OSCR

Functional and effective connectivity alterations relate to cognitive performance in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Dynamic causal modelling ↔ code/Reproduce_figures.m, lines 257–341 · score 0.77 · Bayesian model comparison, posterior probability, model space, PP, Connections
  2. [2] § Materials and methods › MRI acquisition protocol ↔ code/Run_first_level.m, lines 1–16 · score 0.59 · MRI scanner, echo, repetition, TE, TR

Paper

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The authors' code

MATLAB · 500 lines · 12 KB · no license · 1 match

  1. %% Reproduces the figures from the DCM/PEB tutorial papers
  2. %
  3. % Please run the analyses first, by uncommenting the following 4 lines:
  4. %
  5. % Run_first_level;
  6. % Run_second_level;
  7. % close all;
  8. % clear all;
  9. %
  10. % Then run the code below.
  12. %% Load GCM
  13. GCM=load('../analyses/GCM_full_pre_estimated.mat');
  14. GCM=GCM.GCM;
  15. load('../design_matrix.mat','X');
  16. %% Show inputs matrix U and timeseries matrix Y. (Part 1, Figure 2)
  18. DCM = GCM{1};
  20. figure('Name','Input matrix U','NumberTitle','off');
  21. x = (1:length(DCM.U.u))*DCM.U.dt;
  22. imagesc(DCM.U.u > 0); colormap gray;
  23. set(gca,'YTickLabel',round(x(1:500:end)),'YTick',round(1:500:length(x)));
  24. set(gca,'XTickLabel',{'Task','Pictures','Words'},'XTick',1:3);
  25. set(gca,'FontSize',12);
  26. ylabel('Time (secs)');
  28. figure('Name','Timeseries Y','NumberTitle','off');
  29. x = [1:DCM.v]*DCM.Y.dt;
  30. y = DCM.Y.y;
  31. for i = 1:4
  32. subplot(1,4,i);
  33. plot(y(:,i),x,'Color','k');
  34. set(gca,'Ydir','reverse')
  35. set(gca,'XLim',[-2 2],'XTick',[],'FontSize',12)
  36. ylim([0 750]);
  37. if i > 1
  38. set(gca,'YTick',[]);
  39. end
  40. end
  41. %% Switched on vs switched off connection (Part 1, Figure 5)
  42. figure;
  43. x = -4:0.01:4;
  45. subplot(1,2,1);
  46. y = normpdf(x,0,1);
  47. area(x,y);
  48. axis square;
  49. set(gca,'YTick',[],'FontSize',12);
  50. title('Prior - Switched on');
  51. xlabel('Parameter value');
  52. ylabel('Probability');
  54. subplot(1,2,2);
  55. y = normpdf(x,0,0.0001);
  56. area(x,y);
  57. axis square;
  58. title('Prior - Switched off');
  59. xlabel('Parameter value');
  60. ylabel('Probability');
  61. set(gca,'YTick',[],'FontSize',12);
  62. %% Parameters and timeseries from example subject (Part 1, Figure 6)
  64. % Subject
  65. s = 37;
  67. % Unpack DCM
  68. DCM = GCM{s,1};
  69. Ep = spm_vec(DCM.Ep);
  70. Vp = spm_vec(DCM.Vp);
  71. pnames = pz_dcm_get_parameter_names(DCM);
  73. % Re-order as listed in paper
  74. idx_a = reshape(1:16,size(DCM.a));
  75. idx_b = reshape(17:64,size(DCM.b));
  76. idx_c = reshape(65:76,size(DCM.c));
  77. idx = [diag(idx_a); % A intrinsic
  78. spm_vec(idx_a - diag(diag(idx_a))); % A extrinsic
  79. diag(idx_b(:,:,2)); % B pictures intrinsic
  80. diag(idx_b(:,:,3)); % B words intrinsic
  81. idx_c(:,1)]; % C Task driving
  82. idx(idx == 0) = [];
  84. % Further limit to free parameters
  85. idx_on = spm_find_pC(DCM);
  86. idx_both = [];
