OSCR

Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome.

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] § Neuroimaging data acquisition and processing › LEiDA analyses ↔ LEiDA/LEiDA.m, lines 38–112 · score 0.78 · bandpass filtered, Hilbert transform, phase synchrony, BOLD signal, leading eigenvector, V1
  2. [2] § Neuroimaging data acquisition and processing › LEiDA analyses ↔ MUSIC_preadolescents/LEiDA_v6.m, lines 39–115 · score 0.78 · bandpass filtered, Hilbert transform, phase synchrony, BOLD signal, leading eigenvector, V1
  3. [3] § Neuroimaging data acquisition and processing › LEiDA analyses ↔ LEiDA/LEiDA.m, lines 38–112 · score 0.70 · Hilbert transformed, phase synchrony, leading eigenvector, V1, parcellation, instantaneous
  4. [4] § Neuroimaging data acquisition and processing › LEiDA analyses ↔ MUSIC_preadolescents/LEiDA_v6.m, lines 39–115 · score 0.70 · Hilbert transformed, phase synchrony, leading eigenvector, V1, parcellation, instantaneous

Paper

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The authors' code

MATLAB · 299 lines · 11 KB · no license · 2 matches

  1. function LEiDA(varargin)
  3. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  4. %
  5. % LEADING EIGENVECTOR DYNAMICS ANALYSIS (LEiDA)
  6. %
  7. % This function processes, clusters and analyses BOLD data using LEiDA.
  8. % Here the example_BOLD is a dataset containing rest and task conditions
  9. %
  10. % NOTE: Step 4 can be run independently once data is saved by calling
  11. % LEiDA('LEiDA_results.mat')
  12. %
  13. % 1 - Read the BOLD data from the folders and computes the BOLD phases
  14. % - Calculate the instantaneous BOLD synchronization matrix
  15. % - Compute the Leading Eigenvector at each frame from all fMRI scans
  16. % 2 - Cluster the Leading Eigenvectors
  17. % 3 - Compute the probability and lifetimes each cluster in each session
  19. % - Calculate signigifance between tasks
  20. % - Saves the Eigenvectors, Clusters and statistics into LEiDA_results.mat
  21. %
  22. % 4 - Plots FC states and errorbars for each clustering solution
  23. % - Adds an asterisk when results are significantly different between
  24. % tasks
  25. %
  26. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  28. % Joana Cabral Oct 2017
  29. % [email hidden]
  30. %
  31. % First use in
  32. % Cabral, et al. 2017 Scientific reports 7, no. 1 (2017): 5135.
  33. %
  34. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  36. if isempty(varargin) % If no input is given, compute LEiDA
  38. %% 1 - Compute the Leading Eigenvectors from the BOLD datasets
  40. disp('Processing the eigenvectors from BOLD data')
  41. % Load here the BOLD data (which may be in different formats)
  42. % Here the BOLD time courses in AAL parcellation are organized as cells,
  43. % where tc_aal{1,1} corresponds to the BOLD data from subject 1 in
  44. % condition 1 and contains a matrix with lines=N_areas and columns=Tmax.
  46. load example_BOLD.mat tc_aal
