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generate_paper_figures_main_bioRxiv.m
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generate_paper_figures_main_bioRxiv.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% generate_paper_figures_main_bioRxiv.m
%%%
%%% MATLAB script to generate the Main 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;
%% FIGURE 1
% load all data relevant to Figure 1
data_Figure1 = load(sprintf('%s/Figure1.mat', data_figures_folder));
surface_to_plot = surface_midthickness;
num_modes = 200;
parc_name = 'Glasser360';
N_interest = [10, 100, 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],:)];
% load parcellation
parc_name = 'Glasser360';
parc = dlmread(filename_common_parcellation(parc_name, hemisphere));
num_parcels = length(unique(parc(parc>0)));
fig = figure('Position', [200 200 1000 800]);
% =========================================================================
% A left: downsampled surface
% =========================================================================
mode_list = [1,2,3,4,20];
num_modes_to_plot = length(mode_list);
factor_x = 1.02;
factor_y = 1.08;
init_x = 0.02;
init_y = 0.52;
length_x = (0.77 - 1.*init_x)/(factor_x*(num_modes_to_plot-1) + 1);
length_y = (0.98 - init_y)/(factor_y*(2-1) + 1);
num_downsampled_vertices = 500;
surface_downsampled = gifti(sprintf('%s/fsLR_%i_midthickness-%s.surf.gii', data_template_surfaces_folder, num_downsampled_vertices, hemisphere));
data_to_plot = ones(size(surface_downsampled.vertices,1),1);
ax1 = axes('Position', [init_x init_y+factor_y*length_y*(2-1) length_x length_y]);
patch(ax1, 'Vertices', surface_downsampled.vertices, 'Faces', surface_downsampled.faces, 'FaceVertexCData', data_to_plot, ...
'EdgeColor', 'k', 'FaceColor', 'interp', 'FaceLighting', 'gouraud', 'linewidth', 0.5);
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
colormap(ax1, 0.7*ones(1,3))
axis off
axis image
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.75, ax1.Position(3), 0.05], 'string', {'surface mesh'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'arrow', [ax1.Position(1)+ax1.Position(3), init_x+factor_x*length_x*(1+1.3-1)], ...
(ax1.Position(2)+ax1.Position(4)*0.5)*ones(1,2), 'Color', 'k')
annotation(fig, 'textbox', [ax1.Position(1)+ax1.Position(3), ax1.Position(2)+ax1.Position(4)*0.55, 0.05, 0.05], 'string', '$\Delta \psi = -\lambda \psi$', 'edgecolor', 'none', ...
'fontsize', fontsize_axis+2, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex')
% =========================================================================
% A right: eigenmodes
% =========================================================================
for mode_ind=1:num_modes_to_plot
mode = mode_list(mode_ind);
if mode_ind==num_modes_to_plot
ax1 = axes('Position', [0.03+init_x+factor_x*length_x*(mode_ind+1.3-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
else
ax1 = axes('Position', [init_x+factor_x*length_x*(mode_ind+1.3-1) init_y+factor_y*length_y*(2-1) length_x length_y]);
end
patch(ax1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', basis_geometric(:,mode), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax1,bluewhitered)
axis off
axis image
if mode_ind==1
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.8, ax1.Position(3), 0.05], 'string', {'mode 1'; '\psi_1'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==2
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.8, ax1.Position(3), 0.05], 'string', {'mode 2'; '\psi_2'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==3
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.8, ax1.Position(3), 0.05], 'string', {'mode 3'; '\psi_3'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==4
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.8, ax1.Position(3), 0.05], 'string', {'mode 4'; '\psi_4'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
else
annotation(fig, 'textbox', [ax1.Position(1), ax1.Position(2)+ax1.Position(4)*0.8, ax1.Position(3), 0.05], 'string', {'mode N'; '\psi_N'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
end
annotation(fig, 'textbox', [ax1.Position(1)-0.056, ax1.Position(2)+ax1.Position(4)*0.45, 0.08, 0.02], 'string', '...', 'edgecolor', 'none', ...
'fontsize', fontsize_axis*2, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'arrow', [0.07+init_x+factor_x*length_x*(1.5+1.3-1), 0.85], ...
