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ana_fMEG_stats_fetal_Frohlichetal2024.m
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ana_fMEG_stats_fetal_Frohlichetal2024.m
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% This is the script that plots the figures and does the stats
% Joel Frohlich
% University of Tuebingen
clearvars
close all
rng(675)
% Create a directory for the figures
if ~isfolder('Figures'), mkdir('Figures'), end
%%% How to interpret the stimulus/rule labels:
% stim/rule
% DevA = ssss/sssd (global deviant)
% DevB = sssd/ssss (local and global deviant)
% StdA = ssss/ssss (standard)
% StdB = sssd/sssd (local deviant)
% Other abbreviations
% GA = gestational age
% SDNN = standard deviation of the interpeak interval in the MCG (heart rate variability)
% To replicate the manuscript, set this to 'false'
neoLP = false; % load the Neonatal data that were lowpass filtered at 10 Hz to match the fetal filtering settings
load grand_average_ERFs ERFdev ERFstd ID % load ERFs from older fetuses
% Take the average of ERF templates, averging first within subjects
ERFdev = nestedmean(ERFdev,ID);
ERFstd = nestedmean(ERFstd,ID);
T = readtable('dataset_differencesT3T4.csv');
T2 = readtable('Overview_Participants.xlsx');
portion = 'all';
fs = 610.3516; % sampling rate
k = 3; % "spatial" (i.e., time x freq) interpolation factor for time-freq representations
% portion denotes which part of signal should be analysed. To replicate
% results from the manuscript, use 'all' (-200 - 3000 ms)
switch portion
case 'beginning'
tstr = '_T1-3only';
case 'end'
tstr = '_T4only';
case 'all'
tstr = '';
end
%%% LOAD THE DATA %%%
% Note: datestr must be set to the date of files you want to load
datestr = '_24-Jun-2022';
% Add CTW
load(sprintf('CTW_deviantA%s_IAAFT1%s',tstr,datestr))
sub1 = sub;
msctw_devA = median(CTW_devA.rsurr,2); % median across all surrogates
load(sprintf('CTW_deviantB%s_IAAFT1%s',tstr,datestr))
sub2 = sub;
msctw_devB = median(CTW_devB.rsurr,2); % median across all surrogates
load(sprintf('CTW_standardA%s_IAAFT1%s',tstr,datestr))
sub3 = sub;
msctw_stdA = median(CTW_stdA.rsurr,2); % median across all surrogates
load(sprintf('CTW_standardB%s_IAAFT1%s',tstr,datestr))
sub4 = sub;
msctw_stdB = median(CTW_stdB.rsurr,2); % median across all surrogates
% add mean signal value
mval_devA = mean(CTW_devA.erf,2);
mval_devB = mean(CTW_devB.erf,2);
mval_stdA = mean(CTW_stdA.erf,2);
mval_stdB = mean(CTW_stdB.erf,2);
% Now also add LZC
load(sprintf('LZC_deviantA%s_IAAFT1%s',tstr,datestr))
subdevA = sub;
mslzc_devA = median(LZC_devA.rsurr,2); % median across all surrogates
assert(all(sub==sub1),'Subjects loaded in a different order')
load(sprintf('LZC_deviantB%s_IAAFT1%s',tstr,datestr))
subdevB = sub;
mslzc_devB = median(LZC_devB.rsurr,2); % median across all surrogates
assert(all(sub==sub2),'Subjects loaded in a different order')
load(sprintf('LZC_standardA%s_IAAFT1%s',tstr,datestr))
substdA = sub;
mslzc_stdA = median(LZC_stdA.rsurr,2); % median across all surrogates
assert(all(sub==sub3),'Subjects loaded in a different order')
load(sprintf('LZC_standardB%s_IAAFT1%s',tstr,datestr))
substdB = sub;
mslzc_stdB = median(LZC_stdB.rsurr,2); % median across all surrogates
assert(all(sub==sub4),'Subjects loaded in a different order')
% Now also add MSE
load(sprintf('MSE_deviantA%s_IAAFT1%s',tstr,datestr))
msmse_devA = squeeze(median(mean(MSE_devA.rsurr,1),3)); % median across all surrogates (20 timescale average)
msse_devA = squeeze(median(MSE_devA.rsurr(1,:,:),3)); % median across all surrogates (1st timescale only)
assert(all(sub==sub1),'Subjects loaded in a different order')
load(sprintf('MSE_deviantB%s_IAAFT1%s',tstr,datestr))
msmse_devB = squeeze(median(mean(MSE_devB.rsurr,1),3)); % median across all surrogates (20 timescale average)
msse_devB = squeeze(median(MSE_devB.rsurr(1,:,:),3)); % median across all surrogates (1st timescale only)
assert(all(sub==sub2),'Subjects loaded in a different order')
load(sprintf('MSE_standardA%s_IAAFT1%s',tstr,datestr))
msmse_stdA = squeeze(median(mean(MSE_stdA.rsurr,1),3)); % median across all surrogates (20 timescale average)