  47. [n_Subjects, n_Task]=size(tc_aal);
  48. [N_areas, Tmax]=size(tc_aal{1,1});
  50. % Parameters of the data
  51. TR=2; % Repetition Time (seconds)
  53. % Preallocate variables to save FC patterns and associated information
  54. Leading_Eig=zeros(Tmax*n_Subjects,N_areas); % All leading eigenvectors
  55. Time_all=zeros(2, n_Subjects*Tmax); % vector with subject nr and task at each t
  56. t_all=0; % Index of time (starts at 0 and will be updated until n_Sub*Tmax)
  58. % Bandpass filter settings
  59. fnq=1/(2*TR); % Nyquist frequency
  60. flp = .02; % lowpass frequency of filter (Hz)
  61. fhi = 0.1; % highpass
  62. Wn=[flp/fnq fhi/fnq]; % butterworth bandpass non-dimensional frequency
  63. k=2; % 2nd order butterworth filter
  64. [bfilt,afilt]=butter(k,Wn); % construct the filter
  65. clear fnq flp fhi Wn k
  67. for s=1:n_Subjects
  68. for task=1:n_Task
  70. % Get the BOLD signals from this subject in this task
  71. BOLD = tc_aal{s,task};
  72. % [Tmax]=size(BOLD,2); Get Tmax here, if it changes between scans
  73. Phase_BOLD=zeros(N_areas,Tmax);
  75. % Get the BOLD phase using the Hilbert transform
  76. for seed=1:N_areas
  77. BOLD(seed,:)=BOLD(seed,:)-mean(BOLD(seed,:));
  78. signal_filt =filtfilt(bfilt,afilt,BOLD(seed,:));
  79. Phase_BOLD(seed,:) = angle(hilbert(signal_filt));
  80. end
  82. for t=1:Tmax
  84. %Calculate the Instantaneous FC (BOLD Phase Synchrony)
  85. iFC=zeros(N_areas);
  86. for n=1:N_areas
  87. for p=1:N_areas
  88. iFC(n,p)=cos(Phase_BOLD(n,t)-Phase_BOLD(p,t));
  89. end
  90. end
  92. % Get the leading eigenvector
  93. [V1,~]=eigs(iFC,1);
  94. % Make sure the largest component is negative
  95. % This step is important because the same eigenvector can
  96. % be returned either as V or its symmetric -V and we need
  97. % to make sure it is always the same (so we choose always
  98. % the most negative one)
  99. if mean(V1>0)>.5
  100. V1=-V1;
  101. elseif mean(V1>0)==.5 && sum(V1(V1>0))>-sum(V1(V1<0))
  102. V1=-V1;
  103. end
  105. % Save V1 from all frames in all fMRI sessions in Leading eig
  106. t_all=t_all+1; % Update time
  107. Leading_Eig(t_all,:)=V1;
  108. Time_all(:,t_all)=[s task]; % Information that at t_all, V1 corresponds to subject s in a given task
  109. end
  110. end
  111. end
  112. clear BOLD tc_aal signal_filt iFC V1 Phase_BOLD
  114. %% 2 - Cluster the Leading Eigenvectors
  116. disp('Clustering the eigenvectors into')
  117. % Leading_Eig is a matrix containing all the eigenvectors:
  118. % Collumns: N_areas are brain areas (variables)
  119. % Rows: Tmax*n_Subjects are all time points (independent observations)
  121. % Set maximum/minimum number of clusters
  122. % There is no fixed number of states the brain can display
  123. % Extend the range depending on the hypothesis of each work
  124. maxk=12;
  125. mink=3;
  126. rangeK=mink:maxk;
  128. % Set the parameters for Kmeans clustering
  129. Kmeans_results=cell(size(rangeK));
  131. for k=1:length(rangeK)