(ax1.Position(2)+0.025)*ones(1,2), 'Color', 'k')
annotation(fig, 'textbox', [0.0+init_x+factor_x*length_x*(1.06+1.3-1), ax1.Position(2)+0.01, 0.13, 0.02], 'string', {'low frequency /'; 'long wavelength'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'textbox', [0.855, ax1.Position(2)+0.01, 0.13, 0.02], 'string', {'high frequency /'; 'short wavelength'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
% =========================================================================
% B: mode decomposition
% =========================================================================
ax2 = axes('Position', [init_x+factor_x*length_x*(1-1) init_y+factor_y*length_y*(1-1) length_x*3.5 length_y*2]);
xstart = 0.07;
ystart = 0.6;
t1 = text(xstart+0.35, ystart-0.25, '$\displaystyle y(\mathbf{r},t) = \sum_{j=1}^N a_j(t)\psi_j(\mathbf{r})$', ...
'fontsize', fontsize_label*1.3, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
t2 = text(xstart, ystart-0.35, '$\displaystyle y$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.22, t2.Position(2), '$\displaystyle \psi_1$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.22*2, t2.Position(2), '$\displaystyle \psi_2$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.22*3, t2.Position(2), '$\displaystyle \psi_3$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
t3 = text(xstart+0.11, ystart-0.45, '$\displaystyle = a_1 \times$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.11*3, t3.Position(2), '$\displaystyle + a_2 \times$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.11*5, t3.Position(2), '$\displaystyle + a_3 \times$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
text(xstart+0.11*7, t3.Position(2), '$\displaystyle + \ldots$', ...
'fontsize', fontsize_label*1.2, 'horizontalalignment', 'center', 'verticalalignment', 'middle', 'interpreter', 'latex');
axis off
for mode_ind=1:4
if mode_ind>1
mode = mode_list(mode_ind-1);
end
ax2_1 = axes('Position', [0.005+init_x+factor_x*length_x*(mode_ind-1)*0.76 init_y+factor_y*length_y*(0.8-1) length_x*0.4 length_y]);
if mode_ind==1
patch(ax2_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_Figure1.task_map_emp.('social_tom_random'), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
else
patch(ax2_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', basis_geometric(:,mode), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
end
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax2_1,bluewhitered)
axis off
axis image
end
% =========================================================================
% C: data
% =========================================================================
ax3 = axes('Position', [init_x+factor_x*length_x*(4.4-1) init_y+factor_y*length_y*(0.9-1) length_x*3 length_y*0.9]);
axis off
data_to_plot = data_resting;
data_to_plot = calc_normalize_timeseries(data_to_plot');
data_to_plot(isnan(data_to_plot)) = 0;
data_to_plot = data_to_plot';
for ii=1:3
ax3_1 = axes('Position', [ax3.Position(1)+ax3.Position(3)*(0.15+0.05*(ii-1)) ax3.Position(2)+ax3.Position(4)*(0.3-0.23*(ii-1)) length_x*0.4 length_y]);
if ii==1
patch(ax3_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_Figure1.task_map_emp.('motor_cue_avg'), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
elseif ii==2
patch(ax3_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_Figure1.task_map_emp.('emotion_faces_shapes'), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
elseif ii==3
patch(ax3_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_Figure1.task_map_emp.('wm_2bk_0bk'), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
end
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax3_1,bluewhitered)
axis off
axis image
ax3_2 = axes('Position', [ax3_1.Position(1)+0.15 ax3_1.Position(2) ax3_1.Position(3) ax3_1.Position(4)]);
patch(ax3_2, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot(:,ii), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax3_2,bluewhitered)
axis off
axis image
annotation(fig, 'textbox', [ax3_1.Position(1)-0.065, ax3_1.Position(2)+ax3_1.Position(4)*0.4, 0.08, 0.02], 'string', sprintf('task %i', ii), 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
if ii==1
annotation(fig, 'arrow', [ax3.Position(1)+ax3.Position(3)*(0.45+0.05*(1-1)), ax3.Position(1)+ax3.Position(3)*(0.47+0.05*(3-1))], ...
[length_y*0.5+ax3.Position(2)+ax3.Position(4)*(0.3-0.22*(1-1)), length_y*0.4+ax3.Position(2)+ax3.Position(4)*(0.3-0.22*(3-1))], ...
'HeadLength', 6, 'HeadWidth', 6, 'Color', 'k')
end
if ii==2
annotation(fig, 'textbox', [ax3_2.Position(1)-0.068, ax3_2.Position(2)+ax3_2.Position(4)*0.35, 0.08, 0.02], 'string', 'time', 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'textbox', [ax3_1.Position(1)-0.05, ax3.Position(2)+ax3.Position(4)*1.05, ax3_1.Position(3)+0.03, 0.02], 'string', 'task-evoked', 'edgecolor', 'none', ...
'fontsize', fontsize_label, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
annotation(fig, 'textbox', [ax3_2.Position(1)+0.04, ax3.Position(2)+ax3.Position(4)*1.05, ax3_2.Position(3)+0.05, 0.02], 'string', 'spontaneous', 'edgecolor', 'none', ...
