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Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework

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The authors' code

MATLAB · 548 lines · 15 KB · CC-BY-NC-SA-4.0

  1. %% Load the data, plot it and do tensor decomposition
  2. clear; close all; clc
  3. subjID = 'Example_Patient1';
  4. startup_patient;
  6. cd(GridLoc_Folder)
  7. Elec_loc_file = dir('*.xyz');
  8. Elec_loc = readlocs(Elec_loc_file.name);
  10. cd(Spk_Folder)
  11. load([subjID '_hfo_spike_data.mat']);
  12. Data_Signal = pEventData;
  14. cd(Code_Folder)
  16. %==========%
  18. sRate = 500;
  19. channel = 27;
  20. sig = Data_Signal(channel,:);
  22. [wt,F] = cwt(sig,sRate,'FrequencyLimits',[0 200],'VoicesPerOctave',10);
  24. S = abs(wt);
  26. S_complex = wt;
  28. %==========%
  30. Tensor = zeros(size(Data_Signal,1),size(Data_Signal,2),40);
  31. sRate = 500;
  32. inputcase = 2; % 1 is the amplitude , 2 is the real part, 3 is the imaginary part, 4 is complex tensor
  33. for channel = 1:size(Data_Signal,1)
  34. sig = Data_Signal(channel,:);
  36. [wt,F] = cwt(sig,sRate,FrequencyLimits=[0 200]);
  37. S = abs(wt);
  38. S_complex = wt;
  40. switch inputcase
  41. case 1
  42. S = sqrt(squeeze(S));
  43. case 2
  44. S = squeeze(real(S_complex));
  45. case 3
  46. S = squeeze(imag(S_complex));
  47. case 4
  48. S = S_complex;
  49. end
  51. for freq = 1:length(F)
  53. for time = 1:size(Data_Signal,2)
  55. Tensor(channel,time,freq) = S(freq,time);
  56. end
  57. end
  58. end
  61. switch inputcase
  62. case 1
  63. R = 50;
  64. case 2
  65. R = 50;
  66. case 3
  67. R = 50;
  68. case 4
  69. R = 50;
  70. end
  73. % Compute the CPD of the full tensor T.
  74. %options.Algorithm = @cpd_minf;
  75. init = @cpd_gevd;
  76. rng('default');
  77. rng(0)
  78. %
  79. Uhat = cpd(Tensor,R,'Initialization', init);
  81. %====% if simply use the determinstic method, the generalized eignevector
  82. %method for tensor decomposition
  83. %Uhat = cpd_gevd(Tensor,R);
  84. %=====%
  86. Topo_ICA = Uhat{1};
  87. Act = Uhat{2};
  88. Spectrum = Uhat{3};
  90. if(inputcase == 4 )
  91. Topo_ICA = real(Topo_ICA);
  92. Act = real(Act);
  93. Spectrum = real(Spectrum);
  94. end
  96. % save all the components into figures
  97. cd(Fig_Folder)
  98. for r = 1:R
  100. fig1 = figure('visible','off');
  102. subplot (1,3,1);
  103. topoplot(Topo_ICA(:,r),Elec_loc,'plotrad',0.55,'electrodes','on');
  105. subplot (1,3,2);
  106. plot(Act(:,r));
  108. set(gca,'FontSize',10)
  109. subplot (1,3,3);
  110. plot(F,Spectrum(:,r));
  112. set(gca,'FontSize',10)
  113. [~,maxfreqindex] = max(abs(Spectrum(:,r)));
  115. title(['main freq:' num2str(F(maxfreqindex))]);
  116. fig1.Position = [100 100 1000 400];
  117. saveas(fig1,[num2str(r) '.jpg']);
  118. end
  119. %%
  121. %=====Reconstruct the spatialtemporal signal using certain components====%
  122. Topo_ICA = Uhat{1};
  123. Act = Uhat{2};
  124. Spectrum = Uhat{3};
  125. if(inputcase == 4 )
  126. Topo_ICA = real(Topo_ICA);
  127. Act = real(Act);
  128. Spectrum = real(Spectrum);
  129. end
  131. component_selected = [5,49];
  132. freq_select = [14];
  135. Uhat_approx1 = Topo_ICA(:,component_selected); % spatial
