Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit.

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5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches

[1] § Methods › EEG Recording and Analysis › Preprocessing ↔ statistical_learning_analysis_scripts.zip/MainITCScript.m, lines 102–134 · score 0.79 · Artifact Blocking, artifact corrected, outer ring, algorithm, threshold, AB
[2] § Methods › EEG Recording and Analysis › Time Course of Neural Entrainment Over Learning ↔ statistical_learning_analysis_scripts.zip/MainITCScript.m, lines 1541–1584 · score 0.62 · ITC pseudovalue, normalized jackknifed, formula, syllable, word
[3] § Methods › EEG Recording and Analysis › Computation of Intertrial Phase Coherence and Statistical Analysis › Scalp‐Level Statistical Analysis ↔ statistical_learning_analysis_scripts.zip/MainITCScript.m, lines 1340–1409 · score 0.61 · Shaded error bars, standard error, scored ITC, syllable frequency, Topographical, word frequency
[4] § Methods › EEG Recording and Analysis › Computation of Intertrial Phase Coherence and Statistical Analysis ↔ statistical_learning_analysis_scripts.zip/MainITCScript.m, lines 1586–1660 · score 0.59 · phase locked, word structure, coherence, epochs, ITC, syllable
[5] § Results › Source‐Level Analysis › Word Entrainment ↔ statistical_learning_analysis_scripts.zip/MainITCScript.m, lines 1340–1409 · score 0.52 · Shaded error bars, standard error, scored ITC, word frequency, zITC, FXS

