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Shared subcortical-default mode morphological dysconnectivity in adolescent bipolar disorder, major depressive disorder and schizophrenia.

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  1. [1] § Materials and methods › Statistics and reproducibility › Between-group differences in single-subject morphological brain networks ↔ gretna_TFNBS_ftest.m, lines 1–102 · score 0.81 · height enhancement parameters, permutation procedure, post hoc, matrix, TFNBS, threshold
  2. [2] § Materials and methods › Relationships between disorder-related alterations in single-subject morphological brain networks and neurotransmitter maps ↔ moran_randomization.m, the whole file · a weak match · score 0.60 · spectral randomization, Moran, space, models, correlation
  3. [3] § Materials and methods › Statistics and reproducibility › Between-group differences in single-subject morphological brain networks ↔ gretna_FDR.m, the whole file · a weak match · score 0.51 · discovery rate, nonparametric, FDR, threshold

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The authors' code

MATLAB · 589 lines · 28 KB · CC-BY-4.0 · 1 match

  1. function [TFNBS_Result] = gretna_TFNBS_ftest(Data_path, File_filter, M, Mask_net, Pthr, E, H, Cova_path)
  3. %==========================================================================
  4. % This function is used to perform the TFNBS algorithm to search
  5. % connections that show significant group effects for one-way experimental
  6. % design (more than two groups). NB. 1) self-connections are allowed in this
  7. % analysis by setting diagnoal elements to be 1 in Mask_net; 2) only data
  8. % but not covariates are randomly relabeled by reshuffling group membership
  9. % in the permutation procedure.
  10. %
  11. %
  12. % Syntax: function [TFNBS_Result] = gretna_TFNBS_ftest(Data_path, File_filter, M, Mask_net, Pthr, E, H, Cova_path)
  13. %
  14. % Inputs:
  15. % Data_path:
  16. % N*1 cell with each cell denoting the directory where
  17. % connectivity matrices are sorted for each group.
  18. % File_filter:
  19. % N*1 cell with each cell denoting the prefix of those
  20. % connectivity matrices to be processed for each group.
  21. % M:
  22. % The number of permutations.
  23. % Mask_net:
  24. % The matrix mask containing 0 and 1 and only connections
  25. % corresponding to 1 are fed in the TFNBS computation.
  26. % Pthr:
  27. % The FWE-corrected p-value threshold.
  28. % E:
  29. % Extension enhancement parameters(default = 0.5).
  30. % H:
  31. % Height enhancement parameters(default = 2).
  32. % Cova_path (opt):
  33. % N*1 cell with each cell denoting the directory and filename
  34. % of covariates for each group. NB. the group order should
  35. % be the same to that of Data_path.
  36. %
  37. % Output:
  38. % TFNBS_Result.:
  39. % Fmat:
  40. % F values for each edge of interest.
  41. % P_Fmat:
  42. % P values of Fmat.
  43. % DF_Fmat:
  44. % Degree of freedom of Fmat.
  45. % Tmat:
  46. % Post hoc T values for each edge of interest. For n groups,
  47. % the order is: g(1)-g(2), g(1)-g(3), ..., g(1)-g(n),
  48. % g(2)-g(3), ..., g(2)-g(n), ..., g(n-1)-g(n).
  49. % P_Tmat:
  50. % P values of Tmat.
  51. % DF_Tmat:
  52. % Degree of freedom of Tmat.
  53. % TFNBS_f_score:
  54. % TFNBS-based F values for each edge of interest.
  55. % TFNBS_f_pval:
  56. % P values of TFNBS_f_score (FWE-corrected).
  57. % TFNBS_t_score:
  58. % Post hoc TFNBS-based T values for each edge of interest.
  59. % TFNBS_t_pval:
  60. % P values of TFNBS_t_score (un-corrected, two-tailed).
  61. % N_sig_edge:
  62. % The number of edges showing significant group effect.
  63. % Reg_sig_edge:
  64. % Nodal indices linked by those edges showing significant
  65. % group effect.
  66. % TFNBS_f_score_sig_edge:
  67. % TFNBS-based F values for those edges showing significant
  68. % group effect.
  69. % TFNBS_f_pval_sig_edge:
  70. % P values of TFNBS_f_score_sig_edge (FWE-corrected).
  71. % TFNBS_t_score_sig_edge:
  72. % Post hoc TFNBS-based T values for those edges showing
  73. % significant group effect.
