(parc_name_list),2);
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
data_1 = data_SuppFigure8.task_recon_corr_parcellations_geometric.(parc_name_interest);
% connectome eigenmodes
data_2 = data_SuppFigure8.task_recon_corr_parcellations_connectome.(parc_name_interest);
recon_corr = [];
for ii=1:length(contrasts)
contrast = contrasts{ii};
temp_1 = nanmean(data_1.(contrast),1);
temp_2 = nanmean(data_2.(contrast),1);
recon_corr = cat(1, recon_corr, temp_1-temp_2);
end
recon_corr_combined(:,:,parc_name_ind) = recon_corr;
clim_max = max(recon_corr(:,2:end),[],'all');
clim_min = min(recon_corr(:,2:end),[],'all');
clims_combined(parc_name_ind,:) = [clim_min, clim_max];
end
fig = figure('Position', [200 200 1000 600]);
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
data_to_plot = recon_corr_combined(:,:,parc_name_ind);
clim = [min(clims_combined(:,1)), max(clims_combined(:,2))]; % make absolute colorbar for all parcellations
if parc_name_ind<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(parc_name_ind-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(parc_name_ind-4-1)+0.12 init_y+factor_y*length_y*(1-1) length_x length_y]);
end
imagesc(data_to_plot)
for ii=1:length(contrasts)-1
yline(ii+0.5, 'k-');
end
for ii=1:length(tasks)-1
yline(task_contrasts_ind.(tasks{ii})(end)+0.5, 'k-', 'linewidth', 2);
end
caxis(clim)
colormap(ax1, bluewhitered)
set(ax1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ytick', yticks, 'yticklabel', {}, 'ticklabelinterpreter', 'none')
if parc_name_ind>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if parc_name_ind==1 || parc_name_ind==5
set(ax1, 'yticklabel', lower(tasks))
end
title(parc_name_interest, 'fontsize', fontsize_axis, 'fontweight', 'normal')
end
cbar = colorbar;
set(cbar, 'ticklength', 0.02, 'Position', [ax1.Position(1)+ax1.Position(3)*1.1 ax1.Position(2) 0.01 ax1.Position(4)], ...
'fontsize', fontsize_axis-2);
ylabel(cbar, {'reconstruction accuracy difference'; ['(geometric ' char(8211) ' connectome)']}, 'fontsize', fontsize_label)
%% SUPPLEMENTARY FIGURE 9
% load all data relevant to Supplementary Figure 9
data_SuppFigure9 = load(sprintf('%s/SuppFigure9.mat', data_figures_folder));
density_list = 1:13;
density_interest_list = 1:2:13;
density_interest_list_ind = dsearchn(density_list', density_interest_list');
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.07;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
color_shade_cmap = repmat(colors(ii,:),length(density_interest_list),1);
brighten_vec = linspace(0,0.85,length(density_interest_list));
for density_ind=1:length(density_interest_list)
color_shade_cmap(density_ind,:) = brighten(color_shade_cmap(density_ind,:), brighten_vec(density_ind));
end
color_shade_cmap = flipud(color_shade_cmap);
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
plot(data_SuppFigure9.task_recon_corr_geometric.(contrast), '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
for density_ind=1:length(density_interest_list)
density = density_interest_list(density_ind);
density_num = density_interest_list_ind(density_ind);
plot(data_SuppFigure9.task_recon_corr_connectome_density.(contrast)(density_num,:), 'color', color_shade_cmap(density_ind,:), 'linewidth', 2, 'displayname', sprintf('density = %.1f%%', density))
end
hold off;
leg = legend('fontsize', fontsize_axis-1, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/1.4;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
if ii>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(task_name, 'fontsize', fontsize_axis, 'fontweight', 'normal')
end
% rest
ii = 8;
brighten_vec = linspace(0,0.8,length(density_interest_list));
color_shade_cmap = repmat(brighten_vec', 1, 3);
color_shade_cmap = flipud(color_shade_cmap);
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
plot(data_SuppFigure9.resting_recon_corr_geometric, '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