  87. for i = 1:length(idx)
  88. if ismember(idx(i), idx_on)
  89. idx_both(end+1) = idx(i);
  90. end
  91. end
  92. idx = idx_both;
  94. % Filter
  95. Ep = Ep(idx);
  96. Vp = Vp(idx);
  97. pnames = pnames(idx);
  99. % Plot
  100. figure('Name','Parameters and timeseries of example subject','NumberTitle','off');
  101. subplot(2,1,1);
  102. Cp = diag(Vp);
  103. spm_plot_ci(Ep,Cp); hold on;
  104. x = repmat(1:4,1,length(Ep)/4);
  105. set(gca,'XTick',1:length(Ep),'XTickLabel',x);
  107. % Decorate
  108. xlabel('DCM parameter');
  109. ylabel('Posterior');
  111. % Plot example timeseries
  112. subplot(2,1,2);
  113. x = [1:DCM.v]*DCM.Y.dt;
  114. plot(x,DCM.y,'LineWidth',2); hold on
  115. plot(x,DCM.y + DCM.R(:,1:4),':');
  116. legend({'lvF','ldF','rvF','rdF'});
  117. hold on;
  118. x = (1:length(DCM.U.u))*DCM.U.dt;
  119. plot(x,full(DCM.U.u(:,1)),'Color',[0.2 0.2 0.2]);
  120. %% Report explained variance across subjects
  121. GCM_diagnostics = spm_dcm_fmri_check(GCM);
  122. exp_var = cellfun(@(x)x.diagnostics(1),GCM_diagnostics);
  123. fprintf('Mean explained variance: %2.2f std: %2.2f\n',mean(exp_var),std(exp_var));
  124. %% Covariance components (Part 1, Figure A.1)
  125. figure('Name','DCM Covariance components','NumberTitle','off');
  126. for i = 1:4
  127. subplot(1,4,i);
  128. imagesc(GCM{1}.Y.Q{i});
  129. axis tight; axis square; axis off;
  130. colormap gray;
  131. end
  132. %% Plot cartoon of T2* relaxation (Part 1, Figure A.3)
  133. B0 = 3;
  134. gamma = 42.56; % Gyromagnetic ratio (Mhz/Tesla)
  135. larmor = gamma * B0; % MHz
  136. T2 = 0.2; % Transverse time constant, arbitrarily chosen for display (secs)
  137. flipangle = pi/2; % Radians
  139. % Envelope
  140. t = 0:0.001:1; % secs
  141. envelope = sin(flipangle).*exp(-t ./ T2);
  143. figure;plot(t,envelope,'Color','k'); hold on;
  145. % Sine wave
  146. y = sin(flipangle).*sin(larmor.*t) .* exp(-t./T2);
  147. plot(t,y,'Color','k','LineWidth',2);
  148. xlabel('Time');ylabel('Measured signal');
  150. line([T2 T2],[envelope(t==T2) 1],'Color','k');
  151. set(gca,'XTick',[],'YTick',[]);
  152. %% PEB design matrices in colour. (Part 2 Figure 3)
  154. colours = [0 0 0
  155. 100 100 100;
  157. 98 62.4 64.7; % Reds: 2->6
  158. 92.9 43.1 46.7;
  159. 80 26.7 30.6;
  160. 68.2 14.5 18.4;
  161. 53.7 4.3 7.8;
  163. 100 87.8 63.1; % Yellows: 7->11
  164. 95.3 78.4 44.3;
  165. 82 63.5 27.1;
  166. 70.2 51.8 14.9;
  167. 55.3 38.4 4.3;
  169. 51.4 58.8 76.5; % Blues: 12:
  170. 33.3 42.4 63.9;
  171. 22 32.2 54.9;
  172. 13.7 23.9 47.1;
  173. 6.7 15.7 36.9;
  175. 60 87.5 55.3; % greens
  176. 42.4 78.8 36.9;
  177. 28.6 67.8 22.4;
  178. 18.4 58 12.2;
  179. 9.4 45.9 3.5] ./ 100;
  181. reds = 3:7;
  182. yellows = 8:12;
  183. blues = 13:17;
  184. greens = 18:22;
  186. load('../analyses/PEB_B.mat');
  187. PEB = PEB_B;
  189. % We z-score for display purposes - it's not zscored in the analysis
  190. PEB.M.X(:,2:end) = zscore(PEB.M.X(:,2:end) );
  192. XB = PEB.M.X;
  193. XW = PEB.M.W;
  195. XB(:,2) = pz_rescale(XB(:,2),reds(1),reds(end)-0.01);