msse_stdA = squeeze(median(MSE_stdA.rsurr(1,:,:),3)); % median across all surrogates (1st timescale only)
assert(all(sub==sub3),'Subjects loaded in a different order')
load(sprintf('MSE_standardB%s_IAAFT1%s',tstr,datestr))
msmse_stdB = squeeze(median(mean(MSE_stdB.rsurr,1),3)); % median across all surrogates (20 timescale average)
msse_stdB = squeeze(median(MSE_stdB.rsurr(1,:,:),3)); % median across all surrogates (1st timescale only)
assert(all(sub==sub4),'Subjects loaded in a different order')
% Add stochaticity test
load(sprintf('Sto_deviantA%s_IAAFT2%s',tstr,datestr))
assert(all(sub==sub1),'Subjects loaded in a different order')
load(sprintf('Sto_deviantB%s_IAAFT2%s',tstr,datestr))
assert(all(sub==sub2),'Subjects loaded in a different order')
load(sprintf('Sto_standardA%s_IAAFT2%s',tstr,datestr))
assert(all(sub==sub3),'Subjects loaded in a different order')
load(sprintf('Sto_standardB%s_IAAFT2%s',tstr,datestr))
assert(all(sub==sub4),'Subjects loaded in a different order')
% Add permen32
load(sprintf('PermEn32_deviantA%s_IAAFT1%s',tstr,datestr))
mspe32_devA = median(PermEn32_devA.rsurr,2); % median across all surrogates
assert(all(sub==sub1),'Subjects loaded in a different order')
load(sprintf('PermEn32_deviantB%s_IAAFT1%s',tstr,datestr))
mspe32_devB = median(PermEn32_devB.rsurr,2); % median across all surrogates
assert(all(sub==sub2),'Subjects loaded in a different order')
load(sprintf('PermEn32_standardA%s_IAAFT1%s',tstr,datestr))
mspe32_stdA = median(PermEn32_stdA.rsurr,2); % median across all surrogates
assert(all(sub==sub3),'Subjects loaded in a different order')
load(sprintf('PermEn32_standardB%s_IAAFT1%s',tstr,datestr))
mspe32_stdB = median(PermEn32_stdB.rsurr,2); % median across all surrogates
assert(all(sub==sub4),'Subjects loaded in a different order')
% Add permen64
load(sprintf('PermEn64_deviantA%s_IAAFT1%s',tstr,datestr))
mspe64_devA = median(PermEn64_devA.rsurr,2); % median across all surrogates
assert(all(sub==sub1),'Subjects loaded in a different order')
load(sprintf('PermEn64_deviantB%s_IAAFT1%s',tstr,datestr))
mspe64_devB = median(PermEn64_devB.rsurr,2); % median across all surrogates
assert(all(sub==sub2),'Subjects loaded in a different order')
load(sprintf('PermEn64_standardA%s_IAAFT1%s',tstr,datestr))
mspe64_stdA = median(PermEn64_stdA.rsurr,2); % median across all surrogates
assert(all(sub==sub3),'Subjects loaded in a different order')
load(sprintf('PermEn64_standardB%s_IAAFT1%s',tstr,datestr))
mspe64_stdB = median(PermEn64_stdB.rsurr,2); % median across all surrogates
assert(all(sub==sub4),'Subjects loaded in a different order')
% These are the fetuses in ALL conditions
common = intersect( intersect(sub1,sub2), intersect(sub3,sub4) );
fprintf('%i unique subjects entered the analysis\n',length(unique([sub1 sub2 sub3 sub4])))
fprintf('%i subjects common to all conditions entered the analysis\n',length(common))
%% Time-freq transform
% This code performs time-frequency analysis on fetal magnetoencephalography (MEG) data and generates corresponding plots. Here's a summary of each major part:
%
% Time-Frequency Analysis:
% Sets up parameters for the time-frequency analysis, including the sampling frequency (fs), frequency of interest (foi_start to foi_end), and window shift (window_shift).
% Calls the function ro_freq_wavelet_TFT to perform the time-frequency transformation on the data (CTW_devA.erf).
%
% Plotting for Condition Stimulus (sssd Rule: ssss):
% Creates a figure with two subplots.
% Plots the condition/stimulus signal for 'sssd' in the first subplot.
% Plots the time-frequency representation in the second subplot, including contour lines and tone markers.
% Calculates correlation between power and entropy (CTW_devA.trh) for each frequency.
%
% Correlation coefficients and p-values are calculated for each frequency and stored in variables R1, Pr1, R2, Pr2, R3, Pr3, R4, Pr4.
%
% Repeats the Above Steps for Other Conditions:
% Repeats the time-frequency analysis, plotting, and correlation calculation for conditions 'ssss, rule: sssd' (CTW_devB.erf), 'ssss, rule: ssss' (CTW_stdA.erf), and 'sssd, rule: sssd' (CTW_stdB.erf).