for density_ind=1:length(density_interest_list)
density = density_interest_list(density_ind);
density_num = density_interest_list_ind(density_ind);
plot(data_SuppFigure9.resting_recon_corr_connectome_density(density_num,:), 'color', color_shade_cmap(density_ind,:), 'linewidth', 2, 'displayname', sprintf('density = %.1f%%', density))
end
hold off;
leg = legend('fontsize', fontsize_axis-1, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/1.4;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
if ii>4
xlabel('number of modes', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title('rest', 'fontsize', fontsize_axis, 'fontweight', 'normal')
%% SUPPLEMENTARY FIGURE 10
% load all data relevant to Supplementary Figure 10
data_SuppFigure10 = load(sprintf('%s/SuppFigure10.mat', data_figures_folder));
density_list = 0.1:0.1:5;
density_interest_list = [0.1, 1.55];
density_interest_ind = dsearchn(density_list', density_interest_list');
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.06;
init_y = 0.07;
length_x = (1 - 1.4*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
data_to_plot = data_SuppFigure10.task_recon_corr_connectome_density.(contrast);
hold on;
plot(density_interest_list(1)*ones(1,2), [min(data_to_plot), data_to_plot(density_interest_ind(1))-0.001], 'k:', 'linewidth', 1.5);
plot(density_interest_list(2)*ones(1,2), [min(data_to_plot), data_to_plot(density_interest_ind(2))-0.001], 'k:', 'linewidth', 1.5);
plot(density_list, data_to_plot, '.-', 'color', colors(ii,:), 'markersize', 25)
yline(data_SuppFigure10.task_recon_corr_geometric.(contrast), 'k-', 'geometric', 'linewidth', 1.5, 'fontsize', fontsize_axis, 'labelverticalalignment', 'bottom');
hold off;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [-0.2, 5], 'xtick', [0,1:5], ...
'ylim', [min(data_to_plot), max(get(gca,'ylim'))])
if ii>4
xlabel('density (%)', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(task_name, 'fontsize', fontsize_axis, 'fontweight', 'normal')
end
% rest
ii = 8;
data_to_plot = data_SuppFigure10.resting_recon_corr_connectome_density;
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
plot(density_interest_list(1)*ones(1,2), [min(data_to_plot), data_to_plot(density_interest_ind(1))-0.001], 'k:', 'linewidth', 1.5);
plot(density_interest_list(2)*ones(1,2), [min(data_to_plot), data_to_plot(density_interest_ind(2))-0.001], 'k:', 'linewidth', 1.5);
plot(density_list, data_to_plot, '.-', 'color', colors(ii,:), 'markersize', 25)
yline(data_SuppFigure10.resting_recon_corr_geometric, 'k-', 'geometric', 'linewidth', 1.5, 'fontsize', fontsize_axis, 'labelverticalalignment', 'bottom');
hold off;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [-0.2, 5], 'xtick', [0,1:5], ...
'ylim', [min(data_to_plot), max(get(gca,'ylim'))])
if ii>4
xlabel('density (%)', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title('rest', 'fontsize', fontsize_axis, 'fontweight', 'normal')
%% SUPPLEMENTARY FIGURE 11
% load all data relevant to Supplementary Figure 11
data_SuppFigure11 = load(sprintf('%s/SuppFigure11.mat', data_figures_folder));
parc_name_list = {'Schaefer400', 'Schaefer600', 'Schaefer800', 'Schaefer1000'};
cmap1 = lines(7);
cmap2 = cbrewer('qual', 'Set1', 8, 'pchip');
colors = [cmap1(4,:); cmap2(3,:); cmap1(6,:); cmap2(1,:); cmap1(3,:); cmap2(7,:); cmap2(8,:); 0 0 0];
colors2 = [0 0 0; cmap1([4,5,1:3,6:7],:)];
factor_x = 1.3;
factor_y = 1.3;
init_x = 0.05;
init_y = 0.08;
length_x = (1 - 1.5*init_x)/(factor_x*(4-1) + 1);
length_y = (1 - 1.8*init_y)/(factor_y*(2-1) + 1);
fig = figure('Position', [200 200 1000 600]);
for ii=1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
color_shade_cmap = repmat(colors(ii,:),length(parc_name_list),1);
brighten_vec = linspace(0,0.85,length(parc_name_list));
for parc_name_ind = 1:length(parc_name_list)
