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analyze_etkinlab_sessioneffects.m
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analyze_etkinlab_sessioneffects.m
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function results = analyze_etkinlab_sessioneffects(varargin)
% Compute and return conditional correlations, nuisance correlations, and NSR on all subjects x sites.
% import ggmClass/standardize.*
% import ggmClass/covariance.*
addpath(fullfile(getenv('HOME'),'MATLAB','ggmClass'));
% % DANA healthy controls
% tmpdata = load(['../prelim2016-resting-predicts-tms/' ...
% 'tmp_workspace_1.mat'],'obj');
% tmpnames = regexp(tmpdata.obj.subjlabels,'_','split');
% for ii=1:length(tmpnames)
% tmpdata.obj.subjlabels{ii} = tmpnames{ii}{2};
% end
studyname = 'best-eegfmri-cpac';
verbose = false;
if(nargin==0)
plotOnly = false;
elseif(nargin>=1)
plotOnly = varargin{1};
end
if(exist('brewermap'))
colormapfun = @()(flipud(brewermap(length(colormap),'RdYlBu')));
close all;
else
colormapfun = @winter;
end
% test re-test mccc
exportfun = @(filename)(print('-dpng','-r150',filename));
%fname = ['tmp' filesep datestr(now,'dd-mmm-yyyy')];
fname = ['tmp' filesep '15-Oct-2018'];
if(~exist(fname,'dir'))
mkdir(fname)
end
fname = [fname filesep studyname];
if(~exist(fname,'dir'))
mkdir(fname)
end
nmethods = 1;
methodnames = {'sn_denoised'};
switch studyname
case 'pnas'
datadir = fullfile(getenv('HOME'),'MATLAB','kggm2016-paper','data');
studydata.cpac = load(fullfile(datadir,'dataset_pnas_rfmri'));
subjlia = cellfun(@(x)(~isempty(x)),studydata.cpac.confounds);
include_subjects = zeros(1,length(subjlia));
subject_idx = find(subjlia);
fd_thresh = .2;
for subjno=1:length(subject_idx)
subject_no = subject_idx(subjno);
tmpconfounds = studydata.cpac.confounds{1};
sessionno1 = ...
find(strcmp(studydata.cpac.tms{subject_no},'rest'));
sessionno2 = ...
find(strcmp(studydata.cpac.tms{subject_no},'rest1Hzpre'));
if(...
sqrt(mean((...
tmpconfounds{sessionno1}.framewise_displacement).^2))<fd_thresh ...
| ...
sqrt(mean((...
tmpconfounds{sessionno2}.framewise_displacement).^2))<fd_thresh ...
)
include_subjects(subject_no) = 1;
end
end
if(verbose)
disp('Checking Motion Inclusion')
sum(subjlia)
sum(subjlia & include_subjects)
end
studydata.cpac.confounds = studydata.cpac.confounds(...
subjlia & include_subjects);
studydata.cpac.X = studydata.cpac.X(subjlia & include_subjects);
studydata.cpac.subjects = studydata.cpac.subjects(...
subjlia & include_subjects);
studydata.cpac.files = studydata.cpac.files(subjlia & include_subjects);
studydata.session1.compcor = [];
studydata.session2.compcor = [];
studydata.session1.motion = [];
studydata.session2.motion = [];
nsubjects = length(studydata.cpac.X)
for ii=1:nsubjects
try
sessionno = find(strcmp(studydata.cpac.tms{ii},'rest'));
studydata.session1.X{ii} = studydata.cpac.X{ii}(:,:,sessionno);
% studydata.session1.compcor(ii,:,:) = studydata.cpac.confounds{ii}{sessionno}.compcor;
% studydata.session1.motion(ii,:,:) = studydata.cpac.confounds{ii}{sessionno}.motion;
sessionno = find(strcmp(studydata.cpac.tms{ii},'rest1Hzpre'));
studydata.session2.X{ii} = studydata.cpac.X{ii}(:,:,sessionno);
% studydata.session2.compcor(ii,:,:) = studydata.cpac.confounds{ii}{sessionno}.compcor;
% studydata.session2.motion(ii,:,:) = studydata.cpac.confounds{ii}{sessionno}.motion;
catch me
studydata.cpac.subjects{ii}
disp(me)
end
end
case 'corr-bnu1'
datadir = fullfile(getenv('HOME'),'MATLAB','rcubed','data');
pipelinename = 'pipeline_global00';
atlasname = 'CC200';
fname = [fname filesep pipelinename];
if(~exist(fname,'dir'))
mkdir(fname)
end
fname = [fname filesep atlasname];
if(~exist(fname,'dir'))
mkdir(fname)
end
studydata.cpac = load(fullfile(datadir,...