%
% Saving Plots:
% Saves the generated figures in PNG and SVG formats.
% parameters
cfg = [];
cfg.fsample = fs;
cfg.foi_start = 1;
cfg.foi_end = 10;
cfg.window_shift=0.1;
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_devA.erf),cfg);
% plot the condition/stimulus signal
% sssD
myfigure2
subplot(2,1,1)
hold on
plot(nestedmean(squeeze(CTW_devA.erf),sub1),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: sssd, rule: ssss','fontsize',18)
makefighandsome
% plot the condition/stimulus time-freq representation
subplot(2,1,2)
hold on
TFT1 = interp2(squeeze(nestedmean(log10(pow),sub1))',k,'linear');
imagesc(TFT1)
contour(TFT1,10,'LineColor','k');
xax = 1:size(TFT1,2);
tmp=linspace(-200,3000,size(TFT1,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT1,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT1,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: sssd, rule: ssss','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT1,1)).*xax(xidx(1)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(2)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(3)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(4)),1:size(TFT1,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_devA.png')
print('-dsvg','./Figures/TFT_devA.svg')
% correlation between power and entropy
R1 = nan(1,length(foi));
Pr1 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(nestedmean(log10(pow(:,:,ifrq)),sub1));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_devA.trh,2)),'linear');
[r,p] = corr(pw2',nestedmean(squeeze(CTW_devA.trh),sub1)');
R1(ifrq) = r;
Pr1(ifrq) = p;
end
% sssS
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_devB.erf),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(nestedmean(squeeze(CTW_devB.erf),sub2),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: ssss, rule: sssd','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT2 = interp2(squeeze(nestedmean(log10(pow),sub2))',k,'linear');
imagesc(TFT2)
contour(TFT2,10,'LineColor','k');
xax = 1:size(TFT2,2);
tmp=linspace(-200,3000,size(TFT2,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT2,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT2,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: ssss, rule: sssd','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT2,1)).*xax(xidx(1)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(2)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(3)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(4)),1:size(TFT2,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_devB.png')
print('-dsvg','./Figures/TFT_devB.svg')
% correlation between power and entropy
R2 = nan(1,length(foi));
Pr2 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(nestedmean(log10(pow(:,:,ifrq)),sub2));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_devB.trh,2)),'spline');
[r,p] = corr(pw2',nestedmean(squeeze(CTW_devB.trh),sub2)');
R2(ifrq) = r;
Pr2(ifrq) = p;
end
% ssss
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_stdA.erf),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(nestedmean(squeeze(CTW_stdA.erf),sub3),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: ssss, rule: ssss','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT3 = interp2(squeeze(nestedmean(log10(pow),sub3))',k,'linear');
imagesc(TFT3)
contour(TFT3,10,'LineColor','k');
xax = 1:size(TFT3,2);
tmp=linspace(-200,3000,size(TFT3,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT3,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT3,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: ssss, rule: ssss','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT3,1)).*xax(xidx(1)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(2)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(3)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(4)),1:size(TFT3,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_stdA.png')
print('-dsvg','./Figures/TFT_stdA.svg')
% correlation between power and entropy
R3 = nan(1,length(foi));
Pr3 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(nestedmean(log10(pow(:,:,ifrq)),sub3));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_stdA.trh,2)),'spline');
[r,p] = corr(pw2',nestedmean(squeeze(CTW_stdA.trh),sub3)');
R3(ifrq) = r;
Pr3(ifrq) = p;
end
% sssd
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_stdB.erf),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(nestedmean(squeeze(CTW_stdB.erf),sub4),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: sssd, rule: sssd','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT4 = interp2(squeeze(nestedmean(log10(pow),sub4))',k,'linear');
imagesc(TFT4)
contour(TFT4,10,'LineColor','k');
xax = 1:size(TFT4,2);
tmp=linspace(-200,3000,size(TFT4,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT4,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT4,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: sssd, rule: sssd','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT4,1)).*xax(xidx(1)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(2)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(3)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(4)),1:size(TFT4,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_stdB.png')
print('-dsvg','./Figures/TFT_stdB.svg')
% correlation between power and entropy
R4 = nan(1,length(foi));
Pr4 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(nestedmean(log10(pow(:,:,ifrq)),sub4));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_stdB.trh,2)),'spline');
[r,p] = corr(pw2',nestedmean(squeeze(CTW_stdB.trh),sub4)');
R4(ifrq) = r;
Pr4(ifrq) = p;
end
%% Time-freq transform with ERF template subtraction
% Template subtraction means that an average ERF across subjects is
% subtracted from the signal
% NOTE: Template subtraction was NOT performed in the manuscript! If you
% are just trying to replicate our findings, this block of code is
% extraneous.