  136. Uhat_approx2 = Act(:,component_selected); % temporal
  137. Uhat_approx3 = Spectrum(:,component_selected); % spectral
  139. Uhat_approx2 = Uhat_approx2(:,:);
  140. Uhat_approx3 = Uhat_approx3(freq_select,:);
  141. Uhat_approx = {Uhat_approx1,Uhat_approx2,Uhat_approx3};
  143. Tensor_Groundtruth = Tensor(:,:,freq_select);
  146. Num_TBF = length(component_selected);
  149. cd(GridLoc_Folder);
  151. load('UnconstCentLFD.mat')
  152. Norm_K = svds(K,1)^2;
  153. load('Gradient.mat')
  154. load('Laplacian.mat')
  155. load('NewMesh.mat')
  156. load('LFD_Fxd_Vertices.mat')
  157. load('Edge.mat')
  158. V = kron(V,eye(3));
  159. load('Neighbor.mat')
  160. Number_dipole = numel(K(1,:));
  161. Location = New_Mesh(1:3,:);
  162. TRI = currytri.';
  163. Vertice_Location = curryloc;
  164. Number_edge = numel(V(:,1));
  166. cd(Code_Folder)
  168. % Select an extended source and display it on the cortex
  169. CortexViewInit;
  171. figure
  172. h1 = trisurf(TRI,Vertice_Location(1,:),Vertice_Location(2,:),Vertice_Location(3,:),(0)); colorbar;
  173. set(h1,'EdgeColor','None', 'FaceAlpha',1,'FaceLighting','phong');
  174. hold on
  175. hold off
  176. light_position = [3 3 1];
  177. light('Position',light_position);
  178. light_position = [-3 -3 -1];
  179. light('Position',light_position);
  180. colorbar;
  181. x_max = 1;
  182. %x_max = 0.05;
  183. caxis([-x_max x_max]);
  184. view([-1,0.25,0.15])
  185. grid off
  186. axis off
  187. %colorbar('off')
  188. colormap(cmap)
  190. position = [-38,-23,92]';
  191. J = zeros(Number_dipole,1);
  192. dist = norms(Location - position);
  193. index_dist = find(dist<20);
  195. J_abbas = reshape(repmat(J,1,Num_TBF), [3, Number_dipole/3, Num_TBF]);
  196. J_abbas(:,index_dist,:) = 1;
  197. J_abbas_2 = squeeze(reshape(J_abbas,[Number_dipole,1,Num_TBF]));
  198. % % number of J is equal to number of triangles
  199. J_abbas_1 = squeeze(norms(J_abbas,1));
  200. % %================%
  201. J_abbas_1 = sum(J_abbas_1.^2,2);
  202. % %================%
  203. J_init_plot = (J_abbas_1);
  205. figure
  206. h1 = trisurf_customized(TRI,Vertice_Location(1,:),Vertice_Location(2,:),Vertice_Location(3,:),(J_init_plot)); colorbar;
  207. set(h1,'EdgeColor','None', 'FaceAlpha',1,'FaceLighting','phong');
  208. hold on
  209. hold off
  210. light_position = [3 3 1];
  211. light('Position',light_position);
  212. light_position = [-3 -3 -1];
  213. light('Position',light_position);
  214. colorbar;
  215. x_max = max(sum(abs(J_init_plot),2));
  216. if(size(J_init_plot,1) == 1)
  217. x_max = max(abs(J_init_plot));
  218. end
  219. %x_max = 0.05;
  220. caxis([-x_max x_max]);
  221. view(perspect)
  222. grid off
  223. axis off
  224. %colorbar('off')
  225. colormap(cmap)
  227. %
  230. Uhat_J_ini = J_abbas_2;
  231. sum0 = sum(Uhat_J_ini,2);
  232. index_choose_SourceSpatial = find(sum0~=0);
  234. %Tensor;
  235. N_SourceSpatial = Number_dipole; % Number of dipoles, if unconstrained, then is 3*number dipole
  236. N_Temporal = size(Uhat_approx2,1); % Number of time points
  237. N_Spectral = size(Uhat_approx3,1); % Number of frequencies included in the analysis
  238. N_channel = size(Data_Signal,1);
  239. SensorSpaceTensor = zeros(N_channel,N_Temporal,N_Spectral);
