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erfosc_script_jm.m
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erfosc_script_jm.m
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% the rationale for the stuff done in this script is as follows:
% subject specific data handling is done outside functions
% algorithmic pipeline steps are performed inside functions
if ~exist('subj', 'var')
error('a subject needs to be specified');
end
if ~exist('isPilot', 'var')
fprintf('assuming that a regular subject is requested\n');
isPilot = false;
end
if isnumeric(subj) && ~isPilot
erf_osc_datainfo;
subject = subjects(subj);
elseif isnumeric(subj) && isPilot
subject = pilotsubjects(subj);
elseif ~isnumeric(subj)
error('a subject should be identified by means of a scalar number, otherwise the data handling won''t work');
end
if ~exist('dodics_gamma', 'var'), dodics_gamma = false; end
if ~exist('dodics_lowfreq', 'var'), dodics_lowfreq = false; end
if ~exist('dofreq', 'var'), dofreq = false; end
if ~exist('docomp', 'var'), docomp = false; end
if ~exist('getdata', 'var'), getdata = false; end
if ~exist('dofreq_short', 'var'), dofreq_short = false; end
if ~exist('dofreq_short_lowfreq', 'var'), dofreq_short_lowfreq = false; end
if ~exist('docorrelation', 'var'), docorrelation = false; end
if ~exist('docorrelation_lowfreq', 'var'), docorrelation_lowfreq = false; end
if ~exist('dolcmv_parc', 'var'), dolcmv_parc = false; end
if ~exist('dolcmv_parc_msmall', 'var'), dolcmv_parc_msmall = false; end
if ~exist('dolcmv_norm', 'var'), dolcmv_norm = false; end
if ~exist('dolcmv_norm_msmall', 'var'), dolcmv_norm_msmall = false; end
if ~exist('dosplitpow_lcmv', 'var'), dosplitpow_lcmv = false; end
if ~exist('docorrpow_lcmv', 'var'), docorrpow_lcmv = false; end
if ~exist('doPlanar', 'var'), doPlanar = false; end
if ~exist('doglm', 'var'), doglm = false; end
if ~exist('dosplitpow_source', 'var'), dosplitpow_source = false; end
if ~exist('doparcel_erf', 'var'), doparcel_erf = false; end
if ~exist('doresplocked', 'var'), doresplocked = false; end
if ~exist('dobdssp', 'var'), dobdssp = false; end
if ~exist('docorrpow_lcmv_lowfreq', 'var'), docorrpow_lcmv_lowfreq = false; end
if ~exist('docorr_gamma_rt', 'var'), docorr_gamma_rt = false; end
if ~exist('dostat_erf_rt', 'var'), dostat_erf_rt = false; end
if ~exist('dostat_pow_erf', 'var'), dostat_pow_erf = false; end
if docorr_gamma_rt, dofreq_short = true; end
if doparcel_erf, dolcmv_parc = true; end
if dodics_gamma, dofreq = true; end
if dodics_lowfreq, dofreq = true; end
if dofreq, getdata = true; end
if docomp, getdata = true; end
if dofreq_short, getdata = true; end
if dofreq_short_lowfreq, getdata = true; end
if dolcmv_parc, getdata = true; end
if dolcmv_parc_msmall, getdata = true; end
if dolcmv_norm, getdata = true; end
if dosplitpow_lcmv, getdata = true; end
if docorrpow_lcmv, getdata = true; end
if docorrpow_lcmv_lowfreq, getdata = true; end
if doglm, getdata = true; dolcmv_parc = true; end
if dosplitpow_source, getdata = true; end
if dobdssp, getdata = true; end
% this chunk creates 2 data structures [-0.75 0.5]
if getdata
if ~exist('undocomp','var')
undocomp = false;
end
[p,f,e] = fileparts(subject.dataset);
basedir = strrep(p, 'raw', 'processed');
filename_data = fullfile(basedir, sprintf('sub-%03d_cleandata.mat', subj));
load(filename_data);
if undocomp
filename_comp = fullfile(basedir, sprintf('sub-%03d_icaComp.mat', subj));
load(filename_comp);
sel = zeros(0,1);
if ~isempty(subject.ecgcomp)
sel = [sel subject.ecgcomp];
end
if ~isempty(subject.eyecomp)
sel = [sel subject.eyecomp];
end
comp.topo = comp.topo(:,sel);
comp.unmixing = comp.unmixing(sel,:);
else
comp = [];
end
[data_onset, data_shift, data_resp] = erfosc_getdata(dataClean, comp);
clear dataClean;
end
% this chunk does a dss decomposition on the onset-aligned data, and
% applies the topographies to the shift-aligned data
if docomp
[comp_onset, comp_shift, comp_shift2] = erfosc_dss_onset(data_onset, data_shift);
savedir = '/project/3011085.02/analysis/';
save(fullfile(savedir, 'comp/', sprintf('sub-%03d/sub-%03d_comp', subj,subj)), 'comp_onset', 'comp_shift', 'comp_shift2');
end
% this chunk does spectral decomposition
if dofreq_short
peakpicking;%MvE
latency = peaks(subj,1).*[1 1] - 0.02 - [0.20 1./600];%MvE
foi = subject.gammapeak(end).*[1 1];
smo = max(10, diff(subject.gammaband(end,:))./2);
[freq_onset, freq_shift, P] = erfosc_freq(data_onset, data_shift, latency, subject, foi, smo);