Paper

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The authors' code

MATLAB · 1,661 lines · 74 KB · CC-BY-4.0 · 5 matches

% Apply the Infant Pipeline initially applied in Choi, Batterink 2020 to
% the child data from FXS Project
%datafile_names must remain consistent each time script is run.
%notes
%0770 AB algorithm seemed to create completely tiny voltages - excluded anyway though
%% Read files to analyse
clear % clear matlab workspace
clc % clear matlab command window
CurrentDirectory = '/Volumes/Laura_data/Laura Backup/eeglab 2024/';
cd(CurrentDirectory)
eeglab
%Enter the path of the folder that has the raw data to be analyzed
rawdata_location = [CurrentDirectory 'FXS Project/incoming/structured'];
datafile_names=dir(rawdata_location);
datafile_names=datafile_names(~ismember({datafile_names.name},{'.', '..', '.DS_Store'}));
datafile_names={datafile_names.name};
[filepath,name,ext] = fileparts(char(datafile_names{1}));
%Channel Locations
channel_locations = [CurrentDirectory 'FXS Project/channel location file/GSN-HydroCel-129.sfp'];
%Outer ring of channels:
outerlayer_channel = {'E17' 'E38' 'E43' 'E44' 'E48' 'E49' 'E113' 'E114' 'E119' 'E120' 'E121' 'E125' 'E126' 'E127' 'E128' 'E56' 'E63' 'E68' 'E73' 'E81' 'E88' 'E94' 'E99' 'E107'}; % list of channels
%Codes:
%1-12-10
%11-2-4
%8-3-5
%9-7-6
SyllableCodeStrings = {'DIN1', 'DIN2', 'DIN3', 'DIN4', 'DIN5', 'DIN6', 'DIN7', 'DIN8', 'DIN9', 'DI10', 'DI11', 'DI12', ...
'D101', 'D102', 'D103', 'D104', 'D105', 'D106', 'D107', 'D108', 'D109', 'D110', 'D111', 'D112'}; %most subjects have the top row of codes, 2310 has bottom row
WordOnsetStrings = {'DIN1', 'DIN8', 'DIN9', 'DI11', ...
'D101', 'D108', 'D109', 'D111'};
%algorithm parameters for AB algorithm.
threshold = 50; %50 originally; %0 code for didn't apply AB algorithm at all. 999 code for filter from 0.1 to 30 and no alogirthm.
approach = 'total'; %single smoothing matrix used
% ---- Subject Information ---
TDC = {'2481', '1646', '3367', '2860', '1844', '2920', '1136', '2808', '1498', '1655', '2577', '2802', '1343', '1430', ...
'3260', '2181', '1025', '1692', '0076', '0439', '2835', '3186', '0694', '1466', '3107', '2723', '1008', '2313', '1864', '1340', '2805'};
FXS = {'2978', '0836', '1721', '0649', '0468', '0871', '2195', '0913', '1649', '2151', '2472', '2604', '1037', '0392', '1841', '2238', '1798'};
%ASD = {'3463', '2219', '0409', '0502', '0114', '1292', '2410', '2866', '2339', '2100', '0878', '1963','2215', '1995', '2695'};
AllSubjects = [TDC, FXS, ASD];
NewSubjectIDs = {'1798', '1841', '2238', '1963', '1995', '2215', '2695'};
NewSubjectIndices = find(contains(datafile_names, NewSubjectIDs));
AllSubjectIndices = find(contains(datafile_names, [TDC, FXS])); %for now just run TDC and FXS, run ASD later
%% Preprocessing following Fujioka et al. 2011
for subjecti = NewSubjectIndices
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[]; %clear any dataset currently loaded in GUI.
EEG=mff_import([rawdata_location filesep datafile_names{subjecti}]);
[ALLEEG EEG CURRENTSET] = pop_newset( ALLEEG, EEG, CURRENTSET, 'gui','off');
%import channel locations
EEG=pop_chanedit(EEG, 'load',{channel_locations 'filetype' 'autodetect'});
EEG = eeg_checkset( EEG );
% Check whether the channel locations were properly imported. The EEG signals and channel numbers should be same.
if size(EEG.data, 1) ~= length(EEG.chanlocs)
error('The size of the data does not match with channel numbers.');
end
%remove outer ring of channels (see Fujioka et al. 2011)
EEG = pop_select( EEG,'nochannel', outerlayer_channel);
%filter between 0.5 and 20 Hz - same as before.
%ton of 60 hz noise
%filter first with notch, then with buttersworth filter?
EEG = pop_basicfilter( EEG, 1:EEG.nbchan, 'Boundary', 'boundary', 'Cutoff', 60, 'Design', 'notch', 'Filter', 'PMnotch', 'Order', 180, 'RemoveDC', 'on' ); % GUI: 07-Jun-2017 18:55:44
%filter the data using ERPLAB [ 0.5 20]
EEG = pop_basicfilter( EEG, 1:EEG.nbchan, 'Cutoff', [ 0.5 20], 'Design', 'butter', 'Filter', 'bandpass', 'Order', 2, 'RemoveDC', 'on' ); % GUI: 15-Jan-2014 16:49:27
%save the dataset into the appropriate folder
SubjectName = datafile_names{subjecti}(1:4);
EEG.setname = [SubjectName '_preprocessed'];
EEG = pop_saveset( EEG, 'filename', [SubjectName '_preprocessed'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/preprocessed/']); %
end
%% APPLY ARTIFACT BLOCKING (AB) ALGORITHM
for subjecti = NewSubjectIndices
SubjectName = datafile_names{subjecti}(1:4);
%load preprocessed dataset
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_preprocessed.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/preprocessed/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
%outer ring of channels already removed. %remove CZ - algorithm doesn't
%work if ref channel is included.
EEG = pop_select( EEG,'rmchannel', {'Cz'});
%call algorithm
CleanEEG = AB_alg_window(EEG.data,EEG.srate,threshold, approach); %algorithm won't work properly if ref channel is included.
%EEGDataOriginal = EEG.data;
EEG.data = CleanEEG;
%rerefernce the data - I didn't retain the Cz channel because it gave me an
%warning about the same channel label.
EEG = pop_reref( EEG, []);
%save the dataset into the appropriate folder
EEG.setname = [SubjectName '_artifact corrected'];
EEG = pop_saveset( EEG, 'filename', [SubjectName '_artifact corrected'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/ABCleaned/']); %"real" subjects (not pilot subjects) will be named Infant1, Infant2, Infant3, etc.
end
%% CREATE EPOCHS
NumSyllablesPerEpoch = 30; %syllables as in PsychSci Paper corresponding to 9 seconds.
EpochLength = NumSyllablesPerEpoch*0.3; %in seconds --> this assumes presentation rate of 300ms per syllable
for subjecti = NewSubjectIndices %11:length(datafile_names)
SubjectName = datafile_names{subjecti}(1:4);
%load preprocessed dataset
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_artifact corrected.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/ABCleaned/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
UnacceptableJitter = 320; %1000hz sampling rate --> 320 msec rather than 300 msec SOA between syllables is not acceptable. Expect 300
EventsToRemove = [];
for x = 2:size(EEG.event,2) %for each code beginning at the second code (first few are start codes anyway)
if strcmp(EEG.event(x).type, 'DI64')
EventsToRemove = [EventsToRemove, x];
end
end
if size(EventsToRemove, 1) > 0
EEG = pop_selectevent( EEG, 'event', EventsToRemove,'select','inverse','deleteevents','on');
end
%epoch to every 10th word (every 30 syllables in the random condition)
y = 5; %don't include the very first syllable due to sharp auditory onset response; skip over three "start codes" plus first syllable
while y < size(EEG.event,2) %number of events
if contains(EEG.event(y).type, WordOnsetStrings) %ismember( EEG.event(y).type, WordOnsetStrings)
CandidateEpochOnset = y; %found a candidate code to onset to.
syllablecounter = 0;
for z = y:size(EEG.event,2) %candidate code is syllable 1, and so on.
%if a pause or break in auditory stimulation is detected
if EEG.event(z).latency - EEG.event(z-1).latency > UnacceptableJitter %155 %unacceptable level of stim jitter or pause. %should typically have an interstim interval of 154 sampling points (300 ms) --> if not, pause is detected (165 originally)
disp('break in auditory stimulation detected!')
disp(EEG.event(z))
syllablecounter = 0; %reset
y = z;
break
end
if contains( EEG.event(z).type, SyllableCodeStrings)
syllablecounter = syllablecounter + 1;
if syllablecounter == NumSyllablesPerEpoch % 36 syllables without running into a boundary or other unexpected code
EEG = pop_editeventvals(EEG,'changefield',{CandidateEpochOnset,'type','NE'});%NE - New Epoch
disp(CandidateEpochOnset)
y = z; %search for next candidate epoch onset 30 positions later.
break
end
elseif ~ismember( EEG.event(z).type, SyllableCodeStrings) %a 'boundary' event --> reset the counter in this case as it needs to be 36 continuous syllables.
disp('other code found')
disp(EEG.event(z).type)
syllablecounter = 0; %reset
y = z; %if you hit a boundary need to start looking again for a candidate epoch onset...
break
end
end
end
y = y + 1;
end
% STEP 12: Segment data into fixed length epochs
%task_event_markers = {'NE'}; %new epoch events, created in step 11.5 (Entrain)
EEG = eeg_checkset(EEG);
EEG = pop_epoch(EEG, {'NE'}, [0 EpochLength], 'epochinfo', 'yes');
EEG = eeg_checkset( EEG );
EEG = pop_rmbase( EEG, [EEG.times(1) EEG.times(end)]); %baseline correct to the entire epoch
EEG = eeg_checkset( EEG );
EEG.setname = [SubjectName '_epoched'];
EEG = pop_saveset( EEG, 'filename', [SubjectName '_epoched'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/epochs/']); %"real" subjects (not
end
%% LOAD PARAMETERS (recommended by Ana) and RUN FFT
% Output frequencies to keep
foutput = [0.6 10];
% Range of frequnecies to use to compute SNR.
% It is better if it includes the foutput and if the lower limit is bigger than the high-pass filter used in the data
fband = [0.2 15];
% target frequencies if indeicated won't be used to compute the SNR.
% Not really necessary, if left empty should give more or less the same
ftarget = []; % [1.11 3.33] | []
% Bins around each frequnecy bin to compute SNR
binsSNR = [-5 -4 -3 -2 -1 1 2 3 4 5];
% SNR method for the power
% based on the linear fit of log(Power) vs log(freq) of the adjacent
% freuency bins. it seems the most sensitive
typeSNRPWR = 'linear';
% SNR method for the PLV
% In principle it shouldn't be necessary but by computing it sensitivity
% may slightly improve. I put 'none' but you can try 'linear' or 'mean'
typeSNRPLV = 'none';
% Z-score or not the final power.
% Not really necessary, only to center at zero the final data
zscorePWR = 0;
% Z-score or not the final PLV.
% Not really necessary, only to center at zero the final data
zscorePLV = 0;
cd([CurrentDirectory 'FXS Project/infant pipeline/FFT files/']) %so that files go in proper place
DataQualityMtx = [];
for subjecti= NewSubjectIndices %1:length(datafile_names)
clear EEGfrq
SubjectName = datafile_names{subjecti}(1:4);
%load dataset
%load preprocessed dataset
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_epoched.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/epochs/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
EEGfrq = frqa_plvpwr(EEG, 'foutput',foutput,'fband',fband,'binsSNR',binsSNR,...
'typeSNRPWR',typeSNRPWR,'typeSNRPLV',typeSNRPLV,...
'zscorePWR',zscorePWR,'zscorePLV',zscorePLV);
filename = char(strcat(SubjectName, '_Structured_EEGfreq'));
save(filename, 'EEGfrq'); %how to save when filename is a variable set beforehand
end
%% Individual FFT plots
FFTfile_location = [CurrentDirectory 'FXS Project/infant pipeline/FFT files'];
cd(FFTfile_location)
%FFTfiles = dir('*Structured_EEGfreq.mat'); %this only considers the files that were succesfully preprocessed and not those tagged with 'no usable data, all bad epochs.'
%FFTfile_names={FFTfiles.name};
plotchan= 1:104; % we removed the outer ring of electrodes
locsfile = [CurrentDirectory 'FXS Project/channel location file/EGI104InnerChannelsNoCz.locs']; %exported this .locs file one time from EEG dataset. writelocs( chanstruct, filename ); writelocs( EEG.chanlocs, 'testlocs' );
for subji = NewSubjectIndices %1:length(FFTfile_names)