['corr-bnu1_task-rest_' ...
pipelinename '_' atlasname ] ...
));
%subjlia = cellfun(@(x)(~isempty(x)),studydata.cpac.confounds);
subjlia = studydata.cpac.trt_idx;
studydata.cpac.confounds = studydata.cpac.confounds(subjlia);
studydata.cpac.X = studydata.cpac.X(subjlia);
studydata.cpac.subjects = studydata.cpac.subjects(subjlia);
studydata.cpac.files = studydata.cpac.subjects;
nsubjects = length(studydata.cpac.X)
for ii=1:nsubjects
ii
sessionno = find(strcmp(studydata.cpac.sessions,'ses-1'));
studydata.session1.X{ii} = studydata.cpac.X{ii}{sessionno};
sessionno = find(strcmp(studydata.cpac.sessions,'ses-2'));
studydata.session2.X{ii} = studydata.cpac.X{ii}{sessionno};
end
case 'best-eegfmri-cpac'
clear studydata
pipeline = 'etkinlab_analysis';
resttype = '';
n_time = 840;
fname = [fname '-' resttype 'trt-tp1.1-tp2.2' ['-t' num2str(n_time)] ];
if(~exist(fname))
mkdir(fname)
end
fname = fullfile(fname,pipeline);
if(~exist(fname))
mkdir(fname)
end
datadir = fullfile(getenv('HOME'),'MATLAB','kggm2016-paper','data');
studydata = ...
load(fullfile(datadir,'dataset_SchaeferYeo100_best_rfmri_2'));
session1_prefix = ['ses-tp1' 'run-1'];
session1_idx = 1:length(studydata.files)
studydata.session1.subjects = studydata.files
session2_prefix = ['ses-tp2' 'run-2'];
session2_idx = 1:length(studydata.files)
studydata.session2.subjects = studydata.files;
all_subnames = ...
cellfun(@num2str,num2cell(1:17),'UniformOutput',false);
for ii=1:length(all_subnames)
if(ii<=length(studydata.X) && ...
~isempty(studydata.X{ii}{1}))
studydata.session1.X{ii} = ...
squeeze(studydata.X{ii}{1}(10:850,1:100));
else
studydata.session1.X{ii} = [];
studydata.session1.subjects{ii} = [];
end
if(ii<=length(studydata.X) && length(studydata.X{ii})>2 && ...
~isempty(studydata.X{ii}{4}))
studydata.session2.X{ii} = ...
squeeze(studydata.X{ii}{4}(10:850,1:100));
else
studydata.session2.X{ii} = [];
studydata.session2.subjects{ii} = [];
end
tmp_subj_no = [];
end
both_subjects_idx = ...
~(cellfun(@isempty,studydata.session2.subjects) | ...
cellfun(@isempty,studydata.session1.subjects));
studydata.session1.X = studydata.session1.X(both_subjects_idx);
studydata.session2.X = studydata.session2.X(both_subjects_idx);
nsubjects = sum(both_subjects_idx)
studydata.roilabels.table = readtable('/Volumes/MACBOOKUSB/Datasets/Schaefer2018_LocalGlobal/Parcellations/MNI/Schaefer2018_100Parcels_7Networks_order.txt','Delimiter','\t','ReadVariableNames',0);
studydata.subjects.labels = all_subnames;
studydata.subjects.index = both_subjects_idx;
save([fname filesep 'data_' pipeline '_' ...
studyname '_rfmri.mat'],'studydata');
case 'best-eegfmri'
pipeline = 'etkinlab_analysis';
resttype = '';
n_time = 820;
fname = [fname '-' resttype '' ['-t' num2str(n_time)] ];
if(~exist(fname))
mkdir(fname)
end
fname = fullfile(fname,pipeline);
if(~exist(fname))
mkdir(fname)
end
datadir = fullfile(getenv('HOME'),'Downloads');
tmpdata = load(fullfile(datadir,'BEST_Schaefer100PlusSubcortical_BoldSignalsHomAndSNR_w1_05282018.mat'));
% tmpdata = load(fullfile(datadir,'BEST_Schaefer100PlusSubcortical_BoldSignalsHomAndSNR_w1_03152018.mat'));
subNames = ...
regexprep(tmpdata.subNames,{'.*best_','.connectivity.*'},{'',''});
session1_prefix = ['tp1_efmri_' 'rest1'];
session1_idx = ...
find(~cellfun(@isempty,strfind(subNames,session1_prefix)));
studydata.session1.subjects = subNames(session1_idx);
session2_prefix = ['tp2_efmri_' 'rest2'];
session2_idx = ...
