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dics_plot_effect_timecourses.m
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dics_plot_effect_timecourses.m
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function dics_plot_effect_timecourses(sessions, vs, remove_outliers, sjgrs)
addpath(genpath('~/Documents/code/Tools'));
addpath(genpath('~/code/Tools'));
addpath(('~/Documents/code/gramm'));
close all;
set(groot, 'DefaultFigureWindowStyle', 'normal');
sjdat = subjectspecifics('ga');
if ~exist('sessions', 'var'), sessions = [0]; end
if ischar(sessions), sessions = str2double(sessions); end
freqs = dics_freqbands; % retrieve specifications
if ~exist('vs', 'var'), vs = 1:3; end
if ischar(vs), vs = str2double(vs); end
if ~exist('remove_outliers', 'var'), remove_outliers = true; end % default: remove values above 500
if ischar(remove_outliers), remove_outliers = str2double(remove_outliers); end
sjgroups = {'all', 'repeaters', 'alternators', 'repeaters_subsampled'};
sjgroups = {'repeaters', 'alternators'}; % do this first
if ~exist('sjgrs', 'var'), sjgrs = 1:length(sjgroups); end % for all sjs together
if ischar(sjgrs), sjgrs = str2double(sjgrs); end
statmethod = 'glme'; % stick to this
for session = sessions,
for v = vs,
% choose some more settings
for sjgr = sjgrs,
group = sjgroups{sjgr}; % only run once, don't loop
% =========================================== %
% GET PARCELLATED DATA FROM ALL SUBJECTS
% =========================================== %
disp('loading neural data...');
if session == 0, % combine the two sessions
source = load(sprintf('%s/GA-S%d_parcel_%s.mat', sjdat.roidir, ...
1, freqs(v).name));
source = source.source;
source2 = load(sprintf('%s/GA-S%d_parcel_%s.mat', sjdat.roidir, ...
2, freqs(v).name));
assert(isequal(source2.source.label, source.label));
source.pow = cat(2, source.pow, source2.source.pow);
source.trialinfo = cat(1, source.trialinfo, source2.source.trialinfo);
clear source2
else % load each session separately
source = load(sprintf('%s/GA-S%d_parcel_%s.mat', sjdat.roidir, ...
session, freqs(v).name));
source = source.source;
end
fprintf('%s/GA-S%d_parcel_%s.mat \n', sjdat.roidir, session, freqs(v).name);
% hack: pool 3 motor signatures
motor_pool = contains(source.label, {'M1_lateralized', 'glasser_premotor_lateralized_PMd/v', 'jwg_IPS_PCeS_lateralized'});
source.label{end+1} = 'pooled_motor_lateralized';
source.pow(end+1, :, :) = mean(source.pow(motor_pool, :, :));
% remove some ugly stuff in the beginning and between epochs
if session ~= 5,
rmidx = [1:4, 31:41, 45:48, 79:85, 123:length(source.time)];
source.pow(:, :, rmidx) = [];
source.time(rmidx) = [];
end
% REMOVE NANS FROM TIMECOURSE - make the epoch borders smaller
% for better plotting
startSeq = strfind(squeeze(isnan(nanmean(nanmean(source.pow))))', true(1,3));
removeidx = false(1, length(source.time));
for s = 1:length(startSeq),
removeidx(startSeq(s)+1 : startSeq(s)+1) = 1;
end
source.pow(:, :, removeidx) = [];
source.time(removeidx) = [];
% =========================================== %
% LINK TO BEHAVIORAL DATA TABLE
% =========================================== %
tab = readtable(sprintf('%s/allsubjects_meg.csv', sjdat.csvdir));
%% summarize
rep = splitapply(@nanmean, tab.repetition, findgroups(tab.subj_idx));
fprintf('%d repeaters, %d alternators, %d unbiased', ...
length(find(rep > 0)), ...
length(find(rep < 0)), ...