cd([CurrentDirectory 'FXS Project/infant pipeline/FFT files'])
%Load struct_ersp and rand_ersp for each subject
clear Struct_PLV EEGfrq*
SubjectName = datafile_names{subji}(1:4);
%SubjectNumber = extractBetween(FFTfile_names{subji}, 1, 4);
load([SubjectName '_Structured_EEGfreq.mat'])
%load(FFTfile_names{subji})
Struct_PLV = EEGfrq.plv(:,:);
freqvals = EEGfrq.freqs; %freqs based on last file loaded
freqvalindicesunder5Hz = find(freqvals <= 5);
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
%extract neighbouring bins
NeighbouringWordFreqBins = [WordFreq-3, WordFreq-2, WordFreq-1, WordFreq+1, WordFreq+2, WordFreq+3];
NeighbouringSyllableFreqBins = [SyllableFreq-3, SyllableFreq-2, SyllableFreq-1, SyllableFreq+1, SyllableFreq+2, SyllableFreq+3];
%compute actual and normalized ITC values for topoplot
WordITC = Struct_PLV(plotchan, WordFreq);
SyllableITC = Struct_PLV(plotchan, SyllableFreq);
WordNeighboursITC = squeeze(mean(Struct_PLV(plotchan, NeighbouringWordFreqBins ),2));
SyllableNeighboursITC = squeeze(mean(Struct_PLV(plotchan, NeighbouringSyllableFreqBins ),2));
WordITCNormalized = WordITC - WordNeighboursITC;
SyllableITCNormalized = SyllableITC - SyllableNeighboursITC;
cd([CurrentDirectory 'FXS Project/infant pipeline/individual plots'])
figure; plot(freqvals(freqvalindicesunder5Hz), mean(Struct_PLV(plotchan,freqvalindicesunder5Hz),1), 'r');
hold on
xline(1.111, '--')
hold on
xline(2.222, '--')
hold on
xline(3.333, '--')
hold on
legend([SubjectName ' structured'])
figurename = char(strcat(SubjectName, '_Structured'));
print('-dpdf', figurename); %save as postscript in order to merge two files
%topoplot
WordFreqScaleLimits = [-0.25 .25];
SyllableFreqScaleLimits = [-0.6 0.6];
figure;
subplot(2,2,1); topoplot(WordITC, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits , 'shading','interp'); colorbar;
title([SubjectName 'ITC Word Frequency']);
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
subplot(2,2,2); topoplot(SyllableITC, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits , 'shading','interp'); colorbar;
title('ITC Syllable Frequency');
subplot(2,2,3); topoplot(WordITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits , 'shading','interp'); colorbar;
title('Normalized ITC Word Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
subplot(2,2,4); topoplot(SyllableITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits , 'shading','interp'); colorbar;
title('Normalized ITC Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = char(strcat(SubjectName, ' ITC Topoplot'));
print ('-dtiff', filename);
end
%% GRAND AVERAGE - FFT -- ALL PARTICIPANTS - OVERALL EXPOSURE
clear SubjectIDs ITCVars
clear FFTfile* Structured_PLVMtx
clear Structured_PLVMtx
plotchan= 1:104; %SigWordElectrodes; %1:105; % we removed the outer ring of electrodes
locsfile = [CurrentDirectory 'FXS Project/channel location file/EGI104InnerChannelsNoCz.locs']; %exported this .locs file one time from EEG dataset. writelocs( chanstruct, filename ); writelocs( EEG.chanlocs, 'testlocs' );
FFTfile_location = [CurrentDirectory 'FXS Project/infant pipeline/FFT files'];
cd(FFTfile_location)
FFTfiles = dir('*Structured_EEGfreq.mat'); %this only considers the files that were succesfully preprocessed and not those tagged with 'no usable data, all bad epochs.'
FFTfile_names={FFTfiles.name};
SubjectList = [];
GroupName = ASD; %FXS; %TDC; %FXS; %ASD %TDC; %ASD; %FXS; %TDC; %FXS; %TDC; AllSubjects; %
%get subject list
for x = 1:length(FFTfile_names)
SubjectNumber = cell2mat(extractBetween(FFTfile_names{x}, 1, 4));
if ismember(SubjectNumber, GroupName)
SubjectList = [SubjectList, x];
end
end
% -- STRUCTURED --
clear PLVMtx Struct* Rand* Grand* PLV*
for i = SubjectList
display(i)
%Load struct_ersp and rand_ersp for each subject
%load([SubjectID{i} '_EEGfrq.mat'])
load(FFTfile_names{i})
disp(FFTfile_names{i})
PLV = EEGfrq.plv(plotchan,:); %only extracts relevant channels
Structured_PLVMtx(i,:,:) = PLV; %rows without corresponding subjects (e.g., 20) just contains 0s. This is taken into account below by selecting SubjectsToInclude as a dimension.
% -- to extract individual level variables --
freqvals = EEGfrq.freqs; %freqs based on last subject loaded
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
%extract neighbouring bins
NeighbouringWordFreqBins = [WordFreq-3, WordFreq-2, WordFreq-1, WordFreq+1, WordFreq+2, WordFreq+3];
NeighbouringSyllableFreqBins = [SyllableFreq-3, SyllableFreq-2, SyllableFreq-1, SyllableFreq+1, SyllableFreq+2, SyllableFreq+3];
%compute individual actual and normalized ITC values to store for analysis
WordITC = PLV(:, WordFreq);
SyllableITC = PLV(:, SyllableFreq);
WordNeighboursITC = squeeze(mean(PLV(:, NeighbouringWordFreqBins ),2));
SyllableNeighboursITC = squeeze(mean(PLV(:, NeighbouringSyllableFreqBins ),2));
WordITCNormalized = WordITC - WordNeighboursITC;
SyllableITCNormalized = SyllableITC - SyllableNeighboursITC;
%average across channels
MeanWordITC = mean(WordITC,1);
MeanSyllableITC = mean(SyllableITC,1);
MeanWordITCNormalized = mean(WordITCNormalized,1);
MeanSyllableITCNormalized = mean(SyllableITCNormalized,1);
ITCVars(i,:,:,:) = [MeanWordITC, MeanSyllableITC, MeanWordITCNormalized, MeanSyllableITCNormalized];
%store subject and group information
SubjectNumber = cell2mat(extractBetween(FFTfile_names{i}, 1, 4));
if ismember(SubjectNumber, TDC)
GroupNum = 1;
elseif ismember(SubjectNumber, FXS)
GroupNum = 2;
elseif ismember(SubjectNumber, ASD)
GroupNum = 3;
end
SubjectIDs{i,1} = SubjectNumber;
SubjectIDs{i,2} = GroupNum; %also store group membership
%CongruentSubjectIDs{i,:} = SubjectID{i};
end
%freqvals = EEGfrq.freqs; %freqs based on last subject loaded
%average across channels
GrandAveragePLV_Structured = squeeze(mean(Structured_PLVMtx(SubjectList,:,:),1));
GrandAveragePLV_avacrosschans_Structured = mean(GrandAveragePLV_Structured(:,:),1);
%Standard Error
PLV_averageacrosschans_Structured = squeeze(mean(Structured_PLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_Structured = std(PLV_averageacrosschans_Structured(:,:)); %nfreqs
SE_Structured = STD_Structured/sqrt(numel(SubjectList));
NSubjects = numel(SubjectList);
%---- Plots ------
cd([CurrentDirectory 'FXS Project/infant pipeline/grand averages/'])
%truncated
freqvalindicesunder5Hz = find(freqvals <= 5);
figure; plot(freqvals(freqvalindicesunder5Hz), GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz),'r');
hold on
%shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), 'lineprops','r') %old syntax for shadedErrorBar
shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), {'Color', 'r' }, 1);
hold on
xline(1.111, '--')
hold on
xline(2.222, '--')
hold on
xline(3.333, '--')
legend('structured', 'SE')
pdfname = ['ITC FFT All Channels, n = ' num2str(NSubjects)];
%pdfname = ['ITC FFT Auditory ROI'];
print('-dpdf', pdfname); %save as postscript in order to merge two files
% PLOT TOPOS
%figure out freqs of interest
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
%extract neighbouring bins
NeighbouringWordFreqBins = [WordFreq-3, WordFreq-2, WordFreq-1, WordFreq+1, WordFreq+2, WordFreq+3];
NeighbouringSyllableFreqBins = [SyllableFreq-3, SyllableFreq-2, SyllableFreq-1, SyllableFreq+1, SyllableFreq+2, SyllableFreq+3];
Structured_ITC_WordFreq = squeeze(GrandAveragePLV_Structured(:,WordFreq));
Structured_ITC_SyllableFreq = squeeze(GrandAveragePLV_Structured(:,SyllableFreq));
WordNeighboursITC = squeeze(mean(GrandAveragePLV_Structured(:, NeighbouringWordFreqBins ),2));
SyllableNeighboursITC = squeeze(mean(GrandAveragePLV_Structured(:, NeighbouringSyllableFreqBins ),2));
WordITCNormalized = Structured_ITC_WordFreq - WordNeighboursITC;
SyllableITCNormalized = Structured_ITC_SyllableFreq - SyllableNeighboursITC;
WordFreqScaleLimits = [-0.2 .2];
SyllableFreqScaleLimits = [-0.5 0.5];
NormalizedWordFreqScaleLimits = [-0.05 .05];
NormalizedSyllableFreqScaleLimits = [-0.4 0.4];
PValueLimits = [0 0.1];
figure;
%regular raw values
subplot(2,2,1); topoplot(Structured_ITC_WordFreq, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits , 'shading','interp'); colorbar;
title('Structured ITC Word Frequency');
subplot(2,2,2); topoplot(Structured_ITC_SyllableFreq, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits , 'shading','interp'); colorbar;
title('Structured ITC Syllable Frequency');
%normalized
subplot(2,2,3); topoplot(WordITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', NormalizedWordFreqScaleLimits , 'shading','interp'); colorbar;
title('ITC Word Normalized');
subplot(2,2,4); topoplot(SyllableITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', NormalizedSyllableFreqScaleLimits , 'shading','interp'); colorbar;
title('ITC Syllable Normalized');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['Condition x Frequency Topo Plots n = ' num2str(NSubjects)];
print ('-dtiff', filename);
%figure out which electrodes are significant
NormalizedWordITC_SubjectByChannel = Structured_PLVMtx(SubjectList,:,WordFreq) - mean(Structured_PLVMtx(SubjectList,:,NeighbouringWordFreqBins),3); %NSubject x NChan
NormalizedSyllableITC_SubjectByChannel = Structured_PLVMtx(SubjectList,:,SyllableFreq) - mean(Structured_PLVMtx(SubjectList,:,NeighbouringSyllableFreqBins),3);
[SigWordElectrodesArray, PWord] = ttest(NormalizedWordITC_SubjectByChannel, 0, 'tail', 'right'); %one directional t-test, mean is greater than 0
[SigSyllableElectrodesArray, PSyllable] = ttest(NormalizedSyllableITC_SubjectByChannel, 0, 'tail', 'right'); %one directional t-test, mean is greater than 0
SigWordElectrodes = find(SigWordElectrodesArray ==1);
SigSyllableElectrodes = find(SigSyllableElectrodesArray ==1);
NumSigWordElectrodes = numel(SigWordElectrodes);
%plot significant electrodes
figure;
subplot(1,2,1)
topoplot(WordITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', NormalizedWordFreqScaleLimits,'shading','interp', 'emarker2', {SigWordElectrodes ,'p','k', 7, 1} ); colorbar;
title(['Significant Word ITC Electrodes: ' num2str(NumSigWordElectrodes)]);
subplot(1,2,2)
topoplot(SyllableITCNormalized, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', NormalizedSyllableFreqScaleLimits,'shading','interp', 'emarker2', {SigSyllableElectrodes ,'p','k', 7, 1} ); colorbar;
title(['Significant Syllable ITC Electrodes']);
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['Sig Electrodes Topo Plots n = ' num2str(NSubjects)];
print ('-dtiff', filename);
%if you want you could run it from the top one more time to plot the ITC
%graph with only the significant word electrodes
% %create a table
% ITCVarsCell = num2cell(ITCVars);
% ITCVarsWithSubjectIDs = horzcat(SubjectIDs, ITCVarsCell);
%
% ColumnNames = {'Subject', 'Group', 'WordITC', 'SyllableITC', 'WordITCNormalized', 'SyllableITCNormalized'};
% IndividualLevelVariablesTableForAnalysis = cell2table(ITCVarsWithSubjectIDs(1:end,:), 'VariableNames', ColumnNames);
%
% LocationForCSV = [CurrentDirectory 'FXS Project/infant pipeline/grand averages/stats for analysis']; %on the server
% cd(LocationForCSV)
%
% writetable(IndividualLevelVariablesTableForAnalysis,'IndividualLevelVariablesTableForAnalysis.csv')
%% CREATE 100 SURROGATE DATASETS PER PARTICIPANT
%load the same FFT parameters as above.
EpochLength = 9;
cd([CurrentDirectory 'FXS Project/infant pipeline/shuffled FFT files/']) %so that files go in proper place
NumIterations = 100;