if ~exist('savefreq', 'var')
savefreq = false;
end
if savefreq
savedir = '/project/3011085.02/analysis/freq/';%MvE
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_freqshort_mve', subj,subj)), 'freq_shift', 'P', 'latency');
end
end
% this chunk does spectral decomposition
if dofreq_short_lowfreq
load(sprintf('/project/3011085.02/analysis/freq/sub-%03d/sub-%03d_pow_low.mat',subj,subj));
peakpicking;
latency = peaks(subj,1).*[1 1] - 0.02 - [0.4 1./600];%MvE
foi = [peakfreq peakfreq];
smo = 2.5;
[freq_onset, freq_shift, P] = erfosc_freq(data_onset, data_shift, latency, subject, foi, smo);
if ~exist('savefreq', 'var')
savefreq = false;
end
if savefreq
savedir = '/project/3011085.02/analysis/freq';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_freqshort_low', subj,subj)), 'freq_shift', 'P', 'latency');
end
end
% this chunk does spectral decomposition
if dofreq
if ~exist('latency', 'var')
latency = [-inf 0-1./data_onset.fsample];
end
if dodics_lowfreq
if ~exist('peakfreq', 'var')
load(sprintf('/project/3011085.02/analysis/freq/sub-%03d/sub-%03d_pow_low.mat',subj,subj));
end
foi = [peakfreq peakfreq]; smo = 2;
latency = [-0.75+1./data_onset.fsample 0-1./data_onset.fsample];
[freq_onset, freq_shift, P] = erfosc_freq(data_onset, data_shift, latency, subject, foi, smo);
else
[freq_onset, freq_shift, P] = erfosc_freq(data_onset, data_shift, latency, subject);
end
if ~exist('savefreq', 'var')
savefreq = false;
end
if savefreq
savedir = '/project/3011085.02/analysis/freq';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_freq', subj,subj)), 'freq_shift', 'P', 'latency');
end
end
% this chunk does source reconstruction
if dodics_gamma
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
[source_onset, source_shift, Tval, F] = erfosc_dics_gamma(freq_onset, freq_shift, headmodel, sourcemodel);
source_onset = rmfield(source_onset, 'cfg');
source_shift = rmfield(source_shift, 'cfg');
savedir = '/project/3011085.02/analysis/source';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_source', subj,subj)), 'source_onset', 'source_shift', 'Tval', 'F');
end
if dodics_lowfreq
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
[source_onset, source_shift, Tval, F] = erfosc_dics_alpha(freq_onset, freq_shift, headmodel, sourcemodel);
source_onset = rmfield(source_onset, 'cfg');
source_shift = rmfield(source_shift, 'cfg');
savedir = '/project/3011085.02/analysis/source/';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_source_low', subj,subj)), 'source_shift', 'Tval', 'F');
end
if dolcmv_parc
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
load('atlas_subparc374_8k.mat');
if doresplocked
[source_parc] = erfosc_lcmv_parc(data_resp, headmodel, sourcemodel, atlas, doresplocked);
else
[source_parc] = erfosc_lcmv_parc(data_shift, headmodel, sourcemodel, atlas, doresplocked);
savedir = '/project/3011085.02/analysis/source';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_lcmv',subj, subj)), 'source_parc');
end
end
if dolcmv_norm
datadir = '/project/3011085.02/analysis/source';
filename = fullfile(datadir, sprintf('sub-%03d/sub-%03d_lcmv', subj,subj));
load(filename);
tmpcfg = [];
tmpcfg.latency = [-0.2 -1./600];
data_shift = ft_selectdata(tmpcfg, data_shift);
tmpcfg = [];
tmpcfg.covariance = 'yes';
tmp = ft_timelockanalysis(tmpcfg, data_shift);
noise = zeros(numel(source_parc.label),1);
for k = 1:numel(source_parc.label)
tmpF = source_parc.F{k}(1,:);
tmpC = sqrt(tmpF*tmp.cov*tmpF');
noise(k) = tmpC;
end
save(filename, 'noise', '-append');
end
if dolcmv_parc_msmall
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
load('atlas_MSMAll_8k_subparc.mat');
[source_parc] = erfosc_lcmv_parc(data_shift, headmodel, sourcemodel, atlas);
savedir = '/project/3011085.02/analysis/source/';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_lcmv_msmall', subj, subj)), 'source_parc');
end
if dolcmv_norm_msmall
datadir = '/project/3011085.02/analysis/source/';
filename = fullfile(datadir, sprintf('sub-%03d/sub-%03d_lcmv_msmall', subj, subj));
load(filename);
tmpcfg = [];
tmpcfg.latency = [-0.2 -1./600];
data_shift = ft_selectdata(tmpcfg, data_shift);
tmpcfg = [];
tmpcfg.covariance = 'yes';
tmp = ft_timelockanalysis(tmpcfg, data_shift);
noise = zeros(numel(source_parc.label),1);
for k = 1:numel(source_parc.label)
tmpF = source_parc.F{k}(1,:);
tmpC = sqrt(tmpF*tmp.cov*tmpF');
noise(k) = tmpC;
end
save(filename, 'noise', '-append');
end
% this chunk extracts single trial power and amplitude of
