-
Notifications
You must be signed in to change notification settings - Fork 0
/
Supplementary_5_pipeline_verification.m
209 lines (197 loc) · 10 KB
/
Supplementary_5_pipeline_verification.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
% Copyright (c) 2024 Mohammad Al-Sa'd
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this software and associated documentation files (the "Software"),
% to deal in the Software without restriction, including without limitation
% the rights to use, copy, modify, merge, publish, distribute, sublicense,
% and/or sell copies of the Software, and to permit persons to whom the
% Software is furnished to do so, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included
% in all copies or substantial portions of the Software.
%
% THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
% OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
% FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
% THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
% LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
% FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
% DEALINGS IN THE SOFTWARE.
%
% Email: mohammad.al-sad@helsinki.fi, alsad.mohamed@gmail.com
%
% The following reference should be cited whenever this script is used:
% Al‐Sa'd, M., Vanhatalo, S. and Tokariev, A., 2024. Multiplex dynamic
% networks in the newborn brain disclose latent links with neurobehavioral
% phenotypes. Human Brain Mapping, 45(2), https://doi.org/10.1002/hbm.26610
%
% Last Modification: 12-February-2024
%
% Description:
% This script generates Figs. 8-10 in the supplementary material.
%% Initialization
clear; close all; clc;
addpath(genpath('Functions'));
%% Parameters
grp_idx = 'AED'; % HC or AED
fdr_thr = 0.025; % 5% false discovery rate +/- threshold
alpha = 0.05; % Statistical significance level
%% Load density results
D_pos = []; D_neg = [];
for subset = 1:2
load(['Results\' grp_idx '\Selection\subset_' num2str(subset) '_selected'],'K_pos','K_neg');
D_pos = cat(2,D_pos,K_pos);
D_neg = cat(2,D_neg,K_neg);
end
%% Get correlation density
y_pos = []; y_neg = [];
mask_pos = false(length(D_pos),length(D_pos{1}));
mask_neg = false(length(D_neg),length(D_neg{1}));
for i = 1:length(D_pos)
y_pos = cat(1,y_pos,D_pos{i}(:)');
y_neg = cat(1,y_neg,D_neg{i}(:)');
mask_pos(i,:) = abs(D_pos{i}) >= fdr_thr;
mask_neg(i,:) = abs(D_neg{i}) >= fdr_thr;
end
%% Null distribution
R_pos = []; R_neg = [];
P_pos = []; P_neg = [];
for subset = 1:2
load(['Results\' grp_idx '\Selection\subset_' num2str(subset) '_permutations']);
R_pos = cat(2,R_pos,r_pos); P_pos = cat(2,P_pos,p_pos);
R_neg = cat(2,R_neg,r_neg); P_neg = cat(2,P_neg,p_neg);
end
clear p_pos p_neg r_pos r_neg;
K_pos = []; K_neg = [];
for i = 1:size(R_pos,2)
for k = 1:size(R_pos,1)
[k_pos, ~] = corr_density(R_pos(k,i), P_pos(k,i), alpha);
[~, k_neg] = corr_density(R_neg(k,i), P_neg(k,i), alpha);
K_pos = [K_pos k_pos]; K_neg = [K_neg k_neg];
end
end
clear R_pos R_neg P_pos P_neg k_pos k_neg;
%% Density verification results
Km_pos = zeros(7,5); Km_neg = zeros(7,5);
Ks_pos = zeros(7,5); Ks_neg = zeros(7,5);
for test_num = 1:5
load(['Results\' grp_idx '\Verification\subset_1_test_' num2str(test_num)]);
temp1 = Kp; temp2 = Kn;
load(['Results\' grp_idx '\Verification\subset_2_test_' num2str(test_num)]);
KKp = 100.*[temp1; Kp]; KKn = -100.*[temp2; Kn];
Km_pos(:,test_num) = mean(KKp,2);
Km_neg(:,test_num) = mean(KKn,2);
Ks_pos(:,test_num) = std(KKp,[],2);
Ks_neg(:,test_num) = std(KKn,[],2);
end
%% Plotting
XTickString_a{1} = '\begin{tabular}{c}Neurological\\Score (C1)\end{tabular}';
XTickString_a{2} = '\begin{tabular}{c}Neurological\\Score (C2)\end{tabular}';
XTickString_a{3} = '\begin{tabular}{c}Cognition\end{tabular}';
XTickString_a{4} = '\begin{tabular}{c}Language\\Comprehension\end{tabular}';
XTickString_a{5} = '\begin{tabular}{c}Language\\Production\end{tabular}';
XTickString_a{6} = '\begin{tabular}{c}Fine\\Motor Skills\end{tabular}';
XTickString_a{7} = '\begin{tabular}{c}Gross\\Motor Skills\end{tabular}';
XTickString_b{1} = '\begin{tabular}{c}Static FCN\end{tabular}';
XTickString_b{2} = '\begin{tabular}{c}Raw\\mdFCN\end{tabular}';
XTickString_b{3} = '\begin{tabular}{c}Latent\\mdFCN\end{tabular}';
XTickString_b{4} = '\begin{tabular}{c}Decomposed\\mdFCN\end{tabular}';
XTickString_b{5} = '\begin{tabular}{c}Reconstructed\\mdFCN\end{tabular}';
ccc = [0 0.447 0.741; 0.85 0.325 0.098; 0.466 0.674 0.188; ...