  196. XB(:,3) = pz_rescale(XB(:,3),yellows(1),yellows(end)-0.01);
  197. XB(:,4) = pz_rescale(XB(:,4),blues(1),blues(end)-0.01);
  198. XB(:,5) = pz_rescale(XB(:,5),greens(1),greens(end)-0.01);
  200. X = kron(XB,XW);
  202. XB(:,1) = 2;
  204. figure('Name','PEB design matrices','NumberTitle','off');
  205. subplot(1,3,1);
  206. image(XB); colormap(gca,colours); axis square;
  207. xlabel('Covariate'); ylabel('Subject');
  208. set(gca,'FontSize',12);
  209. title('Between-Subjects X_B','FontSize',16);
  211. subplot(1,3,2);
  212. imagesc(XW); colormap gray; axis square;
  213. xlabel('DCM parameter'); ylabel('DCM parameter');
  214. set(gca,'FontSize',12);
  215. title('Within-Subjects X_W','FontSize',16);
  217. subplot(1,3,3);
  218. imagesc(X); colormap(gca,colours); axis square;
  219. xlabel('Group level covariate'); ylabel('Subject level DCM parameter');
  220. set(gca,'FontSize',12);
  221. title('Design matrix X','FontSize',16);
  223. %% PEB GLM parameters (Part 2, Figure 4)
  224. load('../analyses/PEB_B.mat','PEB_B');
  226. nconnections = length(PEB_B.Pnames);
  228. % One bar plot for mean and LI
  229. nx = 2;
  230. Ep = PEB_B.Ep(:,1:nx);
  231. Ep = Ep(:);
  232. Vp = diag(PEB_B.Cp);
  233. Vp = Vp(1:(nconnections * nx));
  235. figure('Name','PEB GLM parameters','NumberTitle','off');
  237. subplot(1,2,1);
  238. spm_plot_ci(Ep,diag(Vp));
  239. xlabel('GLM Parameter');
  240. ylabel('Estimate');
  241. set(gca,'FontSize',12);
  242. hold on;
  243. x=1:nconnections:(nconnections*nx);
  244. for i = 2:length(x)
  245. line([x(i)-0.5 x(i)-0.5],[-2 4],'Color',[0.2 0.2 0.2]);
  246. end
  247. axis square;
  248. title('Group level GLM parameters \theta^{(2)}');
  250. subplot(1,2,2);
  251. bar(diag(PEB_B.Ce),'FaceColor',[1 1 1]*.8);
  252. axis square;
  253. xlabel('Connectivity Parameter');
  254. ylabel('Estimate');
  255. set(gca,'FontSize',12);
  256. title('Random effects variance diag(\Sigma^{(2)})');
  257. %% Explicit model comparison figure (Part 2, Figure 5)
  258. load('../analyses/BMA_B_28models.mat','BMA');
  260. figure('Name','Comparison of pre-defined models','NumberTitle','off');
  262. % Plot model space
  263. subplot(2,3,1);
  264. imagesc(BMA.K)
  265. axis square; colormap gray;
  266. set(gca,'XTick',[],'YTick',[]);
  268. Pp_common = sum(BMA.P,2);
  269. Pp_diff = sum(BMA.P,1);
  271. subplot(2,3,2);
  272. imagesc(BMA.P);
  273. axis square;
  274. xlabel('Model (differences)'); ylabel('Model (commonalities)');
  275. title('Posterior probabilities','FontSize',16);
  276. set(gca,'FontSize',12);
  277. colormap gray;
  279. subplot(2,3,3);
  280. bar(Pp_common); ylim([0 1]); xlim([0 size(Pp_common,1)]);
  281. axis square;
  282. xlabel('Model'); ylabel('Probability');
  283. title('Commonalities','FontSize',16);
  284. set(gca,'FontSize',12);
  286. subplot(2,3,4);
  287. bar(Pp_diff); ylim([0 1]); xlim([0 size(Pp_common,1)]);
  288. axis square;
  289. xlabel('Model'); ylabel('Probability');
  290. title('Differences (LI)','FontSize',16);
  291. set(gca,'FontSize',12);
  293. % Show connections in winning model 4