if docorrelation
% erfosc_comppeaks;
% erfosc_lcmvpeaks
peakpicking;
datadir = '/project/3011085.02/analysis//'
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_source', subj, subj)));
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow', subj, subj)), 'datapst')
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_lcmv', subj, subj)));
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_freqshort', subj, subj)));
[val1 idx1] = sort(datapst.trialinfo(:,1));
datapst.trial = datapst.trial(idx1);
datapst.trialinfo = datapst.trialinfo(idx1,:);
[m, idx] = max(Tval);
pow = (abs(F(idx,:)*transpose(freq_shift.fourierspctrm)).^2)*P;
%pow = log10(pow);
% erf = cellrowselect(comp_shift.trial, comp_id(subj));
% erf = cat(1,erf{:});
% erf = polarity(subj).*ft_preproc_baselinecorrect(erf, 391, 451);
erf = cat(1, datapst.trial{:});
% ix1 = nearest(comp_shift.time{1}, peaks(subj,1));
% ix2 = nearest(comp_shift.time{1}, peaks(subj,2));
ix1 = nearest(datapst.time{1}, peaks(subj,1));
ix2 = nearest(datapst.time{1}, peaks(subj,2));
signpeak = sign(mean(mean(erf(:,ix1:ix2),2)));
erf = signpeak.*erf;
amp = mean(erf(:,ix1:ix2), 2);
[rho, pval] = corr(log10(pow(:)), amp(:), 'type', 'spearman');
save(fullfile(datadir, sprintf('corr/sub-%03d/sub-%03d_corr', subj,subj)), 'amp', 'pow', 'rho', 'pval', 'erf');
end
if docorrelation_lowfreq
erfosc_lcmvpeaks;
datadir = '/project/3011085.02/analysis/'
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_source_alpha', subj, subj)));
load(fullfile(datadir, 'freq/', sprintf('sub-%03d/sub-%03d_splitpow', subj, subj)), 'datapst')
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_lcmv', subj, subj)));
load(fullfile(datadir, 'freq/', sprintf('sub-%03d/sub-%03d_freqshort_alpha', subj, subj)));
[val1 idx1] = sort(datapst.trialinfo(:,1));
datapst.trial = datapst.trial(idx1);
datapst.trialinfo = datapst.trialinfo(idx1,:);
[m, idx] = min(Tval);
pow = (abs(F(idx,:)*transpose(freq_shift.fourierspctrm)).^2)*P;
erf = cat(1, datapst.trial{:});
ix1 = nearest(datapst.time{1}, peaks(subj,1));
ix2 = nearest(datapst.time{1}, peaks(subj,2));
signpeak = sign(mean(mean(erf(:,ix1:ix2),2)));
erf = signpeak.*erf;
amp = mean(erf(:,ix1:ix2), 2);
[rho, pval] = corr(log10(pow(:)), amp(:), 'type', 'spearman');
save(fullfile(datadir,'corr/', sprintf('sub-%03d/sub-%03d_corr_alpha', subj, subj)), 'amp', 'pow', 'rho', 'pval', 'erf');
end
if dosplitpow_lcmv
% erfosc_lcmvpeaks;
peakpicking;
datadir = '/project/3011085.02/analysis/source/';
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_lcmv', subj, subj)));
source_parc.avg = diag(1./noise)*source_parc.avg;
ix1 = nearest(source_parc.time, peaks(subj,1));
ix2 = nearest(source_parc.time, peaks(subj,2));
tmpcfg = [];
tmpcfg.latency = [-0.1 0.5-1./600];
datapst = ft_selectdata(tmpcfg, data_shift);
tmpcfg.latency = [-0.2 -1./600] + peaks(subj,1) - 0.02; %0.01;
datapre = ft_selectdata(tmpcfg, data_shift);
%clear data_shift;
source_parc.avg = ft_preproc_baselinecorrect(source_parc.avg, 1, 60);
[maxval, maxidx] = max(abs(mean(source_parc.avg(:,ix1:ix2),2)));
signpeak = sign(mean(source_parc.avg(maxidx,ix1:ix2),2));
fprintf('parcel with max amplitude = %s\n', source_parc.label{maxidx});
plot_alternative_parcel(source_parc.label{maxidx});
datapst.trial = source_parc.F{maxidx}(1,:)*datapst.trial;
datapst.label = source_parc.label(maxidx);
tmpcfg = [];
tmpcfg.demean = 'yes';
tmpcfg.baselinewindow = [-inf 0];
datapst = ft_preprocessing(tmpcfg, datapst);
tmp = cat(1, datapst.trial{:}).*signpeak; % meak peak up-going
tmp = ft_preproc_baselinecorrect(tmp, 1, 60);
[srt, idx] = sort(mean(tmp(:,ix1:ix2),2));
datapst.trial = datapst.trial(idx);
datapst.trialinfo = datapst.trialinfo(idx,:);
datapre.trial = datapre.trial(idx);
datapre.trialinfo = datapre.trialinfo(idx,:);
data_shift.trial = data_shift.trial(idx);
data_shift.trialinfo = data_shift.trialinfo(idx,:);
% powerspectrum
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'pow';
cfg.tapsmofrq = 10;
cfg.foilim = [0 120];
cfg.trials = 1:100;
cfg.pad = 1;
if doPlanar
cfgb = [];
cfgb.method = 'template';
cfgb.template = 'CTF275_neighb.mat';
cfgb.neighbours = ft_prepare_neighbours(cfgb, datapre);
cfgb.method = 'sincos';
datapre_planar = ft_megplanar(cfgb, datapre);
cfgb = [];
cfgb.method = 'sum';
f1 = ft_combineplanar(cfgb, ft_freqanalysis(cfg, datapre_planar));
cfg.trials = numel(datapre.trial)-100 + (1:100);
f2 = ft_combineplanar(cfgb, ft_freqanalysis(cfg, datapre_planar));
else
f1 = ft_freqanalysis(cfg, datapre);