  132. disp(['- ' num2str(rangeK(k)) ' clusters'])
  133. [IDX, C, SUMD, D]=kmeans(Leading_Eig,rangeK(k),'Replicates',10,'MaxIter',1000,'Display','off','Options',statset('UseParallel',1));
  134. Kmeans_results{k}.IDX=IDX; % Cluster time course - numeric collumn vectos
  135. Kmeans_results{k}.C=C; % Cluster centroids (FC patterns)
  136. Kmeans_results{k}.SUMD=SUMD; % Within-cluster sums of point-to-centroid distances
  137. Kmeans_results{k}.D=D; % Distance from each point to every centroid
  138. end
  140. save LEiDA_results.mat Leading_Eig Time_all Kmeans_results
  142. %% 3 - Analyse the Clustering results
  144. % For every fMRI scan calculate probability and lifetimes of each pattern c.
  145. P=zeros(n_Task,n_Subjects,maxk-mink+1,maxk);
  146. LT=zeros(n_Task,n_Subjects,maxk-mink+1,maxk);
  148. for k=1:length(rangeK)
  149. for task=1:n_Task % 1, Baselineline, 2, baby face task
  150. for s=1:n_Subjects
  152. % Select the time points representing this subject and task
  153. T=((Time_all(1,:)==s)+(Time_all(2,:)==task))>1;
  154. Ctime=Kmeans_results{k}.IDX(T);
  156. for c=1:rangeK(k)
  157. % Probability
  158. P(task,s,k,c)=mean(Ctime==c);
  160. % Mean Lifetime
  161. Ctime_bin=Ctime==c;
  163. % Detect switches in and out of this state
  164. a=find(diff(Ctime_bin)==1);
  165. b=find(diff(Ctime_bin)==-1);
  167. % We discard the cases where state sarts or ends ON
  168. if length(b)>length(a)
  169. b(1)=[];
  170. elseif length(a)>length(b)
  171. a(end)=[];
  172. elseif ~isempty(a) && ~isempty(b) && a(1)>b(1)
  173. b(1)=[];
  174. a(end)=[];
  175. end
  176. if ~isempty(a) && ~isempty(b)
  177. C_Durations=b-a;
  178. else
  179. C_Durations=0;
  180. end
  181. LT(task,s,k,c)=mean(C_Durations)*TR;
  182. end
  183. end
  184. end
  185. end
  187. P_pval=zeros(maxk-mink+1,maxk);
  188. LT_pval=zeros(maxk-mink+1,maxk);
  190. disp('Test significance between Rest and Task')
  191. for k=1:length(rangeK)
  193. disp(['Now running for ' num2str(k) ' clusters'])
  194. for c=1:rangeK(k)
  195. % Compare Probabilities
  196. a=squeeze(P(1,:,k,c)); % Vector containing Prob of c in Baseline
  197. b=squeeze(P(2,:,k,c)); % Vector containing Prob of c in Task
  198. stats=permutation_htest2_np([a,b],[ones(1,numel(a)) 2*ones(1,numel(b))],1000,0.05,'ttest');
  199. P_pval(k,c)=min(stats.pvals);
  201. % Compare Lifetimes
  202. a=squeeze(LT(1,:,k,c)); % Vector containing Lifetimes of c in Baseline
  203. b=squeeze(LT(2,:,k,c)); % Vector containing Lifetimes of c in Task
  204. stats=permutation_htest2_np([a,b],[ones(1,numel(a)) 2*ones(1,numel(b))],1000,0.05,'ttest');
  205. LT_pval(k,c)=min(stats.pvals);
  206. end
  207. end
  208. disp('%%%%% LEiDA SUCCESSFULLY COMPLETED %%%%%%%')
  209. disp('Saving LEiDA results')
  210. save LEiDA_results.mat Leading_Eig Time_all Kmeans_results P LT P_pval LT_pval rangeK
  211. else
  212. load(varargin{1})
  213. end
  215. %% 4 - Plot FC patterns and stastistics between groups
  217. disp(' ')
  218. disp('%%% PLOTS %%%%')