length(find(rep == 0)));
% map table idx to MEG idx
[~, ~, tidx] = intersect(source.trialinfo(:, 18), tab.idx, 'stable');
assert(size(source.trialinfo, 1) == length(tidx));
tab = tab(tidx, :); % keep only that part of the table
assert(isequal(tab.idx, source.trialinfo(:, 18)), 'idx do not match');
% =========================================== %
% SELECT SUBGROUPS
% repeat is [-1, 1] so split around 0
% =========================================== %
if strcmp(group, 'repeaters'),
rm_alternators = find(tab.group < 1);
tab(rm_alternators, :) = [];
source.pow(:, rm_alternators, :) = [];
elseif strcmp(group, 'repeaters_subsampled'),
alternators = repetition.subj_idx(repetition.repeat < 0);
repeaters = repetition.subj_idx(repetition.repeat > 0);
rng(123);
keep_repeaters = randsample(repeaters, length(alternators));
rm_subj = ismember(tab.subj_idx, ...
setdiff(unique(tab.subj_idx), keep_repeaters));
tab(rm_subj, :) = [];
source.pow(:, rm_subj, :) = [];
elseif strcmp(group, 'alternators'),
rm_repeaters = find(tab.group > -1);
tab(rm_repeaters, :) = [];
source.pow(:, rm_repeaters, :) = [];
end
fprintf('\n\n%s: %d subjects\n', group, length(unique(tab.subj_idx)));
% =========================================== %
% DEFINE GROUPINGS OF TRIALS THAT ARE INTERESTING TO PLOT
% =========================================== %
clear contrasts;
[contrasts(1).g, contrasts(1).tid] = findgroups(tab(:, {'subj_idx', 'stimulus', 'response'}));
[contrasts(end+1).g, contrasts(end+1).tid] = findgroups(tab(:, {'subj_idx', 'response', 'prev_resp'}));
[contrasts(end+1).g, contrasts(end+1).tid] = findgroups(tab(:, {'subj_idx', 'stimulus', 'prev_stim'})); % need for motor fig
% some 2-way contrasts
[contrasts(end+1).g, contrasts(end+1).tid] = findgroups(tab(:, {'subj_idx', 'stimulus', 'prev_resp'})); %4 need for parietal fig
[contrasts(end+1).g, contrasts(end+1).tid] = findgroups(tab(:, {'subj_idx', 'hand', 'prev_hand'})); %5 % for motor status
% contrast 6: 4 terms for timecourses
[contrasts(end+1).g, contrasts(end+1).tid] = findgroups(tab(:, {'subj_idx', 'stimulus', 'response', ...
'prev_stim', 'prev_resp'}));
% define time epochs
timewins(1).name = 'pre_ref_time';
timewins(1).samples = 1:8; % pre-reference fixation to 50ms after reference onset
timewins(end+1).name = 'pre_stim_time';
timewins(end).samples = 29:36; % pre-stimulus fixation to 50ms after stimulus onset
timewins(end+1).name = 'reference';
timewins(end).samples = 7:22; % 0-750 ms after reference onset
timewins(end+1).name = 'stimulus';
timewins(end).samples = 35:51; % 0-750 ms after stimulus onset
% =========================================== %
% LOOP OVER ALL CHANNELS
% =========================================== %
useroi_names = {'wang_vfc_IPS2/3', 'wang_vfc_IPS0/1', 'pooled_motor_lateralized'};
useroi_names = {'pooled_motor_lateralized'};
userois = find(ismember(source.label, useroi_names))';
disp(source.label(userois));
for r = userois,
% match the colors for IPS01 and IPS23
cmap = viridis(11);
if r == 11,
cmap = cmap(5, :);
elseif r == 12,
cmap = cmap(5, :);
else
cmap = cmap(end-1, :);
end
for c = 3; %[2, 3, 5],
% =========================================== %
% STATS ON CONTRASTS
% =========================================== %
clear stats;
% DETERMINE GLME FORMULA
if width(contrasts(c).tid) == 2
formula = sprintf('neural_data ~ 1 + %s + (1|subj_idx)', ...
contrasts(c).tid.Properties.VariableNames{2});
elseif width(contrasts(c).tid) == 3
formula = sprintf('neural_data ~ 1 + %s + %s + (1|subj_idx)', ...
contrasts(c).tid.Properties.VariableNames{2}, ...
contrasts(c).tid.Properties.VariableNames{3});
elseif width(contrasts(c).tid) == 4
formula = sprintf('neural_data ~ 1 + %s + %s + %s + (1|subj_idx)', ...
contrasts(c).tid.Properties.VariableNames{2}, ...
contrasts(c).tid.Properties.VariableNames{3}, ...