  240. %
  242. %======= this is the matrix multiplcation method
  243. J1 = kron(eye(N_Spectral),J_abbas_2);
  244. TBF = [];
  245. for nn1 = 1:N_Spectral
  246. TBF = [TBF;Uhat_approx3(nn1,:)'.*Uhat_approx2'];
  247. end
  248. JA = J1*conj(TBF);
  250. K1 = kron(eye(N_Spectral),K);
  251. KJA = K1*JA;
  253. KJA1 = KJA(1:N_channel,:);
  254. KJA1_true= SensorSpaceTensor(:,:,1);
  256. TBF_tensor = zeros(Num_TBF,N_Temporal,N_Spectral);
  257. for nn1 = 1:N_Spectral
  258. TBF_tensor(:,:,nn1) = Uhat_approx3(nn1,:)'.*Uhat_approx2';
  259. end
  261. %=====
  262. Phi_tensor = Tensor_Groundtruth;
  264. Sig_st = 135;
  265. Sig_end = 170;
  266. Noise_st = 1;
  267. Noise_end = 100;
  270. num_it_x = 25;
  271. num_it_y = 25;
  272. Number_iteration = 4;
  274. parms = cell(N_Spectral,1);
  276. for iter_TBF = 1:N_Spectral
  277. Phi = squeeze(Phi_tensor(:,:,iter_TBF));
  278. TBF = squeeze(TBF_tensor(:,:,iter_TBF));
  280. Phi_noisy = Phi(:,:);
  282. [Number_sensor,Number_Source] = size(Phi_noisy);
  284. % Estimating Noise and Initializing
  286. N_wht = Phi_noisy - Phi_noisy*TBF.'*pinv(TBF*TBF.')*TBF;
  287. Noise_only = Phi_noisy(:,Noise_st:Noise_end);
  288. SNR_unused = norm(Phi_noisy,'fro')^2/norm(Noise_only,'fro')^2; % a SNR that is not used
  290. % Noise Estimation try different options
  291. Sigma_inv_half = 1*diag(1./std(Noise_only(:,:),[],2));
  292. %==============%
  294. Sigma_inv_half = diag(repmat(mean(diag(Sigma_inv_half)),[Number_sensor 1]));
  296. %==============%
  297. Phi_noisy = Phi_noisy(:,Sig_st:Sig_end);
  298. TBF = TBF (:,Sig_st:Sig_end);
  299. psi = Phi_noisy*(TBF.')/(TBF*(TBF.'));
  301. % Initializing the Solution
  302. SNR_avg = mean(std(Sigma_inv_half*Phi_noisy(:,1:end),[],2).^2)/mean(std(Sigma_inv_half*Noise_only,[],2).^2)
  303. D_W = ((norms(K+10^-20).^-1));
  304. K_n = (Sigma_inv_half*K).*repmat(D_W,[Number_sensor 1]);
  305. psi_n = Sigma_inv_half*psi;
  306. [~, gam] = eig((Sigma_inv_half^1*K)*((Sigma_inv_half*K).'));
  307. %======%
  308. Smf = 10^3; % smooth factor
  309. %======%
  310. J_ini = (K_n.'*pinv(K_n*K_n.' + mean(diag(gam))*(Smf/SNR_avg)*eye(Number_sensor))*psi_n).*repmat(D_W',[1 Num_TBF]);
  311. %J_ini = randn(size(J_ini))*norm(J_ini,1);
  313. close all
  314. [Number_sensor,Number_Source] = size(Phi_noisy);
  315. epsilon = 0.1;
  317. % Noise Power Parameter (Chi_2 Dist Theory)
  318. prob = 0.95;
  319. beta = icdf('chi2',prob,Number_sensor-1); % Spike Avg
  321. Phi_norm = Sigma_inv_half*(Phi_noisy(:,:));
  322. K_norm = Sigma_inv_half*(K);
  324. power = sum((Sigma_inv_half*Noise_only).^2,1);
  326. power_signal = sqrt(sum(Phi_noisy.^2,2))/size(Phi_noisy,2);
  327. power_noise = sqrt(sum(Noise_only.^2,2))/size(Noise_only,2);
  328. SNR_allchannel = power_signal./power_noise;
  329. SNR = mean(SNR_allchannel);
  330. disp(['SNR of the data is ' num2str(SNR)]);
  332. [X,B] = hist(power,100);
  334. Sum_X = cumsum(X)/sum(X);
  335. ind_90 = find(Sum_X > 0.90); B(ind_90(1,1));
  336. ind_95 = find(Sum_X > 0.95); B(ind_95(1,1));
  337. ind_50 = find(Sum_X > 0.50); B(ind_50(1,1));
  339. CF_min = beta/(norm(Phi_norm,'fro')^2/Number_Source);
  340. %==============%
  341. CF = max(beta/B(ind_50(1,1)),CF_min) ;
  342. %==============%
  344. %=============%