cfg.trials = numel(datapre.trial)-100 + (1:100);
f2 = ft_freqanalysis(cfg, datapre);
end
% TFR
cfg = [];
cfg.method = 'mtmconvol';
cfg.output = 'pow';
cfg.pad = 2;
cfg.foi = [2:2:120];
cfg.tapsmofrq = ones(1,numel(cfg.foi)).*8;
cfg.t_ftimwin = ones(1,numel(cfg.foi)).*0.25;
cfg.toi = (-150:6:90)./300;
cfg.trials = 1:100;
if doPlanar
cfgb = [];
cfgb.method = 'template';
cfgb.template = 'CTF275_neighb.mat';
cfgb.neighbours = ft_prepare_neighbours(cfgb, data_shift);
cfgb.method = 'sincos';
data_shift_planar = ft_megplanar(cfgb, data_shift);
cfgb = [];
cfgb.method = 'sum';
tfr1 = ft_combineplanar(cfgb, ft_freqanalysis(cfg, data_shift_planar));
cfg.trials = numel(data_shift.trial)-100 + (1:100);
tfr2 = ft_combineplanar(cfgb, ft_freqanalysis(cfg, data_shift_planar));
else
tfr1 = ft_freqanalysis(cfg, data_shift);
cfg.trials = numel(data_shift.trial)-100 + (1:100);
tfr2 = ft_freqanalysis(cfg, data_shift);
end
datadir = '/project/3011085.02/analysis/freq';
if doPlanar
save(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow_plcmb',subj, subj)), 'f1', 'f2', 'datapst','idx');
save(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow_tfr_plcmb',subj, subj)), 'tfr1', 'tfr2');
else
save(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow', subj, subj)), 'f1', 'f2', 'datapst');
save(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow_tfr',subj, subj)), 'tfr1', 'tfr2');
end
end
if doglm
load(sprintf('/project/3011085.02/analysis/freq/sub-%03d/sub-%03d_gamma_virtual_channel.mat',subj, subj), 'gammaPow');
if doresplocked
parceldata_shift = data_resp;
else
parceldata_shift = data_shift;
end
parceldata_shift.label = source_parc.label
for k=1:length(source_parc.F);
spatfilter_parcel(k,:) = source_parc.F{k}(1,:);
end
parceldata_shift.trial = spatfilter_parcel*parceldata_shift.trial;
cfg=[];
cfg.lpfilter = 'yes';
cfg.lpfilttype = 'firws';
cfg.lpfreq = 30;
cfg.lpfiltdir = 'onepass-reverse-zerophase';
cfg.preproc.demean = 'yes';
if ~doresplocked
cfg.preproc.baselinewindow = [-0.1 0];
end
parceldata_shift = ft_preprocessing(cfg, parceldata_shift);
if doresplocked
% add nans for short trials
tend = zeros(numel(parceldata_shift.time),1);
for k=1:numel(parceldata_shift.time);
tend(k,1) = parceldata_shift.time{k}(end); % save the last time point for each trial
parceldata_shift.time{k} = [parceldata_shift.time{k}, parceldata_shift.time{k}(end)+1/parceldata_shift.fsample:1/parceldata_shift.fsample:0.4];
parceldata_shift.trial{k} = [parceldata_shift.trial{k}, nan(numel(parceldata_shift.label), numel(parceldata_shift.time{k})-size(parceldata_shift.trial{k},2))];
end
cfg=[];
cfg.latency = [-0.5 0.4];
erfdata = ft_selectdata(cfg, parceldata_shift);
tlck=rmfield(erfdata,{'trial', 'time', 'trialinfo'});
tlck.avg = nanmean(cat(3,erfdata.trial{:}),3);
tlck.dimord = 'chan_time';
tlck.time = erfdata.time{1};
else
cfg=[];
cfg.latency = [-0.1 0.5];
erfdata = ft_selectdata(cfg, parceldata_shift);
cfg=[];
cfg.latency = [-0.1 0];
tmp = ft_selectdata(cfg, erfdata);
tmp.trial = cat(2, tmp.trial{:});
baseline_std = std(tmp.trial, [], 2);
cfg=[];
cfg.vartrllength = 2;
tlck = ft_timelockanalysis(cfg, erfdata);
end
erfdata.trial = cat(3,erfdata.trial{:});
erfdata.trial = permute(erfdata.trial, [3,1,2]);
erfdata.time = erfdata.time{1};
design = [gammaPow;((data_shift.trialinfo(:,5)-data_shift.trialinfo(:,4))/1200)'; ones(size(gammaPow))];
cfg=[];
cfg.glm.statistic = 'beta';
cfg.glm.standardise = false;
for k=1:length(erfdata.label)
dat = [squeeze(erfdata.trial(:,k,:))]';
if doresplocked
dat = (dat - repmat(nanmean(dat,2),[1 length(erfdata.trialinfo)]));
dat(isnan(dat))=0;
else
dat = (dat - repmat(mean(dat,2),[1 length(erfdata.trialinfo)]));
end
tmp = statfun_glm(cfg, dat, design);
betas_tmp(k,:) = tmp.stat(:,1);
end
betas = rmfield(erfdata,{'trial', 'cfg'});
betas.avg = betas_tmp;
betas.time = erfdata.time;
betas.dimord = 'chan_time';
savedir = '/project/3011085.02/analysis/';
if doresplocked
save(fullfile([savedir, 'GLM/', sprintf('sub-%03d/sub-%03d_glm_parcelresp', subj, subj)]), 'betas', 'tlck','tend');
else
save(fullfile([savedir, 'source/', sprintf('sub-%03d/sub-%03d_parcel_blstd', subj, subj)]), 'baseline_std');
save(fullfile([savedir, 'GLM/', sprintf('sub-%03d/sub-%03d_glm_parcel', subj, subj)]), 'betas', 'tlck');
end
end
if dosplitpow_source
datadir = '/project/3011085.02/analysis/freq/';
load(fullfile(datadir, sprintf('sub-%03d/sub-%03d_splitpow_plcmb', subj, subj)), 'datapst'); % take the trial indexes of sorted trials