find(~cellfun(@isempty,strfind(subNames,session2_prefix)));
studydata.session2.subjects = subNames(session2_idx);
all_subnames = ...
cellfun(@num2str,num2cell(3001:3020),'UniformOutput',false);
for ii=1:length(all_subnames)
tmp_subj_no = ...
find(~cellfun(@isempty, ...
strfind(subNames(session1_idx),all_subnames(ii))));
if(~isempty(tmp_subj_no))
studydata.session1.X{ii} = ...
squeeze(tmpdata.signals(session1_idx(tmp_subj_no),1:100,1:820))';
studydata.session1.subjects{ii} = subNames(session1_idx(tmp_subj_no));
else
studydata.session1.X{ii} = [];
studydata.session1.subjects{ii} = [];
end
tmp_subj_no = [];
tmp_subj_no = ...
find(~cellfun(@isempty, ...
strfind(subNames(session2_idx),all_subnames(ii))));
if(~isempty(tmp_subj_no))
studydata.session2.X{ii} = ...
squeeze(tmpdata.signals(session2_idx(tmp_subj_no),1:100,1:820))';
studydata.session2.subjects{ii} = ...
subNames(session2_idx(tmp_subj_no));
else
studydata.session2.X{ii} = [];
studydata.session2.subjects{ii} = [];
end
tmp_subj_no = [];
end
both_subjects_idx = ...
~(cellfun(@isempty,studydata.session2.subjects) | ...
cellfun(@isempty,studydata.session1.subjects));
studydata.session1.X = studydata.session1.X(both_subjects_idx);
studydata.session2.X = studydata.session2.X(both_subjects_idx);
nsubjects = sum(both_subjects_idx)
studydata.session1
studydata.roilabels.table = readtable('/Volumes/MACBOOKUSB/Datasets/Schaefer2018_LocalGlobal/Parcellations/MNI/Schaefer2018_100Parcels_7Networks_order.txt','Delimiter','\t','ReadVariableNames',0);
studydata.subjects.labels = all_subnames;
studydata.subjects.index = both_subjects_idx;
save([fname filesep 'data_' pipeline '_' ...
studyname '_rfmri.mat'],'studydata');
end
if(isfield(studydata,'cpac'))
communityno = 3;
ncommunities = length(unique(studydata.cpac.roilabels.Yeo7));
nodeidx = find(studydata.cpac.roilabels.Yeo7==communityno);
nodeidx = cat(1,nodeidx,find(studydata.cpac.roilabels.Yeo7==4));
nodeidx = cat(1,nodeidx,find(studydata.cpac.roilabels.Yeo7==6));
communitylabel = studydata.cpac.roilabels.Yeo7Label(nodeidx);
if(length(unique(communitylabel))>2)
communitylabel = {'AllNetworks'};
end
communitycolor = studydata.cpac.roilabels.Yeo7(nodeidx);
else
nodeidx = 1:size(studydata.session1.X{1},2);
tmp_community_parts = ...
regexp(studydata.roilabels.table.Var2,{'_'}, 'split');
for ii=1:length(tmp_community_parts)
communitylabels{ii} = tmp_community_parts{ii}{3};
end
communitylabel = grp2idx(communitylabels);
communitycolor = communitylabel;
end
results = struct();
observed_correlation = [];
denoised_correlation = [];
denoised_correlation2 = [];
nuisance_correlation = [];
nsr = [];
if(~plotOnly)
%Y = get_shared_nuisance(squeeze(mean(studydata.signalsSite1,2))');
Y = [];
for sessionno=1:2
for ii=1:nsubjects
if(verbose)
ii
['session' num2str(sessionno)]
end
studydata
X = studydata.(['session' num2str(sessionno)]).X{ii};
X = X(1:n_time,:);
%Y = mean(X,2);
%Y = squeeze(studydata.compcor(ii,:,:));
%Y = cat(2,Y,squeeze(studydata.motion(ii,:,:)));
try
observed_correlation(:,:,ii,sessionno) = ...
standard_correlation(X);
% results.Site1{ii,sessionno} = conditional_correlation(X,Y);
% denoised_correlation(:,:,ii,sessionno) = ...
% results.Site1{ii,sessionno}.corr;
% nuisance_correlation(:,:,ii,sessionno) = ...
