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Trait anxiety is associated with reduced reward-related replay at rest

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › EEG analysis › Representational similarity analysis (RSA) ↔ RSA_code.zip/step01_RAS_main_code.m, lines 1–76 · score 0.85 · evoked signals, pre processed, design matrix, functional localiser, RSA, FL
  2. [2] § Methods › EEG analysis › Neural decoding analysis ↔ RSA_code.zip/step01_RAS_main_code.m, lines 116–254 · score 0.65 · sensor patterns, spatial correlation, intercept, sensitivity, vector, onset
  3. [3] § Results › Neural decoding ↔ RSA_code.zip/step01_RAS_main_code.m, lines 1–76 · score 0.61 · evoked response, functional localiser, scanning, multivariate, models, predicted
  4. [4] § Methods › EEG data acquisition › Data acquisition and preprocessing ↔ RSA_code.zip/step01_RAS_main_code.m, lines 116–254 · score 0.55 · EEG channel, epochs, noise, weighted, MEG, onset

Paper

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The authors' code

MATLAB · 334 lines · 15 KB · CC-BY-4.0 · 4 matches

  1. % RSA analysis (one GLM per sensor, where numObservations = numTrials and numPredictors = numTrialTypes)
  3. % RSA on the evoked signal of the functional localiser
  4. %
  5. % 1.Load in the epoched MEG data for each FL trial. Pre-processes the MEG sensor data prior to GLM
  6. % 2.Define the condition(stimulus)-specific sensor responses (mass univariate analysis on each sensor and timeSlice independently)
  7. % For each subject, sensor and timeSlice
  8. % 1.Dependent variable = trial*1 vector of the specific sensor*timeSlice value for each trial.
  9. % 1.Zscore the dependent variable pre-GLM over trials for each channel and timeBin separately. (simply rescaling the values across trials,
  10. % allows comparable beta value magnitudes and centring acorss the differnet independent GLMs (doen seaperately for sensorys and time bins)
  11. % 2.Independent variables = design matrix of trial condition ID
  12. % 3.Optional multivariate normalisation of the condition-specific sensor responses (1st level betas)
  13. % (pre-whietening of the sensor*condition betas by the multivatiate variance-covariance structure of the noise present in the sensors [which requires shrinkage if using all sensors]).
  14. % Diedrichsen and Kriegeskorte et al 2017, Walther et al 2016
  15. % 4.Choose a distance metric (Euclidian/Mahalanobis, or 1-Pearson's [corr is default])
  16. % 1.For each subject and time-slice a neural RDM, capturing the representational distance between the evoked responses of any two conditions
  17. % 2.Difference neural RDM (from 1st to second FL) captures the change in geometry from start to end of scan. Can additionally smooth the neural RM over time here, before inference.
  18. % 3.The difference RDM can be compared to a model RDM for position and sequence code using a regression analysis
  19. %
  20. % Matthew Nour, London, Dec 2019
  22. clear all;
  23. clc;
  24. close all;
  25. %restoredefaultpath;
  27. [~, pInds] = uperms([1:8],1001);
  28. uniquePerms=pInds;
  30. localiser_names = {'stimFL', 'posFL'}; % change from sFL to pFL
  31. numLocalisers = length(localiser_names);
  32. numberStates = 8;
  33. numberTimePoints = 85;%
  34. %SpecifySubjects
  35. numberSubjects = 68;
  36. numEEGChan = 64;%EEG
  37. eeg_channels = 1:numEEGChan;
  39. % Some options
  40. baseline_correct = 0; % demean the data for each channel and trial by the pre-stimulus baseline %%done
  41. multivariate_normalisation = 1; % whiten the noise structre of the condition-patterns (sensor*condition betas) before calculating the distance metric
  42. shrink_noise = 1; % Ledoit and Wolf, shirnkage of sample covariance matrix to avoid rank deficiency