for subjecti = 1:length(datafile_names)
clear EEGfrq_ByIteration
disp(subjecti)
%load dataset
SubjectName = datafile_names{subjecti}(1:4);
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_epoched.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/epochs/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
%for each 200 "actual onset" event code, get the "urevent" - in EGI, this
%is stored as EEG.event.description
%double check to make sure each epoch has the correct number of events
OnsetCodeStrings = {'NE'};
EventDescriptionMtx = [];
for x = 1:size(EEG.event,2) %epoch
if ismember( EEG.event(x).type, OnsetCodeStrings )
EventDescription = str2double(EEG.event(x).description);
EventDescriptionMtx = [EventDescriptionMtx, EventDescription];
end
end
EventDescriptionMtxUnique = unique(EventDescriptionMtx); %remove duplicates
%load preprocessed AB corrected continuous data
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_artifact corrected.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/ABCleaned/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
ParentEEG = EEG; %parentEEG will now be used for all iterations
%for each iteration
for iter = 1:NumIterations
disp(iter)
%for each UR Event identified above (corresponding to a 200 code), randomly select an onset from -1 to 1 seconds before or
%after to shuffle the timing while preserving the general sleep state
DatapointLength = 1000/EEG.srate;
%plus or minus 900 ms (word rate) from the true event
ShufflingWindowMin = -1*900/(DatapointLength); % -900;
ShufflingWindowMax = 900/(DatapointLength); %+900 datapoints (EEG.srate = 1000).
EventsToAdd = [];
for x = 1:size(ParentEEG.event,2) %number of events
if ismember( str2double(ParentEEG.event(x).description), EventDescriptionMtxUnique)
%randomly select an interval +/- 1.0 seconds
RandomInterval = ShufflingWindowMin + (ShufflingWindowMax-ShufflingWindowMin).*rand(1); %in data points
ShuffledOnsetLatency = ParentEEG.event(x).latency + RandomInterval; %in datapoints
EventsToAdd = [EventsToAdd; 300, ShuffledOnsetLatency];
end
end
%insert new events (300 codes) that are +/- 900 msec prior to the real onsets
EEG = pop_importevent(ParentEEG, 'event', EventsToAdd, 'fields', {'type', 'latency'}, 'append', 'yes', 'optimalign', 'off', 'timeunit', NaN ); %datapoints
EEG = eeg_checkset( EEG );
%epoch to all 300 codes.
EEG = pop_epoch( EEG, { '300'}, [0 EpochLength], 'epochinfo', 'yes'); %epoch all the selected events.
EEG = eeg_checkset( EEG );
EEG = pop_rmbase( EEG, [EEG.times(1) EEG.times(end)]); %baseline correct to the entire epoch (better for running ICA).
EEG = eeg_checkset( EEG );
%double check to make sure each epoch has the correct number of events
for x = 1:size(EEG.epoch,2) %epoch
NumSyllables = 0; %each epoch should have 30 or 31 syllables exactly (31 because the next syllable might just make it into the epoch)
for y = 1:size(EEG.epoch(x).event,2) %event within each epoch
if ismember( EEG.epoch(x).eventtype{y}, SyllableCodeStrings )
NumSyllables = NumSyllables + 1;
end
end
if NumSyllables <30|| NumSyllables >31
disp(['Epoch Error! Epoch to Remove:' num2str(x) ])
disp(['Epoch as only ' num2str(NumSyllables) ' syllable codes, which can happen at the end of a naturally occurring block.'])
else
disp('All epochs contain the expected number of syllable codes!')
end
end
%run FFT
clear EEGfrq
EEGfrq = frqa_plvpwr(EEG, 'foutput',foutput,'fband',fband,'binsSNR',binsSNR,...
'typeSNRPWR',typeSNRPWR,'typeSNRPLV',typeSNRPLV,...
'zscorePWR',zscorePWR,'zscorePLV',zscorePLV);
%for the first iteration ONLY, create the big structure
%this will be filled in eventually with all iterations
if iter == 1
EEGfrq_ByIteration.iteration(1:NumIterations) = EEGfrq; %in order to have a bigger structure with the same structure
disp('creating the big structure for all subsequent iterations...')
end
EEGfrq_ByIteration.iteration(iter) = EEGfrq;
end %end final iteration
% save the big structure
filename = char(strcat(SubjectName, '_EEGfreq_ByIteration'));
save(filename, 'EEGfrq_ByIteration'); %how to save when filename is a variable set beforehand
end
%% EXTRACT PLV FROM TRUE DATA AND SURROGATE DATA, AND PLOT
clear SubjectIDs ITCVars
clear FFTfile* Structured_PLVMtx
clear Structured_PLVMtx
plotchan= 1:104; %SigWordElectrodes; %1:105; % we removed the outer ring of electrodes
locsfile = [CurrentDirectory 'FXS Project/channel location file/EGI104InnerChannelsNoCz.locs']; %exported this .locs file one time from EEG dataset. writelocs( chanstruct, filename ); writelocs( EEG.chanlocs, 'testlocs' );
FFTfile_location = [CurrentDirectory 'FXS Project/infant pipeline/FFT files'];
cd(FFTfile_location)
FFTfiles = dir('*Structured_EEGfreq.mat'); %this only considers the files that were succesfully preprocessed and not those tagged with 'no usable data, all bad epochs.'
FFTfile_names={FFTfiles.name};
FFTsurrogatefile_location = [CurrentDirectory 'FXS Project/infant pipeline/shuffled FFT files'];
SubjectList = [];
GroupName = FXS; %ASD %TDC; %ASD; %FXS; %TDC; %FXS; %TDC; AllSubjects; %
%get subject list
for x = 1:length(FFTfile_names)
SubjectNumber = cell2mat(extractBetween(FFTfile_names{x}, 1, 4));
if ismember(SubjectNumber, GroupName)
SubjectList = [SubjectList, x];
end
end
% -- STRUCTURED --
clear PLVMtx Struct* Rand* Grand* PLV*
TruePLVMtx = [];
SurrogatePLVMtx = [];
PLVSurrogate = [];
for i = SubjectList
clear EEGfrq* SurrogateDataFileName SurrogateDatasetsMtx
display(i)
%load "true" data
cd(FFTfile_location)
load(FFTfile_names{i})
disp(FFTfile_names{i})
PLVTrue = EEGfrq.plv(plotchan,:); %only extracts relevant channels
TruePLVMtx(i,:,:) = PLVTrue; %rows without corresponding subjects (e.g., 20) just contains 0s. This is taken into account below by selecting SubjectsToInclude as a dimension.
%load corresponding surrogate data
cd(FFTsurrogatefile_location)
SurrogateDataFileName = [char(extractBetween(FFTfile_names{i}, 1,4)) '_EEGfreq_ByIteration.mat'];
load(SurrogateDataFileName)
for iterationnum = 1:size(EEGfrq_ByIteration.iteration,2)
SurrogateDatasetsMtx(iterationnum,:,:) = EEGfrq_ByIteration.iteration(iterationnum).plv(plotchan,:); %nchan x nfreqs for each surrogate dataset; ndataset x nchan x nfreq
end
PLVSurrogate = squeeze(mean(SurrogateDatasetsMtx,1)); %average across the 5 surrogate datasets for the subject
SurrogatePLVMtx(i,:,:) = PLVSurrogate ; %nchan x nfreqs
end
% -- to extract individual level variables --
freqvals = EEGfrq.freqs; %freqs based on last subject loaded
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
GrandAverageTruePLV = squeeze(mean(TruePLVMtx(SubjectList,:,:),1));
GrandAverageTruePLV_avacrosschans = mean(GrandAverageTruePLV(:,:),1);
GrandAverageSurrogatePLV = squeeze(mean(SurrogatePLVMtx(SubjectList,:,:),1));
GrandAverageSurrogatePLV_avacrosschans = mean(GrandAverageSurrogatePLV(:,:),1);
%Standard Error
TruePLV_averageacrosschans = squeeze(mean(TruePLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_TruePLV = std(TruePLV_averageacrosschans(:,:)); %nfreqs
SE_TruePLV = STD_TruePLV /sqrt(numel(SubjectList));
SurrogatePLV_averageacrosschans = squeeze(mean(SurrogatePLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_SurrogatePLV = std(SurrogatePLV_averageacrosschans(:,:)); %nfreqs
SE_SurrogatePLV = STD_SurrogatePLV /sqrt(numel(SubjectList));
NSubjects = numel(SubjectList);
%---- Plots ------
cd([CurrentDirectory 'FXS Project/infant pipeline/grand averages/'])
%truncated
freqvalindicesunder5Hz = find(freqvals <= 5);
figure; plot(freqvals(freqvalindicesunder5Hz), GrandAverageTruePLV_avacrosschans(freqvalindicesunder5Hz),'r');
hold on
%shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), 'lineprops','r') %old syntax for shadedErrorBar
shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAverageTruePLV_avacrosschans(freqvalindicesunder5Hz), SE_TruePLV(freqvalindicesunder5Hz), {'Color', 'r' }, 1);
hold on
plot(freqvals(freqvalindicesunder5Hz), GrandAverageSurrogatePLV_avacrosschans(freqvalindicesunder5Hz),'b');
hold on
shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAverageSurrogatePLV_avacrosschans(freqvalindicesunder5Hz), SE_SurrogatePLV(freqvalindicesunder5Hz), {'Color', 'b' }, 1);
hold on
xline(1.111, '--')
hold on
xline(2.222, '--')
hold on
xline(3.333, '--')
legend('structured', 'SE', 'surrogate', 'SE')
pdfname = ['ITC FFT True data versus Surrogate data, All Channels, n = ' num2str(NSubjects)];
%pdfname = ['ITC FFT Auditory ROI'];
print('-dpdf', pdfname); %save as postscript in order to merge two files
% plot distribution
WordFreqScaleLimits = [-0.25 0.25]; %[-0.15 .15]; %so that 0 is centered on green.
SyllableFreqScaleLimits = [-0.5 .5];
DifferenceEffectScaleLimits = [-0.05 0.05];
figure;
subplot(2,2,1)
topoplot(GrandAverageTruePLV(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('True Data, Word Frequency');
subplot(2,2,2)
topoplot(GrandAverageTruePLV(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('True Data, Syllable Frequency');
subplot(2,2,3)
topoplot(GrandAverageSurrogatePLV(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('Surrogate Data, Word Frequency');
subplot(2,2,4)
topoplot(GrandAverageSurrogatePLV(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('Surrogate Data, Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['Topographical Distributions, True versus Surrogate, n = ' num2str(NSubjects)];
print ('-dtiff', filename);
%difference topo
WordFreqDifference = GrandAverageTruePLV(:,WordFreq) - GrandAverageSurrogatePLV(:,WordFreq);
SyllableFreqDifference = GrandAverageTruePLV(:,SyllableFreq) - GrandAverageSurrogatePLV(:,SyllableFreq);
% figure;
% subplot(2,2,1)
% %topoplot(WordFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', DifferenceEffectScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
% topoplot(WordFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', DifferenceEffectScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp', 'emarker2', {SignificantElectrodes_Word ,'p','k', 10, 1}); colorbar;
% title('True minus Surrogate, Word Frequency');
% subplot(2,2,2)
% %topoplot(SyllableFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
% topoplot(SyllableFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp', 'emarker2', {SignificantElectrodes_Syllable,'p','k', 10, 1}); colorbar;
% title('True minus Surrogate, Syllable Frequency');
% set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
% filename = ['Topographical Distributions, True versus Surrogate, Difference, Sig Electrodes'];
% print ('-dtiff', filename);
figure;
subplot(2,2,1)
topoplot(WordFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', DifferenceEffectScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%topoplot(WordFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', DifferenceEffectScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp', 'emarker2', {SignificantElectrodes_Word ,'p','k', 10, 1}); colorbar;
title('True minus Surrogate, Word Frequency');
subplot(2,2,2)
topoplot(SyllableFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%topoplot(SyllableFreqDifference, locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp', 'emarker2', {SignificantElectrodes_Syllable,'p','k', 10, 1}); colorbar;
title('True minus Surrogate, Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