'fontsize', fontsize_label, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
end
ax3_3 = axes('Position', [ax3.Position(1)+ax3.Position(3)*(0.62+0.05*(ii-1)) ax3.Position(2)+ax3.Position(4)*(1.4-1) length_x length_y*0.43]);
parc_name = 'Glasser360';
parc = dlmread(filename_common_parcellation(parc_name, hemisphere));
data_parc_emp = calc_parcellate(parc, data_resting);
data_parc_emp = calc_normalize_timeseries(data_parc_emp');
data_parc_emp(isnan(data_parc_emp)) = 0;
imagesc(data_parc_emp'*data_parc_emp/T)
caxis([-1 1])
colormap(ax3_3, bluewhitered)
% colorbar
set(gca, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xtick', [], 'ytick', [])
axis image
xlabel('region', 'fontsize', fontsize_axis)
ylabel('region', 'fontsize', fontsize_axis)
title('FC', 'fontsize', fontsize_axis, 'fontweight', 'normal')
% =========================================================================
% D: reconstruction accuracy
% =========================================================================
ax5 = axes('Position', [0.05 0.1 0.35 0.35]);
hold on;
for jj=1:length(N_interest)
xline(N_interest(jj), 'k:', 'linewidth', 1.5, 'HandleVisibility', 'off');
end
for ii=1:length(representative_contrasts)
contrast = representative_contrasts{ii};
plot(nanmean(task_recon_corr_geometric.(contrast),1), 'color', colors(ii,:), 'linewidth', 2, 'displayname', lower(tasks{ii}))
end
plot(nanmean(resting_recon_corr_geometric,1), 'color', colors(ii+1,:), 'linewidth', 2, 'displayname', lower('REST'))
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)
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
% =========================================================================
% E: empirical and reconstructed task maps
% =========================================================================
factor_x = 1;
factor_y = 1.02;
init_x = ax5.Position(1)+ax5.Position(3)*1.28;
init_y = ax5.Position(2)*0.5;
length_x = (ax5.Position(3)*0.9)/(factor_x*(length(N_interest)+1-1) + 1);
length_y = (ax5.Position(2)+ax5.Position(4)-init_y)/(factor_y*(length(representative_contrasts)-1) + 1);
% empirical
for ii = 1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
data_to_plot = data_Figure1.task_map_emp.(contrast);
ax6 = axes('Position', [init_x+factor_x*length_x*(1-1) init_y+factor_y*length_y*(length(representative_contrasts)-ii) length_x length_y]);
patch(ax6, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax6, bluewhitered)
axis off
axis image
annotation(fig, 'textbox', [ax6.Position(1)-0.035, ax6.Position(2)+ax6.Position(4)*0.4, 0.01, 0.01], 'string', task_name, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'middle')
if ii==1
annotation(fig, 'textbox', [ax6.Position(1)+ax6.Position(3)*0.44, ax6.Position(2)+ax6.Position(4)*0.93, 0.01, 0.01], 'string', 'data', 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
end
end
% reconstruction
for ii = 1:length(representative_contrasts)
contrast = representative_contrasts{ii};
task_name = lower(tasks{ii});
for jj = 1:length(N_interest)
N = N_interest(jj);
data_to_plot = data_Figure1.task_map_recon.(contrast)(:,jj);
ax6 = axes('Position', [init_x+factor_x*length_x*(jj) init_y+factor_y*length_y*(length(representative_contrasts)-ii) length_x length_y]);
patch(ax6, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax6, bluewhitered)
axis off
axis image
if ii==1
annotation(fig, 'textbox', [ax6.Position(1)+ax6.Position(3)*0.44-0.035, ax6.Position(2)+ax6.Position(4)*0.93, 0.08, 0.01], 'string', {'recon'; sprintf('(N = %i)', N)}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
end
if ii==1 && jj>=2
annotation(fig, 'arrow', [ax6.Position(1)+ax6.Position(3)*0.6, ax6.Position(1)+ax6.Position(3)*0.57], ...
[ax6.Position(2)+ax6.Position(4)*0.1, ax6.Position(2)+ax6.Position(4)*0.18], ...
'LineStyle', 'none', 'HeadLength', 6, 'HeadWidth', 6, 'Color', 'k')
annotation(fig, 'arrow', [ax6.Position(1)+ax6.Position(3)*0.87, ax6.Position(1)+ax6.Position(3)*0.84], ...
[ax6.Position(2)+ax6.Position(4)*0.24, ax6.Position(2)+ax6.Position(4)*0.32], ...