% results.Site1{ii,sessionno}.nuisance;
% nsr(ii,sessionno) = results.Site1{ii,sessionno}.NSR;
denoised_correlation2(:,:,ii,sessionno) = ...
standard_correlation_sn(X);
catch me
disp(me)
results.Site1{ii,sessionno} = {};
disp('Session')
sessionno
disp('Subject')
ii
end
% if(ii==1)
% demo_conditional_correlation(X,Y);
% end
end
end
if(isfield(studydata,'subjects'))
subject_labels = ...
studydata.subjects.labels(studydata.subjects.index)
end
% results.denoised2 = denoised_correlation;
% results.nuisance = nuisance_correlation;
% results.nsr = nsr;
results.denoised2 = denoised_correlation2;
results.sn_denoised = denoised_correlation2;
results.observed = observed_correlation;
if(exist('subject_labels'))
results.subject_labels = subject_labels;
end
sitelabels = ones(1,2*nsubjects);
% if(exist('brewermap'))
% colormapfun = @()(flipud(brewermap(length(colormap),'RdYlBu')));
% close all;
% else
% colormapfun = @winter;
% end
%
% % test re-test mccc
% exportfun = @(filename)(print('-dpng','-r150',filename));
% fname = ['tmp' filesep datestr(now,'dd-mmm-yyyy')];
%
% if(~exist(fname,'dir'))
% mkdir(fname)
% end
%
% % upper_idx = find(reshape(triu(ones(p,p),1), [p*p 1]));
% % features = reshape(results.denoised(upper_idx),[p*p nsubjects 2]);
%
% nmethods = 2;
% methodnames = {'observed','sn_denoised'};
for methodno=1:nmethods
methodname = methodnames{methodno};
p = size(results.(methodname),1);
graphs = results.(methodname)(nodeidx,:,:,:);
results.cpac.(methodname).trt_reliability = ...
helper_node_mccc(graphs);
results.cpac.(methodname).trt_reliability.nodeidx = nodeidx;
tmp_kendallW = [];
for nodeno=1:size(graphs,1)
for subjectno=1:nsubjects
tmp_kendallW(nodeno,subjectno) = ...
reliability.kendallsW(...
cat(1,squeeze(graphs(nodeno,:,subjectno,1)),...
squeeze(graphs(nodeno,:,subjectno,2)))' ...
);
end
end
results.cpac.(methodname).trt_reliability.kendallsw = tmp_kendallW;
save([fname filesep 'trt_' studyname '_rfmri.mat'],'results');
tmp_kendallW = [];
if(~exist(fullfile(fname,methodname)))
mkdir(fullfile(fname,methodname));
end
ba_nodes = nodeidx;
trid_idx = find(reshape(triu(ones(p,p),1),[p^2 1]));
all_y_diff = [];
all_x_mean = [];
tmp_propLOA = [];
for subjectno=1:nsubjects
% [altmanplot, kendallcoef, propLOA, y_diff, x_mean] = ...
% plot_bland_altman_agreement(graphs(ba_nodes, ba_nodes,subjectno,1),...
% graphs(ba_nodes, ba_nodes,subjectno,2));
rater1 = graphs(ba_nodes,ba_nodes,subjectno,1);
rater2 = graphs(ba_nodes,ba_nodes,subjectno,2);
rater1 = rater1(trid_idx);
rater2 = rater2(trid_idx);
baStats = reliability.bland_altman_MD(rater1,rater2);
y_diff = baStats.D;
x_mean = baStats.M;
propLOA = nan;
kendallcoef = reliability.kendallsW(cat(2,rater1,rater2));
% savefilename = fullfile(fname, methodname, ...
% ['agreement_subj' num2str(subjectno)]);
% altmanplot.export('file_name', ...
% savefilename,'file_type','pdf');
tmp_kendallW(subjectno) = kendallcoef;
tmp_propLOA(subjectno) = propLOA;
all_y_diff(:,subjectno) = y_diff;
all_x_mean(:,subjectno) = x_mean;
close;
end
loa_matrix = zeros(p^2,3);
for ii=1:size(all_y_diff,1)
bastats = reliability.bland_altman_loa(...
all_x_mean(ii,:)', ...
all_y_diff(ii,:)' ...
);
loa_matrix(trid_idx(ii),1) = bastats.stats.mu;
loa_matrix(trid_idx(ii),2) = bastats.stats.ci(1); loa_matrix(trid_idx(ii),3) = bastats.stats.ci(2);
end
figure;
%scatter(mean(all_x_mean,2),mean(all_y_diff,2))
loa_int = [];
loa_int(1,1) = mean(loa_matrix(trid_idx,2));
loa_int(1,2) = mean(loa_matrix(trid_idx,3));
loa_int(2:3,1) = prctile(loa_matrix(trid_idx,2),[2.5 97.5]);
loa_int(2:3,2) = prctile(loa_matrix(trid_idx,3),[2.5 97.5]);
[altmanplot tmp_propLOA] = ...
plot_bland_altman_agreement(mean(all_x_mean,2), ...
mean(all_y_diff,2), ...
loa_int ...