title('True minus Surrogate, Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['Topographical Distributions, True versus Surrogate, Difference, n = ' num2str(NSubjects)];
print ('-dtiff', filename);
%% EXTRACT PLV FROM TRUE DATA AND SURROGATE DATA, AND PLOT AS Z SCORES
clear SubjectIDs ITCVars
clear FFTfile* Structured_PLVMtx
clear Structured_PLVMtx
plotchan= 1:104; %SigWordElectrodes; %1:105; % we removed the outer ring of electrodes
locsfile = [CurrentDirectory 'FXS Project/channel location file/EGI104InnerChannelsNoCz.locs']; %exported this .locs file one time from EEG dataset. writelocs( chanstruct, filename ); writelocs( EEG.chanlocs, 'testlocs' );
FFTfile_location = [CurrentDirectory 'FXS Project/infant pipeline/FFT files'];
cd(FFTfile_location)
FFTfiles = dir('*Structured_EEGfreq.mat'); %this only considers the files that were succesfully preprocessed and not those tagged with 'no usable data, all bad epochs.'
FFTfile_names={FFTfiles.name};
FFTsurrogatefile_location = [CurrentDirectory 'FXS Project/infant pipeline/shuffled FFT files'];
SubjectList = [];
SubjectIDList = []; %for bookkeeping for later analyses
GroupName = ASD; %FXS; %TDC; %FXS; %TDC; AllSubjects; %
%get subject list
for x = 1:length(FFTfile_names)
SubjectNumber = cell2mat(extractBetween(FFTfile_names{x}, 1, 4));
if ismember(SubjectNumber, GroupName)
SubjectList = [SubjectList, x];
SubjectIDList = [SubjectIDList; str2double(SubjectNumber)];
end
end
% -- STRUCTURED --
clear PLVMtx Struct* Rand* Grand* PLV*
TruePLVMtx = [];
PLVSurrogate = [];
SurrogatePLVMtx = [];
zscoreTruePLVMtx = [];
for i = SubjectList
clear EEGfrq* SurrogateDataFileName SurrogateDatasetsMtx zscoreTruePLV
display(i)
%load "true" data
cd(FFTfile_location)
load(FFTfile_names{i})
disp(FFTfile_names{i})
PLVTrue = EEGfrq.plv(plotchan,:); %only extracts relevant channels
TruePLVMtx(i,:,:) = PLVTrue; %rows without corresponding subjects (e.g., 20) just contains 0s. This is taken into account below by selecting SubjectsToInclude as a dimension.
%load corresponding surrogate data
cd(FFTsurrogatefile_location)
SurrogateDataFileName = [char(extractBetween(FFTfile_names{i}, 1,4)) '_EEGfreq_ByIteration.mat'];
load(SurrogateDataFileName)
for iterationnum = 1:size(EEGfrq_ByIteration.iteration,2)
SurrogateDatasetsMtx(iterationnum,:,:) = EEGfrq_ByIteration.iteration(iterationnum).plv(plotchan,:); %nchan x nfreqs for each surrogate dataset; ndataset x nchan x nfreq
end
% Z score the data according to the surrogate distribution
% each subject serves as their own Z score distribution
Mean_Surrogate = squeeze(mean(SurrogateDatasetsMtx,1)); %average across the 100 surrogate datasets for the subject
STD_Surrogate = squeeze(std(SurrogateDatasetsMtx)); %std deviation
%compute z score
zscoreTruePLV = (PLVTrue - Mean_Surrogate) ./ STD_Surrogate;
SurrogatePLVMtx(i,:,:) = Mean_Surrogate; %stores the mean surrogate ITCs for each subject; nregion x nfreqs
zscoreTruePLVMtx(i,:,:) = zscoreTruePLV; %stores the z scored actual observed data for each subject, nregion x nfreqs
end
% -- to extract individual level variables --
freqvals = EEGfrq.freqs; %freqs based on last subject loaded
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
GrandAverageTruePLV = squeeze(mean(TruePLVMtx(SubjectList,:,:),1));
GrandAverageTruePLV_avacrosschans = mean(GrandAverageTruePLV(:,:),1);
GrandAverageSurrogatePLV = squeeze(mean(SurrogatePLVMtx(SubjectList,:,:),1));
GrandAverageSurrogatePLV_avacrosschans = mean(GrandAverageSurrogatePLV(:,:),1);
%Standard Error
TruePLV_averageacrosschans = squeeze(mean(TruePLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_TruePLV = std(TruePLV_averageacrosschans(:,:)); %nfreqs
SE_TruePLV = STD_TruePLV /sqrt(numel(SubjectList));
SurrogatePLV_averageacrosschans = squeeze(mean(SurrogatePLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_SurrogatePLV = std(SurrogatePLV_averageacrosschans(:,:)); %nfreqs
SE_SurrogatePLV = STD_SurrogatePLV /sqrt(numel(SubjectList));
%Z scores
GrandAverageZscore = squeeze(mean(zscoreTruePLVMtx(SubjectList,:,:),1)); %nelectrodes x nfreqs
GrandAverageZscore_avacrosselectrodes = mean(GrandAverageZscore,1); %across all electrodes
%standard error
ZScoreTruePLV_averageacrosschans = squeeze(mean(zscoreTruePLVMtx(SubjectList,:,:),2)); %now nsubj x nfreq
STD_ZScoreTruePLV = std(ZScoreTruePLV_averageacrosschans(:,:)); %nfreqs
SE_ZScoreTruePLV = STD_ZScoreTruePLV /sqrt(numel(SubjectList));
NSubjects = numel(SubjectList);
%---- Plots ------
cd([CurrentDirectory 'FXS Project/infant pipeline/grand averages/z scored grand averages/'])
%truncated
freqvalindicesunder5Hz = find(freqvals <= 5);
%this is the same plot as created in the above cell
% figure; plot(freqvals(freqvalindicesunder5Hz), GrandAverageTruePLV_avacrosschans(freqvalindicesunder5Hz),'r');
% hold on
% %shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), 'lineprops','r') %old syntax for shadedErrorBar
% shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAverageTruePLV_avacrosschans(freqvalindicesunder5Hz), SE_TruePLV(freqvalindicesunder5Hz), {'Color', 'r' }, 1);
% hold on
% plot(freqvals(freqvalindicesunder5Hz), GrandAverageSurrogatePLV_avacrosschans(freqvalindicesunder5Hz),'b');
% hold on
% shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAverageSurrogatePLV_avacrosschans(freqvalindicesunder5Hz), SE_SurrogatePLV(freqvalindicesunder5Hz), {'Color', 'b' }, 1);
% hold on
% xline(1.111, '--')
% hold on
% xline(2.222, '--')
% hold on
% xline(3.333, '--')
% legend('structured', 'SE', 'surrogate', 'SE')
% pdfname = ['ITC True data versus Surrogate data, All Channels, n = ' num2str(NSubjects)];
% %pdfname = ['ITC FFT Auditory ROI'];
% print('-dpdf', pdfname); %save as postscript in order to merge two files
% z score plot as sanity check
%truncated
%freqvalindicesunder5Hz = find(freqvals <= 5);
figure; plot(freqvals(freqvalindicesunder5Hz), GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz),'r');
hold on
%shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), 'lineprops','r') %old syntax for shadedErrorBar
shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz), SE_ZScoreTruePLV(freqvalindicesunder5Hz), {'Color', 'r' }, 1);
hold on
xline(1.111, '--')
hold on
xline(2.222, '--')
hold on
xline(3.333, '--')
yline(0, 'k-', 'LineWidth', 1)
ylabel('ZITC')
xlabel('Frequency (Hz)')
legend('ZITC', 'SE')
pdfname = ['Z scored ITC, All Channels, n = ' num2str(NSubjects)];
print('-dpdf', pdfname); %save as postscript in order to merge two files
% plot topographical distribution of z scores
WordFreqScaleLimits = [-1 1]; %[-0.15 .15]; %so that 0 is centered on green.
SyllableFreqScaleLimits = [-8 8];
DifferenceEffectScaleLimits = [-0.05 0.05];
figure;
subplot(1,2,1)
topoplot(GrandAverageZscore(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('ZITC, Word Frequency');
subplot(1,2,2)
topoplot(GrandAverageZscore(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
title('ZITC, Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'landscape', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['ZITC Topographical Distributions, n = ' num2str(NSubjects)];
print ('-dtiff', filename);
print ('-dpdf', filename);
%plot significaint electrodes that show significant entrainment at each frequency,
%use a one-sample t-test against 0
WordZITCByElectrodesandBySubject = zscoreTruePLVMtx(SubjectList, :, WordFreq);
SyllableZITCByElectrodesandBySubject = zscoreTruePLVMtx(SubjectList, :, SyllableFreq);
[SigWordElectrodesArray, PWord] = ttest(WordZITCByElectrodesandBySubject, 0, 'tail', 'right'); %one directional t-test, mean is greater than 0
[SigSyllableElectrodesArray, PSyllable] = ttest(SyllableZITCByElectrodesandBySubject, 0, 'tail', 'right'); %one directional t-test, mean is greater than 0
SigWordElectrodes = find(SigWordElectrodesArray ==1);
SigSyllableElectrodes = find(SigSyllableElectrodesArray ==1);
NumSigWordElectrodes = numel(SigWordElectrodes);
NumSigSyllableElectrodes = numel(SigSyllableElectrodes);
figure;
subplot(1,2,1)
topoplot(GrandAverageZscore(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits,'shading','interp', 'emarker2', {SigWordElectrodes ,'p','k', 7, 1} ); colorbar;
title(['Significant Word ITC Electrodes: ' num2str(NumSigWordElectrodes)]);
subplot(1,2,2)
topoplot(GrandAverageZscore(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits,'shading','interp', 'emarker2', {SigSyllableElectrodes ,'p','k', 7, 1} ); colorbar;
title(['Significant Syllable ITC Electrodes: ' num2str(NumSigSyllableElectrodes)]);
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['ZITC Topo, Sig Electrodes, n = ' num2str(NSubjects)];
print ('-dtiff', filename);
% extract individual level variables for statistical analysis
ZScoreITCWordFreqMtx = zscoreTruePLVMtx(SubjectList,:, WordFreq); %nsubj X nchan
ZScoreITCSyllableFreqMtx = zscoreTruePLVMtx(SubjectList,:, SyllableFreq);
%ZScoreWLI = (ZScoreITCWordFreqMtx ./ ZScoreITCSyllableFreqMtx); %no longer sure whether the WLI is that informative considering we are now already normalizing by the surrogate shuffled data for each participant and electrode
%variables based on channel configurations
allchans= 1:104;
ZScoreITCWordFreqMtx_allchans = mean(ZScoreITCWordFreqMtx(:,allchans),2);
ZScoreITCSyllableFreqMtx_allchans = mean(ZScoreITCSyllableFreqMtx(:,allchans),2);
%ZScoreWLI_allchans = mean(ZScoreWLI(:,allchans),2);
SPSSToCompare = [ZScoreITCWordFreqMtx_allchans, ZScoreITCSyllableFreqMtx_allchans];
CorrespondingSubjectIDs = SubjectIDList;
%can build the array one group at a time
%% GROUP COMPARISONS - COMPARING TWO GROUPS AT TIME - ZITC GRAND AVERAGE
Group1Name = TDC;
Group2Name = FXS;
% -- GROUP 1 ---
Group1SubjectList = [];
%get subject list
for x = 1:length(FFTfile_names)
SubjectNumber = cell2mat(extractBetween(FFTfile_names{x}, 1, 4));
if ismember(SubjectNumber, Group1Name)
Group1SubjectList = [Group1SubjectList, x];
end
end
% -- GROUP 2 ---
Group2SubjectList = [];
%get subject list
for x = 1:length(FFTfile_names)
SubjectNumber = cell2mat(extractBetween(FFTfile_names{x}, 1, 4));
if ismember(SubjectNumber, Group2Name)
Group2SubjectList = [Group2SubjectList, x];
end
end
% -- GROUP 1 --
clear PLVMtx Struct* Rand* Grand* PLV*
Group1TruePLVMtx = [];
Group1SurrogatePLVMtx = [];
Group1zscoreTruePLVMtx = [];
for i = Group1SubjectList
clear EEGfrq* SurrogateDataFileName SurrogateDatasetsMtx zscoreTruePLV
display(i)
%load "true" data
cd(FFTfile_location)
load(FFTfile_names{i})
disp(FFTfile_names{i})
PLVTrue = EEGfrq.plv(plotchan,:); %only extracts relevant channels
Group1TruePLVMtx(i,:,:) = PLVTrue; %rows without corresponding subjects (e.g., 20) just contains 0s. This is taken into account below by selecting SubjectsToInclude as a dimension.
%load corresponding surrogate data
cd(FFTsurrogatefile_location)
SurrogateDataFileName = [char(extractBetween(FFTfile_names{i}, 1,4)) '_EEGfreq_ByIteration.mat'];
load(SurrogateDataFileName)
for iterationnum = 1:size(EEGfrq_ByIteration.iteration,2)
SurrogateDatasetsMtx(iterationnum,:,:) = EEGfrq_ByIteration.iteration(iterationnum).plv(plotchan,:); %nchan x nfreqs for each surrogate dataset; ndataset x nchan x nfreq
end
% Z score the data according to the surrogate distribution
% each subject serves as their own Z score distribution
Mean_Surrogate = squeeze(mean(SurrogateDatasetsMtx,1)); %average across the 100 surrogate datasets for the subject
STD_Surrogate = squeeze(std(SurrogateDatasetsMtx)); %std deviation
