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III_I_REL_ResultsPartIII.m
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%3. Results
% In this code, results and figures associated with following section are created
% Section 3.3. Within-segment functional connectivity
% Please see:
% manuscript: ""
% and for the following dataset: https://openneuro.org/datasets/ds004386
% Merve Kaptan, [email protected]
% 22.12.2022
%------------------------------------------
% Calculate the results & make figures
clc
clear all
close all
%% setup paths and directories
dataDir = '/data/pt_02098/RELIABILITY_FC/Reliability_Spinal_RestingStatefMRI/derivatives'
cd(dataDir)
subjects = dir('sub-ZS*');
% exclude problematic subjects 9, 18, 30!!! Because of bad ECG
subjects([9,18,30]) = [];
saveFolderName = '_Denoised_Max_Segmental'
subjects = {subjects.name};
saveDir = ['/data/pt_02098/RELIABILITY_FC/Reliability_Spinal_RestingStatefMRI/derivatives/ICCandCon' saveFolderName '/']
printPath = ['/data/pt_02098/RELIABILITY_FC/Reliability_Spinal_RestingStatefMRI/derivatives/Figures' saveFolderName '/'];
codeDir = '/data/pt_02098/RELIABILITY_FC/Reliability_Spinal_RestingStatefMRI/derivatives/Code'
addpath(genpath(codeDir))
load('/data/pt_02098/RELIABILITY_FC/Reliability_Spinal_RestingStatefMRI/derivatives/subjectsSegmentalLevels.mat')
Levels = SubjectsSegLevels;
if ~exist(printPath)
mkdir(printPath)
end
if ~exist(saveDir)
mkdir(saveDir)
end
order = {'slice-wise [mean] + avg & simple corr'}
saveICC = 1;
saveCorrs = 1;
CalculateICC =1;
printMode = 1;
plotType = 'avgOverSes';
groupLevelICC = 1;
fileNames = {'denoised_max'}
signalFolder = 'signalFiles';
analysisSpace = 'ns' %native space
sessions = {'manual_denoised', 'auto_denoised'}
if isequal(analysisSpace, 'ts')
aspace = 'templspace' %'templspace' %or subjspace
elseif isequal(analysisSpace, 'ns')
aspace = 'nativespace';
end
colorDD = [102,194,165]./255;
colorVV = [252,141,98]./255 ;
colorwDV = [141,160,203]./255;
colorbDV = [231,138,195]./255;
legendNames = {'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'T1'};
o = 1;
f = 1;
%%
load([saveDir filesep order{o} '_' aspace '_' fileNames{f} 'ICC.mat']);
datasets1 = ICCvalue(1,:);
datasets1 = fliplr(datasets1);
datasets2 = squeeze(ICCvalue_CI(1,:,:));
datasets2 = flipud(datasets2);
colors = colorDD;
RELhelper_PlotICC(datasets1, datasets2, legendNames, colors, 'DD',subjects, order{1})
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ICCSegment_DD_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
datasets1 = ICCvalue(2,:);
datasets1 = fliplr(datasets1);
datasets2 = squeeze(ICCvalue_CI(2,:,:));
datasets2 = flipud(datasets2);
colors = colorVV;
RELhelper_PlotICC(datasets1, datasets2, legendNames, colors, 'VV',subjects, order{1})
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ICCSegment_VV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
datasets1 = ICCvalue(3,:);
datasets1 = fliplr(datasets1);
datasets2 = squeeze(ICCvalue_CI(3,:,:));
datasets2 = flipud(datasets2);
colors = [141,160,203]./255;
RELhelper_PlotICC(datasets1, datasets2, legendNames, colors, 'wDV',subjects, order{1})
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ICCSegment_wDV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
datasets1 = ICCvalue(4,:);
datasets1 = fliplr(datasets1);
datasets2 = squeeze(ICCvalue_CI(4,:,:));
datasets2 = flipud(datasets2);
colors = [231,138,195]./255;
RELhelper_PlotICC(datasets1, datasets2, legendNames, colors, 'bDV',subjects, order{1})
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ICCSegment_bDV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
load([saveDir filesep order{o} '_' aspace '_' fileNames{f} 'corrs.mat']);
DD = (DD_session1 + DD_session2)./2;
DD = fliplr(DD);
fiGure = figure;
fiGure.Position = [0 0 800 800];
hold on
box off
YLims = [-.1 .6]
ylabel('r');
XLabels= legendNames;
xlim([0 numel(XLabels)+1]);
xticks([1:numel(XLabels)]);
ylim(YLims)
%yticks(-0.5:0.2:1)
ax = gca;
ax.FontSize = 15;
subtitle(order{o})
yline(0, 'LineWidth', 2)
h1 = boxplot(DD, ...
