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Timelimit02_ERP_cluster.m
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Timelimit02_ERP_cluster.m
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%% CLUSTER-based permutation tests on ERPs
%=========================================================================%
% AUTHOR: Bianca Trovo ([email protected])
% DATE: created on April/May 2019
% EXPERIMENT: Timelimit_2018
%{
SCOPE: compute the
OUTPUT: R{condi}or P{condi} in
FIXME:
%}
%=========================================================================%
%% START of the script
%% Housekeeping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clear workspace (if needed)
if input('clear all? (1/0) ... ')
clearvars; close all;
end
% set paths (if needed)
BT_setpath
% choose subj & go to the right folder
BT_getsubj
clear LevelAnalysis name numlines prompt subj_folders
%% More specific paths (maybe set this in the start script)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
behavioral_folder= [results_Path, '/Behaviour']; % it can be also current_subj_folder
if ~exist(fullfile(behavioral_folder)); mkdir(fullfile(behavioral_folder)); end;
timeseries_folder= [results_Path, '/Timeseries']; % it can be also current_subj_folder
if ~exist(fullfile(timeseries_folder)); mkdir(fullfile(timeseries_folder)); end;
cd(timeseries_folder);
powerspectra_folder= [results_Path, '/Powerspect']; % it can be also current_subj_folder
if ~exist(fullfile(powerspectra_folder)); mkdir(fullfile(powerspectra_folder)); end;
cd(powerspectra_folder);
correlation_folder= [results_Path, '/Correlations']; % it can be also current_subj_folder
if ~exist(fullfile(correlation_folder)); mkdir(fullfile(correlation_folder)); end;
regression_folder= [results_Path, '/Regressions']; % it can be also current_subj_folder
if ~exist(fullfile(regression_folder)); mkdir(fullfile(regression_folder)); end;
statistics_folder= [results_Path, '/Statistics']; % it can be also current_subj_folder
if ~exist(fullfile(statistics_folder)); mkdir(fullfile(statistics_folder)); end;
%% 1) Time-series CORRELATIONS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data
% load 'R_all_ER1'; load 'R_all_ER2';
% load 'R_all_ER1Log'; load 'R_all_ER2Log';
load 'R_all_ER1Log_semiLogX'; load 'R_all_ER2Log_semiLogX'; % correggi
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
RPsubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
nSubjs= length(RPsubjs);
% R_all=R_all';
R_some= {};
for subi=1:nSubjs;
R_some{subi}= R_all{RPsubjs(subi)}
end
% R_some= R_some';
isequal(R_all{13},R_some{6}); % IT WORKS but you should automatize it
% Run cluster test
% ROI for RP
% chans = {'EEG20','EEG21','EEG29','EEG30','EEG31','EEG39','EEG40'};
% single-channel
% chans= {'EEG030'};
chans= {'EEG*'};
latency= [-2 -0.2]; %-200ms = point of no return;
nSubjs = 22;
% Prepare the 'design'. Since Fieldrip does not know the the simple t-test
% against 0, we have to use the paired t-test (against 0).
design = zeros(2, 2*nSubjs);
design(1, 1:nSubjs)= 1; % condition 1 (data)
design(1, (nSubjs+1):(2*nSubjs))= 2; % condition 2 (0, value against which we compare the data).