);
savefilename = fullfile(fname, methodname, ...
['group_edgelevel_mdplot']);
altmanplot.export('file_name', ...
savefilename,'file_type','pdf');
results.cpac.(methodname).trt_reliability.edgeidx = trid_idx;
results.cpac.(methodname).trt_reliability.y_diff = all_y_diff;
results.cpac.(methodname).trt_reliability.x_mean = all_x_mean;
results.cpac.(methodname).trt_reliability.loa_matrix = ...
sparse(loa_matrix);
results.cpac.(methodname).trt_reliability.propLOA = tmp_propLOA;
results.cpac.(methodname).trt_reliability.global_kendallsw = tmp_kendallW;
save([fname filesep 'trt_' studyname '_rfmri.mat'],'results','-append');
end
try
mean(results.cpac.sn_denoised.trt_reliability.kendallsw-...
results.cpac.observed.trt_reliability.kendallsw,2);
catch
end
% Compare Agreement With and Without Successive Normalization
% nboot = 10;
% mccc = zeros(p,1);
% mccc_ci = zeros(p,2);
% mccc_boot = zeros(p,nboot);
% similarity = zeros(2*nsubjects,2*nsubjects);
% for nodeno=1:length(nodeidx)
% jj = nodeidx(nodeno)
% features1 = ...
% reshape(permute(squeeze(results.(methodnames{1})(jj,:,:,:)),...
% [1 3 2]),[p*2 nsubjects]);
% features2 = ...
% reshape(permute(squeeze(results.(methodnames{2})(jj,:,:,:)),...
% [1 3 2]),[p*2 nsubjects]);
% similarity = similarity + cov(cat(2,features1,features2));
% [mccc(jj),~,mccc_ci(jj,:),mccc_boot(jj,:)] = ...
% reliability.mccc(features1(1:p,:),features2(1:p,:));
% mccc(jj)
% mccc_ci(jj,:)
% end
% similarity = similarity./length(nodeidx);
% results.cpac.sn_gm_agreement.mccc = real(mccc);
% results.cpac.sn_gm_agreement.mccc_ci = real(mccc_ci);
% results.cpac.sn_gm_agreement.similarity = similarity;
% results.cpac.sn_gm_agreement.nodeidx = nodeidx;
%
% results = setfield(results,'sn_denoised',[]);
% results = setfield(results,'observed',[]);
% results = setfield(results,'denoised2',[]);
% save([fname filesep 'trt_' studyname '_rfmri.mat'],'results','-append');
else
load([fname filesep 'trt_' studyname '_rfmri.mat'],'results');
if(~isempty(results.observed))
results = setfield(results,'sn_denoised',[]);
results = setfield(results,'observed',[]);
results = setfield(results,'denoised2',[]);
save([fname filesep 'trt_' studyname ...
'_rfmri.mat'],'results','-append');
end
if(isfield(results,'subject_labels'))
subject_labels = ...
results.subject_labels;
end
end
% Plotting code only
figure;
set(gcf,'Position',[50 75 1200 600]);
for methodno=1:nmethods
methodname = methodnames{methodno};
y_methods = results.cpac.(methodname).trt_reliability.mccc;
y_lo = results.cpac.(methodname).trt_reliability.mccc_ci(:,1);
y_hi = results.cpac.(methodname).trt_reliability.mccc_ci(:,2);
grammobj(methodno,1) = plot_reliability_dotplot(y_methods, ...
y_lo, ...
y_hi, ...
methodnames, ...
communitycolor ...
);
switch methodname
case 'sn_denoised'
grammobj(methodno,1).set_title(['ICC ' 'after centering']);
case 'observed'
grammobj(methodno,1).set_title(['ICC ' 'before centering']);
otherwise
grammobj(methodno,1).set_title(['ICC for ' methodname]);
end
end
grammobj.draw();
if(strcmp(studyname,'best-eegfmri')|| strcmp(studyname,'best-eegfmri-cpac'))
savefilename = fullfile(fname, ['sessioneffects_reliability' '_' ...
'before_after_sn']);
else
savefilename = fullfile(fname, ['sessioneffects_reliability' '_' ...