color_shade_cmap(parc_name_ind,:) = brighten(color_shade_cmap(parc_name_ind,:), brighten_vec(parc_name_ind));
end
color_shade_cmap = flipud(color_shade_cmap);
if ii<=4
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
end
hold on;
data_to_plot = data_SuppFigure11.task_recon_corr_geometric.(contrast);
data_to_plot_x = 100*(1:num_modes)/sum(cortex);
plot(data_to_plot_x, data_to_plot, '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
for parc_name_ind = 1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
parc = dlmread(filename_common_parcellation(parc_name_interest, hemisphere));
parcels = unique(parc(parc>0));
num_parcels = length(unique(parc(parc>0)));
if num_parcels<200
num_modes = num_parcels;
else
num_modes = 200;
end
data_to_plot = data_SuppFigure11.task_recon_corr_discrete_connectome.(parc_name_interest).(contrast);
data_to_plot_x = 100*(1:num_modes)/num_parcels;
plot(data_to_plot_x, data_to_plot, 'color', color_shade_cmap(parc_name_ind,:), 'linewidth', 2, 'displayname', parc_name_interest)
end
hold off;
leg = legend('fontsize', fontsize_axis-1, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/1.4;
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1e-4, 1e2], 'ylim', [0 1], 'xscale', 'linear')
if ii>4
xlabel('% of available modes', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title(task_name, 'fontsize', fontsize_axis, 'fontweight', 'normal')
ax1_inset = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.5 ax1.Position(2)+ax1.Position(4)*0.55 length_x*0.48 length_y*0.15]);
data_to_plot = data_SuppFigure11.task_recon_corr_geometric.(contrast);
data_to_plot_x = 100*(1:num_modes)/sum(cortex);
plot(data_to_plot_x, data_to_plot, '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
set(gca, 'fontsize', fontsize_axis-2, 'ticklength', [0.02 0.02], 'xtick', [0.2, 0.4, 0.6], 'ylim', [0 1], 'xscale', 'linear')
xlabel('%', 'fontsize', fontsize_label-2)
box on
end
% rest
ii = 8;
brighten_vec = linspace(0,0.8,length(parc_name_list));
color_shade_cmap = repmat(brighten_vec', 1, 3);
color_shade_cmap = flipud(color_shade_cmap);
ax1 = axes('Position', [init_x+factor_x*length_x*(ii-4-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
data_to_plot = data_SuppFigure11.resting_recon_corr_geometric;
data_to_plot_x = 100*(1:num_modes)/sum(cortex);
plot(data_to_plot_x, data_to_plot, '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
for parc_name_ind=1:length(parc_name_list)
parc_name_interest = parc_name_list{parc_name_ind};
parc = dlmread(filename_common_parcellation(parc_name_interest, hemisphere));
parcels = unique(parc(parc>0));
num_parcels = length(unique(parc(parc>0)));
if num_parcels<200
num_modes = num_parcels;
else
num_modes = 200;
end
data_to_plot = data_SuppFigure11.resting_recon_corr_discrete_connectome.(parc_name_interest);
data_to_plot_x = 100*(1:num_modes)/num_parcels;
plot(data_to_plot_x, data_to_plot, 'color', color_shade_cmap(parc_name_ind,:), 'linewidth', 2, 'displayname', parc_name_interest)
end
hold off;
leg = legend('fontsize', fontsize_axis-1, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/1.4;
if ii>4
xlabel('% of available modes', 'fontsize', fontsize_label)
end
if ii==1 || ii==5
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
end
title('rest', 'fontsize', fontsize_axis, 'fontweight', 'normal')
ax1_inset = axes('Position', [ax1.Position(1)+ax1.Position(3)*0.5 ax1.Position(2)+ax1.Position(4)*0.55 length_x*0.48 length_y*0.15]);
data_to_plot = data_SuppFigure11.resting_recon_corr_geometric;
data_to_plot_x = 100*(1:num_modes)/sum(cortex);
plot(data_to_plot_x, data_to_plot, '--', 'color', 'k', 'linewidth', 2, 'displayname', 'geometric')
set(gca, 'fontsize', fontsize_axis-2, 'ticklength', [0.02 0.02], 'xtick', [0.2, 0.4, 0.6], 'ylim', [0 1], 'xscale', 'linear')
xlabel('%', 'fontsize', fontsize_label-2)
box on
%% SUPPLEMENTARY FIGURE 12
surface_to_plot = surface_midthickness;
medial_wall = find(~cortex);