design(2, 1:nSubjs) = 1:nSubjs; % Indicate the subject number (for pairing, alghough not useful here)
design(2, (nSubjs+1):(2*nSubjs)) = 1:nSubjs;
% Make *fake data*
R_contr= {};
for subi=1:nSubjs;
templateER.avg = R_all{subi}.avg; % some or all
R_contr{subi}= templateER;
R_contr{subi}.avg= zeros(nChans,nTimes);
end
cfg = [];
cfg.method = 'montecarlo';
cfg.channel = chans;
cfg.latency= latency;
cfg.avgoverchan = 'yes';
% cfg.avgovertime= 'yes';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.05; % lower to 0.01
cfg.correcttail = 'alpha'; %only for two sided t-tests
cfg.statistic = 'ft_statfun_depsamplesT';
% cfg.statistic = 'ft_statfun_indepsamplesF'; % if you want
% to run the ANOVA
cfg.design = design;
cfg.ivar = 1;
cfg.uvar = 2;
cfg.numrandomization = 500; % to try put 500 and final put 1000
% aaron's way
cfg_neighb=[];
% cfg_neighb.layout = '/Users/bt_neurospin/Repos/matlab_internal/turbo_mne/eeg_64_NM20884N.lay';
cfg_neighb.layout= 'eeg_64_NM20884N.lay';
cfg_neighb.method = 'distance';
neighbs = ft_prepare_neighbours(cfg_neighb);
cfg.neighbours = neighbs;
% STATS
stat = ft_timelockstatistics(cfg, R_all{:}, R_contr{:}); %R_some or R_all
cd(statistics_folder);
% save stat_corr_ER1b stat; save stat_corr_ER2b stat; save stat_corr_ER2Logb stat; save stat_corr_ER2Logc stat; save stat_corr_ER2Loge stat;
% save stat_corr_ER2Logf stat; save stat_corr_ER2Logg stat; save stat_corr_ER2Logh stat;
save('stat_corr_ER2Log_wd_22','-v7.3'); % wd= wide: All channels, latency= -3s 0s
save('stat_corr_ER2Log_nr_22','-v7.3'); % nr= narrow: All channels, latency= -2s -0.2s
save('stat_corr_ER2Log_hp_22','-v7.3'); % hp= hypothesis: ROI channels, latency= -2 -0.2s
save('stat_corr_ER1Log_wd_12','-v7.3'); % wd= wide: All channels, latency= -3s 0s
save('stat_corr_ER1Log_nr_12','-v7.3'); % nr= narrow: All channels, latency= -2s -0.2s
save('stat_corr_ER1Log_hp_12','-v7.3'); % hp= hypothesis: ROI channels, latency= -2 -0.2s
stat.posclusters; stat.negclusters;
%% Plot significant clusters
stat.stat2= stat.stat.*(stat.posclusterslabelmat == 1);
cfg_fig= [];
cfg_fig.parameter= 'stat2';
cfg_fig.zlim= 'maxabs';
figure; ft_singleplotER(cfg_fig,stat);
%
% stat.stat_neg= stat.stat.*(stat.negclusterslabelmat == 1);
% cfg= [];
% cfg.parameter= 'stat3';
% cfg.zlim= 'maxabs';
% figure; ft_singleplotTFR(cfg,stat);
% % figure; ft_topoplotTFR(cfg,stat);
cfg = [];
% cfg.alpha = 0.05;
% cfg.parameter = 'raweffect';
% cfg.zlim = [-1e-27 1e-27];
cfg.layout = 'eeg_64_NM20884N.lay';
% cfg.saveaspng= 'clusterplot4';
ft_clusterplot(cfg,stat);
figure;
% define parameters for plotting
timestep = 0.05; %(in seconds)
sampling_rate = 500;
sample_count = length(stat.time);
j = [-1:timestep:0]; % Temporal endpoints (in seconds) of the ERP average computed in each subplot
m = [1:timestep*sampling_rate:sample_count]; % temporal endpoints in MEEG samples
% get relevant (significant) values
pos_cluster_pvals = [stat.posclusters(:).prob];
% In case you have downloaded and loaded the data, ensure stat.cfg.alpha exist
if ~isfield(stat.cfg,'alpha'); stat.cfg.alpha = 0.025; end; % stat.cfg.alpha was moved as the downloaded data was processed by an additional FieldTrip function to anonymize the data.
pos_signif_clust = find(pos_cluster_pvals < stat.cfg.alpha);
pos = ismember(stat.posclusterslabelmat, pos_signif_clust);
% First ensure the channels to have the same order in the average and in the statistical output.