contrasts(c).tid.Properties.VariableNames{4});
elseif width(contrasts(c).tid) == 5
formula = sprintf('neural_data ~ 1 + %s + %s + %s + %s + (1|subj_idx)', ...
contrasts(c).tid.Properties.VariableNames{2}, ...
contrasts(c).tid.Properties.VariableNames{3}, ...
contrasts(c).tid.Properties.VariableNames{4}, ...
contrasts(c).tid.Properties.VariableNames{5});
else
assert(1==0, 'this contrast is not well defined!');
end
disp(formula);
% =========================================== %
% LOOP OVER TIMECOURSE
% =========================================== %
if c > 7,
num_b = width(contrasts(c).tid) - 1 + 2;
else
num_b = width(contrasts(c).tid) - 1;
end
timeidx = source.time;
stats.beta = nan(num_b, length(timeidx));
stats.ci_low = nan(num_b, length(timeidx));
stats.ci_high = nan(num_b, length(timeidx));
stats.pval = nan(num_b, length(timeidx));
% LOOP OVER TIME
for tp = 1:length(timeidx)
% grab single-trial data at this timepoint
tab.neural_data = squeeze(source.pow(r, :, tp))';
% remove outliers
if remove_outliers,
tab{tab.neural_data > 500, 'neural_data'} = nan;
end
if all(isnan(tab.neural_data)), continue; end
% actual fit, this takes a while
glme = fitglme(tab, formula);
coeff = glme.Coefficients;
% save for plotting - skip the Intercept
stats.beta(:, tp) = coeff.Estimate(2:end);
stats.pval(:, tp) = coeff.pValue(2:end);
stats.ci_low(:, tp) = coeff.Lower(2:end);
stats.ci_high(:, tp) = coeff.Upper(2:end);
end
stats.names = glme.CoefficientNames(2:end);
% FDR correction
[~, crit_p] = fdr_bh(stats.pval(:), 0.05);
crit_p = 0.01;
% only plot points for significant timewindows, for two contrasts
stats.b_h = stats.beta;
stats.b_h(stats.pval > min(crit_p, 0.05)) = nan;
% =========================================== %
% PLOT AGAIN WITHOUT GRAMM
% =========================================== %
for bidx = 1:num_b,
close all;
%subplot(3,3,[1 2]); hold on;
subplot(5,8,[1 2 3]); hold on;
% cmap = inferno(10);
% THEN THE DATA ON TOP - npoint x nside x nline
boundedline(1:length(timeidx), stats.beta(bidx, :), ...
permute(cat(3, stats.beta(bidx, :) - stats.ci_low(bidx, :), ...
stats.ci_high(bidx, :) - stats.beta(bidx, :)), [2 3 1]), ...
'nan', 'gap', 'cmap', cmap, 'alpha');
axis tight;
ymin = min(get(gca, 'ylim'))-1;
plot(1:length(timeidx), stats.b_h(bidx, :), '.', 'color', ...
cmap, 'markerfacecolor', cmap, 'markersize', 8);
% layout
axis tight;
hline(0);
plot_timename(source.time, 0.2);
% xlabel('Time (s)');
axis tight;
% ylabel({'Effect size (\Delta%)'; sprintf('effect of %s', stats.names{bidx}); ...
% sprintf('in \\bf\\%s\\rm-band (%d-%d Hz)', freqs(v).name, ...
% freqs(v).freq-freqs(v).tapsmofrq, freqs(v).freq+freqs(v).tapsmofrq)}, ...
% 'interpreter', 'tex');
offsetAxes;
set(gca, 'fontsize', 6);
switch source.label{r}
case 'wang_vfc_IPS0/1'
ylim([-2.5 3]); % set manually for different subplots
case 'wang_vfc_IPS2/3'
ylim([-1 1.8]); % set manually for different subplots
case 'pooled_motor_lateralized'
if c == 3,
ylim([-1, 1]);
end
end
tightfig;
print(gcf, '-dpdf', sprintf('%s/dics_effect_timecourse_c%d_%s_%s_%s_%s.pdf', sjdat.figsdir, ...
c, freqs(v).name, regexprep(source.label{r}, '/', ''), group, stats.names{bidx}));
end
end % contrast
end % roi
end
end % freq
end % session
end % function