  219. disp(['Choose number of clusters between ' num2str(rangeK(1)) ' and ' num2str(rangeK(end)) ])
  220. Pmin_pval=min(P_pval(P_pval>0));
  221. LTmin_pval=min(LT_pval(LT_pval>0));
  222. if Pmin_pval<LTmin_pval
  223. [k,~]=ind2sub([length(rangeK),max(rangeK)],find(P_pval==Pmin_pval));
  224. else
  225. [k,~]=ind2sub([length(rangeK),max(rangeK)],find(LT_pval==LTmin_pval));
  226. end
  227. disp(['Note: The most significant difference is detected with K=' num2str(rangeK(k)) ' (p=' num2str(min(Pmin_pval,LTmin_pval)) ')'])
  229. % To correct for multiple comparisons, you can divide p by the number of
  230. % clusters considered
  232. K = input('Number of clusters: ');
  233. Best_Clusters=Kmeans_results{rangeK==K};
  234. k=find(rangeK==K);
  236. % Clusters are sorted according to their probability of occurrence
  237. ProbC=zeros(1,K);
  238. for c=1:K
  239. ProbC(c)=mean(Best_Clusters.IDX==c);
  240. end
  241. [~, ind_sort]=sort(ProbC,'descend');
  243. % Get the K patterns
  244. V=Best_Clusters.C(ind_sort,:);
  245. [~, N]=size(Best_Clusters.C);
  246. Order=[1:2:N N:-2:2];
  248. figure
  249. colormap(jet)
  250. % Pannel A - Plot the FC patterns over the cortex
  251. % Pannel B - Plot the FC patterns in matrix format
  252. % Pannel C - Plot the probability of each state in each condition
  253. % Pannel D - Plot the lifetimes of each state in each condition
  255. for c=1:K
  256. subplot(4,K,c)
  257. % This needs function plot_nodes_in_cortex.m and aal_cog.m
  258. plot_nodes_in_cortex(V(c,:))
  259. title({['State #' num2str(c)]})
  260. subplot(4,K,K+c)
  261. FC_V=V(c,:)'*V(c,:);
  262. li=max(abs(FC_V(:)));
  263. imagesc(FC_V(Order,Order),[-li li])
  264. axis square
  265. title('FC pattern')
  266. ylabel('Brain area #')
  267. xlabel('Brain area #')
  269. subplot(4,K,2*K+c)
  270. Rest=squeeze(P(1,:,k,ind_sort(c)));
  271. Task=squeeze(P(2,:,k,ind_sort(c)));
  272. bar([mean(Rest) mean(Task)],'EdgeColor','w','FaceColor',[.5 .5 .5])
  273. hold on
  274. % Error bar containing the standard error of the mean
  275. errorbar([mean(Rest) mean(Task)],[std(Rest)/sqrt(numel(Rest)) std(Task)/sqrt(numel(Task))],'LineStyle','none','Color','k')
  276. set(gca,'XTickLabel',{'Rest', 'Task'})
  277. if P_pval(k,ind_sort(c))<0.05
  278. plot(1.5,max([mean(Rest) mean(Task)])+.01,'*k')
  279. end
  280. if c==1
  281. ylabel('Probability')
  282. end
  283. box off
  285. subplot(4,K,3*K+c)
  286. Rest=squeeze(LT(1,:,k,ind_sort(c)));
  287. Task=squeeze(LT(2,:,k,ind_sort(c)));
  288. bar([mean(Rest) mean(Task)],'EdgeColor','w','FaceColor',[.5 .5 .5])
  289. hold on
  290. errorbar([mean(Rest) mean(Task)],[std(Rest)/sqrt(numel(Rest)) std(Task)/sqrt(numel(Task))],'LineStyle','none','Color','k')
  291. set(gca,'XTickLabel',{'Rest', 'Task'})
  292. if LT_pval(k,ind_sort(c))<0.05
  293. plot(1.5,max([mean(Rest) mean(Task)])+.01,'*k')
  294. end
  295. if c==1
  296. ylabel('Lifetime (seconds)')
  297. end
  298. box off
  299. end