% This might not be the case, because ft_math might shuffle the order
[i1,i2] = match_str(R_all{1}.label, stat.label);
% plot
for k = 1:20;
subplot(4,5,k);
cfg = [];
cfg.xlim=[j(k) j(k+1)];
% cfg.zlim = [-5e-14 5e-14];
pos_int = zeros(numel(R_all{1}.label),1);
pos_int(i1) = all(pos(i2, m(k):m(k+1)), 2);
cfg.highlight = 'on';
cfg.highlightchannel = find(pos_int);
cfg.comment = 'xlim';
cfg.commentpos = 'title';
cfg.layout = 'eeg_64_NM20884N.lay';
ft_topoplotER(cfg, GAVG_R);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signrank test (move somewhere else?)
RPSubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
nRP= length(RPSubjs);
R_CUBE= zeros(60,2001,nRP);
for subi=1:RPSubjs;
R_CUBE(:,:,subi) = myft_ftavg2dataCube(R_all{subi});
end
TAIL= 'both';
for i=1:nChans
for j= 1:nTimes
[P(i,j)] = signrank(squeeze(R_CUBE(i,j,:)));
end
end
SR=500;
t0= 3;
tAx = [0:2000]./SR - 3;
figure; plot(tAx, P(28,:), 'o','Linewidth',2);
title('Correlations between RP average amplitudes from -1s to -0.2s and participants Waiting Times (N=22)');
Xlabel('Time(s)'); Ylabel('P values');%legend('Correlations p-values','P-value= 0.05','Location','Best');
hold on; plot([tAx(1) tAx(end)],[0.05 0.05],'r','Linewidth',2);
txt = 'P-value= 0.05';
text(-2, 0.07,txt,'FontSize',14);
plot([0 0],[0 1],'k--','Linewidth',1.5);
%%%%%%%%%%%%%%%%%
% RPSubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
% nRP= length(RPSubjs);
% B_CUBE= zeros(60,2001,nRP);
%
% for subi=1:RPSubjs;
%
% B_CUBE(:,:,subi) = myft_ftavg2dataCube(B_all{subi});
%
% end
%
% TAIL= 'both';
% for i=1:nChans
%
% for j= 1:nTimes
%
% [P(i,j)] = signrank(squeeze(B_CUBE(i,j,:)));
%
% end
%
% end
%
% SR=500;
% t0= 3;
% tAx = [0:2000]./SR - 3;
% figure; plot(tAx, P(28,:), 'o','Linewidth',2);
% title('Correlations between RP average amplitudes from -1s to -0.2s and participants Waiting Times (N=22)');
% Xlabel('Time(s)'); Ylabel('P values');%legend('Correlations p-values','P-value= 0.05','Location','Best');
% hold on; plot([tAx(1) tAx(end)],[0.05 0.05],'r','Linewidth',2);
% txt = 'P-value= 0.05';
% text(-2, 0.07,txt,'FontSize',14);
% plot([0 0],[0 1],'k--','Linewidth',1.5);
%% 1b) Time-series: REGRESSIONS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data
load 'B_all_ER1'; load 'B_all_ER2';
load 'B_all_ER1Log'; load 'B_all_ER2Log';
load 'B_all_ER1Log_semiLogX'; load 'B_all_ER2Log_semiLogX';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
RPsubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
nSubjs= length(RPsubjs);
B_all=B_all';
B_some= {};
for subi=1:nSubjs;
B_some{subi}= B_all{RPsubjs(subi)}
end
B_some= B_some';
isequal(B_all{13},B_some{6}); % IT WORKS but you should automatize it
% Run cluster test
% ROI for RP
% chans = {'EEG20','EEG21','EEG29','EEG30','EEG31','EEG39','EEG40'};
% single-channel
% chans= {'EEG030'};
chans= {'EEG*'};
latency= [-2 -0.2]; %-200ms = point of no return;
nSubjs = 22;
% Prepare the 'design'. Since Fieldrip does not know the the simple t-test
% against 0, we have to use the paired t-test (against 0).
design = zeros(2, 2*nSubjs);
design(1, 1:nSubjs)= 1; % condition 1 (data)
design(1, (nSubjs+1):(2*nSubjs))= 2; % condition 2 (0, value against which we compare the data).