  44. do_zscore = 0;
  45. do_demean = 1;
  46. cons1st = 0;
  49. RDM = nan(numLocalisers, numberSubjects, numberStates, numberStates, numberTimePoints, size(uniquePerms,1)); %rdm store
  50. chIdx_num_log = cell(numLocalisers, numberSubjects);
  52. load('rules_stimulus_tostate_all.mat')%%use stimulus transfer states
  53. load reward_index.mat
  55. for whichLocaliser = 1:numLocalisers
  56. switch localiser_names{whichLocaliser}
  57. case 'stimFL'
  58. ConDir = uigetdir('./FunctionalLocalizer_Data');
  59. DirCon = dir(fullfile(ConDir,'FunctionalLocalizer_*.mat')); %%%% find all the set file in your folder
  60. FileNamesCon = {DirCon.name};
  61. %
  62. case 'posFL'
  63. ConDir = uigetdir('./Position_Data');
  64. DirCon = dir(fullfile(ConDir,'position_*.mat')); %%%% find all the set file in your folder
  65. FileNamesCon = {DirCon.name};
  67. end
  69. for iSj = 1:68
  71. tic
  72. disp(['whichLocaliser = ' num2str(whichLocaliser)])
  73. disp(['sub = ' num2str(iSj)])
  75. % load the preprocessed FL/position data and identify EEG channels
  76. load ([ConDir,'/',char(FileNamesCon{iSj})])
  79. %% load labels
  80. stimulus_type=marker_type;
  81. %%stimulus transfer state
  83. for state=1:8
  84. stim=state+10;
  85. index_1=find(stimulus_type(:,1)==stim);
  86. stimulus_type(index_1)=rules_stimulus_tostate_all(iSj,state);
  87. end
  90. if reward_index(iSj,1)==1
  92. stimulus_type=stimulus_type;
  94. else
  96. index1=find(stimulus_type==1);
  97. index2=find(stimulus_type==2);
  98. index3=find(stimulus_type==3);
  99. index4=find(stimulus_type==4);
  100. index5=find(stimulus_type==5);
  101. index6=find(stimulus_type==6);
  102. index7=find(stimulus_type==7);
  103. index8=find(stimulus_type==8);
  105. stimulus_type(index1)=5;
  106. stimulus_type(index2)=6;
  107. stimulus_type(index3)=7;
  108. stimulus_type(index4)=8;
  109. stimulus_type(index5)=1;
  110. stimulus_type(index6)=2;
  111. stimulus_type(index7)=3;
  112. stimulus_type(index8)=4;
  113. end
  115. %
  116. %% fix the mapping from state(S1:S8) to picture_stimuli
  117. % this is the state ID (i.e. structural position within 2 sequences). The picID is below
  118. labStm_pic =stimulus_type;
  120. % now clean by goodchanels (lcidata = [EEGchannels, timePoints, epoched trials from all runs]
  121. if baseline_correct
  122. baseline_time = 1:11;
  123. for k = 1:size(stim_data,3)
  124. tmp = mean(stim_data(:, baseline_time(1):baseline_time(end), k), 2);
  125. stim_data(:, :, k) = stim_data(:, :, k) - repmat(tmp, 1, size(stim_data,2));
  126. end
  127. lcidata = stim_data;
  128. else
  130. lcidata = stim_data;
  131. end
  133. % logical vector, 1 = good channel
  134. chIdx = ones(numEEGChan,1);
  136. chIdx = logical(chIdx); % don't know why
  140. for iT= 1:numberTimePoints%size(lcidata,2) % time post stim onset
  142. % 1st level GLM on MEG data to identify stimulus-sepcific sensor weights. GLM on each sensor and iT separately, collapsing over trials
  143. % clear these variables every time slice
  144. if cons1st
  145. reg_log = nan(length(eeg_channels), numberStates+1);
  146. else
  147. reg_log = nan(length(eeg_channels), numberStates);
  148. end
  149. % the log of regressors over all sensors (included and excluded - 272) at this timeslice ((without intercept)
  150. norm_reg_log = nan(length(eeg_channels), numberStates); % (used for multivariate normalisation)
  151. res_log = nan(length(eeg_channels), size(lcidata,3)); % [chan * trial] log of the trial-wise residuals from the 1st level GLM for each sensor (used for multivariate normalisation)
  152. rdm_log = nan(numberStates,numberStates);