cfg = [];
cfg.fsample = fs;
cfg.foi_start = 1;
cfg.foi_end = 10;
cfg.window_shift=0.1;
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_devA.erf-ERFdev),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(mean(squeeze(CTW_devA.erf)-ERFdev),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: sssd, rule: ssss; ERF subtracted','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT1 = interp2(squeeze(mean(log10(pow)))',k,'linear');
imagesc(TFT1)
contour(TFT1,10,'LineColor','k');
xax = 1:size(TFT1,2);
tmp=linspace(-200,3000,size(TFT1,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT1,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT1,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: sssd, rule: ssss','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT1,1)).*xax(xidx(1)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(2)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(3)),1:size(TFT1,1),'w','linewidth',2)
plot(ones(1,size(TFT1,1)).*xax(xidx(4)),1:size(TFT1,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_devA_subtr.png')
print('-dsvg','./Figures/TFT_devA_subtr.svg')
% correlation between power and entropy
R1 = nan(1,length(foi));
Pr1 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(mean(log10(pow(:,:,ifrq))));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_devA.trh,2)),'linear');
[r,p] = corr(pw2',mean(squeeze(CTW_devA.trh))');
R1(ifrq) = r;
Pr1(ifrq) = p;
end
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_devB.erf-ERFdev),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(mean(squeeze(CTW_devB.erf-ERFdev)),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: ssss, rule: sssd, ERF subtracted','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT2 = interp2(squeeze(mean(log10(pow)))',k,'linear');
imagesc(TFT2)
contour(TFT2,10,'LineColor','k');
xax = 1:size(TFT2,2);
tmp=linspace(-200,3000,size(TFT2,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT2,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT2,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: ssss, rule: sssd','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT2,1)).*xax(xidx(1)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(2)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(3)),1:size(TFT2,1),'w','linewidth',2)
plot(ones(1,size(TFT2,1)).*xax(xidx(4)),1:size(TFT2,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_devB_subtr.png')
print('-dsvg','./Figures/TFT_devB_subtr.svg')
% correlation between power and entropy
R2 = nan(1,length(foi));
Pr2 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(mean(log10(pow(:,:,ifrq))));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_devB.trh,2)),'spline');
[r,p] = corr(pw2',mean(squeeze(CTW_devB.trh))');
R2(ifrq) = r;
Pr2(ifrq) = p;
end
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_stdA.erf-ERFstd),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(mean(squeeze(CTW_stdA.erf-CTW_stdA.erf-ERFstd)),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: ssss, rule: ssss, ERF subtracted','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT3 = interp2(squeeze(mean(log10(pow)))',k,'linear');
imagesc(TFT3)
contour(TFT3,10,'LineColor','k');
xax = 1:size(TFT3,2);
tmp=linspace(-200,3000,size(TFT3,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT3,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT3,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: ssss, rule: ssss, ERF subtracted','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT3,1)).*xax(xidx(1)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(2)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(3)),1:size(TFT3,1),'w','linewidth',2)
plot(ones(1,size(TFT3,1)).*xax(xidx(4)),1:size(TFT3,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_stdA_subtr.png')
print('-dsvg','./Figures/TFT_stdA_subtr.svg')
% correlation between power and entropy
R3 = nan(1,length(foi));
Pr3 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(mean(log10(pow(:,:,ifrq))));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_stdA.trh,2)),'spline');
[r,p] = corr(pw2',mean(squeeze(CTW_stdA.trh))');
R3(ifrq) = r;
Pr3(ifrq) = p;
end
[pow,pow_full,n,unit,foi_target,foi_delta,foi,DAT] = ro_freq_wavelet_TFT(squeeze(CTW_stdB.erf-ERFstd),cfg);
myfigure2
subplot(2,1,1)
hold on
plot(mean(squeeze(CTW_stdB.erf-ERFstd)),'b','linewidth',2)
xticks([])
xlabel('time (ms)')
ylabel('Field strength (AU)')
title('stimulus: sssd, rule: sssd, ERF subtracted','fontsize',18)
makefighandsome
subplot(2,1,2)
hold on
TFT4 = interp2(squeeze(mean(log10(pow)))',k,'linear');
imagesc(TFT4)
contour(TFT4,10,'LineColor','k');
xax = 1:size(TFT4,2);
tmp=linspace(-200,3000,size(TFT4,2));
xidx = find(mod(round(tmp,-1),600)==0);
tmp2 = 1:size(TFT4,2);
xticks(tmp2(xidx))
xticklabels([0:600:3000])
xlabel('time (ms)')
yax = linspace(0,size(TFT4,1),length(foi));
yidx = find(mod(foi,1)==0);
yticks(yax(yidx))
yticklabels(round(foi(yidx)))
ylabel('Frequency (Hz)')
title('stimulus: sssd, rule: sssd','fontsize',18)
% Mark the tones
plot(ones(1,size(TFT4,1)).*xax(xidx(1)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(2)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(3)),1:size(TFT4,1),'w','linewidth',2)
plot(ones(1,size(TFT4,1)).*xax(xidx(4)),1:size(TFT4,1),'w','linewidth',2)
%mycolorbar
caxis([1.5 3])
makefighandsome
colormap hot
print('-dpng','./Figures/TFT_stdB_subtr.png')
print('-dsvg','./Figures/TFT_stdB_subtr.svg')
% correlation between power and entropy
R4 = nan(1,length(foi));
Pr4 = nan(1,length(foi));
for ifrq = 1:size(pow,3) % for each frequency
pw = squeeze(mean(log10(pow(:,:,ifrq))));
pw2 = interp1(linspace(-200,3000,size(pow,2)),pw,linspace(-200,3000,size(CTW_stdB.trh,2)),'spline');
[r,p] = corr(pw2',mean(squeeze(CTW_stdB.trh))');
R4(ifrq) = r;
Pr4(ifrq) = p;
end
%% Build table
load Data_traces
% Count how many fetuses had more than one visit
% Identifies unique IDs in the dataset (Datatable.ID).