'LineStyle', 'none', 'HeadLength', 6, 'HeadWidth', 6, 'Color', 'k')
elseif ii==2 && jj>=2
annotation(fig, 'arrow', [ax6.Position(1)+ax6.Position(3)*0.82, ax6.Position(1)+ax6.Position(3)*0.79], ...
[ax6.Position(2)+ax6.Position(4)*0.8, ax6.Position(2)+ax6.Position(4)*0.72], ...
'LineStyle', 'none', 'HeadLength', 6, 'HeadWidth', 6, 'Color', 'k')
end
end
end
% arrows from D to E and surface reconstructions
yloc_min = 0.1;
yloc_space = 0.2;
for jj = 1:length(N_interest)
N = N_interest(jj);
annotation(fig, 'line', (ax5.Position(1)+ax5.Position(3)*(N/num_modes))*ones(1,2), ...
[init_y*0.75, init_y*(yloc_min+yloc_space*(jj-1))], ...
'Color', 'k', 'LineStyle', ':', 'linewidth', 1.5)
annotation(fig, 'line', [ax5.Position(1)+ax5.Position(3)*(N/num_modes), init_x+factor_x*length_x*(jj)+length_x*0.5], ...
(init_y*(yloc_min+yloc_space*(jj-1)))*ones(1,2), ...
'Color', 'k', 'LineStyle', ':', 'linewidth', 1.5)
annotation(fig, 'arrow', init_x+factor_x*length_x*(jj)+length_x*0.5*ones(1,2), ...
[init_y*(yloc_min+yloc_space*(jj-1)), init_y*0.99], ...
'HeadLength', 6, 'HeadWidth', 6, 'Color', 'k', 'LineStyle', ':', 'linewidth', 1.5)
basis = basis_geometric(:,1:N);
coeffs = calc_eigendecomposition(surface_to_plot.vertices, basis);
new_vertices = basis*coeffs;
surface_reconstruction = surface_to_plot;
surface_reconstruction.vertices = new_vertices;
inset_length = 0.05;
ax5_inset = axes('Position', [ax5.Position(1)+ax5.Position(3)*(N/num_modes)-inset_length/2 ax5.Position(2)+ax5.Position(4) inset_length inset_length]);
patch(ax5_inset, 'Vertices', surface_reconstruction.vertices, 'Faces', surface_reconstruction.faces, 'FaceVertexCData', zeros(size(surface_reconstruction.vertices,1),1), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax5_inset, 0.7*ones(1,3))
axis off
axis image
end
% =========================================================================
% F: FC
% =========================================================================
extra_diagonal = 7;
factor_y = 1.6;
init_x = ax6.Position(1)+ax6.Position(3)*0.45;
init_y = init_y*0.95;
length_x = ax5.Position(3)*0.9;
length_y = (ax5.Position(2)+ax5.Position(4)-init_y)/(factor_y*(length(N_interest)-1) + 1);
for jj = 1:length(N_interest)
N = N_interest(jj);
point_start = 1-0.5; point_end = num_parcels+extra_diagonal+0.5;
ax7 = axes('Position', [init_x init_y+factor_y*length_y*(length(N_interest)-jj) length_x length_y]);
imagesc(data_Figure1.FC_combined(:,:,jj))
hold on;
plot([point_start+1+extra_diagonal point_end], point_start*ones(1,2), 'k-', 'linewidth', 1)
plot(point_end*ones(1,2), [point_start point_end-1-extra_diagonal], 'k-', 'linewidth', 1)
plot([point_start point_end-1-extra_diagonal], point_end*ones(1,2), 'k-', 'linewidth', 1)
plot(point_start*ones(1,2), [point_start+1+extra_diagonal point_end], 'k-', 'linewidth', 1)
for ii=1:num_parcels-1
plot((point_start+1+extra_diagonal+(ii-1))*ones(1,2), point_start+[0 1]+(ii-1), 'k-', 'linewidth', 1)
plot(point_start+1+extra_diagonal+[0 1]+(ii-1), (point_start+1+(ii-1))*ones(1,2), 'k-', 'linewidth', 1)
plot((point_start+1+(ii-1))*ones(1,2), point_start+1+extra_diagonal+[0 1]+(ii-1), 'k-', 'linewidth', 1)
plot(point_start+[0 1]+(ii-1), (point_start+1+extra_diagonal+(ii-1))*ones(1,2), 'k-', 'linewidth', 1)
end
hold off;
caxis([-1 1])
colormap(ax7, bluewhitered)
cbar = colorbar;
set(ax7, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02])
ylabel(cbar, 'FC', 'fontsize', fontsize_label)
axis image
axis off
text(-5, (num_parcels+extra_diagonal)/2, 'data', 'rotation', 90, 'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
annotation(fig, 'textbox', [ax7.Position(1)+ax7.Position(3)*0.35, ax7.Position(2)+ax7.Position(4)*0.95, 0.08, 0.01], 'string', {'recon'; sprintf('(N = %i)', N)}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'horizontalalignment', 'center', 'verticalalignment', 'bottom')