%compute z score
zscoreTruePLV = (PLVTrue - Mean_Surrogate) ./ STD_Surrogate;
Group1SurrogatePLVMtx(i,:,:) = Mean_Surrogate; %stores the mean surrogate ITCs for each subject; nregion x nfreqs
Group1zscoreTruePLVMtx(i,:,:) = zscoreTruePLV; %stores the z scored actual observed data for each subject, nregion x nfreqs
end
% -- GROUP 2 --
clear PLVMtx Struct* Rand* Grand* PLV*
Group2TruePLVMtx = [];
Group2SurrogatePLVMtx = [];
Group2zscoreTruePLVMtx = [];
for i = Group2SubjectList
clear EEGfrq* SurrogateDataFileName SurrogateDatasetsMtx zscoreTruePLV
display(i)
%load "true" data
cd(FFTfile_location)
load(FFTfile_names{i})
disp(FFTfile_names{i})
PLVTrue = EEGfrq.plv(plotchan,:); %only extracts relevant channels
Group2TruePLVMtx(i,:,:) = PLVTrue; %rows without corresponding subjects (e.g., 20) just contains 0s. This is taken into account below by selecting SubjectsToInclude as a dimension.
%load corresponding surrogate data
cd(FFTsurrogatefile_location)
SurrogateDataFileName = [char(extractBetween(FFTfile_names{i}, 1,4)) '_EEGfreq_ByIteration.mat'];
load(SurrogateDataFileName)
for iterationnum = 1:size(EEGfrq_ByIteration.iteration,2)
SurrogateDatasetsMtx(iterationnum,:,:) = EEGfrq_ByIteration.iteration(iterationnum).plv(plotchan,:); %nchan x nfreqs for each surrogate dataset; ndataset x nchan x nfreq
end
% Z score the data according to the surrogate distribution
% each subject serves as their own Z score distribution
Mean_Surrogate = squeeze(mean(SurrogateDatasetsMtx,1)); %average across the 100 surrogate datasets for the subject
STD_Surrogate = squeeze(std(SurrogateDatasetsMtx)); %std deviation
%compute z score
zscoreTruePLV = (PLVTrue - Mean_Surrogate) ./ STD_Surrogate;
Group2SurrogatePLVMtx(i,:,:) = Mean_Surrogate; %stores the mean surrogate ITCs for each subject; nregion x nfreqs
Group2zscoreTruePLVMtx(i,:,:) = zscoreTruePLV; %stores the z scored actual observed data for each subject, nregion x nfreqs
end
% -- to extract individual level variables --
freqvals = EEGfrq.freqs; %freqs based on last subject loaded
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
% - Group 1 -
Group1GrandAverageTruePLV = squeeze(mean(Group1TruePLVMtx(Group1SubjectList,:,:),1));
Group1GrandAverageTruePLV_avacrosschans = mean(Group1GrandAverageTruePLV(:,:),1);
Group1GrandAverageSurrogatePLV = squeeze(mean(Group1SurrogatePLVMtx(Group1SubjectList,:,:),1));
Group1GrandAverageSurrogatePLV_avacrosschans = mean(Group1GrandAverageSurrogatePLV(:,:),1);
%Standard Error
Group1TruePLV_averageacrosschans = squeeze(mean(Group1TruePLVMtx(Group1SubjectList,:,:),2)); %now nsubj x nfreq
Group1STD_TruePLV = std(Group1TruePLV_averageacrosschans(:,:)); %nfreqs
Group1SE_TruePLV = Group1STD_TruePLV /sqrt(numel(Group1SubjectList));
Group1SurrogatePLV_averageacrosschans = squeeze(mean(Group1SurrogatePLVMtx(Group1SubjectList,:,:),2)); %now nsubj x nfreq
Group1STD_SurrogatePLV = std(Group1SurrogatePLV_averageacrosschans(:,:)); %nfreqs
Group1SE_SurrogatePLV = Group1STD_SurrogatePLV /sqrt(numel(Group1SubjectList));
%Z scores
Group1GrandAverageZscore = squeeze(mean(Group1zscoreTruePLVMtx(Group1SubjectList,:,:),1));
Group1GrandAverageZscore_avacrosselectrodes = mean(Group1GrandAverageZscore,1);
%standard error
Group1ZScoreTruePLV_averageacrosschans = squeeze(mean(Group1zscoreTruePLVMtx(Group1SubjectList,:,:),2)); %now nsubj x nfreq
Group1STD_ZScoreTruePLV = std(Group1ZScoreTruePLV_averageacrosschans(:,:)); %nfreqs
Group1SE_ZScoreTruePLV = Group1STD_ZScoreTruePLV /sqrt(numel(Group1SubjectList));
% - Group 2 -
Group2GrandAverageTruePLV = squeeze(mean(Group2TruePLVMtx(Group2SubjectList,:,:),1));
Group2GrandAverageTruePLV_avacrosschans = mean(Group2GrandAverageTruePLV(:,:),1);
Group2GrandAverageSurrogatePLV = squeeze(mean(Group2SurrogatePLVMtx(Group2SubjectList,:,:),1));
Group2GrandAverageSurrogatePLV_avacrosschans = mean(Group2GrandAverageSurrogatePLV(:,:),1);
%Standard Error
Group2TruePLV_averageacrosschans = squeeze(mean(Group2TruePLVMtx(Group2SubjectList,:,:),2)); %now nsubj x nfreq
Group2STD_TruePLV = std(Group2TruePLV_averageacrosschans(:,:)); %nfreqs
Group2SE_TruePLV = Group2STD_TruePLV /sqrt(numel(Group2SubjectList));
Group2SurrogatePLV_averageacrosschans = squeeze(mean(Group2SurrogatePLVMtx(Group2SubjectList,:,:),2)); %now nsubj x nfreq
Group2STD_SurrogatePLV = std(Group2SurrogatePLV_averageacrosschans(:,:)); %nfreqs
Group2SE_SurrogatePLV = Group2STD_SurrogatePLV /sqrt(numel(Group2SubjectList));
%Z scores
Group2GrandAverageZscore = squeeze(mean(Group2zscoreTruePLVMtx(Group2SubjectList,:,:),1));
Group2GrandAverageZscore_avacrosselectrodes = mean(Group2GrandAverageZscore,1);
%standard error
Group2ZScoreTruePLV_averageacrosschans = squeeze(mean(Group2zscoreTruePLVMtx(Group2SubjectList,:,:),2)); %now nsubj x nfreq
Group2STD_ZScoreTruePLV = std(Group2ZScoreTruePLV_averageacrosschans(:,:)); %nfreqs
Group2SE_ZScoreTruePLV = Group2STD_ZScoreTruePLV /sqrt(numel(Group2SubjectList));
NSubjectsGroup1 = numel(Group1SubjectList);
NSubjectsGroup2 = numel(Group2SubjectList);
%---- Plots ------
cd([CurrentDirectory 'FXS Project/infant pipeline/grand averages/z scored grand averages/'])
%truncated
%freqvalindicesunder5Hz = find(freqvals <= 5);
freqvalindicesunder5Hz = find(freqvals <= 4);
%z score plot
figure; h1 = plot(freqvals(freqvalindicesunder5Hz), Group1GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz),'k', 'LineWidth', 2);
hold on
%shadedErrorBar(freqvals(freqvalindicesunder5Hz),GrandAveragePLV_avacrosschans_Structured(freqvalindicesunder5Hz), SE_Structured(freqvalindicesunder5Hz), 'lineprops','r') %old syntax for shadedErrorBar
h = shadedErrorBar(freqvals(freqvalindicesunder5Hz),Group1GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz), Group1SE_ZScoreTruePLV(freqvalindicesunder5Hz), {'Color', 'k' }, 1);
hold on
h2 = plot(freqvals(freqvalindicesunder5Hz), Group2GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz),'r', 'LineWidth', 2);
hold on
h = shadedErrorBar(freqvals(freqvalindicesunder5Hz),Group2GrandAverageZscore_avacrosselectrodes(freqvalindicesunder5Hz), Group2SE_ZScoreTruePLV(freqvalindicesunder5Hz), {'Color', 'r' }, 1);
hold on
xline(1.111, '--')
hold on
xline(2.222, '--')
hold on
xline(3.333, '--')
yline(0, 'k-', 'LineWidth', 1)
ylabel('ZITC')
xlabel('Frequency (Hz)')
%legend([h1, h2], {'TDC', 'FXS'})
set(gca, 'FontSize', 14);
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'portrait', 'PaperPosition', [0.5 0.5 8.0 10.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
figurename = ['Z scored ITC, Two Group Comparison, All Channels, No Legend, nGroup1 = ' num2str(NSubjectsGroup1), ' nGroup2 = ' num2str(NSubjectsGroup2) ];
exportgraphics(gcf, [figurename '.pdf'], 'ContentType','vector'); %this should import as a vector now
%print('-dpdf', figurename); %save as postscript in order to merge two files
%print('-dtiff', figurename); %save as postscript in order to merge two files
% plot topographical distribution of z scores
WordFreqScaleLimits = [-1 1]; %[-0.15 .15]; %so that 0 is centered on green.
SyllableFreqScaleLimits = [-8 8];
DifferenceEffectScaleLimits = [-0.05 0.05];
figure;
subplot(2,2,1)
topoplot(Group1GrandAverageZscore(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%title('TDC Word Frequency');
subplot(2,2,2)
topoplot(Group1GrandAverageZscore(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%title('TDC Syllable Frequency');
subplot(2,2,3)
topoplot(Group2GrandAverageZscore(:,WordFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', WordFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%title('FXS Word Frequency');
subplot(2,2,4)
topoplot(Group2GrandAverageZscore(:,SyllableFreq), locsfile, 'style', 'map', 'colormap', jet, 'maplimits', SyllableFreqScaleLimits, 'plotrad',.5,'headrad',.5,'shading','interp'); colorbar;
%title('FXS Syllable Frequency');
set(gcf,'PaperPositionMode','manual', 'PaperOrientation', 'landscape', 'PaperPosition', [0.05 0.15 12.0 8.50]) %corresponds to settings on Print Preview. %good measurements for not cutting off title.
filename = ['ZITC Group Comparison Topographical Distributions, no titles, nGroup1 = ' num2str(NSubjectsGroup1), ' nGroup2 = ' num2str(NSubjectsGroup2)];
exportgraphics(gcf, filename + ".pdf", 'ContentType','vector');
%print ('-dtiff', filename);
%print ('-dpdf', filename);
%% JACKKNIFE PROCEDURE TO ESTIMATE ITPC RELIABILITY
%load the same FFT parameters as above. (LOAD PARAMETERS cell)
ParticipantLevelData = [];
PseudovalsByParticipantByEpoch = []; %timecourse data
subjectcounter = 0;
for subji = AllSubjectIndices %length(datafile_names)
SubjectName = datafile_names{subji}(1:4); %string
SubjectNumber = str2double(SubjectName); %number
disp(subji)
disp(SubjectName)
subjectcounter = subjectcounter + 1;
%figure out GroupID
clear GroupID
if any(strcmp(SubjectName, TDC))
GroupID = 1;
elseif any(strcmp(SubjectName, FXS))
GroupID = 2;
elseif any(strcmp(SubjectName, ASD))
GroupID = 3;
else
GroupID = 0; %unclassified, but at least it won't disrupt the loop
end
%load dataset
clear EEGfrq
STUDY = []; CURRENTSTUDY = 0; ALLEEG = []; EEG=[]; CURRENTSET=[];
EEG = pop_loadset( 'filename', [SubjectName '_epoched.set'], 'filepath', [CurrentDirectory 'FXS project/infant pipeline/epochs/']);
[ALLEEG, EEG, CURRENTSET] = eeg_store( ALLEEG, EEG );
ParentEEG = EEG;
NumTrials = EEG.trials;
FullTrialArray = 1:NumTrials;
NumberofChannels = size(EEG.chanlocs,2);
% ------
%create temporarily subdivided datasets and run the function on each dataset
%first create the big frq structure
EEGfrq = frqa_plvpwr(EEG, 'foutput',foutput,'fband',fband,'binsSNR',binsSNR,...
'typeSNRPWR',typeSNRPWR,'typeSNRPLV',typeSNRPLV,...
'zscorePWR',zscorePWR,'zscorePLV',zscorePLV);
freqvals = EEGfrq.freqs; %freqs based on last subject loaded
[~, WordFreq] = min(abs(freqvals-1.1111));
[~, SyllableFreq] = min(abs(freqvals-3.3333)); %syllable freq = 3.33 Hz
%for normalization, extract neighbouring bins excluding the word,
%harmonics, and syllable frequencies.
[~, Harmonic1Freq] = min(abs(freqvals-2.22222)); %syllable freq = 3.33 Hz
[~, Harmonic2Freq] = min(abs(freqvals-4.44444)); %syllable freq = 3.33 Hz
NormalizationFreqBins = setdiff(find(freqvals <= 5), [WordFreq, SyllableFreq, Harmonic1Freq, Harmonic2Freq]);
%compute ITC
AllTrialsWordITC = EEGfrq.plv(:,WordFreq);
AllTrialsSyllableITC = EEGfrq.plv(:,SyllableFreq);
AllTrialsNormalizedBinsITC = squeeze(mean(EEGfrq.plv(:, NormalizationFreqBins ),2));
AllTrialsWordITCNormalized = AllTrialsWordITC - AllTrialsNormalizedBinsITC;
AllTrialsSyllableITCNormalized = AllTrialsSyllableITC - AllTrialsNormalizedBinsITC;
EEGfrq_jackknifed.replication(1:NumTrials) = EEGfrq; %preallocate - delete extra rows later - in order to have a bigger structure with the same structure
WordITCJackknifed_ByReplication= [];
SyllableITCJackknifed_ByReplication = [];
WordITCNormalizedJackknifed_ByReplication = [];
SyllableITCNormalizedJackknifed_ByReplication = [];
for replication = 1:NumTrials
disp(['Replication Number :' num2str(replication)])
%compute what ITC would be without that trial
TrialArrayLessOneTrial = setdiff(FullTrialArray, replication); %removes N trial
EEGLessOneTrial = pop_select( ParentEEG, 'trial', TrialArrayLessOneTrial);
%run command on subsampled trials
EEGfrqLessOneTrial = frqa_plvpwr(EEGLessOneTrial, 'foutput',foutput,'fband',fband,'binsSNR',binsSNR,...