% Counts the number of fetuses with more than one visit (nages > 1) and prints the count.
IDs = unique(Datatable.ID);
cnt = 0;
for isub = 1:length(IDs)
idx = Datatable.ID == IDs(isub);
nages = length(unique(Datatable.GA(idx)));
if nages > 1, cnt = cnt + 1; end
end
fprintf('%i fetuses gave data at more than one visit\n',cnt)
% Next, we add columns to Datatable for conditions, SDNN (standard deviation of NN intervals), XChrom (sex), DD (difference between global deviant and standard), and BMI (maternal body mass index).
% add SDNN to the main table and create a simpler column with condition labels and add SDNN to the main table
Datatable.condition = nan(size(Datatable,1),1);
Datatable.SDNN = nan(size(Datatable,1),1);
Datatable.XChrom = nan(size(Datatable,1),1);
Datatable.DD = nan(size(Datatable,1),1);
Datatable.BMI = nan(size(Datatable,1),1);
BMI = readtable('./BMI.xls');
for irow = 1:size(Datatable,1)
% Add condition label (simple to read)
if strcmp(Datatable.Blocklabel(irow,:),'A (ssss)')
Datatable.condition(irow) = 1;
elseif strcmp(Datatable.Blocklabel(irow,:),'B (sssd)')
Datatable.condition(irow) = 2;
else
error('condition not recognized')
end
% Add SDNN (note: SDNN is the same for all data from one block)
for jrow = 1:size(T,1)
if T.ID(jrow) == Datatable.ID(irow) && T.GA(jrow) == Datatable.GA(irow) ...
&& strcmp(T.Block{jrow},Datatable.Blocklabel(irow,1))
Datatable.SDNN(irow) = T.SDNN(jrow);
% Also add the difference (global dev vs. std) of differences (T4 - T3)
NDX = find(T.ID == Datatable.ID(irow) & T.GA == Datatable.GA(irow) ...
& strcmp(T.Block,Datatable.Blocklabel(irow,1)) & T.tonepart==2);
GD = NDX(find(contains(T.condition(NDX),'global_deviant'))); % global deviant
GS = NDX(find(~contains(T.condition(NDX),'global_deviant'))); % global standard
Datatable.DD(irow) = T.value(GD) - T.value(GS);
end
end
% Add sex as a covariate
for jrow = 1:size(T2,1)
if T2.ID(jrow) == Datatable.ID(irow)
switch T2.FetalSex{jrow}
case 'm'
Datatable.XChrom(irow) = 1;
Datatable.Sex{irow} = 'M';
break
case 'f'
Datatable.XChrom(irow) = 2;
Datatable.Sex{irow} = 'F';
break
otherwise
continue
end
end
end
% Add maternal BMI as a covariate
for jrow = 1:size(BMI,1)
tmpid = str2double(BMI.Pat_ID{jrow}(2:end));
if tmpid == Datatable.ID(irow)
Datatable.BMI(irow) = BMI.BMIVorSS(jrow);
end
end
end
% Concatenate corresponding values
%%% Mean signal value %%%
Datatable.mval_dev = [mval_devA; mval_devB];
Datatable.mval_std = [mval_stdA; mval_stdB];
%%% CTW %%%
% add CTW
Datatable.CTW_dev = [CTW_devA.full CTW_devB.full]';
Datatable.CTW_std = [CTW_stdA.full CTW_stdB.full]';
% Median CTW surrogate
Datatable.msctw_dev = [msctw_devA; msctw_devB];
Datatable.msctw_std = [msctw_stdA; msctw_stdB];