end
%%% panel letters
annotation(fig, 'textbox', [0.01, 0.99, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.01, 0.73, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.52, 0.73, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.01, 0.5, 0.01, 0.01], 'string', 'D', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.47, 0.5, 0.01, 0.01], 'string', 'E', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.84, 0.5, 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_figures_folder));
surface_to_plot = surface_midthickness;
points_ind = data_Figure2.points_ind;
parcel_points = data_Figure2.parcel_points;
colors_schematic = lines(3);
mode_list = [1,2,3,4,20];
num_modes_to_plot = length(mode_list);
fig = figure('Position', [200 200 1000 600]);
% =========================================================================
% A: schematic
% =========================================================================
factor_x = 1.02;
factor_y = 0.6;
init_x = 0.02;
init_y = 0.57;
length_x = (0.89 - 1.*init_x)/(factor_x*(num_modes_to_plot+2-1) + 1);
length_y = (0.98 - init_y)/(factor_y*(2-1) + 1);
ax1_1 = axes('Position', [0.03+init_x+factor_x*length_x*(1-1) init_y+factor_y*length_y*(1.4-1) length_x*1.2 length_y*1.2]);
data_to_plot = zeros(size(surface_to_plot.vertices,1),1);
patch(ax1_1, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot, ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax1_1, 0.8*ones(1,3))
axis off
axis image
for basis_ind=1:4
if basis_ind==1
connectome = data_Figure2.network_geometric;
basis_name = {'geometric'};
ax1_2_positions = [0.04 ax1_1.Position(2)+ax1_1.Position(4)*0.81 length_x*0.55 length_y*0.3];
box1_positions = [ax1_1.Position(1)+ax1_1.Position(3)*0.25 ax1_1.Position(2)+ax1_1.Position(4)*0.55 ax1_1.Position(3)*0.1 ax1_1.Position(3)*0.15];
elseif basis_ind==2
connectome = data_Figure2.network_EDR;
basis_name = {'EDR'};
ax1_2_positions = [0.15 ax1_1.Position(2)+ax1_1.Position(4)*0.81 ax1_2_positions(3) ax1_2_positions(4)];
box1_positions = [ax1_1.Position(1)+ax1_1.Position(3)*0.65 ax1_1.Position(2)+ax1_1.Position(4)*0.6 ax1_1.Position(3)*0.1 ax1_1.Position(3)*0.15];
elseif basis_ind==3
connectome = data_Figure2.network_connectome_1;
basis_name = 'connectome';
ax1_2_positions = [0.057 ax1_1.Position(2)-ax1_1.Position(4)*0.1 ax1_2_positions(3) ax1_2_positions(4)];
box1_positions = [ax1_1.Position(1)+ax1_1.Position(3)*0.35 ax1_1.Position(2)+ax1_1.Position(4)*0.3 ax1_1.Position(3)*0.1 ax1_1.Position(3)*0.15];
elseif basis_ind==4
connectome = data_Figure2.network_connectome_2;
ax1_2_positions = [0.13 ax1_1.Position(2)-ax1_1.Position(4)*0.1 ax1_2_positions(3) ax1_2_positions(4)];
box1_positions = [ax1_1.Position(1)+ax1_1.Position(3)*0.65 ax1_1.Position(2)+ax1_1.Position(4)*0.4 ax1_1.Position(3)*0.1 ax1_1.Position(3)*0.15];
end
[ind_y, ind_x] = find(triu(connectome>0));
ax1_2 = axes('Position', ax1_2_positions);
hold on;
for ii=1:size(ind_x,1)
if connectome(ind_y(ii), ind_x(ii))==1
what_color = colors_schematic(1,:);
linewidth = 1.5;
elseif connectome(ind_y(ii), ind_x(ii))==2
what_color = colors_schematic(2,:);
linewidth = 1.5;
elseif connectome(ind_y(ii), ind_x(ii))==3
what_color = 'm';%colors_schematic(3,:);
linewidth = 2.5;
end
plot3(parcel_points([ind_y(ii), ind_x(ii)],1), parcel_points([ind_y(ii), ind_x(ii)],2), parcel_points([ind_y(ii), ind_x(ii)],3), '-', 'linewidth', linewidth, 'color', what_color)
end
for ii=1:size(parcel_points,1)
plot3(parcel_points(ii,1), parcel_points(ii,2), parcel_points(ii,3), 'ko', 'markersize', 2, 'markerfacecolor', 'k')
end
hold off;
view([-90 0])
set(ax1_2, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'ylim', [-68 -62], 'zlim', [22 28], 'xtick', [], 'ytick', [], 'ztick', [], 'linewidth', 2)
axis off
axis square
box1 = annotation('rectangle', box1_positions , ...