load(fullfile(datadir, 'atlas_subparc374_8k.mat'))
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
foi = subject.gammaband;
[source_parc] = erfosc_lcmv_parc_gamma(data_shift, headmodel, sourcemodel, atlas, foi);
parceldata_shift = data_shift;
parceldata_shift.label = source_parc.label
for k=1:length(source_parc.F);
spatfilter_parcel(k,:) = source_parc.F{k}(1,:);
end
parceldata_shift.trial = spatfilter_parcel*parceldata_shift.trial;
% sort based on erf amplitude (as in datapst)
erftrlinfo = datapst.trialinfo(:,1);
parceltrlinfo=parceldata_shift.trialinfo(:,1);
[~, order] = ismember(erftrlinfo, parceltrlinfo);
parceldata_shift.trialinfo = parceldata_shift.trialinfo(order, :); % sort parceldata on erf amp (low-high)
parceldata_shift.trial = parceldata_shift.trial(order);
% TFR
cfg = [];
cfg.method = 'mtmconvol';
cfg.output = 'pow';
cfg.pad = 2;
cfg.foi = 20:2:100
cfg.tapsmofrq = ones(1,numel(cfg.foi)).*8;
cfg.t_ftimwin = ones(1,numel(cfg.foi)).*0.25;
cfg.toi = (-300:6:150)./300;
cfg.trials = 1:100;
tfr_high_1 = ft_freqanalysis(cfg, parceldata_shift); % low ERF amp
cfg.trials = numel(data_shift.trial)-100 + (1:100);
tfr_high_2 = ft_freqanalysis(cfg, parceldata_shift); % high ERF amp
cfg = [];
cfg.method = 'mtmconvol';
cfg.output = 'pow';
cfg.pad = 2;
cfg.foi = 2:2:30
cfg.taper = 'hanning';
cfg.t_ftimwin = ones(1,numel(cfg.foi)).*0.25;
cfg.toi = (-300:6:150)./300;
cfg.trials = 1:100;
tfr_low_1 = ft_freqanalysis(cfg, parceldata_shift); % low ERF amp
cfg.trials = numel(data_shift.trial)-100 + (1:100);
tfr_low_2 = ft_freqanalysis(cfg, parceldata_shift); % high ERF amp
savedir = '/project/3011085.02/analysis/source/';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_splitpow_source', subj, subj)), 'tfr_low_1','tfr_low_2','tfr_high_1', 'tfr_high_2')
end
if doparcel_erf
parceldata_shift = data_shift;
parceldata_shift.label = source_parc.label;
for k=1:length(source_parc.F);
spatfilter_parcel(k,:) = source_parc.F{k}(1,:);
end
parceldata_shift.trial = spatfilter_parcel*parceldata_shift.trial;
[~, idx] = sort(parceldata_shift.trialinfo(:,6)-parceldata_shift.trialinfo(:,5), 'ascend');
parceldata_shift.trial = parceldata_shift.trial(idx);
parceldata_shift.trialinfo = parceldata_shift.trialinfo(idx);
savedir = '/project/3011085.02/analysis/erf/';
save(fullfile(savedir, sprintf('sub-%03d/sub-%03d_erfparc', subj, subj)), 'parceldata_shift');
end
if docorrpow_lcmv
peakpicking;
datadir = '/project/3011085.02/analysis/';
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_lcmv', subj, subj)));
source_parc.avg = diag(1./noise)*source_parc.avg;
ix1 = nearest(source_parc.time, peaks(subj,1));
ix2 = nearest(source_parc.time, peaks(subj,2));
tmpcfg = [];
tmpcfg.latency = [-0.1 0.5-1./600];
datapst = ft_selectdata(tmpcfg, data_shift);
tmpcfg.latency = [-0.2 -1./600] + peaks(subj,1) - 0.02;
datapre = ft_selectdata(tmpcfg, data_shift);
source_parc.avg = ft_preproc_baselinecorrect(source_parc.avg, 1, 60);
[maxval, maxidx] = max(abs(mean(source_parc.avg(:,ix1:ix2),2)));
signpeak = sign(mean(source_parc.avg(maxidx,ix1:ix2),2));
fprintf('parcel with max amplitude = %s\n', source_parc.label{maxidx});
for k = 1:numel(source_parc.label)
F(k,:) = source_parc.F{k}(1,:);
end
datapst.trial = F*datapst.trial;
datapst.label = source_parc.label;
tmpcfg = [];
tmpcfg.demean = 'yes';
tmpcfg.baselinewindow = [-inf 0];
tmpcfg.lpfilter = 'yes';
tmpcfg.lpfreq = 30;
tmpcfg.lpfilttype = 'firws';
tmpcfg.lpfiltdir = 'onepass-reverse-zerophase'
datapst = ft_preprocessing(tmpcfg, datapst);
tmpcfg = [];
tmpcfg.latency = peaks(subj,:);%JM
tmpcfg.avgovertime = 'yes';
datapeak = ft_selectdata(tmpcfg,datapst);
X = cat(2,datapeak.trial{:});
Xpow = abs(mean(X,2));
signswap = diag(sign(mean(X,2)));
X = signswap*X; % let the amplitude be on average positive
X = standardise(X,2);
tmpcfg = [];
tlckpst = ft_timelockanalysis(tmpcfg, datapst);
tlckpst.avg = signswap*tlckpst.avg;
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_source', subj, subj)));
if ~exist('freq_shift')
load(fullfile(datadir, 'freq/', sprintf('sub-%03d/sub-%03d_freqshort', subj, subj)));
end
[m, idx] = max(Tval);
pow = (abs(F(idx,:)*transpose(freq_shift.fourierspctrm)).^2)*P;
pow = standardise(log10(pow(:)));
rho = corr(X', pow, 'type', 'spearman'); %MvE
tmp = load(sprintf('/project/3011085.02/processed/sub-%03d/ses-meg01/sub-%03d_eyedata.mat', subj, subj));
% get gamma-ERF correlation, accounting for pupil diameter, without confounding eye position.