  154. for iSensor = 1:size(lcidata,1) % sensor index w.,r.t. subject-specific goodSensors
  156. %objective_sensor_id = chIdx_number(iSensor); % to slot into a matrix of 272 channels [or all conisdered channels] (some of which are nan, if bad)
  158. % reset for each sensor
  159. X_stim = zeros(size(lcidata,3),numberStates); % [trials * stimID)
  160. Y = nan(size(lcidata,3),1); % [trials * 1] = the specific sensor*iT value for each trial
  162. % Y - identify correct iT slice for this sensor, over all trials
  163. Y(:,1) = squeeze(lcidata(iSensor, iT, :)); % have already cropped the lcidata, so can index from iT take into account the epoching from -700ms
  164. if do_zscore
  165. Y = zscore(Y); % [trials * 1], MEG data zscored over all trials, per time-point and sensor (Luyckz et al 2019). This scales the Y so that it is similar for all GLM's, but does not ensure that the value of each trial (Y(t)) is scaled w.r.t. that trial's overall sensor-specific timecourse (is want this then set zscore_by_iT = 1)
  166. end
  168. if do_demean
  169. Y = Y-nanmean(Y); % [trials * 1], MEG data zscored over all trials, per time-point and sensor (Luyckz et al 2019). This scales the Y so that it is similar for all GLM's, but does not ensure that the value of each trial (Y(t)) is scaled w.r.t. that trial's overall sensor-specific timecourse (is want this then set zscore_by_iT = 1)
  170. end
  173. % the trial-by-trial regressor values (state ID)
  174. for iTrial = 1:size(lcidata,3)
  175. X_stim(iTrial, labStm_pic(iTrial)) = 1;
  176. end
  178. if cons1st
  179. design_matrix = [X_stim,ones(size(X_stim,1),1)];
  180. else
  181. design_matrix = [X_stim];
  182. end
  185. this_weights = pinv(design_matrix)*Y;
  186. reg_log(iSensor, :) = this_weights;
  188. if multivariate_normalisation
  189. this_residuals = Y - (design_matrix * this_weights);
  190. res_log(iSensor, :) = this_residuals; % [chan * trial] residuals(²Ð²î) over trials
  191. end
  193. end % end sensors
  196. if multivariate_normalisation
  198. % calculate the sensor*sensor spatial correlation structure of noise (residuals)
  199. trialByChanResid = squeeze(res_log(chIdx, :))'; % note the transpose (columns are random variable input to covCor)
  201. if shrink_noise
  202. [noiseCorr shrink_factor(whichLocaliser, iSj)] = covCor(trialByChanResid); % the method of Ledoit and Wolf 2004, referenced in Walther et al 2016 [shrinks the sample var-cov matirx towards the diagonal covariance matrix, to avoid rank deficiency]
  203. else
  204. noiseCorr = 1/(size(trialByChanResid,1)) * trialByChanResid' * trialByChanResid; % Walther et al 2016 (chan*chan variance-covariance matrix)
  205. end
  207. if rank(noiseCorr) < size(noiseCorr,1) % shrinking avoids rank deficiency
  208. warning([[char(FileNamesCon{iSj})] ' ID ' num2str(iSj) ' ' localiser_names{whichLocaliser} ' noninvertable noise covariance matrix. ? numChan < numTrials '])
  209. end
  211. % normalise the regression weights of each sensor by the
  212. % spatial noise correlation between sensors (the
  213. % vaiance-covariance matrix noiseCorr). Do this for each
  214. % condition separately
  215. for k=1:numberStates
  216. norm_reg_log(chIdx, k) = squeeze(reg_log(chIdx,k))' * noiseCorr^(-0.5); % post-multiply activity estimates to achieve multivariate noise normalisation (pre-whitening)
  217. end
  219. end % multivariate normalisation
  222. norm_reg_log_untouched = norm_reg_log(:,1:numberStates);
  223. reg_log_untouched = reg_log(:,1:numberStates);
  225. for iPerm=1:length(uniquePerms)
  226. % can do a lop over condition label permutaitons here
  227. this_perm =uniquePerms(iPerm,:);
  232. norm_reg_log = norm_reg_log_untouched(:,this_perm);
  233. reg_log = reg_log_untouched(:,this_perm);