'color', 'k', 'linewidth', 2);
box2 = annotation('rectangle', [ax1_2_positions(1)+ax1_2_positions(3)*0.08 ax1_2_positions(2)-ax1_2_positions(4)*0.08 ax1_2_positions(3)*0.84 ax1_2_positions(4)+ax1_2_positions(4)*0.16], ...
'color', 'k', 'linewidth', 2);
if basis_ind==1 || basis_ind==2
annotation('line', [box1.Position(1) box2.Position(1)], [box1.Position(2)+box1.Position(4) box2.Position(2)], ...
'color', 'k', 'linewidth', 2);
annotation('line', [box1.Position(1)+box1.Position(3) box2.Position(1)+box2.Position(3)], [box1.Position(2)+box1.Position(4) box2.Position(2)], ...
'color', 'k', 'linewidth', 2);
annotation('textbox', [box2.Position(1), box2.Position(2)+box2.Position(4)*0.95, box2.Position(3), 0.02], ...
'string', basis_name, 'edgecolor', 'none', 'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'bottom');
elseif basis_ind==3
annotation('line', [box1.Position(1) box2.Position(1)], [box1.Position(2) box2.Position(2)+box2.Position(4)], ...
'color', 'k', 'linewidth', 2);
annotation('line', [box1.Position(1)+box1.Position(3) box2.Position(1)+box2.Position(3)], [box1.Position(2) box2.Position(2)+box2.Position(4)], ...
'color', 'k', 'linewidth', 2);
annotation('textbox', [box2.Position(1), box2.Position(2)-box2.Position(4)*0.15, box2.Position(3)*2.3, 0.02], ...
'string', basis_name, 'edgecolor', 'none', 'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'top');
annotation('line', [box2.Position(1)+box2.Position(3)*0.9 box2.Position(1)+box2.Position(3)*1.38], [box2.Position(2)+box2.Position(4)*0.75 box2.Position(2)+box2.Position(4)*0.65], ...
'color', 'm', 'linewidth', 3);
annotation('line', [box2.Position(1)+box2.Position(3)*0.88 box2.Position(1)+box2.Position(3)*1.38], [box2.Position(2)+box2.Position(4)*0.45 box2.Position(2)+box2.Position(4)*0.5], ...
'color', 'm', 'linewidth', 3);
annotation('line', [box2.Position(1)+box2.Position(3)*0.95 box2.Position(1)+box2.Position(3)*1.38], [box2.Position(2)+box2.Position(4)*0.12 box2.Position(2)+box2.Position(4)*0.2], ...
'color', 'm', 'linewidth', 3);
elseif basis_ind==4
annotation('line', [box1.Position(1) box2.Position(1)], [box1.Position(2) box2.Position(2)+box2.Position(4)], ...
'color', 'k', 'linewidth', 2);
annotation('line', [box1.Position(1)+box1.Position(3) box2.Position(1)+box2.Position(3)], [box1.Position(2) box2.Position(2)+box2.Position(4)], ...