'typeSNRPWR',typeSNRPWR,'typeSNRPLV',typeSNRPLV,...
'zscorePWR',zscorePWR,'zscorePLV',zscorePLV);
EEGfrq_jackknifed.replication(replication) = EEGfrqLessOneTrial;
%compute individual actual and normalized ITC values to store for analysis
WordITCJackknifed = EEGfrqLessOneTrial.plv(:,WordFreq);
SyllableITCJackknifed = EEGfrqLessOneTrial.plv(:,SyllableFreq);
OtherFreqsITCJackknifed = squeeze(mean(EEGfrqLessOneTrial.plv(:, NormalizationFreqBins ),2));
WordITCNormalizedJackknifed = WordITCJackknifed - OtherFreqsITCJackknifed;
SyllableITCNormalizedJackknifed = SyllableITCJackknifed - OtherFreqsITCJackknifed;
%store
WordITCJackknifed_ByReplication(replication,:) = WordITCJackknifed; %Nreplications x NElectrodes
SyllableITCJackknifed_ByReplication(replication,:) = SyllableITCJackknifed; %Nreplications x NElectrodes
WordITCNormalizedJackknifed_ByReplication(replication,:) = WordITCNormalizedJackknifed; %Nreplications x NElectrodes
SyllableITCNormalizedJackknifed_ByReplication(replication,:) = SyllableITCNormalizedJackknifed; %Nreplications x NElectrodes
clear EEGfrqLessOneTrial
end
%save the participant's EEG freq matrix
cd([CurrentDirectory 'FXS Project/infant pipeline/jackknifed FFT files/'])
disp(SubjectNumber)
%filename = strcat(num2str(SubjectNumber), '_EEGfrq_jackknifed');
filename = [SubjectName '_EEGfrq_jackknifed'];
save(filename, 'EEGfrq_jackknifed');
%average steps to save in participant matrix
%average across channels
WordITCJackknifed_ByReplication_meanacrosschans = mean(WordITCJackknifed_ByReplication,2);
SyllableITCJackknifed_ByReplication_meanacrosschans = mean(SyllableITCJackknifed_ByReplication,2);
WordITCNormalizedJackknifed_ByReplication_meanacrosschans = mean(WordITCNormalizedJackknifed_ByReplication,2);
SyllableITCNormalizedJackknifed_ByReplication_meanacrosschans = mean(SyllableITCNormalizedJackknifed_ByReplication,2);
%average across replications
MeanWordITCJackknifed_ByReplication_meanacrosschans = mean(WordITCJackknifed_ByReplication_meanacrosschans);
MeanSyllableITCJackknifed_ByReplication_meanacrosschans = mean(SyllableITCJackknifed_ByReplication_meanacrosschans);
MeanWordITCNormJackknifed_ByReplication_meanacrosschans = mean( WordITCNormalizedJackknifed_ByReplication_meanacrosschans);
MeanSyllableITCNormJackknifed_ByReplication_meanacrosschans = mean(SyllableITCNormalizedJackknifed_ByReplication_meanacrosschans);
%original ITC values as computed across all trials (from above), average across channels
AllTrialsWordITC_meanacrosschans = mean(AllTrialsWordITC,1);
AllTrialsSyllableITC_meanacrosschans = mean(AllTrialsSyllableITC,1);
AllTrialsWordITCNormalized_meanacrosschans = mean(AllTrialsWordITCNormalized,1);
AllTrialsSyllableITCNormalized_meanacrosschans = mean(AllTrialsSyllableITCNormalized,1);
%jackknife pseudovals - a pseudo-value is calculated as the difference
%between the whole sample estimate and the partial estimate. This tells
%you whether each individual trial contributes to lowering or increasing the ITC and by how much. Perfectly inversely correlated with jackknifed
%ITC values (rest of the sample ITC). So a higher pseudoval would
%indicate that the contribution of that specific trial INCREASES the
%overall ITC (such that when that trial is removed, the remaining trial
%ITC is lower). Formula from https://www.mattcraddock.com/blog/2020/09/07/jackknife-intertrial-coherence-and-phase/
WordITCPseudovals_ByReplication_meanacrosschans = NumTrials*AllTrialsWordITC_meanacrosschans - ((NumTrials-1) *WordITCJackknifed_ByReplication_meanacrosschans); %Ntrial
SyllableITCPseudovals_ByReplication_meanacrosschans = NumTrials*AllTrialsSyllableITC_meanacrosschans - ((NumTrials-1) *SyllableITCJackknifed_ByReplication_meanacrosschans);
WordITCNormalizedPseudovals_ByReplication_meanacrosschans = NumTrials*AllTrialsWordITCNormalized_meanacrosschans - ((NumTrials-1) *WordITCNormalizedJackknifed_ByReplication_meanacrosschans); %Ntrial
SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans = NumTrials*AllTrialsSyllableITCNormalized_meanacrosschans - ((NumTrials-1) *SyllableITCNormalizedJackknifed_ByReplication_meanacrosschans);
%exact values to store in participant level matrices
%mean of pseudovalues can be viewed as a bias reduced version of the original statistic
MeanWordITCPseudovals_ByReplication_meanacrosschans = mean(WordITCPseudovals_ByReplication_meanacrosschans);
MeanSyllableITCPseudovals_ByReplication_meanacrosschans = mean(SyllableITCPseudovals_ByReplication_meanacrosschans);
MeanWordITCNormalPseudovals_ByReplication_meanacrosschans = mean(WordITCNormalizedPseudovals_ByReplication_meanacrosschans);
MeanSyllableITCNormalPseudovals_ByReplication_meanacrosschans = mean(SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans);
%standard deviation of pseudovalues
StDevWordITCPseudovals_ByReplication_meanacrosschans = std(WordITCPseudovals_ByReplication_meanacrosschans);
StDevSyllableITPseudovals_ByReplication_meanacrosschans = std(SyllableITCPseudovals_ByReplication_meanacrosschans);
StDevWordITCNormalPseudovals_ByReplication_meanacrosschans = std(WordITCNormalizedPseudovals_ByReplication_meanacrosschans);
StDevSyllableITCNormalPseudovals_ByReplication_meanacrosschans = std(SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans);
%take the highest 5 and 10 pseudovalues:
%5
Max5WordITCPseudovals_ByReplication_meanacrosschans = mean(maxk(WordITCPseudovals_ByReplication_meanacrosschans, 5));
Max5SyllableITPseudovals_ByReplication_meanacrosschans = mean(maxk(SyllableITCPseudovals_ByReplication_meanacrosschans, 5));
Max5WordITCNormalPseudovals_ByReplication_meanacrosschans = mean(maxk(WordITCNormalizedPseudovals_ByReplication_meanacrosschans, 5));
Max5SyllableITCNormalPseudovals_ByReplication_meanacrosschans = mean(maxk(SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans, 5));
%10th
Max10WordITCPseudovals_ByReplication_meanacrosschans = mean(maxk(WordITCPseudovals_ByReplication_meanacrosschans, 10));
Max10SyllableITPseudovals_ByReplication_meanacrosschans = mean(maxk(SyllableITCPseudovals_ByReplication_meanacrosschans, 10));
Max10WordITCNormalPseudovals_ByReplication_meanacrosschans = mean(maxk(WordITCNormalizedPseudovals_ByReplication_meanacrosschans, 10));
Max10SyllableITCNormalPseudovals_ByReplication_meanacrosschans = mean(maxk(SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans, 10));
%Trial indices associated with >0 Positive Pseudovalues
%positive pseudovalues means that the trial was contributing positively to the overall
%ITC estimate; in other words, when that trial is removed, ITC across
%the remaining trials DECREASES.
%A negative pseudovalue means that the trial was disrupting or reducing
%phase coherence potentially due to noise, phase jitter, or phase-locking at a different angle. In other words, removing that trial INCREASES ITPC
%this could give insight to the temporal trajecotry of participants' learning.
PositiveContributingTrialsToWordITC= find(WordITCPseudovals_ByReplication_meanacrosschans >0);
PositiveContributingTrialsToSyllableITC = find(SyllableITCPseudovals_ByReplication_meanacrosschans >0);
PositiveContributingTrialsToWordITCNormalized= find(WordITCNormalizedPseudovals_ByReplication_meanacrosschans >0);
PositiveContributingTrialsToSyllableITCNormalized= find(SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans >0);
%summarize the total number of positive contributing trials - perhaps
%this is a proxy of participants' overall time spent parsing or
%processing the word structures
NumberPositiveContributingTrialsToWordITC = numel(PositiveContributingTrialsToWordITC);
NumberPositiveContributingTrialsToSyllableITC = numel(PositiveContributingTrialsToSyllableITC);
NumberPositiveContributingTrialsToWordITCNormalized = numel(PositiveContributingTrialsToWordITCNormalized);
NumberPositiveContributingTrialsToSyllableITCNormalized = numel(PositiveContributingTrialsToSyllableITCNormalized);
ParticipantLevelData(subjectcounter,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:,:) = [SubjectNumber, GroupID, AllTrialsWordITC_meanacrosschans, AllTrialsSyllableITC_meanacrosschans, ...
AllTrialsWordITCNormalized_meanacrosschans, AllTrialsSyllableITCNormalized_meanacrosschans, MeanWordITCPseudovals_ByReplication_meanacrosschans, MeanSyllableITCPseudovals_ByReplication_meanacrosschans , ...
MeanWordITCNormalPseudovals_ByReplication_meanacrosschans, MeanSyllableITCNormalPseudovals_ByReplication_meanacrosschans, ...
Max5WordITCPseudovals_ByReplication_meanacrosschans, Max5SyllableITPseudovals_ByReplication_meanacrosschans, ...
Max5WordITCNormalPseudovals_ByReplication_meanacrosschans, Max5SyllableITCNormalPseudovals_ByReplication_meanacrosschans, Max10WordITCPseudovals_ByReplication_meanacrosschans, Max10SyllableITPseudovals_ByReplication_meanacrosschans, ...
Max10WordITCNormalPseudovals_ByReplication_meanacrosschans, Max10SyllableITCNormalPseudovals_ByReplication_meanacrosschans, ...
NumberPositiveContributingTrialsToWordITC, NumberPositiveContributingTrialsToSyllableITC, NumberPositiveContributingTrialsToWordITCNormalized, ...
NumberPositiveContributingTrialsToSyllableITCNormalized];
%now we will save the pseudovals to enable time course analysis:
TrialArrayToUse = [1:NumTrials]';
PseudovalArrays = [WordITCPseudovals_ByReplication_meanacrosschans, SyllableITCPseudovals_ByReplication_meanacrosschans, ...
WordITCNormalizedPseudovals_ByReplication_meanacrosschans, SyllableITCNormalizedPseudovals_ByReplication_meanacrosschans];
SubjectIDRepArray = repmat(SubjectNumber, size(TrialArrayToUse));
GroupIDRepArray = repmat(GroupID, size(TrialArrayToUse));
CurrentSubjectData = [SubjectIDRepArray, GroupIDRepArray, TrialArrayToUse, PseudovalArrays];
PseudovalsByParticipantByEpoch = vertcat( PseudovalsByParticipantByEpoch , CurrentSubjectData);
end
cd([CurrentDirectory 'FXS Project/infant pipeline/jackknifed FFT files/summary tables']) %so that files go in proper place
%after all participants have run, create and save as a table
%save as table
ColumnNames = {'Subject', 'Group', 'AllTrialsWordITC', 'AllTrialsSyllableITC', 'AllTrialsWordITCNormalized', 'AllTrialsSyllableITCNormalized', ...
'MeanWordITCPseudovals', 'MeanSyllableITCPseudovals', 'MeanWordITCNormalPseudovals_ByReplication_meanacrosschans', 'MeanSyllableITCNormalPseudovals_ByReplication_meanacrosschans', ...
'Max5WordITCPseudovals', 'Max5SyllableITPseudovals', 'Max5WordITCNormPseudovals', 'Max5SyllableITCNormPseudovals', 'Max10WordITCPseudovals', 'Max10SyllableITPseudovals', ...
'Max10WordITCNormalPseudovals', 'Max10SyllableITCNormalPseudovals', 'NumberPosContributingTrialsToWordITC', 'NumberPosContributingTrialsToSyllableITC', ...
'NumberPosContributingTrialsToWordITCNorm' 'NumberPosContributingTrialsToSyllableITCNorm'};
ParticipantDataTableForAnalysis = array2table(ParticipantLevelData(1:end,:), 'VariableNames', ColumnNames);
writetable(ParticipantDataTableForAnalysis,'ParticipantDataTableForAnalysis.csv')
%Table 2 - timecourse data
ColumnNames2 = {'Subject', 'Group', 'EpochNum', 'WordITCPseudoval', 'SyllableITCPseudoval', 'WordITCNormPseudoval', 'SyllableITCNormPseudoval'};
PseudovalTimecourseTableForAnalysis = array2table(PseudovalsByParticipantByEpoch, 'VariableNames', ColumnNames2);
writetable(PseudovalTimecourseTableForAnalysis,'PseudovalTimecourseTableForAnalysis.csv')