%%% LZC %%%
% add LZC
Datatable.LZC_dev = [LZC_devA.full LZC_devB.full]';
Datatable.LZC_std = [LZC_stdA.full LZC_stdB.full]';
% median surrogate LZC
Datatable.mslzc_dev = [mslzc_devA; mslzc_devB];
Datatable.mslzc_std = [mslzc_stdA; mslzc_stdB];
% add PermEn32
Datatable.PE32_dev = [PermEn32_devA.full PermEn32_devB.full]';
Datatable.PE32_std = [PermEn32_stdA.full PermEn32_stdB.full]';
% median surrogate PermEn32
Datatable.mspe32_dev = [mspe32_devA; mspe32_devB];
Datatable.mspe32_std = [mspe32_stdA; mspe32_stdB];
% add PermEn64
Datatable.PE64_dev = [PermEn64_devA.full PermEn64_devB.full]';
Datatable.PE64_std = [PermEn64_stdA.full PermEn64_stdB.full]';
% median surrogate PermEn64
Datatable.mspe64_dev = [mspe64_devA; mspe64_devB];
Datatable.mspe64_std = [mspe64_stdA; mspe64_stdB];
%%% MSE %%%
% add MSE (average across n timescales)
Datatable.MSE_dev = [mean(squeeze(MSE_devA.full)) mean(squeeze(MSE_devB.full))]';
Datatable.MSE_std = [mean(squeeze(MSE_stdA.full)) mean(squeeze(MSE_stdB.full))]';
% median surrogate MSE
Datatable.msmse_dev = [msmse_devA msmse_devB]';
Datatable.msmse_std = [msmse_stdA msmse_stdB]';
%%% SampEn %%%
% add SE (i.e., sample entropy, first timescale only)
Datatable.SE_dev = [squeeze(MSE_devA.full(1,:)) squeeze(MSE_devB.full(1,:))]';
Datatable.SE_std = [squeeze(MSE_stdA.full(1,:)) squeeze(MSE_stdB.full(1,:))]';
% median surrogate SE
Datatable.msse_dev = [msse_devA msse_devB]';
Datatable.msse_std = [msse_stdA msse_stdB]';
% truncation (chop off parts of signal to match the surrogate signal)
%%% CTW %%%
% add CTW
Datatable.CTW_devT = [CTW_devA.trunc CTW_devB.trunc]';
Datatable.CTW_stdT = [CTW_stdA.trunc CTW_stdB.trunc]';
%%% LZC %%%
% add LZC
Datatable.LZC_devT = [LZC_devA.trunc LZC_devB.trunc]';
Datatable.LZC_stdT = [LZC_stdA.trunc LZC_stdB.trunc]';
%%% PermEn %%%
% add PermEn32
Datatable.PE32_devT = [PermEn32_devA.trunc PermEn32_devB.trunc]';
Datatable.PE32_stdT = [PermEn32_stdA.trunc PermEn32_stdB.trunc]';
% add PermEn64
Datatable.PE64_devT = [PermEn64_devA.trunc PermEn64_devB.trunc]';
Datatable.PE64_stdT = [PermEn64_stdA.trunc PermEn64_stdB.trunc]';
%%% MSE %%%
% add MSE (average across n timescales)
Datatable.MSE_devT = [mean(squeeze(MSE_devA.trunc)) mean(squeeze(MSE_devB.trunc))]';
Datatable.MSE_stdT = [mean(squeeze(MSE_stdA.trunc)) mean(squeeze(MSE_stdB.trunc))]';
%%% SampEn %%%
% add SE (i.e., sample entropy, first timescale only)
Datatable.SE_devT = [squeeze(MSE_devA.trunc(1,:)) squeeze(MSE_devB.trunc(1,:))]';
Datatable.SE_stdT = [squeeze(MSE_stdA.trunc(1,:)) squeeze(MSE_stdB.trunc(1,:))]';
%%%%%%%%%%%%%% subtraction (subtract out the ERF template) %%%%%%%%%%%%%%%%
% NOTE - This was NOT done in the manuscript!