  345. alpha_vec = 0.35;
  347. Num_alpha = numel(alpha_vec);
  348. %=============%
  349. % Variables to save results
  350. J_sol = zeros(Number_dipole,Num_TBF,Number_iteration,Num_alpha);
  351. Y_sol = zeros(Number_edge,Num_TBF,Number_iteration,Num_alpha);
  352. W_sol = zeros(Number_dipole,Num_TBF,Number_iteration,Num_alpha);
  353. W_d_sol = zeros(Number_edge,Num_TBF,Number_iteration,Num_alpha);
  354. J_sol_2 = J_sol;
  355. Y_sol_2 = Y_sol;
  357. % Weighting the depth - initializing weighting matrix
  358. M = size(K,2);
  359. N = size(V,1);
  360. W = ones(M,Num_TBF);
  361. W_d = ones(N,Num_TBF);
  363. % Initialize optimization parameters - set number of iterations, etc.
  364. Lambda_min = Norm_K*sqrt(Number_sensor)/max(norms(K.'*Phi_noisy));
  366. C_t = (TBF*(TBF.'));
  367. TBF_norm = TBF.'*(C_t\TBF);
  368. T_norm = (TBF.')/C_t;
  369. alpha = alpha_vec;
  370. lambda = 6*max(1, Lambda_min);
  371. W_v = (V.')*V;
  372. L_v = 1.1*svds(W_v,1);
  373. x_0 = zeros(Number_dipole,Num_TBF);
  374. eps = 10^-3;
  375. betta_tilda = (norm(Phi_norm,'fro')^2/SNR)/CF;
  377. Abbas = K_norm*(K_norm.');
  378. [U, D, ~] = svd(Abbas);
  379. K_U = U.'*K_norm;
  381. parms{iter_TBF}.lambda = lambda;
  382. parms{iter_TBF}.betta_tilda = betta_tilda;
  383. parms{iter_TBF}.Phi_norm = Phi_norm;
  384. parms{iter_TBF}.TBF = TBF;
  385. parms{iter_TBF}.TBF_norm = TBF_norm;
  386. parms{iter_TBF}.T_norm = T_norm;
  387. parms{iter_TBF}.U = U;
  388. parms{iter_TBF}.D = D;
  389. parms{iter_TBF}.K_norm = K_norm;
  390. parms{iter_TBF}.K_U = K_U;
  392. end
  394. num_it_new = 0;
  395. stop_crt = 10^-3;
  397. % Initialize the optimization problem, Loop through till convergence,
  398. % update the weights using the iterative re-weighting schema and solve
  399. % agian until convergence or counter overflow.
  401. stop_itr = 0;
  402. weight_it = 0;
  403. max_weight_itr_num = Number_iteration;
  404. t1 = tic;
  405. res_obj_combined = cell(0);
  406. for i_alpha = 1:Num_alpha
  407. alpha = alpha_vec(i_alpha);
  408. x_0 = J_ini;
  409. stop_itr = 0;
  410. weight_it = 0;
  412. W = ones(M,Num_TBF);
  413. W_d = ones(N,Num_TBF);
  415. while (~stop_itr)
  416. weight_it = weight_it + 1;
  417. if weight_it > max_weight_itr_num
  418. stop_itr = 1;
  419. end
  421. [J,Y,res_obj] = FISTA_ADMM_IRES_Tensor (alpha, W_v, V, L_v,x_0, W, W_d, eps,num_it_x,num_it_y,num_it_new,parms);
  422. res_obj_combined{end+1} = res_obj;
  423. x_0 = J;
  424. J_sol(:,:,weight_it,i_alpha) = J;
  425. Y_sol(:,:,weight_it,i_alpha) = Y;
  427. J_n = reshape(J, [3, Number_dipole/3, Num_TBF]);
  428. Ab_J = squeeze(norms(J_n))+10^-20;
  429. if(size(Ab_J,1) == 1)
  430. Ab_J = Ab_J';
  431. end
  432. Y_n = reshape(V*J, [3, Number_edge/3, Num_TBF]);
  433. Ab_Y = squeeze(norms(Y_n))+10^-20;
  434. if(size(Ab_Y,1) == 1)
  435. Ab_Y = Ab_Y';
  436. end
  438. W_old = W;
  439. W_tr = 1./(0 + Ab_J./(repmat(max(Ab_J),[Number_dipole/3 1])) + (epsilon+10^-16) );
  440. W = circshift(upsample(W_tr,3),[0 0]) + circshift(upsample(W_tr,3),[1 0]) + circshift(upsample(W_tr,3),[2 0]);
  441. W_d_old = W_d;
  442. W_d_tr = 1./(0 + Ab_Y./(repmat(max(Ab_Y),[Number_edge/3 1]))+(epsilon+10^-16));