'before_after_sn' '_' communitylabel{1}]);
end
savefig(savefilename);
grammobj.export('file_name', ...
savefilename,'file_type','pdf');
exportfun(savefilename);
figure;
addpath('~/MATLAB/packages/Violinplot-Matlab');
set(gcf,'Position',[25 75 400 600]);
vplotobj = violinplot(results.cpac.('sn_denoised').trt_reliability.mccc);
title('ROI ICC Distribution')
set(gcf,'Color',[.9 .9 .9]);
set(gcf,'Color',[.9 .9 .9]);
set(gca,'fontsize',20)
savefilename = fullfile(fname, ['MCCC_Summary.png']);
export_fig(savefilename,'-transparent');
pause(3)
figure;
addpath('~/MATLAB/packages/Violinplot-Matlab');
set(gcf,'Position',[25 75 400 600]);
vplotobj = violinplot(results.cpac.('sn_denoised').trt_reliability.global_kendallsw);
title('Subjects Kendall (W) Distribution')
set(gcf,'Color',[.9 .9 .9]);
set(gcf,'Color',[.9 .9 .9]);
set(gca,'fontsize',20)
savefilename = fullfile(fname, ['KendallW_Summary.png']);
export_fig(savefilename,'-transparent');
pause(3)
% Plot full mccc node distributions on the same plot
for methodno=1:nmethods
if(nmethods>1)
y_methods = cat(2,...
results.cpac.(methodnames{1}).trt_reliability.mccc,...
results.cpac.(methodnames{2}).trt_reliability.mccc ...
);
% y_methods_lo = cat(2,...
% results.cpac.(methodnames{1}).trt_reliability.mccc_ci(nodeidx,1),...
% results.cpac.(methodnames{2}).trt_reliability.mccc_ci(nodeidx,1) ...
% );
% y_methods_hi = cat(2,...
% results.cpac.(methodnames{1}).trt_reliability.mccc_ci(nodeidx,2),...
% results.cpac.(methodnames{2}).trt_reliability.mccc_ci(nodeidx,2) ...
% );
% y_groups = {};
% y_groups{1} = repmat(methodnames(1),[1 size(y_methods,1)]);
% y_groups{2} = repmat(methodnames(2),[1 size(y_methods,1)]);
%
% Plot cornerhistogram
grammobj = plot_reliability_cornerhist(y_methods, ...
{'Before centering','After centering'}, ...
communitycolor ...
);
figure;
set(gcf,'Position',[25 75 850 750]);
grammobj.draw();
if(strcmp(studyname,'best-eegfmri'))
savefilename = fullfile(fname, ['sessioneffects_reliability' '_' ...
'difference' '_' methodnames{1} '_' methodnames{2}] ...
);
else
savefilename = fullfile(fname, ...
['sessioneffects_reliability' '_' ...
'difference' '_' methodnames{1} '_' methodnames{2} ...
communitylabel{1} ...
] ...
);
end
savefig(savefilename);
grammobj.export('file_name',savefilename,'file_type','pdf');
exportfun(savefilename);
y_methods = cat(2,...
mean(results.cpac.(methodnames{1}).trt_reliability.kendallsw,2),...
mean(results.cpac.(methodnames{2}).trt_reliability.kendallsw,2) ...
);
% Plot cornerhistogram
grammobj = plot_reliability_cornerhist(y_methods, ...
{'Before centering','After centering'}, ...
communitycolor ...
);
figure;
set(gcf,'Position',[25 75 850 750]);
grammobj.draw();
savefilename = fullfile(fname, ...
['sessioneffects_concordance' '_' ...
'difference' '_' methodnames{1} '_' methodnames{2} ...
communitylabel{1} ...
] ...
);
savefig(savefilename);
grammobj.export('file_name',savefilename,'file_type','pdf');
exportfun(savefilename);
else
y_methods = ...
results.cpac.(methodnames{1}).trt_reliability.mccc;
end
end
addpath('~/MATLAB/packages/Violinplot-Matlab');
figure;
set(gcf,'Position',[25 75 1200 650]);
%vplotobj = violinplot(results.cpac.(methodname).trt_reliability.kendallsw');
vplotobj = plot_violinplot(...
results.cpac.(methodname).trt_reliability.kendallsw', ...
{methodname}, ...
communitycolor ...
);
if(iscell(communitylabel))
communitylabelname = communitylabel{1}
else
communitylabelname = ''
end
savefilename = fullfile(fname, ...
['sessioneffects_violinplot' '_' ...
methodname ...
communitylabelname ...
] ...
);
savefig(savefilename);
exportfun(savefilename);
% Export kendallsW with powers parameters into a single table.
reliability_table = table();
methodname = 'sn_denoised';
for subjno=1:nsubjects
if(isfield(studydata,'cpac') && isfield(studydata.cpac,'confounds'))
power_table = studydata.cpac.confounds{subjno}{1}.powers_params(:,...