design(2, 1:nSubjs) = 1:nSubjs; % Indicate the subject number (for pairing, alghough not useful here)
design(2, (nSubjs+1):(2*nSubjs)) = 1:nSubjs;
% Make *fake data*
B_contr= {};
for subi=1:nSubjs;
templateER.avg = B_all{subi}.avg;
B_contr{subi}= templateER;
B_contr{subi}.avg= zeros(nChans,nTimes)
end
cfg = [];
cfg.method = 'montecarlo';
cfg.channel = chans;
cfg.latency= latency;
% cfg.avgoverchan = 'yes';
% cfg.avgovertime= 'yes';
cfg.correctm = 'cluster';
cfg.clusteralpha = 0.01;
cfg.correcttail = 'alpha'; %only for two sided t-tests
cfg.statistic = 'ft_statfun_depsamplesT';
% cfg.statistic = 'ft_statfun_indepsamplesF'; % if you want
% to run the ANOVA
cfg.design = design;
cfg.ivar = 1;
cfg.uvar = 2;
cfg.numrandomization = 500;
% aaron's way
% cfg_neighb=[];
% % cfg_neighb.layout = '/Users/bt_neurospin/Repos/matlab_internal/turbo_mne/eeg_64_NM20884N.lay';
% cfg_neighb.layout= 'eeg_64_NM20884N.lay';
% cfg_neighb.method = 'distance';
% neighbs = ft_prepare_neighbours(cfg_neighb);
% cfg.neighbours = neighbs;
% elie's way
cfg_neighb=[];
% cfg_neighb.layout = '/Users/bt_neurospin/Repos/matlab_internal/turbo_mne/eeg_64_NM20884N.lay';
cfg_neighb.layout= 'eeg_64_NM20884N.lay';
cfg_neighb.method = 'distance';
cfg_neighb.neighbourdist = .18;%.18 %.13
cfg_neighb.sens= B_all{2}.elec;
neighbs = ft_prepare_neighbours(cfg_neighb);
cfg.neighbours = neighbs;
% STATS
stat = ft_timelockstatistics(cfg, B_all{:}, B_contr{:}); %R_all
cd(statistics_folder);
% save stat_regr_ER1b stat;
% save stat_regr_ER2b stat;
% save stat_regr_ER2Logb stat;
% save stat_regr_ER2Logc stat;
% save stat_regr_ER2Loge stat;
% save stat_regr_ER2Logf stat;
% save stat_regr_ER2Logg stat;
% save stat_regr_ER2Logh stat;
save('stat_regr_ER2Log_wd_22','-v7.3'); % wd= wide: All channels, latency= -3s 0s
save('stat_regr_ER1Log_nr_22','-v7.3'); % nr= narrow: All channels, latency= -2s -0.2s
save('stat_regr_ER1Log_hp_22','-v7.3'); % hp= hypothesis: ROI channels, latency= -2 -0.2s
save('stat_regr_ER2Log_wd_12','-v7.3'); % wd= wide: All channels, latency= -3s 0s
save('stat_regr_ER1Log_nr_12','-v7.3'); % nr= narrow: All channels, latency= -2s -0.2s
save('stat_regr_ER1Log_hp_12','-v7.3'); % hp= hypothesis: ROI channels, latency= -2 -0.2s
stat.posclusters; stat.negclusters;
% Plot significant clusters
stat.stat2= stat.stat.*(stat.posclusterslabelmat == 1);
cfg_fig= [];
cfg_fig.parameter= 'stat2';
cfg_fig.zlim= 'maxabs';
figure; ft_singleplotER(cfg_fig,stat);
%
% stat.stat_neg= stat.stat.*(stat.negclusterslabelmat == 1);
% cfg= [];
% cfg.parameter= 'stat3';
% cfg.zlim= 'maxabs';
% figure; ft_singleplotTFR(cfg,stat);
% % figure; ft_topoplotTFR(cfg,stat);
cfg = [];
cfg.alpha = 0.05;
% cfg.parameter = 'raweffect';
% cfg.zlim = [-1e-27 1e-27];
cfg.layout = 'eeg_64_NM20884N.lay';
% cfg.saveaspng= 'clusterplot4';
ft_clusterplot(cfg,stat);
figure;
% define parameters for plotting
timestep = 0.05; %(in seconds)
sampling_rate = 500;
sample_count = length(stat.time);
j = [-1:timestep:0]; % Temporal endpoints (in seconds) of the ERP average computed in each subplot
m = [1:timestep*sampling_rate:sample_count]; % temporal endpoints in MEEG samples
% get relevant (significant) values
pos_cluster_pvals = [stat.posclusters(:).prob];
% In case you have downloaded and loaded the data, ensure stat.cfg.alpha exist
if ~isfield(stat.cfg,'alpha'); stat.cfg.alpha = 0.025; end; % stat.cfg.alpha was moved as the downloaded data was processed by an additional FieldTrip function to anonymize the data.