  236. if multivariate_normalisation
  237. rdm_log(:,:) = 1-corr(squeeze(norm_reg_log(chIdx, 1:numberStates)), 'Type', 'Pearson');% dissimilarity = 1-Pearson's correlation (invariant to scaling differences, but sensitive to baseline shifts)
  238. rsa_log(:,:)= corr(squeeze(norm_reg_log(chIdx, 1:numberStates)), 'Type', 'Pearson');
  239. else
  240. rdm_log(:,:) = 1-corr(squeeze(reg_log(chIdx, 1:numberStates)), 'Type', 'Pearson'); % dissimilarity = 1-Pearson's correlation (invariant to scaling differences, but sensitive to baseline shifts)
  241. rsa_log(:,:)= corr(squeeze(reg_log(chIdx, 1:numberStates)), 'Type', 'Pearson');
  242. end
  245. RSA(whichLocaliser, iSj,:,:,iT,iPerm) = rsa_log(:,:);
  246. RDM(whichLocaliser, iSj,:,:,iT,iPerm) = rdm_log(:,:); % [loc, sub, 8,8 it, permNum] the resulting pairwise distances between multi-sensor patterns between each pair of conditions
  247. end
  248. end % iT
  249. toc
  250. end %subject
  252. end %end loop on localiser
  253. save(['positionALL_BC', num2str(baseline_correct), '_NORM_', num2str(multivariate_normalisation), 'SN', num2str(shrink_noise), 'Zscore', num2str(do_zscore), 'demean', num2str(do_demean), 'cons1st', num2str(cons1st)] ,'RDM','-v7.3')
  254. save(['positionALL_RSA_BC', num2str(baseline_correct), '_NORM_', num2str(multivariate_normalisation), 'SN', num2str(shrink_noise), 'Zscore', num2str(do_zscore), 'demean', num2str(do_demean), 'cons1st', num2str(cons1st)] ,'RSA','-v7.3')
  258. %% change from sFL to pFL
  261. dRDM =squeeze(RDM(1,:,:,:,:,:))-squeeze(RDM(2,:,:,:,:,:));
  263. save dRDM.mat dRDM
  265. %%
  266. clear;clc;
  267. load dRDM.mat
  268. numberSubjects=68;
  270. %%
  271. smooth_k = 3.1
  272. if smooth_k>0
  273. for iSj = 1:numberSubjects
  274. for iPerm = 1:1001
  275. dRDM_2=squeeze(dRDM(iSj,:,:,:,iPerm));
  276. for i1 = 1:8
  277. for i2 = 1:8
  278. if isnan(dRDM_2(i1,i2,1)) || i1<i2
  279. continue
  280. else
  281. if smooth_k == 1.5
  282. dRDM_2(i1,i2,:) = imgaussfilt(squeeze(dRDM_2(i1,i2,:)),smooth_k);
  283. elseif smooth_k == 3.1
  284. dRDM_2(i1,i2,:) = imgaussfilt(squeeze(dRDM_2(i1,i2,:)),smooth_k-0.1);
  285. else
  286. dRDM_2(i1,i2,:) = smoothdata(squeeze(dRDM_2(i1,i2,:)),'gaussian',smooth_k);
  287. end
  288. dRDM_2(i2,i1,:) = dRDM_2(i1,i2,:);
  289. end
  290. end
  291. end
  292. dRDM_new(iSj,:,:,:,iPerm)=dRDM_2;
  293. end
  294. end
  295. end
  296. save dRDM_new.mat dRDM_new
  297. %%
  298. numberSubjects = 68;
  299. postion_matrix=[0,0,0,0,1,0,0,0;0,0,0,0,0,1,0,0;0,0,0,0,0,0,1,0;0,0,0,0,0,0,0,1;1,0,0,0,0,0,0,0;0,1,0,0,0,0,0,0;0,0,1,0,0,0,0,0;0,0,0,1,0,0,0,0]
  300. postion_matrix=squareform(postion_matrix);
  302. within_seq_matrix_reward=[1,1,1,1,0,0,0,0;1,1,1,1,0,0,0,0;1,1,1,1,0,0,0,0;1,1,1,1,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0]
  303. for i=1:8
  304. within_seq_matrix_reward(i,i)=0
  305. end
  306. within_seq_matrix_reward=squareform(within_seq_matrix_reward);
  308. within_seq_matrix_neutral=[0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,0,0,0,0;0,0,0,0,1,1,1,1;0,0,0,0,1,1,1,1;0,0,0,0,1,1,1,1;0,0,0,0,1,1,1,1]
  309. for i=1:8
  310. within_seq_matrix_neutral(i,i)=0
  311. end
  312. within_seq_matrix_neutral=squareform(within_seq_matrix_neutral);
  315. cons1st1=ones(28,1);
  316. matrix=[postion_matrix' within_seq_matrix_reward' within_seq_matrix_neutral' cons1st1];
  319. distance_beta = nan(numberSubjects, 85, 1,4);
  321. for sub=1:numberSubjects
  322. for iT=1:85
  323. for iprem=1:1001
  324. y=squeeze(dRDM_new(sub,:,:,iT,iprem));
  325. y=squareform(y);
  326. bbb=pinv(matrix)*(y');
  328. distance_beta(sub,iT,iprem,:)=bbb;
  330. end
  331. end
  332. end
  334. save distance_beta.mat distance_beta