0.494 0.184 0.556; 0.929 0.694 0.125]; p1 = [];
legned_str = {'low-$\delta$','high-$\delta$','$\theta$','$\alpha$','$\beta$'};
figure('Color',[1,1,1],'Position',[25 25 1400 450]);
h1 = bar(100.*y_pos,1,'hist'); hold on;
h2 = bar(100.*y_neg,1,'hist'); grid on;
for i = 1:5, h1(i).FaceColor = ccc(i,:); h2(i).FaceColor = ccc(i,:); p1(i) = h1(i); end
h3 = fill([-10 -10 10 10], 100.*[-1*fdr_thr fdr_thr fdr_thr -1*fdr_thr], [0.75 0.75 0.75]);
set(h3,'FaceAlpha',0.5,'Linestyle','none');
for i = 1:size(y_pos,1)
for j = 1:size(y_pos,2)
plot(repmat(0.5+(i-1),1,10),linspace(-100,100,10),'k-');
end
end
set(gca,'fontweight','bold','FontSize',22,'XGrid','off');
ylabel('Network Density (\%)','Interpreter','latex','FontSize',26);
legend(p1,legned_str,'Orientation','horizontal', ...
'Location','southeast','interpreter','latex','fontsize',26);
a = gca; a.XAxis.FontSize = 18;
set(gca,'xtick',1:size(y_pos,1),'xticklabel',XTickString_a,'TickLabelInterpreter','latex', ...
'XTickLabelRotationMode','manual');
axis([0.5 size(y_pos,1)+0.5 -89 89]);
set(gca,'TickLabelInterpreter','latex','YTick',-80:20:80);
title(grp_idx,'interpreter','latex','fontsize',32);
set(gcf,'Units','inches'); screenposition = get(gcf,'Position');
set(gcf,'PaperPosition',[0 0 screenposition(3:4)],'PaperSize',screenposition(3:4));
figure('Color',[1,1,1],'Position',[50 50 1400 700]); ax1 = axes();
histogram(1128.*K_pos,'Normalization','probability','FaceColor',[1,0.41,0.16]); hold on;
histogram(-1128.*K_neg,'Normalization','probability','FaceColor',[0.07,0.62,1]); grid on;
set(gca,'fontsize',26,'fontweight','bold','YScale','log','Xtick',-5:5,'Xticklabels',[5:-1:0 1:5]);
legend('Positively Correlated','Negatively Correlated','Interpreter','latex','FontSize',32);
xlabel('Number of Significant Connections','Interpreter','latex','FontSize',32);
ylabel({'Probability of Finding','Significant Connections'},'Interpreter','latex','FontSize',32);
axis([-5 5 1e-6 1.5]);
annotation(gcf,'textbox',[0.120142857142857 0.55 0.3 0.188923076923077],...
'VerticalAlignment','middle',...
'String',grp_idx,...
'HorizontalAlignment','center',...
'FontWeight','bold',...
'FontSize',50,...
'FitBoxToText','off',...