pos_signif_clust = find(pos_cluster_pvals < stat.cfg.alpha);
pos = ismember(stat.posclusterslabelmat, pos_signif_clust);
% First ensure the channels to have the same order in the average and in the statistical output.
% This might not be the case, because ft_math might shuffle the order
[i1,i2] = match_str(R_all{1}.label, stat.label);
% plot
for k = 1:20;
subplot(4,5,k);
cfg = [];
cfg.xlim=[j(k) j(k+1)];
% cfg.zlim = [-5e-14 5e-14];
pos_int = zeros(numel(R_all{1}.label),1);
pos_int(i1) = all(pos(i2, m(k):m(k+1)), 2);
cfg.highlight = 'on';
cfg.highlightchannel = find(pos_int);
cfg.comment = 'xlim';
cfg.commentpos = 'title';
cfg.layout = 'eeg_64_NM20884N.lay';
ft_topoplotER(cfg, GAVG_B);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Signrank test (move somewhere else?)
RPSubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
nRP= length(RPSubjs);
R_CUBE= zeros(60,2001,nRP);
for subi=1:RPSubjs;
R_CUBE(:,:,subi) = myft_ftavg2dataCube(R_all{subi});
end
TAIL= 'both';
for i=1:nChans
for j= 1:nTimes
[P(i,j)] = signrank(squeeze(R_CUBE(i,j,:)));
end
end
SR=500;
t0= 3;
tAx = [0:2000]./SR - 3;
figure; plot(tAx, P(28,:), 'o','Linewidth',2);
title('Correlations between RP average amplitudes from -1s to -0.2s and participants Waiting Times (N=22)');
Xlabel('Time(s)'); Ylabel('P values');%legend('Correlations p-values','P-value= 0.05','Location','Best');
hold on; plot([tAx(1) tAx(end)],[0.05 0.05],'r','Linewidth',2);
txt = 'P-value= 0.05';
text(-2, 0.07,txt,'FontSize',14);
plot([0 0],[0 1],'k--','Linewidth',1.5);
%%%%%%%%%%%%%%%%%
% RPSubjs= [3 6 7 8 10 13 15 17 18 19 20 21];
% nRP= length(RPSubjs);
% B_CUBE= zeros(60,2001,nRP);
%
% for subi=1:RPSubjs;
%
% B_CUBE(:,:,subi) = myft_ftavg2dataCube(B_all{subi});
%
% end
%
% TAIL= 'both';
% for i=1:nChans
%
% for j= 1:nTimes
%
% [P(i,j)] = signrank(squeeze(B_CUBE(i,j,:)));
%
% end
%
% end
%
% SR=500;
% t0= 3;
% tAx = [0:2000]./SR - 3;
% figure; plot(tAx, P(28,:), 'o','Linewidth',2);
% title('Correlations between RP average amplitudes from -1s to -0.2s and participants Waiting Times (N=22)');
% Xlabel('Time(s)'); Ylabel('P values');%legend('Correlations p-values','P-value= 0.05','Location','Best');
% hold on; plot([tAx(1) tAx(end)],[0.05 0.05],'r','Linewidth',2);
% txt = 'P-value= 0.05';
% text(-2, 0.07,txt,'FontSize',14);
% plot([0 0],[0 1],'k--','Linewidth',1.5);