step01_RAS_main_code.m, under CC-BY-4.0 · at the source

Overview

Authors: Qianqian Yu1,2,3,4, Yue-jia Luo1,3,5, Ray Dolan1,6,7, Jianxin Ou1,2, Chuwen Huang4, Haiteng Wang1, Zhibing Xiao1, Matthew Nour6,8, Yunzhe Liu1,2
ORCID iDs: Qianqian Yu, Yue-jia Luo, Ray Dolan, Jianxin Ou, Haiteng Wang, Zhibing Xiao, Yunzhe Liu
  1. State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University,Beijing, China
  2. Chinese Institute for Brain Research,Beijing, China
  3. School of Psychology, Center for Brain Disorders and Cognitive Science, Shenzhen University,Shenzhen, China
  4. Center for Neurocognition and Social Behavior, Institute of Artificial Intelligence, Shenzhen University of Advanced Technology,Shenzhen, China
  5. Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, China
  6. Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Wellcome Centre for Human Neuroimaging, University College London,London, UK
  7. Department of Psychiatry, Universitätsmedizin Berlin,Berlin, Germany
  8. Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, University of Oxford,Oxford, UK
Institutions: Beijing Normal University (China); Chinese Institute for Brain Research (China); Shenzhen University (China); Shenzhen University of Advanced Technology (China); University College London (United Kingdom); Charité - Universitätsmedizin Berlin (Germany); University of Oxford (United Kingdom)
Journal: n/a, volume 16, issue 1, article 7975
Dates: received 30 March 2024; accepted 11 August 2025; published online 26 August 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1038/s41467-025-63281-w · PMCID PMC12381021 · OpenAlex W4413680364
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Single-unit activity, calcium imaging
Keywords: Human behaviour, Decision, Learning and memory
MeSH: Anxiety*, Learning*, Rest*, Reward*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: National Natural Science Foundation of China (32271093, 31920103009, ZD153); Shenzhen-Hong Kong Institute of Brain Science ((2022SHIBS0003); Fundamental Research Funds for the Central Universities; National Social Science Fund of China
Citations: cited by 1 paper (Europe PMC); 93 references in the paper

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Version 3, 28 September 2026

  • Funding: added National Natural Science Foundation of China: 32271093, 31920103009, ZD153; Shenzhen-Hong Kong Institute of Brain Science: (2022SHIBS0003; Fundamental Research Funds for the Central Universities; National Social Science Fund of China

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Cite

This paper

Yu, Q., Luo, Y.-j., Dolan, R., Ou, J., Huang, C., Wang, H., Xiao, Z., Nour, M., & Liu, Y. (2025). Trait anxiety is associated with reduced reward-related replay at rest. Nature Communications, 16(1), 7975. https://doi.org/10.1038/s41467-025-63281-w

BibTeX

@article{yu2025trait,
author = {Yu, Qianqian and Luo, Yue-jia and Dolan, Ray and Ou, Jianxin and Huang, Chuwen and Wang, Haiteng and Xiao, Zhibing and Nour, Matthew and Liu, Yunzhe},
title = {{Trait anxiety is associated with reduced reward-related replay at rest}},
journal = {Nature Communications},
year = {2025},
volume = {16},
number = {1},
pages = {7975},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-025-63281-w},
url = {https://doi.org/10.1038/s41467-025-63281-w},
pmcid = {PMC12381021}
}

RIS

TY - JOUR
AU - Yu, Qianqian
AU - Luo, Yue-jia
AU - Dolan, Ray
AU - Ou, Jianxin
AU - Huang, Chuwen
AU - Wang, Haiteng
AU - Xiao, Zhibing
AU - Nour, Matthew
AU - Liu, Yunzhe
TI - Trait anxiety is associated with reduced reward-related replay at rest
T2 - Nature Communications
J2 - Nat Commun
PY - 2025
DA - 2025
VL - 16
IS - 1
SP - 7975
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-025-63281-w
UR - https://doi.org/10.1038/s41467-025-63281-w
LA - en
ER -

CSL-JSON

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"family": "Yu",
"given": "Qianqian"
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{
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{
"family": "Xiao",
"given": "Zhibing"
},
{
"family": "Nour",
"given": "Matthew"
},
{
"family": "Liu",
"given": "Yunzhe"
}
],
"container-title-short": "Nat Commun",
"volume": "16",
"issue": "1",
"page": "7975",
"DOI": "10.1038/s41467-025-63281-w",
"PMCID": "PMC12381021",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-025-63281-w",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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