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analyze_etkinlab_sess_length.m
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analyze_etkinlab_sess_length.m
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function results = analyze_etkinlab_sess_length()
studyname = 'best-eegfmri-cpac'
addpath(fullfile(getenv('HOME'),'MATLAB','ggmClass'));
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'};
load(['tmp' filesep '15-Oct-2018' filesep 'best-eegfmri-cpac-trt-t840/etkinlab_analysis/data_etkinlab_analysis_best-eegfmri-cpac_rfmri.mat']);
nsubjects = length(studydata.session1.X);
p = size(studydata.session1.X{1},2);
% Get community labels
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;
results = {};
results.cpac = {};
results.community = communitylabel;
results.communitycolor = communitycolor;
% Get network estimate in increments of 80 measurements
% (80*.7 = 56 seconds)
n_step = 80;
n_minutes = 10;
n_trials = 1;
for minuteno=1:1:n_minutes
tmp_kendallW = zeros(1,nsubjects);
tmp_mccc = zeros(p,1);
for trialno=1:n_trials
corr_mat = helper_corr_session(studydata,minuteno*n_step);
results.networks = corr_mat;
graphs = results.networks(nodeidx,:,:,:);
tmp_kendallW = tmp_kendallW + helper_kendallW(graphs,nodeidx);
trt_reliability = helper_node_mccc(graphs);
tmp_mccc = tmp_mccc + real(trt_reliability.mccc);
end
results.kendallW(:,minuteno) = tmp_kendallW/n_trials;
results.mccc(:,minuteno) = tmp_mccc/n_trials;
end
dlmwrite([fname filesep 'trt_' studyname '_mccc.txt'],results.mccc,'precision',6);
dlmwrite([fname filesep 'trt_' studyname '_kendallw.txt'],results.kendallW,'precision',6);
mccc_summary(:,1) = mean(results.mccc,1);
mccc_summary(:,2) = prctile(results.mccc,[5]);
mccc_summary(:,3) = prctile(results.mccc,[95]);
kw_summary(:,1) = mean(results.kendallW,1);
kw_summary(:,2) = prctile(results.kendallW,[5]);
kw_summary(:,3) = prctile(results.kendallW,[95]);
dlmwrite([fname filesep 'trt_' studyname '_mccc_summary.txt'],mccc_summary,'precision',4);
dlmwrite([fname filesep 'trt_' studyname '_kendallw_summary.txt'],kw_summary,'precision',4);
% 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
end
function corr_mat = helper_corr_session(studydata,n_time)
[n p] = size(studydata.(['session1']).X{1});
nsubjects = length(studydata.(['session1']).X);
step_t = n_time
verbose = false;
nsessions = 2;
corr_mat = zeros(p,p,nsubjects,nsessions);
for sessionno=1:nsessions
for ii=1:nsubjects
if(verbose)
ii
['session' num2str(sessionno)]
end
X = studydata.(['session' num2str(sessionno)]).X{ii};
start_idx = randi([9 max(9,(n-9-step_t))]);
X = X(start_idx+1:start_idx+n_time,:);
try
corr_mat(:,:,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
end
function output = helper_kendallW(graphs,nodeidx)
p = size(graphs,1);
nsubjects = size(graphs,3);
tmp_kendallW = nan(1,nsubjects);
ba_nodes = nodeidx;
trid_idx = find(reshape(triu(ones(p,p),1),[p^2 1]));
for subjectno=1:nsubjects
rater1 = graphs(ba_nodes,ba_nodes,subjectno,1);
rater2 = graphs(ba_nodes,ba_nodes,subjectno,2);
rater1 = rater1(trid_idx);
rater2 = rater2(trid_idx);
tmp_kendallW(subjectno) = reliability.kendallsW(cat(2,rater1,rater2));
end
output = tmp_kendallW;
end
function output = helper_mccc()
end
function output = conditional_correlation(X,Y)
% Only uses usual column standardize (i.e. correlation)
output = struct();
[Sigma results] = covariance.conditional_sample_covariance_separate(X, ...
struct('verbose',false,...
'nuisance',Y) ...
);
output.corr = results.Sigma;
output.nuisance = results.nCorr;
output.corr2 = covariance.mle_sample_covariance(results.X_perpY, ...
struct('standardize', false));
output.NSR = results.NSR;
end
function Sigma = standard_correlation(X)
% Usual standard correlation matrix
[Sigma results] = covariance.mle_sample_covariance(X, ...
struct('standardize',false));
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 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 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
verbose = false;
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)
if(verbose)
jj = nodeno
else
jj = nodeno;
if(mod(jj,10)==0)
disp(sprintf('MCCC %2.2f Complete',jj/p));
end
end
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;
if(verbose)
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
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