'color', 'k', 'linewidth', 2);
end
end
% =========================================================================
% B: eigenmodes
% =========================================================================
for basis_ind=1:2
if basis_ind==2
data_to_plot_evec = basis_connectome;
basis_name = {'connectome'};
elseif basis_ind==1
data_to_plot_evec = basis_EDR;
basis_name = {'EDR'};
end
for mode_ind=1:num_modes_to_plot
mode = mode_list(mode_ind);
if mode_ind==num_modes_to_plot
ax2 = axes('Position', [0.07+0.03+init_x+factor_x*length_x*(mode_ind+2-1) init_y+factor_y*length_y*(basis_ind-1) length_x length_y]);
else
ax2 = axes('Position', [0.07+init_x+factor_x*length_x*(mode_ind+2-1) init_y+factor_y*length_y*(basis_ind-1) length_x length_y]);
end
patch(ax2, 'Vertices', surface_to_plot.vertices, 'Faces', surface_to_plot.faces, 'FaceVertexCData', data_to_plot_evec(:,mode), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax2,bluewhitered)
axis off
axis image
if basis_ind==1
if mode_ind==1
annotation(fig, 'textbox', [ax2.Position(1), ax2.Position(2)+ax2.Position(4)*1.4, ax2.Position(3), 0.05], 'string', {'mode 1'; '\psi_1'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==2
annotation(fig, 'textbox', [ax2.Position(1), ax2.Position(2)+ax2.Position(4)*1.4, ax2.Position(3), 0.05], 'string', {'mode 2'; '\psi_2'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==3
annotation(fig, 'textbox', [ax2.Position(1), ax2.Position(2)+ax2.Position(4)*1.4, ax2.Position(3), 0.05], 'string', {'mode 3'; '\psi_3'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
elseif mode_ind==4
annotation(fig, 'textbox', [ax2.Position(1), ax2.Position(2)+ax2.Position(4)*1.4, ax2.Position(3), 0.05], 'string', {'mode 4'; '\psi_4'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
else
annotation(fig, 'textbox', [ax2.Position(1), ax2.Position(2)+ax2.Position(4)*1.4, ax2.Position(3), 0.05], 'string', {'mode N'; '\psi_N'}, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
end
if mode_ind==1
annotation(fig, 'textbox', [ax2.Position(1)-0.08, ax2.Position(2)+ax2.Position(4)*0.45, 0.08, 0.02], 'string', basis_name, 'edgecolor', 'none', ...
'fontsize', fontsize_axis, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
end
annotation(fig, 'textbox', [ax2.Position(1)-0.056, ax2.Position(2)+ax2.Position(4)*0.45, 0.08, 0.02], 'string', '...', 'edgecolor', 'none', ...
'fontsize', fontsize_axis*2, 'fontweight', 'normal', 'horizontalalignment', 'center', 'verticalalignment', 'middle')
end
% =========================================================================
% C: comparison of resting reconstruction accuracy
% =========================================================================
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],:)];
ax3 = axes('Position', [0.05 0.077 0.35 0.45]);
hold on;
plot(nanmean(resting_recon_corr_geometric,1), 'color', colors2(1,:), 'linewidth', 2, 'displayname', 'geometric')
plot(nanmean(resting_recon_corr_connectome,1), 'color', colors2(2,:), 'linewidth', 2, 'displayname', 'connectome')
plot(nanmean(resting_recon_corr_connectome_density_matched,1), 'color', colors2(3,:), 'linewidth', 2, 'displayname', 'connectome (density matched)')
plot(nanmean(resting_recon_corr_EDR,1), 'color', colors2(4,:), 'linewidth', 2, 'displayname', 'EDR')
hold off;
leg = legend('fontsize', fontsize_axis, 'location', 'southeast', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(ax3, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ylim', [0 1])
xlabel('number of modes', 'fontsize', fontsize_label)
ylabel('reconstruction accuracy', 'fontsize', fontsize_label)
% =========================================================================
% D: comparison of task reconstruction accuracy
% =========================================================================
factor_x = 1.15;
init_x = ax3.Position(1)+ax3.Position(3)*0.85;
init_y = ax3.Position(2);
length_x = (0.9 - init_x)/(factor_x*(3+1-1) + 1);
length_y = ax3.Position(4);
yticks = [];
for ii=1:length(tasks)
yticks = [yticks, mean(task_contrasts_ind.(tasks{ii}))];
end
data_1 = task_recon_corr_geometric;
recon_corr_combined = zeros(length(contrasts), num_modes, 3);
clims_combined = zeros(3,2);
for kk=1:3
if kk==1
data_2 = task_recon_corr_connectome;
elseif kk==2
data_2 = task_recon_corr_connectome_density_matched;
elseif kk==3
data_2 = task_recon_corr_EDR;
end
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(:,:,kk) = recon_corr;
clim_max = max(recon_corr(:,2:end),[],'all');
clim_min = min(recon_corr(:,2:end),[],'all');
clims_combined(kk,:) = [clim_min, clim_max];
end
for kk=1:3
if kk==1
title_text = {'connectome'};
elseif kk==2
title_text = {'connectome'; '(density matched)'};
elseif kk==3
title_text = {'EDR'};
end
data_to_plot = recon_corr_combined(:,:,kk);
% clim = clims_combined(kk,:);
clim = [min(clims_combined(:,1)), max(clims_combined(:,2))]; % make absolute colorbar for all three eigenmodes
ax4 = axes('Position', [init_x+factor_x*length_x*(kk) init_y length_x length_y]);
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(ax4, bluewhitered)
% cbar = colorbar;
set(ax4, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', [1 num_modes], 'ytick', yticks, 'yticklabel', lower(tasks), 'ticklabelinterpreter', 'none')
if kk~=1
set(ax4, 'yticklabel', {})
end
if kk==2
xlabel('number of modes', 'fontsize', fontsize_label)
end
title(title_text, 'fontweight', 'normal', 'fontsize', fontsize_axis)
end
cbar = colorbar;
set(cbar, 'ticklength', 0.02, 'Position', [ax4.Position(1)+ax4.Position(3)*1.1 ax4.Position(2) 0.01 ax4.Position(4)], ...