[1 3 6]);
tmp_table = power_table;
tmp_table.Properties.VariableNames(2:3) = ...
strcat(tmp_table.Properties.VariableNames(2:3),{'_ses1'});
power_table = studydata.cpac.confounds{subjno}{2}.powers_params(:,...
[1 3 6]);
tmp_table2 = power_table;
tmp_table2.Properties.VariableNames(2:3) = ...
strcat(tmp_table2.Properties.VariableNames(2:3),{'_ses2'});
tmp_table = horzcat(tmp_table,tmp_table2(:,2:3));
flag_reliability_vs_motion = 1;
else
tmp_table = table();
try
tmp_table.subject = subjno;
tmp_table.subjectlabel = subject_labels(subjno);
catch me
disp(me)
end
end
tmp_table.kendallw = ...
[results.cpac.(methodname).trt_reliability.kendallsw(:,subjno)'];
reliability_table = vertcat(reliability_table,tmp_table);
flag_reliability_vs_motion = 0;
end
save([fname filesep 'table_kendallw_' methodname ],'reliability_table');
writetable(reliability_table, [fname filesep 'table_kendallw_' methodname '.csv'],'Delimiter','\t');
if(flag_reliability_vs_motion)
% Scatterplot
communityno = 3;
y_methods = cat(2,(reliability_table.rootMeanSquareFD_ses1+ ...
reliability_table.rootMeanSquareFD_ses2)/2, ...
mean(reliability_table.kendallw(:, ...
communitycolor==communityno),2) ...
);
grammobj = plot_reliability_glm(y_methods, ...
{'RMSE FD','Kendall W'}, ...
communityno ...
);
figure;
set(gcf,'Position',[25 75 850 750]);
grammobj.draw();
% scatter( (reliability_table.rootMeanSquareFD_ses1+ ...
% reliability_table.rootMeanSquareFD_ses2)/2, ...
% mean(reliability_table.kendallw,2), 50);
savefilename = fullfile(fname, ...
['sessioneffects_kendallw' '_' ...
methodname ...
communitylabel{1} ...
] ...
);
savefig(savefilename);
grammobj.export('file_name',savefilename,'file_type','pdf');
exportfun(savefilename);
end
% % Compare consistency of successive normalization to global signal regression
%
% y_methods = results.cpac.sn_gm_agreement.mccc(nodeidx);
% y_lo = results.cpac.sn_gm_agreement.mccc_ci(nodeidx,1);
% y_hi = results.cpac.sn_gm_agreement.mccc_ci(nodeidx,2);
% figure;
% set(gcf,'Position',[50 75 1200 600]);
% grammobj = plot_reliability_dotplot(y_methods, ...
% y_lo, ...
% y_hi, ...
% 'Before/After SN Agreement', ...
% communitycolor ...
% );
% grammobj.set_title(['ICC Method Agreement']);
% grammobj.draw();
% exportfun(fullfile(fname,['sessioneffects_reliability' ...
% '2_' communitylabel{1} ]));
%
end
function output = conditional_correlation(X,Y)
% Only uses usual column standardize (i.e. correlation)
output = struct();
n1 = size(X,1);
n2 = size(Y,1);
if(n2>n1)
Y = Y(end-n1+1:end,:);
elseif(n1>n2)
X = X(end-n2+1:end,:);
end
[Sigma results] = covariance.conditional_sample_covariance_separate(X, ...
struct('verbose',true,...
'nuisance',Y) ...
);
output.corr = results.Sigma;
output.nuisance = results.nCorr;
output.corr2 = covariance.mle_sample_covariance(results.X_perpY, ...
struct('standardize', 'cols'));
output.NSR = results.NSR;
end
function Sigma = standard_correlation(X)
% Usual standard correlation matrix
[Sigma results] = covariance.mle_sample_covariance(X, ...
struct('standardize','cols'));
end
function Sigma = standard_correlation_sn(X)
% Automatically applies row-first successive norm
%standardize.successive_normalize(X');
[Xnew] = standardize.successive_normalize(X');
[Sigma results] = corr(Xnew');
% [Sigma results] = covariance.mle_sample_covariance(X,
% struct('standardize',true));
%
end
function trt_reliability = helper_node_mccc(graphs)
%
% Inputs
% - graphs is p x p x subjects x {rater|session}
p = size(graphs,1);
nsubjects = size(graphs,3);
if(p<=50)
nodeidx = 1:p;
end
nboot = 50;
mccc = zeros(p,1);
mccc_ci = zeros(p,2);
mccc_var = zeros(p,1);
mccc_wvar = zeros(p,1);
mccc_boot = zeros(p,nboot);
similarity = zeros(2*nsubjects,2*nsubjects);
for nodeno=1:p;
jj = nodeno;
%jj = nodeidx(nodeno);
features = squeeze(graphs(jj,:,:,:));
similarity = similarity + cov(cat(2,features(:,:,1),features(:,:,2)));
end
similarity = similarity./p;
for nodeno=1:p
%jj = nodeidx(nodeno)
jj = nodeno
features = squeeze(graphs(jj,:,:,:));
[mccc(jj), MCCC, mccc_ci(jj,:), mccc_boot(jj,:)] = ...