'Colors', colorDD ,...
'Symbol','.r', ...
'Widths', 0.3);
set(h1,'linew',3)
lines = findobj(gcf, 'type', 'line', 'Tag', 'Median');
set(lines, 'Color', 'k');
MeanDD = mean(DD, 'omitnan')
for ml = 1:numel(MeanDD)
line([ml-0.15 ml+0.15], [MeanDD(ml) MeanDD(ml)], ...
'Color', colorDD, 'LineWidth',3, 'LineStyle','-')
end
xticklabels(legendNames)
positions = [1:numel(legendNames)]
for l = 1:numel(legendNames)
scatter(ones(1,size(DD,1)).*(positions(l)+(rand(1,size(DD,1))-0.5)/10),DD(:,l),[] ,colorDD ,'filled', ...
'MarkerFaceAlpha', 0.4, 'MarkerEdgeAlpha', 0.4)
end
ylim(YLims)
grid on
box off
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ConnSegment_DD_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
VV = (VV_session1 + VV_session2)./2;
VV = fliplr(VV);
fiGure = figure;
fiGure.Position = [0 0 800 800];
hold on
box off
YLims = [-.1 .6]
ylabel('r');
XLabels= legendNames;
xlim([0 numel(XLabels)+1]);
xticks([1:numel(XLabels)]);
ylim(YLims)
%yticks(-0.5:0.2:1)
ax = gca;
ax.FontSize = 15;
subtitle(order{o})
yline(0, 'LineWidth', 2)
h1 = boxplot(VV, ...
'Colors', colorVV ,...
'Symbol','.r', ...
'Widths', 0.3);
set(h1,'linew',3)
lines = findobj(gcf, 'type', 'line', 'Tag', 'Median');
set(lines, 'Color', 'k');
MeanVV = mean(VV, 'omitnan');
for ml = 1:numel(MeanVV)
line([ml-0.15 ml+0.15], [MeanVV(ml) MeanVV(ml)], ...
'Color', colorVV, 'LineWidth',3, 'LineStyle','-')
end
xticklabels(legendNames)
positions = [1:numel(legendNames)]
for l = 1:numel(legendNames)
scatter(ones(1,size(VV,1)).*(positions(l)+(rand(1,size(VV,1))-0.5)/10),VV(:,l),[] ,colorVV ,'filled', ...
'MarkerFaceAlpha', 0.4, 'MarkerEdgeAlpha', 0.4)
end
ylim(YLims)
grid on
box off
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ConnSegment_VV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
load([saveDir filesep order{o} '_' aspace '_' fileNames{f} 'corrs.mat']);
wDV = (within_session1 + within_session2)./2;
wDV = fliplr(wDV);
fiGure = figure;
fiGure.Position = [0 0 800 800];
hold on
box off
YLims = [-.1 .6]
ylabel('r');
XLabels= legendNames;
xlim([0 numel(XLabels)+1]);
xticks([1:numel(XLabels)]);
ylim(YLims)
%yticks(-0.5:0.2:1)
ax = gca;
ax.FontSize = 15;
subtitle(order{o})
yline(0, 'LineWidth', 2)
h1 = boxplot(wDV, ...
'Colors', colorwDV ,...
'Symbol','.r', ...
'Widths', 0.3);
set(h1,'linew',3)
lines = findobj(gcf, 'type', 'line', 'Tag', 'Median');
set(lines, 'Color', 'k');
MeanwDV = mean(wDV, 'omitnan');
for ml = 1:numel(MeanwDV)
line([ml-0.15 ml+0.15], [MeanwDV(ml) MeanwDV(ml)], ...