MainITCScript.m, under CC-BY-4.0 · at the source

OverviewAuthors: Laura J Batterink1, Yanchen Liu2, Grace Westerkamp2, Jae Citarella2, Peyton Siekierski2,3, Lynxie Voorhees2, Lauren E Ethridge4,5, Elizabeth Smith6, Rana Elmaghraby2,3, Craig A Erickson2,3, Zag ElSayed7, Anubhuti Goel8,9, Steve W Wu10,11, Ernest V Pedapati2,3,10,11
ORCID iDs: Laura J Batterink, Yanchen Liu, Grace Westerkamp, Peyton Siekierski, Lauren E Ethridge, Elizabeth Smith, Zag ElSayed, Steve W Wu, Ernest V Pedapati
Department of Psychology; Western Centre for Brain and Mind, Western Institute for Neuroscience, University of Western Ontario, London, Ontario, Canada
Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
Department of Pediatrics, Section on Developmental and Behavioral Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA
Department of Psychology, University of Oklahoma, Norman, Oklahoma, USA
Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
School of Information Technology, University of Cincinnati, Ohio, USA
Neuroscience Graduate Program, University of California, Riverside, Riverside, California, USA
Department of Psychology, University of California, Riverside, Riverside, California, USA
Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
Institutions: Western University (Canada); Cincinnati Children's Hospital Medical Center (United States); University of Cincinnati (United States); University of Oklahoma Health Sciences Center (United States); University of Oklahoma (United States); University of California, Riverside (United States)
Journal: Autism research : official journal of the International Society for Autism Research, volume 19, issue 8, article e70312
Dates: received 29 October 2025; accepted 3 July 2026; published online 12 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/aur.70312 · PMID 42437723 · PMCID PMC13472007 · OpenAlex W4415071967
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.09.681399
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), autism (population), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: auditory processing, EEG, electroencephalography, fragile X syndrome, language development, neural entrainment, statistical learning
MeSH: Fragile X Syndrome*, Learning Disabilities*, Speech*, Speech Perception*, Adolescent, Auditory Cortex, Child, Electroencephalography, Female, Humans, Language Development Disorders, Male, Temporal Lobe (* major topic)
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Eunice Kennedy Shriver National Institute of Child Health and Human Development (5R01HD108222-03, 5R01HD108222‐03); NICHD NIH HHS (R01 HD108222)
Citations: not cited yet (Europe PMC); 105 references in the paper
AbstractThe abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Zenodo 18746668

License: CC-BY-4.0

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 3 files

Software Heritage: not checked

Found in: “Data Availability Statement”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (3 files), EEGLAB (1 file), ERPLAB (1 file), shadedErrorBar (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

5 files

statistical_learning_analysis_scripts.zip/MainITCScript.m, MATLAB, 1,661 lines, 5 matches
statistical_learning_analysis_scripts.zip/frqa_norm.m, MATLAB, 313 lines
statistical_learning_analysis_scripts.zip/frqa_plvpwr.m, MATLAB, 192 lines
statistical_learning_analysis_scripts.zip/frqa_plvpwr_nanmedian.m, MATLAB, 192 lines
statistical_learning_analysis_scripts.zip/frqa_powernorm.m, MATLAB, 275 lines

At the source: doi.org/10.5281/zenodo.18746668

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

Datasets cited

zenodo:17435554, at Zenodo; found in “Data Availability Statement”

Code and data availability statement

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it points to a dataset: Zenodo 17435554
it points to the authors' code: Zenodo 18746668

Read it in the paper: doi.org/10.1002/aur.70312.

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Version 2, 28 September 2026

Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 7 keywords, 13 MeSH terms, 2 funders, 104 references.

Cite

This paper

Batterink, L. J., Liu, Y., Westerkamp, G., Citarella, J., Siekierski, P., Voorhees, L., Ethridge, L. E., Smith, E., Elmaghraby, R., Erickson, C. A., ElSayed, Z., Goel, A., Wu, S. W., & Pedapati, E. V. (2026). Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit. Autism research : official journal of the International Society for Autism Research, 19(8), e70312. https://doi.org/10.1002/aur.70312

BibTeX

@article{batterink2026aberrant, author = {Batterink, Laura J and Liu, Yanchen and Westerkamp, Grace and Citarella, Jae and Siekierski, Peyton and Voorhees, Lynxie and Ethridge, Lauren E and Smith, Elizabeth and Elmaghraby, Rana and Erickson, Craig A and ElSayed, Zag and Goel, Anubhuti and Wu, Steve W and Pedapati, Ernest V}, title = {{Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit}}, journal = {Autism research : official journal of the International Society for Autism Research}, year = {2026}, month = jul, volume = {19}, number = {8}, pages = {e70312}, publisher = {Wiley}, issn = {1939-3792}, doi = {10.1002/aur.70312}, url = {https://doi.org/10.1002/aur.70312}, pmid = {42437723}, pmcid = {PMC13472007} }

RIS

TY - JOUR AU - Batterink, Laura J AU - Liu, Yanchen AU - Westerkamp, Grace AU - Citarella, Jae AU - Siekierski, Peyton AU - Voorhees, Lynxie AU - Ethridge, Lauren E AU - Smith, Elizabeth AU - Elmaghraby, Rana AU - Erickson, Craig A AU - ElSayed, Zag AU - Goel, Anubhuti AU - Wu, Steve W AU - Pedapati, Ernest V TI - Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit T2 - Autism research : official journal of the International Society for Autism Research J2 - Autism Res PY - 2026 DA - 2026/07/12 VL - 19 IS - 8 SP - e70312 SN - 1939-3792 PB - Wiley DO - 10.1002/aur.70312 UR - https://doi.org/10.1002/aur.70312 LA - en ER -

CSL-JSON

{ "id": "10.1002/aur.70312", "type": "article-journal", "title": "Aberrant Neural Entrainment to Word-Level Speech Patterns in Fragile X Syndrome: Evidence for a Statistical Learning Deficit", "container-title": "Autism research : official journal of the International Society for Autism Research", "author": [ { "family": "Batterink", "given": "Laura J" }, { "family": "Liu", "given": "Yanchen" }, { "family": "Westerkamp", "given": "Grace" }, { "family": "Citarella", "given": "Jae" }, { "family": "Siekierski", "given": "Peyton" }, { "family": "Voorhees", "given": "Lynxie" }, { "family": "Ethridge", "given": "Lauren E" }, { "family": "Smith", "given": "Elizabeth" }, { "family": "Elmaghraby", "given": "Rana" }, { "family": "Erickson", "given": "Craig A" }, { "family": "ElSayed", "given": "Zag" }, { "family": "Goel", "given": "Anubhuti" }, { "family": "Wu", "given": "Steve W" }, { "family": "Pedapati", "given": "Ernest V" } ], "container-title-short": "Autism Res", "volume": "19", "issue": "8", "page": "e70312", "DOI": "10.1002/aur.70312", "PMID": "42437723", "PMCID": "PMC13472007", "ISSN": "1939-3792", "publisher": "Wiley", "URL": "https://doi.org/10.1002/aur.70312", "language": "en", "issued": { "date-parts": [ [ 2026, 7, 12 ] ] } }

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