reliability.mccc(features(:,:,1)',features(:,:,2)');
mccc_var(jj) = MCCC.normVind;
mccc_wvar(jj) = MCCC.normVdep;
disp(sprintf('MCCC: %2.4f, CI: (%2.4f,%2.4f), WithinVar: %.4f, TotalVar: %.4f', ...
mccc(jj), mccc_ci(jj,1), mccc_ci(jj,2), mccc_wvar(jj), mccc_var(jj)))
end
trt_reliability.mccc = real(mccc);
trt_reliability.mccc_tvar = mccc_var;
trt_reliability.mccc_wvar = mccc_wvar;
trt_reliability.mccc_ci = real(mccc_ci);
trt_reliability.mccc_boot = real(mccc_boot);
trt_reliability.similarity = similarity;
end
function site_effect = detect_site_effect(X,sitelabels)
corrfun = @(X)(corr(X)); %@(X)(covariance.rank_sample_covariance(X(:,1:min(50,size(X,2)))));
upper_idx = find(reshape(triu(ones(size(X,2), size(X,3)),1), [1 size(X,2)^2]));
X = reshape(X,[size(X,1) size(X,2)*size(X,3)]);
tmpSimilarity = corrfun(X(:,upper_idx)');
[hom sep mw] = compareWithinAndBetweenGroupsSim(tmpSimilarity,sitelabels);
site_effect.similarity = tmpSimilarity;
site_effect.within = hom;
site_effect.between = sep;
site_effect.stat = mw;
site_effect.ratio = (hom-sep)/(hom+sep);
end
function [nuisance] = get_shared_nuisance(Y);
% Y is time-series x subjects
Yz = zscore(Y')';
[U S V] = svd(Y);
nfactors = min(size(Y,2),5);
nuisance = U(1:nfactors,:);
nuisance = reshape(nuisance,[size(Y,1) nfactors]);
end
function output = get_influence_diagnostic(X)
output = struct();
[Sigmasb Sigmas] = covjackknife(X,[1 2 3]);
output.nansubjects = isnan(squeeze(sum(sum(Sigmas,1),2)));
Sigmasb(isnan(Sigmasb)) = 0;
Sigmas(isnan(Sigmas)) = 0;
[output.influence output.norminfluence] = influence(Sigmasb,Sigmas);
end
function vplotobj = plot_violinplot(datamatrix,methodnames,community)
addpath('~/MATLAB/packages/Violinplot-Matlab');
ncommunities = 7;
vcolormap = brewermap(ncommunities,'Spectral');
vplotobj = violinplot(datamatrix, ...
'ShowNotches');
for plotno=1:length(vplotobj)
set(vplotobj(plotno).ViolinPlot, ...
'FaceColor',vcolormap(community(plotno),:) ...
);
set(vplotobj(plotno).ScatterPlot, ...
'MarkerEdgeColor', vcolormap(community(plotno),:) ...
);
set(vplotobj(plotno).ScatterPlot, ...
'CData', vcolormap(community(plotno),:) ...
);
set(vplotobj(plotno).ScatterPlot, ...
'MarkerFaceColor', vcolormap(community(plotno),:) ...
);
set(vplotobj(plotno).MeanPlot, ...
'Color', vcolormap(community(plotno),:) ...
);
end
xlabel('ROIs','fontsize',20,'fontweight','bold');
ylabel('Reliability', 'fontsize',20,'fontweight','bold');
title(regexprep(methodnames{1},'_','-'));
xtickvals = get(gca,'XTick');
xticklabels = get(gca,'XTickLabels');
set(gca,'FontSize',18, ...
'XTick',xtickvals(1:4:end), ...
'XTickLabels',xticklabels(1:4:end) ...
)
%'XTickLabelRotation',90)
end
function grammobj = plot_reliability_dotplot(y_methods,y_lo,y_hi,methodnames,community)
geom_type = 'geom_point';
color_type = 'common';
ncommunities = 7;
alternate_color = zeros(1,length(y_methods));
switch color_type
case 'odd-even'
alternate_color(1:2:end) = 1;
alternate_color(2:2:end) = 2;
mycolormap = brewermap(length(unique(alternate_color))*...
ncommunities,...
'Paired' ...