cfg=[];
cfg.vartrllength = 2;
cfg.keeptrials = 'yes';
eye = ft_timelockanalysis(cfg,tmp.data_shift);
cfg=[];
cfg.toilim = [-0.2 -1./600] + peaks(subj,1) - 0.02;
pupild = erfosc_regress_eye(ft_redefinetrial(cfg, eye), {'UADC007'}, {'visAngleX', 'visAngleY'});
cfg=[];
cfg.avgovertime = 'yes';
pupild = ft_selectdata(cfg, pupild);
pupild = standardise(pupild.trial);
partialrho1 = partialcorr(X', pow, pupild, 'type', 'spearman');
% get correlation gamma-ERF, accounting for eye position, without confound pupil diameter
idx = match_str(eye.label, {'visAngleX', 'visAngleY'});
eye.trial(:,end+1,:) = (eye.trial(:,idx(1),:).^2 + eye.trial(:,idx(2),:).^2).^0.5;
eye.label{end+1} = 'distance';
cfg=[];
cfg.toilim = [-0.2 -1./600] + peaks(subj,1) - 0.02;
eyepos = erfosc_regress_eye(ft_redefinetrial(cfg, eye), {'distance'}, {'UADC007'});
cfg=[];
cfg.avgovertime = 'yes';
distance = ft_selectdata(cfg, eyepos);
distance = standardise(distance.trial);
partialrho2 = partialcorr(X', pow, distance, 'type', 'spearman');
% get correlation gamma-ERF, accounting for eye position and pupil
% diameter (both not confounded by the other)
partialrho3 = partialcorr(X', pow, [distance, pupild], 'type', 'spearman');
load atlas_subparc374_8k
exclude_label = match_str(atlas.parcellationlabel, {'L_???_01', 'L_MEDIAL.WALL_01', 'R_???_01', 'R_MEDIAL.WALL_01'}); %MvE
source = [];
source.brainordinate = atlas;
source.label = atlas.parcellationlabel;
source.rho = zeros(374,1);
source.partialrho = zeros(374,1);
source.pow = zeros(374,1);
source.dimord = 'chan';
indx = 1:374;
indx(exclude_label) = [];
source.rho(indx) = rho;
source.partialrho(indx,1) = partialrho1;
source.partialrho(indx,2) = partialrho2;
source.partialrho(indx,3) = partialrho3;
source.pow(indx) = Xpow(:);
datadir = '/project/3011085.02/analysis/corr/';
save(fullfile(datadir, sprintf('sub-%03d/sub-%03d_corrpowlcmv_gamma',subj, subj)), 'source', 'pow', 'X', 'tlckpst')%, 'pupild', 'distance');
end
if dobdssp
load(fullfile(subject.mridir,'preproc','headmodel.mat'));
load(fullfile(subject.mridir,'preproc','sourcemodel2d.mat'));
load('atlas_subparc374_8k.mat');
cfg = [];
cfg.latency = [-0.75 1-1./data_onset.fsample];
data_onset = ft_selectdata(cfg, data_onset);
if ~isfield(sourcemodel, 'leadfield')
cfg = [];
cfg.headmodel = ft_convert_units(headmodel, 'm');
cfg.grid = ft_convert_units(sourcemodel, 'm');
cfg.grad = ft_convert_units(data_onset.grad, 'm');
cfg.channel = data_onset.label;
cfg.singleshell.batchsize = 2000;
sourcemodel = ft_prepare_leadfield(cfg);
end
indx = match_str(atlas.parcellationlabel,{'R_19_B05_07'});%;'R_18_B05_02';'R_18_B05_03';'R_18_B05_04'});
p_indx = ismember(atlas.parcellation,indx);
s = sourcemodel;
outside = find(~p_indx);
for k = outside(:)'
s.leadfield{k} = [];
end
s.inside = p_indx;
cfg = [];
cfg.grid = s;
cfg.dssp.n_space = min(sum(p_indx),50);
cfg.dssp.n_intersect = 0.7;
cfg.dssp.n_in = cfg.dssp.n_space;
cfg.dssp.n_out = 100;
data19 = ft_denoise_dssp(cfg, data_onset);
indx = match_str(atlas.parcellationlabel,atlas.parcellationlabel(contains(atlas.parcellationlabel,'R_17')));%;'R_18_B05_02';'R_18_B05_03';'R_18_B05_04'});
p_indx = ismember(atlas.parcellation,indx);
s = sourcemodel;
outside = find(~p_indx);
for k = outside(:)'
s.leadfield{k} = [];
end
s.inside = p_indx;
cfg = [];
cfg.grid = s;
cfg.dssp.n_space = 'interactive';%min(sum(p_indx),50);
cfg.dssp.n_intersect = 'interactive';%0.7;
cfg.dssp.n_in = 'interactive';%cfg.dssp.n_space;
cfg.dssp.n_out = 'interactive';%100;
data17 = ft_denoise_dssp(cfg, data_onset);
cfg = [];
cfg.vartrllength = 2;
cfg.preproc.demean = 'yes';