%%% CTW %%%
% add CTW
Datatable.CTW_devS = [CTW_devA.subtr CTW_devB.subtr]';
Datatable.CTW_stdS = [CTW_stdA.subtr CTW_stdB.subtr]';
%%% LZC %%%
% add LZC
Datatable.LZC_devS = [LZC_devA.subtr LZC_devB.subtr]';
Datatable.LZC_stdS = [LZC_stdA.subtr LZC_stdB.subtr]';
%%% PermEn %%%
% add PermEn32
Datatable.PE32_devS = [PermEn32_devA.subtr PermEn32_devB.subtr]';
Datatable.PE32_stdS = [PermEn32_stdA.subtr PermEn32_stdB.subtr]';
% add PermEn64
Datatable.PE64_devS = [PermEn64_devA.subtr PermEn64_devB.subtr]';
Datatable.PE64_stdS = [PermEn64_stdA.subtr PermEn64_stdB.subtr]';
%%% MSE %%%
% add MSE (average across n timescales)
Datatable.MSE_devS = [mean(squeeze(MSE_devA.subtr)) mean(squeeze(MSE_devB.subtr))]';
Datatable.MSE_stdS = [mean(squeeze(MSE_stdA.subtr)) mean(squeeze(MSE_stdB.subtr))]';
%%% SampEn %%%
% add SE (i.e., sample entropy, first timescale only)
Datatable.SE_devS = [squeeze(MSE_devA.subtr(1,:)) squeeze(MSE_devB.subtr(1,:))]';
Datatable.SE_stdS = [squeeze(MSE_stdA.subtr(1,:)) squeeze(MSE_stdB.subtr(1,:))]';
%%%%%%%%%%%%% DETERMINISTIC/STOCHASTIC TEST %%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Stochasticity %%%
Datatable.Sto_dev = [Sto_devA.full Sto_devB.full]';
Datatable.Sto_std = [Sto_stdA.full Sto_stdB.full]';
% With ERF subtraction
Datatable.Sto_devS = [Sto_devA.subtr Sto_devB.subtr]';
Datatable.Sto_stdS = [Sto_stdA.subtr Sto_stdB.subtr]';
%%%%%%%%%%%%%%%%% Random amplitude PermEn32 %%%%%%%%%%%%%%%%%%%%%%%%%%%
Datatable.dev_amp = cell(size(Datatable,1),1);
Datatable.std_amp = cell(size(Datatable,1),1);
Datatable.dev_phi = cell(size(Datatable,1),1);
Datatable.std_phi = cell(size(Datatable,1),1);
Datatable.dev_ra = cell(size(Datatable,1),1);
Datatable.std_ra = cell(size(Datatable,1),1);
for irow = 1:size(Datatable,1)
gsn = Datatable.global_standards_normalized{irow};
Datatable.std_amp{irow} = abs(fft(gsn));
Datatable.std_phi{irow} = angle(fft(gsn));
gdn = Datatable.global_deviants_normalized{irow};
Datatable.dev_amp{irow} = abs(fft(gdn));
Datatable.dev_phi{irow} = angle(fft(gdn));
end
% Interaction effect: Delta^I (i) = H(A_i, Phi_i) - 0.5*[mean_j H(A_i, Phi_j) + mean_j H(A_j, Phi_i)]
% Amplitude effect: Delta^A (i) = mean_j H(A_i, Phi_j) - mean_{j,k} H(A_j, Phi_k)
% Phase effect: Delta^Phi (i) = mean_j H(A_j, Phi_i) - mean_{j,k} H(A_j, Phi_k)
% "Total" effect: Delta^T (i) = H(A_i, Phi_i) - mean_{j,k} H(A_j, Phi_k)
m = 3;
tol = 1e-9; % define a tolerance for the decomp
lag1 = 32;
tau1 = round((lag1/1000)/(1/fs));
lag2 = 64;
tau2 = round((lag2/1000)/(1/fs));
H = nan(size(Datatable,1)); % matrix of entropies, line of identity gives the "true" entropies
for irow = 1:size(H,1)
for icol = 1:size(H,2)
tmp = ifft(Datatable.dev_amp{irow}.*exp(1j.*Datatable.dev_phi{icol}), 'symmetric'); % devaint response
Hdev(irow,icol) = PermEn(tmp,m,tau1);
tmp = ifft(Datatable.std_amp{irow}.*exp(1j.*Datatable.std_phi{icol}), 'symmetric'); % standard response
Hstd(irow,icol) = PermEn(tmp,m,tau1);
end
end
for isub = 1:size(Datatable,1)
assert(Datatable.PE32_std(isub) == Hstd(isub,isub),'Entropies don''t match') % sanity check
assert(Datatable.PE32_dev(isub) == Hdev(isub,isub),'Entropies don''t match') % sanity check
Datatable.PE32AC_dev(isub) = mean(Hdev(isub,:)) - mean(Hdev,[1 2]); % amplitude contribution
Datatable.PE32PC_dev(isub) = mean(Hdev(:,isub)) - mean(Hdev,[1 2]); % phase contribution
Datatable.PE32XC_dev(isub) = Hdev(isub,isub) - 0.5*(mean(Hdev(:,isub)) + mean(Hdev(isub,:))); % interaction contribution
assert((Hdev(isub,isub) - mean(Hdev,[1 2])) - (Datatable.PE32XC_dev(isub) ...
+ (Datatable.PE32AC_dev(isub) + Datatable.PE32PC_dev(isub))/2) < tol,'Components don''t add up');
Datatable.PE32AC_std(isub) = mean(Hstd(isub,:)) - mean(Hstd,[1 2]); % amplitude contribution
Datatable.PE32PC_std(isub) = mean(Hstd(:,isub)) - mean(Hstd,[1 2]); % phase contribution
Datatable.PE32XC_std(isub) = Hstd(isub,isub) - 0.5*(mean(Hstd(:,isub)) + mean(Hstd(isub,:))); % interaction contribution
assert((Hstd(isub,isub) - mean(Hstd,[1 2])) - (Datatable.PE32XC_std(isub) ...