  294. BMA.Kname(BMA.K(4,:)==1)
  296. % Show connections in winning model 15
  297. BMA.Kname(BMA.K(15,:)==1)
  299. % BMA (specific models) before and after thresholding
  300. nconnections = length(BMA.Pnames);
  302. % One bar plot for mean and LI
  303. nx = 2;
  304. Ep = BMA.Ep(1:(nconnections * nx));
  305. Ep = Ep(:);
  306. Vp = BMA.Cp;
  307. Vp = Vp(1:(nconnections * nx));
  309. subplot(2,3,5);
  310. spm_plot_ci(Ep,diag(Vp));
  311. xlabel('GLM Parameter');
  312. ylabel('Estimate');
  313. set(gca,'FontSize',12);
  314. hold on;
  315. x=1:nconnections:(nconnections*nx);
  316. for i = 2:length(x)
  317. line([x(i)-0.5 x(i)-0.5],[-2 4],'Color',[0.2 0.2 0.2]);
  318. end
  319. axis square;
  320. title('Bayesian Model Average');
  322. % Threshold commonalities
  323. Ep(1:nconnections) = Ep(1:nconnections) .* (BMA.Pw > 0.95)';
  324. Vp(1:nconnections) = Vp(1:nconnections) .* (BMA.Pw > 0.95)';
  326. % Threshold differences
  327. Ep(nconnections+1:end) = Ep(nconnections+1:end) .* (BMA.Px > 0.95)';
  328. Vp(nconnections+1:end) = Vp(nconnections+1:end) .* (BMA.Px > 0.95)';
  330. subplot(2,3,6);
  331. spm_plot_ci(Ep,diag(Vp));
  332. xlabel('GLM Parameter');
  333. ylabel('Estimate');
  334. set(gca,'FontSize',12);
  335. hold on;
  336. x=1:nconnections:(nconnections*nx);
  337. for i = 2:length(x)
  338. line([x(i)-0.5 x(i)-0.5],[-2 4],'Color',[0.2 0.2 0.2]);
  339. end
  340. axis square;
  341. title('Thresholded');
  342. %% Family comparisons (Part 2, Figure 6)
  343. load('../analyses/BMA_fam_task.mat');
  344. load('../analyses/BMA_fam_b_dv.mat');
  345. load('../analyses/BMA_fam_b_lr.mat');
  347. rows = 1; cols = 3;
  349. figure('Name','Family comparison','NumberTitle','off');
  351. subplot(rows,cols,1);
  352. imagesc(fam_task.family.post); axis square; colormap gray;
  353. ylabel('Commonalities');xlabel('Differences');
  354. title('Factor 1: Task','FontSize',16);
  355. set(gca,'FontSize',12,'YTick',1:size(fam_task.family.post,1));
  357. subplot(rows,cols,2);
  358. imagesc(fam_b_dv.family.post); axis square; colormap gray;
  359. ylabel('Commonalities');xlabel('Differences');
  360. title('Factor 2: Dorsoventral','FontSize',16);
  361. set(gca,'FontSize',12,'YTick',1:size(fam_b_dv.family.post,1));
  363. subplot(rows,cols,3);
  364. imagesc(fam_b_lr.family.post); axis square; colormap gray;
  365. ylabel('Commonalities');xlabel('Differences');
  366. title('Factor 3: Left/right','FontSize',16);
  367. set(gca,'FontSize',12,'YTick',1:size(fam_b_lr.family.post,1));
  368. %% BMA after automatic search (Part 2, Figure 7)
  370. load('../analyses/BMA_search_B.mat','BMA_B');
  372. nconnections = length(BMA_B.Pnames);
  374. % One bar plot for mean and LI
  375. nx = 2;
  376. p = 1:(nconnections * nx);
  377. Ep = BMA_B.Ep(p);
  378. Pp = BMA_B.Pp(p);
  379. Vp = diag(BMA_B.Cp);
  380. Vp = Vp(p);
  382. figure('Name','BMA - automatic search','NumberTitle','off');
  384. subplot(1,2,1);
  385. spm_plot_ci(Ep,diag(Vp));
  386. xlabel('GLM Parameter');
  387. ylabel('Estimate');
  388. set(gca,'FontSize',12);