LEiDA.m at commit 8676662, no license · at the source

Overview

Authors: Marie-Stephanie Cahart1, Owen O’ Daly1, Ziyuan Cai1, Nicole Mariani2, Alessandra Borsini2, Valeria Mondelli2, Brandi Eiff1, Silvia Rota1, Timothy Nicholson3, Laila Rida1, Adam Hampshire1, Ottavia Dipasquale1, Lia Fernandes4,5, Federico Turkheimer1,6, Steven CR Williams1, Daniel Martins1,4,5
ORCID iDs: Nicole Mariani, Valeria Mondelli, Timothy Nicholson, Adam Hampshire, Federico Turkheimer, Steven CR Williams, Daniel Martins
  1. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
  2. Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
  3. Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
  4. Department of Clinical Neursciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
  5. RISE-HEALTH (Neurosciences thematic line), Department of Clinical Neursciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
  6. Institute for Human and Synthetic Minds, King's College London, UK
Institutions: King's College London (United Kingdom); Universidade do Porto (Portugal)
Journal: Brain, behavior, & immunity - health, volume 54, article 101274
Dates: received 18 January 2026; accepted 24 May 2026; published online 25 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bbih.2026.101274 · PMID 42253624 · PMCID PMC13234210 · OpenAlex W7162292871
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: Post-COVID-19 syndrome, Resting-state fMRI, Dynamic functional connectivity, LEiDA, Brain states, Attention, Limbic system, Default mode network, Neuroinflammation
Topic: Long-Term Effects of COVID-19 (Neurology, Medicine), according to OpenAlex
Funding: National Institute for Health and Care Research; South London and Maudsley NHS Foundation Trust (R0-01); NIHR Imperial Biomedical Research Centre; King&amp;apos;s College London; King&amp;apos;s College Hospital NHS Foundation Trust; NIHR Maudsley BRC
Citations: not cited yet (Europe PMC); 68 references in the paper

Abstract

Background: Post-COVID-19 Syndrome (PCS) is characterised by persistent fatigue, cognitive impairments, and affective symptoms, yet its underlying neural mechanisms remain poorly understood. While static neuroimaging studies have identified resting-state connectivity abnormalities in PCS, such approaches fail to capture the brain's dynamic functional organisation. This represents a missed opportunity to understand how alterations in large-scale network interactions may contribute to the fluctuating symptom profile of PCS. Cognitive and emotional processes rely on the brain's capacity to flexibly reconfigure large-scale networks over time; disruptions in this dynamic repertoire may therefore play a role in PCS pathophysiology.

Methods: Resting-state fMRI data were acquired from 20 individuals with PCS (mean age = 41.8 years, SD = 9.4) and 20 age- and sex-matched healthy controls (mean age = 40.6 years, SD = 8.1) using a multi-echo sequence. Following denoising with multi-echo independent component analysis, we applied Leading Eigenvector Dynamics Analysis (LEiDA) to identify recurrent patterns of whole-brain phase synchrony. The optimal number of dynamic brain states was determined using the Dunn index. For each state, we quantified probability of occurrence, lifetime, and transition probabilities, and mapped spatial topographies onto canonical functional networks. Group differences were assessed using ANCOVAs controlling for age, sex, and handedness. Exploratory associations with clinical symptoms, cognitive performance, and inflammatory markers were examined using both frequentist and Bayesian approaches.

Results: Five recurrent dynamic brain states were identified. Compared with controls, PCS participants showed reduced probability of occurrence and shorter lifetime of a visual/dorsal attention state, alongside increased probability of a limbic/default mode network (DMN) state. PCS was also characterised by tentative reduced transitions between visual/dorsal attention and frontoparietal–DMN states, and increased transitions from somatomotor/visual states toward the limbic-DMN configuration. Exploratory analyses (uncorrected for multiple comparisons) suggested that greater expression of the limbic-DMN state was associated with lower global cognitive performance (MoCA) and higher serum IL-1β levels, although these associations did not survive correction for multiple comparisons.

Conclusions: PCS is associated with a reorganisation of intrinsic brain dynamics, marked by a shift from externally oriented attentional states toward limbic-DMN configurations and reduced transition flexibility. These findings suggest that PCS may involve alterations in the dynamic balance of large-scale brain systems supporting attention and internally oriented processing. While exploratory, the observed patterns are consistent with a potential link between brain-state dynamics, cognitive function, and inflammatory signalling, and provide a systems-level framework for future studies of post-viral brain dysfunction.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

juanitacabral/LEiDA

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 867666230bfd6d68c4945cf88df1ff9b6f115fbf, 12 May 2025
Languages: MATLAB (44)
Size: 133 files, 44 scripts
Software Heritage: not archived
Found in: the text, “LEiDA analyses”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (10 files), Signal Processing Toolbox (6 files), SPM (4 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
45 files
At the source: github.com/juanitacabral/LEiDA

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 44 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Data will be made available on request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Daniel Martins (0000-0003-3731-4508); removed Daniel Martins

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 16 authors, 9 keywords, 6 funders, 68 references.