'Color', colorwDV, 'LineWidth',3, 'LineStyle','-')
end
xticklabels(legendNames)
positions = [1:numel(legendNames)]
for l = 1:numel(legendNames)
scatter(ones(1,size(wDV,1)).*(positions(l)+(rand(1,size(wDV,1))-0.5)/10),wDV(:,l),[] ,colorwDV ,'filled', ...
'MarkerFaceAlpha', 0.4, 'MarkerEdgeAlpha', 0.4)
end
grid on
box off
if printMode
print([ printPath filesep ...
'Figure_' sessions{:} '_ConnSegment_wDV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
end
%%
bDV = (between_session1 + between_session2)./2;
bDV = fliplr(bDV);
fiGure = figure;
fiGure.Position = [0 0 800 800];
hold on
box off
YLims = [-.1 .6]
ylabel('r');
XLabels= legendNames;
xlim([0 numel(XLabels)+1]);
xticks([1:numel(XLabels)]);
ylim(YLims)
%yticks(-0.5:0.2:1)
ax = gca;
ax.FontSize = 15;
subtitle(order{o})
yline(0, 'LineWidth', 2)
h1 = boxplot(bDV, ...
'Colors', colorbDV ,...
'Symbol','.r', ...
'Widths', 0.3);
set(h1,'linew',3)
lines = findobj(gcf, 'type', 'line', 'Tag', 'Median');
set(lines, 'Color', 'k');
MeanbDV = mean(bDV, 'omitnan');
for ml = 1:numel(MeanbDV)
line([ml-0.15 ml+0.15], [MeanbDV(ml) MeanbDV(ml)], ...
'Color', colorbDV, 'LineWidth',3, 'LineStyle','-')
end
xticklabels(legendNames)
positions = [1:numel(legendNames)]
for l = 1:numel(legendNames)
scatter(ones(1,size(bDV,1)).*(positions(l)+(rand(1,size(bDV,1))-0.5)/10),bDV(:,l),[] ,colorbDV ,'filled', ...
'MarkerFaceAlpha', 0.4, 'MarkerEdgeAlpha', 0.4)
end
grid on
box off
print([ printPath filesep ...
'Figure_' sessions{:} '_ConnSegment_bDV_' num2str(numel(subjects)) '_' fileNames{f} '.tif'],'-dtiff')
[row,col] =find(isnan(DD));
DD(row,:) = [];
csvwrite('tmp.csv', DD);
palm -i tmp.csv -twotail -quiet;
t_values = load('palm_dat_tstat_c1.csv');
p_values_c = load('palm_dat_tstat_fwep_c1.csv');
save([saveDir filesep 'corrPALM_DD_' fileNames{f} '.mat'],'t_values', 'p_values_c')
unix('rm -f tmp.csv');
unix('rm -f palm_*.csv');
[row,col] =find(isnan(VV));
VV(row,:) = [];
csvwrite('tmp.csv', VV);
palm -i tmp.csv -twotail -quiet;
t_values = load('palm_dat_tstat_c1.csv');
p_values_c = load('palm_dat_tstat_fwep_c1.csv');
save([saveDir filesep 'corrPALM_VV_' fileNames{f} '.mat'],'t_values', 'p_values_c')
unix('rm -f tmp.csv');
unix('rm -f palm_*.csv');
[row,col] =find(isnan(wDV));
wDV(row,:) = [];
csvwrite('tmp.csv', wDV);
palm -i tmp.csv -twotail -quiet;
t_values = load('palm_dat_tstat_c1.csv');
p_values_c = load('palm_dat_tstat_fwep_c1.csv');
save([saveDir filesep 'corrPALM_wDV_' fileNames{f} '.mat'],'t_values', 'p_values_c')
unix('rm -f tmp.csv');
unix('rm -f palm_*.csv');
[row,col] =find(isnan(bDV));
bDV(row,:) = [];
csvwrite('tmp.csv', bDV);
palm -i tmp.csv -twotail -quiet;
t_values = load('palm_dat_tstat_c1.csv');
p_values_c = load('palm_dat_tstat_fwep_c1.csv');
save([saveDir filesep 'corrPALM_bDV_' fileNames{f} '.mat'],'t_values', 'p_values_c')
unix('rm -f tmp.csv');
unix('rm -f palm_*.csv');