  389. hold on;
  390. x=1:nconnections:(nconnections*nx);
  391. for i = 2:length(x)
  392. line([x(i)-0.5 x(i)-0.5],[-2 4],'Color',[0.2 0.2 0.2]);
  393. end
  394. axis square;
  395. title('Bayesian Model Average');
  397. % Threshold
  398. Ep = Ep .* (Pp(:) > 0.95);
  399. Vp = Vp .* (Pp(:) > 0.95);
  401. subplot(1,2,2);
  402. spm_plot_ci(Ep,diag(Vp));
  403. xlabel('GLM Parameter');
  404. ylabel('Estimate');
  405. set(gca,'FontSize',12);
  406. hold on;
  407. x=1:nconnections:(nconnections*nx);
  408. for i = 2:length(x)
  409. line([x(i)-0.5 x(i)-0.5],[-2 4],'Color',[0.2 0.2 0.2]);
  410. end
  411. axis square;
  412. title('Thresholded');
  414. % Show connections in BMA
  415. BMA.Kname(BMA.K(4,:)==1)
  418. %% Leave-one-out cross validation (Part 2, Figure 8)
  419. load('../analyses/LOO_rdF_words.mat','qE','qC','Q');
  420. load('../design_matrix.mat','X');
  422. % Subject order
  423. k = 1:size(X,1);
  425. figure('Name','Leave one out cross validation)','NumberTitle','off');
  427. figure;
  428. subplot(1,2,1), spm_plot_ci(qE(k),qC(k)), hold on
  429. plot(X(k,2),'--','Color','K','LineWidth',2), hold off
  430. xlabel('Subject','FontSize',12), ylabel('Predicted subject effect','FontSize',12)
  431. title('Out of sample estimates','FontSize',16)
  432. axis tight, axis square;
  433. set(gca,'FontSize',12);
  435. % count the number of subjects where preditcion was within 99% CI
  436. %--------------------------------------------------------------------------
  437. ci = 0.99;
  438. ci = 1 - (1-ci)/2;
  439. ci = spm_invNcdf(ci);
  440. c = ci*sqrt(qC);
  441. lower = qE - c;
  442. higher = qE + c;
  444. sum(X(:,2)' >= lower & X(:,2)' <= higher)
  446. % classical inference on classification accuracy
  447. %--------------------------------------------------------------------------
  448. [T,df] = spm_ancova(X(:,1:2),[],qE(:),[0;1]);
  449. r = corrcoef(qE(:),X(:,2));
  450. r = full(r(1,2));
  452. if isnan(T)
  453. p = NaN;
  454. else
  455. p = 1 - spm_Tcdf(T,df(2));
  456. end
  457. str = sprintf('corr(df:%-2.0f) = %-0.2f: p = %-0.5f',df(2),r,p);
  459. subplot(1,2,2)
  460. plot(X(:,2),qE,'.','Markersize',8,'Color','k')
  461. xlabel('Group effect','FontSize',12), ylabel('Estimate','FontSize',12)
  462. title(str,'FontSize',16)
  463. set(gca,'FontSize',12);
  464. axis square;lsline;
  466. %% PEB precision components (Part 2, Figure 9)
  467. load('../analyses/PEB_B.mat');
  468. PEB = PEB_B;
  470. n = 4;
  472. nq = length(PEB.M.Q);
  473. ns = size(GCM,1);
  474. np = nq; % number of dcm parameters
  476. figure;
  478. subplot(1,n+2,1);
  479. imagesc( kron(eye(ns), np) );
  480. colormap gray;
  481. axis square;
  482. set(gca,'XTick',[],'YTick',[]);
  484. subplot(1,n+2,2);
  485. Q0=eye(np);
  486. imagesc(Q0);
  487. colormap gray;
  488. axis square;
  489. set(gca,'XTick',[],'YTick',[]);
  491. i = 3;
  492. for q = [1:n-1 nq]
  493. Q = PEB.M.Q{q};
  494. subplot(1,n+2,i);
  495. imagesc(Q);
  496. colormap gray;
  497. axis square;
  498. set(gca,'XTick',[],'YTick',[]);
  499. i = i + 1;
  500. end