'EdgeColor','none','Interpreter','latex');
title({'Network Density (\%)',''},'Interpreter','latex','FontSize',32);
ax2 = axes('Position', get(ax1,'Position'),'XAxisLocation','top','Color','none','XColor','k');
set(ax2,'fontsize',26,'fontweight','bold');
ax2.YAxis.Visible = 'off'; ax2.XLim = [-5 5]; ax2.XTick = -5:5;
ax2.XTickLabel = [round(100.*(5:-1:1)./1128,2) 0 round(100.*(1:5)./1128,2)];
set(gcf,'Units','inches'); screenposition = get(gcf,'Position');
set(gcf,'PaperPosition',[0 0 screenposition(3:4)],'PaperSize',screenposition(3:4));
for i = 1:size(Km_pos,1)
mean_pos = Km_pos(i,:); mean_neg = Km_neg(i,:);
std_pos = Ks_pos(i,:); std_neg = Ks_neg(i,:);
figure('Color',[1,1,1],'Position',[25 25 750 350]);
stem(1:length(mean_pos),mean_pos,'filled','Color',[0.85,0.33,0.1], ...
'LineStyle','-','LineWidth',12,'Marker','none'); hold on
stem(1:length(mean_neg),mean_neg,'filled','Color',[0,0.45,0.74], ...
'LineStyle','-','LineWidth',12,'Marker','none'); grid on;
errorbar(1:length(mean_pos),mean_pos,zeros(1,length(mean_pos)),2.*std_pos, ...
'LineStyle','none','Color',[0.85,0.33,0.1],'LineWidth',3,'CapSize',12,'Marker','none');
errorbar(1:length(mean_neg),mean_neg,2.*std_neg,zeros(1,length(mean_neg)), ...
'LineStyle','none','Color',[0,0.45,0.74],'LineWidth',3,'CapSize',12,'Marker','none');
for j = 1:length(mean_pos)
plot(repmat(0.5+(j-1),1,10),linspace(-100,100,10),'k-');
end
ymax = max([-1.*min(mean_neg-2.*std_neg) max(mean_pos+2.*std_pos)]);
ymin = min([min(mean_neg-2.*std_neg) -1.*max(mean_pos+2.*std_pos)]);
if(ymax < 5 && ymin > -5)
set(gca,'fontweight','bold','FontSize',18,'XGrid','off', ...
'xtick',1:5,'xticklabel','','YTick',-100:1:100);
axis([0.5 length(mean_pos)+0.5 -5.5 5.5]);
elseif(ymax < 15 || ymin > -15)
set(gca,'fontweight','bold','FontSize',18,'XGrid','off', ...
'xtick',1:5,'xticklabel','','YTick',-100:5:100);
axis([0.5 length(mean_pos)+0.5 -17 17]);
else
set(gca,'fontweight','bold','FontSize',18,'XGrid','off', ...
'xtick',1:5,'xticklabel','','YTick',-100:20:100);
axis([0.5 length(mean_pos)+0.5 ymin-5 ymax+5]);
end
ylabel('Network Density (\%)','Interpreter','latex','FontSize',20);
a = gca; a.XAxis.FontSize = 14;
set(gca,'xticklabel',XTickString_b,'TickLabelInterpreter','latex','XTickLabelRotationMode','manual');
set(gcf,'Units','inches'); screenposition = get(gcf,'Position');
set(gcf,'PaperPosition',[0 0 screenposition(3:4)],'PaperSize',screenposition(3:4));
end
%% Saving
opt = input('Do you want to save results (Y/N)\n','s');
if(opt == 'y' || opt == 'Y')
print(1,['Results\Figures\Supp_Fig_8_correlation_density_' grp_idx],'-dpdf','-r400');
print(2,['Results\Figures\Supp_Fig_8_null_distribution_' grp_idx],'-dpdf','-r400');
print(3,['Results\Figures\Supp_Fig_9_verification_1_' grp_idx],'-dpdf','-r400');
print(4,['Results\Figures\Supp_Fig_9_verification_2_' grp_idx],'-dpdf','-r400');
print(5,['Results\Figures\Supp_Fig_10_verification_1_' grp_idx],'-dpdf','-r400');
print(6,['Results\Figures\Supp_Fig_10_verification_2_' grp_idx],'-dpdf','-r400');
print(7,['Results\Figures\Supp_Fig_10_verification_3_' grp_idx],'-dpdf','-r400');
print(8,['Results\Figures\Supp_Fig_10_verification_4_' grp_idx],'-dpdf','-r400');
print(9,['Results\Figures\Supp_Fig_10_verification_5_' grp_idx],'-dpdf','-r400');
end