  443. W_d = circshift(upsample(W_d_tr,3),[0 0]) + circshift(upsample(W_d_tr,3),[1 0]) + circshift(upsample(W_d_tr,3),[2 0]);
  445. W_sol(:,:,weight_it,i_alpha) = W;
  446. W_d_sol(:,:,weight_it,i_alpha) = W_d;
  447. if (norm(W-W_old)/norm(W_old) < stop_crt && norm(W_d-W_d_old)/norm(W_d_old) < stop_crt)
  448. stop_itr = 1;
  449. end
  450. clc
  451. figure
  452. h1 = trisurf_customized(TRI,Vertice_Location(1,:),Vertice_Location(2,:),Vertice_Location(3,:),sum((Ab_J),2)); colorbar
  453. set(h1,'EdgeColor','None', 'FaceAlpha',1,'FaceLighting','phong');
  454. light_position = [3 3 1];
  455. light('Position',light_position);
  456. light_position = [-3 -3 -1];
  457. light('Position',light_position);
  458. colorbar;
  459. x_max = max(abs(sum(J,2)));
  460. if isnan(x_max)
  461. x_max = 1;
  462. end
  463. caxis([-x_max x_max]);
  464. view(232,-5)
  466. grid off
  467. colormap(cmap)
  468. cd(Fig_Folder)
  469. cd('STSI')
  470. name_fig = ['TBF_1st_Iteration_',num2str(weight_it),'.fig'];
  471. saveas(gcf,name_fig)
  472. name_fig = ['TBF_1st_Iteration_',num2str(weight_it),'.jpeg'];
  473. saveas(gcf,name_fig)
  475. end
  477. close all;
  478. end
  479. t_elaps = toc(t1);
  480. disp(['total time elapsed in hours: ' num2str(t_elaps/3600)]);
  482. %%
  483. J = squeeze(J_sol(:,:,end,1));
  484. Num_TBF = size(J,2);
  486. J_abbas = reshape(J, [3, Number_dipole/3, Num_TBF]);
  487. % number of J is equal to number of triangles
  488. J_abbas_1 = squeeze(norms(J_abbas,1));
  489. %================%
  490. J_abbas_2 = J_abbas_1.^2*sum(squeeze(var(TBF_tensor(),[],2)),2);
  491. J_abbas_2 = J_abbas_2(:);
  492. %================%
  493. J_init = (J_abbas_2);
  495. figure
  496. h1 = trisurf_customized(TRI,Vertice_Location(1,:),Vertice_Location(2,:),Vertice_Location(3,:),(J_init)); colorbar;
  497. set(h1,'EdgeColor','None', 'FaceAlpha',1,'FaceLighting','phong');
  498. hold on
  499. hold off
  500. light_position = [3 3 1];
  501. light('Position',light_position);
  502. light_position = [-3 -3 -1];
  503. light('Position',light_position);
  504. colorbar;
  505. x_max = max(sum(abs(J_init),2));
  506. if(size(J_init,1) == 1)
  507. x_max = max(abs(J_init));
  508. end
  509. caxis([-x_max x_max]);
  510. view(-119,12)
  511. grid off
  512. axis off
  513. %colorbar('off')
  514. colormap(cmap)
  516. % Now instead of plotting the whole J distribution, we find clusters (patches)
  517. % of J that has magnitude corssing a threshold
  519. J_col = J_abbas_2;
  520. %================%
  521. Thr = 1/64;
  522. %================%
  523. J_col(abs(J_col)<Thr*max(abs(J_col)))=0;
  524. IND = Find_Patch(Edge, 1, J_col);
  525. J_init = IND;
  527. J_seg = IND;
  528. J_abbas_2 = J_abbas_2.*IND;
  530. figure
  531. h1 = trisurf_customized(TRI,Vertice_Location(1,:),Vertice_Location(2,:),Vertice_Location(3,:),(J_abbas_2));
  532. hold off
  533. colorbar;
  534. set(h1,'EdgeColor','None', 'FaceAlpha',1,'FaceLighting','phong');
  535. light_position = [3 3 1];
  536. light('Position',light_position);
  537. light_position = [-3 -3 -1];
  538. light('Position',light_position);
  539. colorbar;
  540. x_max = max(sum(abs(J_abbas_2),2));
  541. caxis([-x_max x_max]);
  543. view(232,-5)
  544. grid off
  545. axis on
  546. colormap(cmap)
  548. cd(Code_Folder)