cfg.preproc.baselinewindow = [-0.1 0];
cfg.covariance = 'yes';
cfg.removemean = 'yes';
tlck = ft_timelockanalysis(cfg, data_onset);
tlck19 = ft_timelockanalysis(cfg, data19);
tlck17 = ft_timelockanalysis(cfg, data17);
s = sourcemodel;
cfg = [];
cfg.method = 'lcmv';
cfg.headmodel = headmodel;
cfg.grid = s;
cfg.keepleadfield = 'yes';
cfg.lcmv.keepfilter = 'yes';
cfg.lcmv.fixedori = 'yes';
cfg.lcmv.lambda = '20%';
%cfg.lcmv.weightnorm = 'unitnoisegain'; % this confuses me in terms of the
%unit-gain inspection when keeping the leadfields: it's also just a scaling
cfg.lcmv.keepleadfield = 'yes';
cfg.lcmv.keepori = 'yes';
source = ft_sourceanalysis(cfg, tlck);
source19 = ft_sourceanalysis(cfg, tlck19);
source17 = ft_sourceanalysis(cfg, tlck17);
cfg = [];
cfg.latency = [-0.5 -1./600];
datapre = ft_selectdata(cfg, data_onset);
data19pre = ft_selectdata(cfg, data19);
data17pre = ft_selectdata(cfg, data17);
cfg.latency = [0.25 0.75-1./600];
datapst = ft_selectdata(cfg ,data_onset);
data19pst = ft_selectdata(cfg, data19);
data17pst = ft_selectdata(cfg, data17);
F = zeros(size(source.pos,1), numel(data19.label));
F19 = zeros(size(source19.pos,1), numel(data19.label));
F17 = zeros(size(source17.pos,1), numel(data19.label));
F(source.inside,:) = cat(1, source.avg.filter{:});
F19(source19.inside,:) = cat(1, source19.avg.filter{:});
F17(source17.inside,:) = cat(1, source17.avg.filter{:});
cfg = [];
cfg.method = 'mtmfft';
%cfg.foilim = [30 100];
%cfg.pad = 8;
%cfg.tapsmofrq = 16;
cfg.taper = 'hanning';
cfg.output = 'pow';
fpre = ft_freqanalysis(cfg, datapre);
fpst = ft_freqanalysis(cfg, datapst);
f19pre = ft_freqanalysis(cfg, data19pre);
f19pst = ft_freqanalysis(cfg, data19pst);
f17pre = ft_freqanalysis(cfg, data17pre);
f17pst = ft_freqanalysis(cfg, data17pst);
f1pre = ft_checkdata(f1pre,'cmbrepresentation','fullfast');
f1pst = ft_checkdata(f1pst,'cmbrepresentation','fullfast');
f2pre = ft_checkdata(f2pre,'cmbrepresentation','fullfast');
f2pst = ft_checkdata(f2pst,'cmbrepresentation','fullfast');
for k = 1:numel(f1pre.freq)
pow1pre(:,k) = abs(sum(F1.*(F1*f1pre.crsspctrm(:,:,k)),2));
pow1pst(:,k) = abs(sum(F1.*(F1*f1pst.crsspctrm(:,:,k)),2));
pow2pre(:,k) = abs(sum(F2.*(F2*f2pre.crsspctrm(:,:,k)),2));
pow2pst(:,k) = abs(sum(F2.*(F2*f2pst.crsspctrm(:,:,k)),2));
end
end
if docorrpow_lcmv_lowfreq
peakpicking;
datadir = '/project/3011085.02/analysis/';
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_lcmv', subj, subj)));
source_parc.avg = diag(1./noise)*source_parc.avg;
ix1 = nearest(source_parc.time, peaks(subj,1));
ix2 = nearest(source_parc.time, peaks(subj,2));
tmpcfg = [];
tmpcfg.latency = [-0.1 0.5-1./600];
datapst = ft_selectdata(tmpcfg, data_shift);
tmpcfg.latency = [-0.2 -1./600] + peaks(subj,1) - 0.02;
source_parc.avg = ft_preproc_baselinecorrect(source_parc.avg, 1, 60);
[maxval, maxidx] = max(abs(mean(source_parc.avg(:,ix1:ix2),2)));
signpeak = sign(mean(source_parc.avg(maxidx,ix1:ix2),2));
fprintf('parcel with max amplitude = %s\n', source_parc.label{maxidx});
for k = 1:numel(source_parc.label)
F(k,:) = source_parc.F{k}(1,:);
end
datapst.trial = F*datapst.trial;
datapst.label = source_parc.label;
tmpcfg = [];
tmpcfg.demean = 'yes';
tmpcfg.baselinewindow = [-inf 0];
tmpcfg.lpfilter = 'yes';
tmpcfg.lpfreq = 30;
tmpcfg.lpfilttype = 'firws';
tmpcfg.lpfiltdir = 'onepass-reverse-zerophase';
datapst = ft_preprocessing(tmpcfg, datapst);
tmpcfg = [];
tmpcfg.latency = peaks(subj,:);
tmpcfg.avgovertime = 'yes';
datapeak = ft_selectdata(tmpcfg,datapst);
X = cat(2,datapeak.trial{:});
Xpow = abs(mean(X,2));
signswap = diag(sign(mean(X,2)));