Cite

This paper

Cahart, M.-S., O’ Daly, O., Cai, Z., Mariani, N., Borsini, A., Mondelli, V., Eiff, B., Rota, S., Nicholson, T., Rida, L., Hampshire, A., Dipasquale, O., Fernandes, L., Turkheimer, F., Williams, S. C., & Martins, D. (2026). Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome. Brain, behavior, & immunity - health, 54, 101274. https://doi.org/10.1016/j.bbih.2026.101274

BibTeX

@article{cahart2026brain,
author = {Cahart, Marie-Stephanie and O’ Daly, Owen and Cai, Ziyuan and Mariani, Nicole and Borsini, Alessandra and Mondelli, Valeria and Eiff, Brandi and Rota, Silvia and Nicholson, Timothy and Rida, Laila and Hampshire, Adam and Dipasquale, Ottavia and Fernandes, Lia and Turkheimer, Federico and Williams, Steven CR and Martins, Daniel},
title = {{Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome}},
journal = {Brain, behavior, \& immunity - health},
year = {2026},
month = may,
volume = {54},
pages = {101274},
publisher = {Elsevier},
issn = {2666-3546},
doi = {10.1016/j.bbih.2026.101274},
url = {https://doi.org/10.1016/j.bbih.2026.101274},
pmid = {42253624},
pmcid = {PMC13234210}
}

RIS

TY - JOUR
AU - Cahart, Marie-Stephanie
AU - O’ Daly, Owen
AU - Cai, Ziyuan
AU - Mariani, Nicole
AU - Borsini, Alessandra
AU - Mondelli, Valeria
AU - Eiff, Brandi
AU - Rota, Silvia
AU - Nicholson, Timothy
AU - Rida, Laila
AU - Hampshire, Adam
AU - Dipasquale, Ottavia
AU - Fernandes, Lia
AU - Turkheimer, Federico
AU - Williams, Steven CR
AU - Martins, Daniel
TI - Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome
T2 - Brain, behavior, & immunity - health
J2 - Brain Behav Immun Health
PY - 2026
DA - 2026/05/25
VL - 54
SP - 101274
SN - 2666-3546
PB - Elsevier
DO - 10.1016/j.bbih.2026.101274
UR - https://doi.org/10.1016/j.bbih.2026.101274
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.bbih.2026.101274",
"type": "article-journal",
"title": "Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in post-COVID-19 syndrome",
"container-title": "Brain, behavior, & immunity - health",
"author": [
{
"family": "Cahart",
"given": "Marie-Stephanie"
},
{
"family": "O’ Daly",
"given": "Owen"
},
{
"family": "Cai",
"given": "Ziyuan"
},
{
"family": "Mariani",
"given": "Nicole"
},
{
"family": "Borsini",
"given": "Alessandra"
},
{
"family": "Mondelli",
"given": "Valeria"
},
{
"family": "Eiff",
"given": "Brandi"
},
{
"family": "Rota",
"given": "Silvia"
},
{
"family": "Nicholson",
"given": "Timothy"
},
{
"family": "Rida",
"given": "Laila"
},
{
"family": "Hampshire",
"given": "Adam"
},
{
"family": "Dipasquale",
"given": "Ottavia"
},
{
"family": "Fernandes",
"given": "Lia"
},
{
"family": "Turkheimer",
"given": "Federico"
},
{
"family": "Williams",
"given": "Steven CR"
},
{
"family": "Martins",
"given": "Daniel"
}
],
"container-title-short": "Brain Behav Immun Health",
"volume": "54",
"page": "101274",
"DOI": "10.1016/j.bbih.2026.101274",
"PMID": "42253624",
"PMCID": "PMC13234210",
"ISSN": "2666-3546",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.bbih.2026.101274",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
25
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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