Reproduce_figures.m at commit 82bf91f, no license · at the source

Overview

Authors: Elisa Leveraro1, Emilio Cipriano2, Giada Lombardi1,3, Daniela Currò4, Francesco Tazza1,5, Giovanni Novi5, Alice Laroni1,5, Paola Gazzola6, Maria Malentacchi7, Loredana Petrucci8, Caterina Lapucci5, Matilde Inglese1,5
ORCID iDs: Emilio Cipriano, Francesco Tazza, Caterina Lapucci
  1. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother–Child Health (DINOGMI), University of Genoa, Genoa 16132, Italy
  2. Department of Physics (DIFI), University of Genoa, Genoa 16146, Italy
  3. Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London SE5 8AB, UK
  4. Department of Neurology, Ospedale San Paolo, Savona 17100, Italy
  5. IRCCS Azienda Ospedaliera Metropolitana—IRCCS AOM, Genova 16132, Italy
  6. Department of Neurology, PA. Micone Hospital, ASL3, Genoa 16154, Italy
  7. Department of Neurology and CRESM, San Luigi Gonzaga University Hospital, Orbassano 10043, Italy
  8. Department of Neurology, Hospital Sant'Andrea, La Spezia 19124, Italy
Institutions: University of Genoa (Italy); King's College London (United Kingdom); Ospedale San Paolo (Italy); IRCCS Azienda Ospedaliera Metropolitana (Italy); Ospedale San Luigi Gonzaga (Italy)
Journal: Brain communications, volume 8, issue 5, article fcag334
Dates: received 14 April 2026; accepted 17 August 2026; published online 1 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag334 · PMID 42729900 · PMCID PMC13563299 · OpenAlex W7204900927
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), other condition (population)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging, Machine learning
Keywords: neuromyelitis optica spectrum disorder, functional connectivity, effective connectivity, default mode network, ventral attention network
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Fondazione Italiana Sclerosi Multipla
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

The neural mechanisms underlying cognitive dysfunction in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD) remain unclear. Functional connectivity (FC) studies have revealed network-level alterations, but little is known about the effective interactions that support compensatory reorganization.

To address this gap, we investigated how alterations in functional and effective connectivity within large-scale networks relate to cognitive performance in AQP4 + NMOSD.

To this end, 20 AQP4 + NMOSD patients and 20 age- and sex-matched healthy controls underwent 3-T structural MRI and resting-state functional MRI. Seed-based and region-of-interest connectivity analyses focused on the default mode network and ventral attention network. Directional interactions were assessed using dynamic causal modelling (DCM). Cognitive performance was evaluated with the Brief International Cognitive Assessment for Multiple Sclerosis and the Controlled Oral Word Association Test (COWAT).