  74. % TFNBS_t_pval_sig_edge:
  75. % P values of TFNBS_t_score_sig_edge (un-corrected, two-tailed).
  76. %
  77. % References:
  78. % Baggio et al., 2018, Statistical inference in brain graphs using threshold-free
  79. % network-based statistics.
  80. %
  81. % Rui WANG, IBRR, SCNU, Guangzhou, 2019/10/11, [email hidden]
  82. % Yuping YANG, IBRR, SCNU, Guangzhou, 2019/11/30, [email hidden]
  83. % Jinhui WANG, IBRR, SCNU, Guangzhou, 2019/11/30, [email hidden]
  84. %==========================================================================
  86. if nargin == 5
  87. E = 0.5; H = 2;
  88. end
  90. if nargin == 6
  91. H = 2;
  92. end
  94. Ind_mask = find(triu(Mask_net)); % to support functional network analysis, that is, allow within-network self-connections
  96. N_group = length(Data_path);
  97. if N_group == 2
  98. error('This function is used for comparison among more than two groups! For two groups, use gretna_TFNBS_ttest.m!');
  99. end
  101. N_sub = zeros(N_group,1);
  102. Data = cell(N_group,1);
  104. %% reorganize data
  105. for i_group = 1:N_group
  107. cd (Data_path{i_group})
  108. Mat_file = ls([File_filter{i_group} '*.mat']);
  110. if ~isempty(Mat_file)
  111. N_sub(i_group) = size(Mat_file,1);
  112. Data_igroup = zeros(N_sub(i_group),length(Ind_mask));
  114. for i_sub = 1:N_sub(i_group)
  115. Ind_mat = load(Mat_file(i_sub,:));
  116. Name_field = fieldnames(Ind_mat);
  117. Ind_mat = Ind_mat.(Name_field{1});
  118. Data_igroup(i_sub,:) = Ind_mat(Ind_mask);
  119. end
  120. else
  121. Mat_file = ls([File_filter{i_group} '*.txt']);
  123. N_sub(i_group) = size(Mat_file,1);
  124. Data_igroup = zeros(N_sub(i_group),length(Ind_mask));
  126. for i_sub = 1:N_sub(i_group)
  127. Ind_mat = load(Mat_file(i_sub,:));
  128. Data_igroup(i_sub,:) = Ind_mat(Ind_mask);
  129. end
  130. end
  131. Data{i_group} = Data_igroup;
  132. end
  134. %% ancova or anova for real data
  135. if nargin == 8
  136. N_cova = length(Cova_path);
  138. if N_cova ~= N_group
  139. error('The number of covariate files is not equal to the number of groups!');
  140. end
  142. Cova = cell(N_cova,1);
  144. for i_cova = 1:N_cova
  145. [~,~,ext] = fileparts(Cova_path{i_cova});
  147. switch ext
  148. case '.txt'
  149. Cova{i_cova} = load(Cova_path{i_cova});
  150. case '.mat'
  151. Ind_cova = load(Cova_path{i_cova});
  152. Name_field = fieldnames(Ind_cova);
  153. Cova{i_cova} = Ind_cova.(Name_field{1});
  154. end
  155. end
  156. [Fvec,P_Fvec,DF_Fvec,Post_Tvec, Post_P_Tvec, Post_DF_Tvec] = gretna_ancova1(Data,Cova);
  157. else
  158. [Fvec,P_Fvec,DF_Fvec,Post_Tvec, Post_P_Tvec, Post_DF_Tvec] = gretna_ancova1(Data);
  159. end
  161. %% TFNBS for real data
  162. % F test
  163. Fmat = zeros(size(Mask_net));
  164. P_Fmat = zeros(size(Mask_net));
  165. DF_Fmat.group = zeros(size(Mask_net));
  166. DF_Fmat.error = zeros(size(Mask_net));
  168. Fmat(Ind_mask) = Fvec;
  169. Fmat = Fmat + triu(Fmat,1)'; % to support functional network analysis, that is, allow within-network self-connections
  170. Fmat(isnan(Fmat)) = 0;
  172. P_Fmat(Ind_mask) = P_Fvec;
  173. P_Fmat = P_Fmat + triu(P_Fmat,1)'; % to support functional network analysis, that is, allow within-network self-connections