Example_Patient1_Event1_pHFO.m at commit 98ec2c9, under CC-BY-NC-SA-4.0 · at the source

Overview

Authors: Xiyuan Jiang1, Zhengxiang Cai1, Colton Gonsisko1, Gregory A Worrell2, Bin He1
ORCID iDs: Xiyuan Jiang, Zhengxiang Cai, Colton Gonsisko, Gregory A Worrell, Bin He
  1. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
  2. Department of Neurology, Mayo Clinic, Rochester, MN 55905
Institutions: Carnegie Mellon University (United States); Mayo Clinic (United States)
Journal: n/a, volume 122, issue 50, article e2510015122
Dates: received 23 April 2025; accepted 26 October 2025; published online 8 December 2025; in print 16 December 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1073/pnas.2510015122 · PMCID PMC12718345 · OpenAlex W4417134461
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), epilepsy (population), clinical / translational (subfield)
Methods: Single-unit activity, calcium imaging, Physiology & signal measures, Spectral & time-frequency
Keywords: source imaging, source localization, EEG, interictal biomarker, epilepsy
MeSH: Brain*, Brain Mapping*, Drug Resistant Epilepsy*, Adolescent, Adult, Electroencephalography, Female, Humans, Male, Middle Aged, Seizures, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HHS | NIH | National Institute of Biomedical Imaging and Bioengineering (EB029365); NINDS NIH HHS (R01 NS127849, RF1 NS131069, R01 NS124564, R01 NS096761, RF1 NS124564); NIBIB NIH HHS (T32 EB029365); HHS | NIH | National Institute of Neurological Disorders and Stroke (NS096761 NS127849 NS131069 NS124564)
Citations: cited by 5 papers (Europe PMC); 79 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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bfinl/STSI

License: CC-BY-NC-SA-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 98ec2c90434f01624cd843b374f3ffc40c11dc28, 8 December 2025
Languages: MATLAB (15)
Size: 36 files, 15 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (4 files), Wavelet Toolbox (3 files), FieldTrip (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
17 files
At the source: github.com/bfinl/STSI

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

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;
  • 15 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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.

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1073/pnas.2510015122.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 12 MeSH terms, 4 funders, 73 references.

Cite

This paper

Jiang, X., Cai, Z., Gonsisko, C., Worrell, G. A., & He, B. (2025). Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework. Proceedings of the National Academy of Sciences of the United States of America, 122(50), e2510015122. https://doi.org/10.1073/pnas.2510015122

BibTeX

@article{jiang2025mapping,
author = {Jiang, Xiyuan and Cai, Zhengxiang and Gonsisko, Colton and Worrell, Gregory A and He, Bin},
title = {{Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2025},
volume = {122},
number = {50},
pages = {e2510015122},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2510015122},
url = {https://doi.org/10.1073/pnas.2510015122},
pmcid = {PMC12718345}
}

RIS

TY - JOUR
AU - Jiang, Xiyuan
AU - Cai, Zhengxiang
AU - Gonsisko, Colton
AU - Worrell, Gregory A
AU - He, Bin
TI - Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2025
DA - 2025
VL - 122
IS - 50
SP - e2510015122
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2510015122
UR - https://doi.org/10.1073/pnas.2510015122
LA - en
ER -

CSL-JSON

{
"id": "10.1073/pnas.2510015122",
"type": "article-journal",
"title": "Mapping epileptogenic brain using a unified spatial–temporal–spectral source imaging framework",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
{
"family": "Jiang",
"given": "Xiyuan"
},
{
"family": "Cai",
"given": "Zhengxiang"
},
{
"family": "Gonsisko",
"given": "Colton"
},
{
"family": "Worrell",
"given": "Gregory A"
},
{
"family": "He",
"given": "Bin"
}
],
"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "122",
"issue": "50",
"page": "e2510015122",
"DOI": "10.1073/pnas.2510015122",
"PMCID": "PMC12718345",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
"URL": "https://doi.org/10.1073/pnas.2510015122",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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

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