Overall, cognition was quite preserved in patients, except for reduced COWAT scores. Functional connectivity analyses revealed increased intra-network functional connectivity but decreased anterior–posterior default mode network integration. Dynamic causal modelling showed asymmetric prefrontal–posterior interactions, with stronger top-down excitatory influences from prefrontal to posterior regions.

Taken together, these findings indicate that AQP4 + NMOSD patients exhibit altered functional brain organization despite relatively preserved cognition, suggesting a pattern of large-scale network reorganization that may support cognitive performance.

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

Repository

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pzeidman/dcm-peb-example

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 82bf91f0ad0de8f7ca652a55a1261743ff8a30c0, 12 July 2019
Languages: MATLAB (6)
Size: 333 files, 6 scripts
Software Heritage: archived
Found in: the text, “Dynamic causal modelling”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: SPM (5 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
7 files
At the source: github.com/pzeidman/dcm-peb-example.git

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

The data presented in this study are available upon request from the corresponding author.

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 3, 28 September 2026

  • Funding: added Fondazione Italiana Sclerosi Multipla

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 43 references.

Cite

This paper

Leveraro, E., Cipriano, E., Lombardi, G., Currò, D., Tazza, F., Novi, G., Laroni, A., Gazzola, P., Malentacchi, M., Petrucci, L., Lapucci, C., & Inglese, M. (2026). Functional and effective connectivity alterations relate to cognitive performance in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder. Brain communications, 8(5), fcag334. https://doi.org/10.1093/braincomms/fcag334

BibTeX

@article{leveraro2026functional,
author = {Leveraro, Elisa and Cipriano, Emilio and Lombardi, Giada and Currò, Daniela and Tazza, Francesco and Novi, Giovanni and Laroni, Alice and Gazzola, Paola and Malentacchi, Maria and Petrucci, Loredana and Lapucci, Caterina and Inglese, Matilde},
title = {{Functional and effective connectivity alterations relate to cognitive performance in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder}},
journal = {Brain communications},
year = {2026},
month = sep,
volume = {8},
number = {5},
pages = {fcag334},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag334},
url = {https://doi.org/10.1093/braincomms/fcag334},
pmid = {42729900},
pmcid = {PMC13563299}
}

RIS

TY - JOUR
AU - Leveraro, Elisa
AU - Cipriano, Emilio
AU - Lombardi, Giada
AU - Currò, Daniela
AU - Tazza, Francesco
AU - Novi, Giovanni
AU - Laroni, Alice
AU - Gazzola, Paola
AU - Malentacchi, Maria
AU - Petrucci, Loredana
AU - Lapucci, Caterina
AU - Inglese, Matilde
TI - Functional and effective connectivity alterations relate to cognitive performance in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/09/01
VL - 8
IS - 5
SP - fcag334
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag334
UR - https://doi.org/10.1093/braincomms/fcag334
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag334",
"type": "article-journal",
"title": "Functional and effective connectivity alterations relate to cognitive performance in aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder",
"container-title": "Brain communications",
"author": [
{
"family": "Leveraro",
"given": "Elisa"
},
{
"family": "Cipriano",
"given": "Emilio"
},
{
"family": "Lombardi",
"given": "Giada"
},
{
"family": "Currò",
"given": "Daniela"
},
{
"family": "Tazza",
"given": "Francesco"
},
{
"family": "Novi",
"given": "Giovanni"
},
{
"family": "Laroni",
"given": "Alice"
},
{
"family": "Gazzola",
"given": "Paola"
},
{
"family": "Malentacchi",
"given": "Maria"
},
{
"family": "Petrucci",
"given": "Loredana"
},
{
"family": "Lapucci",
"given": "Caterina"
},
{
"family": "Inglese",
"given": "Matilde"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "5",
"page": "fcag334",
"DOI": "10.1093/braincomms/fcag334",
"PMID": "42729900",
"PMCID": "PMC13563299",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag334",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
1
]
]
}
}

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

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