X = signswap*X; % let the amplitude be on average positive
X = standardise(X,2);
tmpcfg = [];
tlckpst = ft_timelockanalysis(tmpcfg, datapst);
tlckpst.avg = signswap*tlckpst.avg;
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_source_low', subj, subj)));
load(fullfile(datadir, 'freq/', sprintf('sub-%03d/sub-%03d_freqshort_low', subj, subj)));
[m, idx] = min(Tval);
pow = (abs(F(idx,:)*transpose(freq_shift.fourierspctrm)).^2)*P;
pow = standardise(log10(pow(:)));
rho = corr(X', pow, 'type', 'spearman'); %MvE
load atlas_subparc374_8k
exclude_label = match_str(atlas.parcellationlabel, {'L_???_01', 'L_MEDIAL.WALL_01', 'R_???_01', 'R_MEDIAL.WALL_01'}); %MvE
source = [];
source.brainordinate = atlas;
source.label = atlas.parcellationlabel;
source.rho = zeros(374,1);
source.pow = zeros(374,1);
source.dimord = 'chan';
indx = 1:374;
indx(exclude_label) = [];
source.rho(indx) = rho;
source.pow(indx) = Xpow(:);
datadir = '/project/3011085.02/analysis/';
save(fullfile(datadir, 'corr/', sprintf('sub-%03d/sub-%03d_corrpowlcmv_low', subj, subj)), 'source', 'pow', 'X', 'tlckpst');
end
if docorr_gamma_rt
load(sprintf('/project/3011085.02/analysis/behavior/sub-%03d/sub-%03d_rt.mat',subj, subj));
datadir = '/project/3011085.02/analysis/';
load(fullfile(datadir, 'source/', sprintf('sub-%03d/sub-%03d_source', subj, subj)));
pow = (abs(F*transpose(freq_shift.fourierspctrm)).^2)*P;
pow = log10(pow);
s = std(pow, [], 2);
u = mean(pow, 2);
pow = (pow-repmat(u, [1 size(pow,2)]))./repmat(s, [1 size(pow,2)]);
rho = corr(rt, pow', 'type', 'spearman'); %MvE
rho=rho';
filename = sprintf('/project/3011085.02/analysis/corr/sub-%03d/sub-%03d_corr_3Dgamma_rt.mat', subj, subj);
save(filename, 'rho')
end
if dostat_pow_erf
if ~exist('GA'); GA = input('send out single subject analyses (0), or continue to group analysis (1)?'); end
if ~exist('whichFreq'); whichFreq = input('gamma (1), lowfreq (2)?'); end
if ~GA
sel = setdiff(1:33,10);
for k = sel(:)'
if whichFreq==1
qsubfeval('erfosc_execute_pipeline','erfosc_script_jm',k,{'docorrpow_lcmv',1}, {'dofreq_short', 1},{'savefreq', 0},'memreq',8*1024^3,'timreq',59*60,'batchid',sprintf('subj%03d',k));
elseif whichFreq==2
qsubfeval('erfosc_execute_pipeline','erfosc_script_jm',k,{'docorrpow_lcmv_lowfreq',1}, {'dofreq_short_lowfreq', 1},{'savefreq', 0},'memreq',8*1024^3,'timreq',59*60,'batchid',sprintf('subj%03d',k));
end
end
else
load atlas_subparc374_8k.mat
datadir = '/project/3011085.02/analysis/';
erf_osc_datainfo;
k=1;
for subj = allsubs
if whichFreq==1
filename = fullfile([datadir, 'corr/', sprintf('sub-%03d/sub-%03d_corrpowlcmv_gamma.mat', subj, subj)]);
elseif whichFreq==2
filename = fullfile([datadir, 'corr/', sprintf('sub-%03d/sub-%03d_corrpowlcmv_low.mat', subj, subj)]);
end
load(filename,'source');
if k==1
S=source;
S.rho = source.rho;
if whichFreq==1
S_pupild.rho = source.partialrho(:,1);
S_xy.rho = source.partialrho(:,2);
S_eye.rho = source.partialrho(:,3);
end
else
S.rho(:,k)=source.rho;
if whichFreq==1
S_pupild.rho(:,k) = source.partialrho(:,1);
S_xy.rho(:,k) = source.partialrho(:,2);
S_eye.rho(:,k) = source.partialrho(:,3);
end
end
k=k+1;
end
S.rho = S.rho';
exclude_label = match_str(atlas.parcellationlabel, {'L_???_01', 'L_MEDIAL.WALL_01', 'R_???_01', 'R_MEDIAL.WALL_01'}); %MvE
S.rho(:, exclude_label) = nan; %MvE
S.dimord = 'rpt_chan_freq';
S.freq = 0;
S2 = S;
S2.rho(:) = 0;
n = 32;
cfgs = [];
cfgs.method='montecarlo';
cfgs.design=[ones(1,n) ones(1,n)*2;1:n 1:n];
cfgs.statistic='ft_statfun_wilcoxon';
cfgs.numrandomization=10000;
cfgs.ivar=1;
cfgs.uvar=2;
cfgs.parameter='rho';