'fontsize', fontsize_axis-2);
ylabel(cbar, {'reconstruction accuracy difference'; ['(geometric ' char(8211) ' others)']}, 'fontsize', fontsize_label)
%%% panel letters
annotation(fig, 'textbox', [0.01, 0.99, 0.01, 0.01], 'string', 'A', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.3, 0.99, 0.01, 0.01], 'string', 'B', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.01, 0.57, 0.01, 0.01], 'string', 'C', 'edgecolor', 'none', ...
'fontsize', 20, 'fontweight', 'b', 'horizontalalignment', 'center')
annotation(fig, 'textbox', [0.45, 0.57, 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_figures_folder));
surface_to_plot = surface_midthickness;
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('Position', [200 200 1000 800]);
xlims = [2 num_modes+2];
ylims = [7.5e-4 1e-1];
N_interest = [50, 100, 200];
wavelengths = [60, 40, 30];
factor_x = 1.2;
init_x = 0.07;
init_y = 0.65;
length_x = (0.98 - init_x)/(factor_x*(2-1) + 1);
length_y = (0.94 - init_y)/(factor_y*(1-1) + 1);
% =========================================================================
% A left: power spectrum (HCP)
% =========================================================================
data_to_plot = nanmean(spectrum_HCP,2);
ax1_1 = axes('Position', [init_x+factor_x*length_x*(1-1) init_y length_x length_y]);
hold on;
bar(1:num_modes, data_to_plot, 'displayname', 'HCP')
for jj=1:length(N_interest)
if jj==1
xline(N_interest(jj), 'k:', 'linewidth', 2, 'label', {'wavelength'; sprintf('~%i mm', wavelengths(jj))}, 'labelhorizontalalignment', 'left', 'HandleVisibility', 'off');
else
xline(N_interest(jj), 'k:', 'linewidth', 2, 'label', sprintf('~%i mm', wavelengths(jj)), 'labelhorizontalalignment', 'left', 'HandleVisibility', 'off');
end
end
hold off;
leg = legend('fontsize', fontsize_label, 'location', 'east', 'interpreter', 'none', 'box', 'off', 'numcolumns', 1);
leg.ItemTokenSize = leg.ItemTokenSize/2;
set(ax1_1, 'fontsize', fontsize_axis, 'ticklength', [0.02 0.02], 'xlim', xlims, 'ylim', ylims, 'yscale', 'log')
xlabel('mode', 'fontsize', fontsize_label)
ylabel_h = ylabel('normalized power (log scale)', 'fontsize', fontsize_label);
% surface reconstructions
for jj = 1:length(N_interest)
N = N_interest(jj);
basis = basis_geometric(:,1:N);
coeffs = calc_eigendecomposition(surface_to_plot.vertices, basis);
new_vertices = basis*coeffs;
surface_reconstruction = surface_to_plot;
surface_reconstruction.vertices = new_vertices;
inset_length = 0.05;
ax1_1_inset = axes('Position', [ax1_1.Position(1)+ax1_1.Position(3)*(N/(xlims(2)+3))-inset_length/2 ax1_1.Position(2)+ax1_1.Position(4) inset_length inset_length]);
patch(ax1_1_inset, 'Vertices', surface_reconstruction.vertices, 'Faces', surface_reconstruction.faces, 'FaceVertexCData', zeros(size(surface_reconstruction.vertices,1),1), ...
'EdgeColor', 'none', 'FaceColor', 'interp');
if strcmpi(hemisphere, 'lh')
view([-90 0]);
elseif strcmpi(hemisphere, 'rh')
view([90 0]);
end
camlight('headlight')
material dull
colormap(ax1_1_inset, 0.7*ones(1,3))
axis off
axis image
end
% =========================================================================
% A right: power spectrum (NeuroVault)
% =========================================================================
data_to_plot = nanmean(spectrum_neurovault,2);
ax1_2 = axes('Position', [init_x+factor_x*length_x*(2-1) init_y+factor_y*length_y*(1-1) length_x length_y]);
hold on;
bar(1:num_modes, data_to_plot, 'displayname', 'NeuroVault')