+ (Datatable.PE32AC_std(isub) + Datatable.PE32PC_std(isub))/2) < tol,'Components don''t add up');
end
assert(all([Datatable.ID] == [sub1'; sub2']),'Subject IDs don''t match')
assert(all([Datatable.ID] == [sub3'; sub4']),'Subject IDs don''t match')
%% Prepare data for linear mixed models
% This block of code prepares a large table (Bigtable) for a linear mixed model by stacking and organizing various features from the original data table (Datatable). Here's a summary:
%
% 1. Data Stacking:
%
% Creates a new table (Bigtable) by stacking two copies of the original data table (Datatable).
%
% 2. Feature Concatenation:
%
% Concatenates various signal features for standards and deviants separately. Features include CTW, LZC, PE32, PE64, MSE, SE, and a binary indicator for stochasticity (Sto).
% The data is organized to have the first half of rows corresponding to standards and the second half to deviants.
%
% 3. Additional Features:
%
% Concatenates additional features, such as truncated (trn) and template subtracted (sbt) versions of CTW, LZC, PE32, PE64, MSE, and SE.
% Includes columns for amplitude contributions (pe32ac), phase contributions (pe32pc), and interaction contributions (pe32xc).
%
% 4. Additional Information:
%
% Concatenates mean signal values (mval), a binary indicator for rule violation (Violation), and a variable indicating the global rule (GlobalRule).
% Creates a binary variable (Stimulus) based on whether there is a violation compared to the global rule.
%
% 5. Log Transformation:
%
% Applies a log transformation to the SDNN (standard deviation of NN intervals) and the maximum peak amplitude (MPA).
%
% 6. Adding T4 - T3 Difference:
%
% Adds a column (T4T3) representing the difference between T4 and T3 values from another data table (T).
% NOTE: we did not use this information in the manuscript.
%
% 7. Sanity Check:
%
% Asserts that the length of unique values in the T4T3 column is equal to the size of Bigtable for a sanity check.
%
% 8. Removing Unnecessary Columns:
%
% Removes unnecessary columns from Bigtable to clean up and streamline the data for further analysis.
Bigtable = [Datatable; Datatable]; % stack tables
Bigtable.T4T3 = nan(size(Bigtable,1),1);
% First half of rows is standards, second half of rows are deviants
Bigtable.CTW = [Datatable.CTW_std; Datatable.CTW_dev];
Bigtable.LZC = [Datatable.LZC_std; Datatable.LZC_dev];
Bigtable.PE32 = [Datatable.PE32_std; Datatable.PE32_dev];
Bigtable.PE64 = [Datatable.PE64_std; Datatable.PE64_dev];
Bigtable.MSE = [Datatable.MSE_std; Datatable.MSE_dev];
Bigtable.SE = [Datatable.SE_std; Datatable.SE_dev];
Bigtable.Sto = strcmp([Datatable.Sto_std; Datatable.Sto_dev],'stochastic');
Bigtable.CTWtrn = [Datatable.CTW_stdT; Datatable.CTW_devT];
Bigtable.LZCtrn = [Datatable.LZC_stdT; Datatable.LZC_devT];
Bigtable.PE32trn = [Datatable.PE32_stdT; Datatable.PE32_devT];
Bigtable.PE64trn = [Datatable.PE64_stdT; Datatable.PE64_devT];
Bigtable.MSEtrn = [Datatable.MSE_stdT; Datatable.MSE_devT];
Bigtable.SEtrn = [Datatable.SE_stdT; Datatable.SE_devT];
Bigtable.CTWsbt = [Datatable.CTW_stdS; Datatable.CTW_devS];
Bigtable.LZCsbt = [Datatable.LZC_stdS; Datatable.LZC_devS];
Bigtable.PE32sbt = [Datatable.PE32_stdS; Datatable.PE32_devS];
Bigtable.PE64sbt = [Datatable.PE64_stdS; Datatable.PE64_devS];
Bigtable.MSEsbt = [Datatable.MSE_stdS; Datatable.MSE_devS];
Bigtable.SEsbt = [Datatable.SE_stdS; Datatable.SE_devS];
Bigtable.Stosbt = strcmp([Datatable.Sto_stdS; Datatable.Sto_devS],'stochastic');
Bigtable.mslzc = [Datatable.mslzc_std; Datatable.mslzc_dev];
Bigtable.msctw = [Datatable.msctw_std; Datatable.msctw_dev];
Bigtable.mspe32 = [Datatable.mspe32_std; Datatable.mspe32_dev];
Bigtable.mspe64 = [Datatable.mspe64_std; Datatable.mspe64_dev];
Bigtable.msmse = [Datatable.msmse_std; Datatable.msmse_dev];
Bigtable.msse = [Datatable.msse_std; Datatable.msse_dev];
Bigtable.pe32ac = [Datatable.PE32AC_std; Datatable.PE32AC_dev];
Bigtable.pe32pc = [Datatable.PE32PC_std; Datatable.PE32PC_dev];