  174. DF_Fmat.group(Ind_mask) = DF_Fvec.group;
  175. DF_Fmat.group = DF_Fmat.group + triu(DF_Fmat.group,1)'; % to support functional network analysis, that is, allow within-network self-connections
  176. DF_Fmat.error(Ind_mask) = DF_Fvec.error;
  177. DF_Fmat.error = DF_Fmat.error + triu(DF_Fmat.error,1)'; % to support functional network analysis, that is, allow within-network self-connections
  179. F_thr = linspace(0,max(Fvec),101);
  180. TFNBS_f_score = zeros([size(Mask_net),101]);
  182. for i_thr = 1:101
  183. F_suprathres = Fmat;
  184. F_suprathres(F_suprathres >= F_thr(i_thr)) = nan;
  185. F_suprathres(~isnan(F_suprathres)) = 0;
  186. F_suprathres(isnan(F_suprathres)) = 1;
  188. [F_ci, F_n_node] = components(sparse(F_suprathres));
  189. F_n_com = length(F_n_node);
  191. for i_com = 1:F_n_com
  192. F_ind_subn = find(F_ci == i_com);
  194. F_subn = F_suprathres(F_ind_subn,F_ind_subn);
  195. F_subn = triu(F_subn); % to support functional network analysis, that is, allow within-network self-connections
  196. F_size = sum(F_subn(:)); % to support functional network analysis, that is, allow within-network self-connections
  198. F_subn(F_subn~=0) = (F_size^E)*(F_thr(i_thr)^H);
  199. F_subn = F_subn + triu(F_subn,1)';
  200. TFNBS_f_score(F_ind_subn,F_ind_subn,i_thr) = F_subn;
  201. end
  202. end
  204. TFNBS_f_score = sum(TFNBS_f_score,3);
  206. % post hoc T test
  207. Tmat = zeros([size(Mask_net),size(Post_Tvec,1)]);
  208. P_Tmat = zeros([size(Mask_net),size(Post_Tvec,1)]);
  209. DF_Tmat = zeros([size(Mask_net),size(Post_Tvec,1)]);
  211. for i_post = 1:size(Post_Tvec,1)
  212. Tmat_i_post = zeros(size(Mask_net));
  213. P_Tmat_i_post = zeros(size(Mask_net));
  214. DF_Tmat_i_post = zeros(size(Mask_net));
  216. Tmat_i_post(Ind_mask) = Post_Tvec(i_post,:);
  217. Tmat_i_post = Tmat_i_post + triu(Tmat_i_post,1)'; % to support functional network analysis, that is, allow within-network self-connections
  218. Tmat(:,:,i_post) = Tmat_i_post;
  219. Tmat(isnan(Tmat)) = 0;
  221. P_Tmat_i_post(Ind_mask) = Post_P_Tvec(i_post,:);
  222. P_Tmat_i_post = P_Tmat_i_post + triu(P_Tmat_i_post,1)'; % to support functional network analysis, that is, allow within-network self-connections
  223. P_Tmat(:,:,i_post) = P_Tmat_i_post;
  225. DF_Tmat_i_post(Ind_mask) = Post_DF_Tvec(i_post,:);
  226. DF_Tmat_i_post = DF_Tmat_i_post + triu(DF_Tmat_i_post,1)'; % to support functional network analysis, that is, allow within-network self-connections
  227. DF_Tmat(:,:,i_post) = DF_Tmat_i_post;
  228. end
  230. TFNBS_t_score = zeros([size(Mask_net),size(Post_Tvec,1)]);
  231. for i_post = 1:size(Post_Tvec,1)
  232. TFNBS_t_score_i_post = zeros([size(Mask_net),101]);
  234. % only positive
  235. if min(Post_Tvec(i_post,:)) >= 0
  236. T_thr_pos = linspace(0,max(Post_Tvec(i_post,:)),101);
  238. for i_thr = 1:101
  239. T_suprathres_pos = Tmat(:,:,i_post);
  240. T_suprathres_pos(T_suprathres_pos >= T_thr_pos(i_thr)) = nan;
  241. T_suprathres_pos(~isnan(T_suprathres_pos)) = 0;
  242. T_suprathres_pos(isnan(T_suprathres_pos)) = 1;
  244. [T_ci_pos, T_n_node_pos] = components(sparse(T_suprathres_pos));
  245. T_n_com_pos = length(T_n_node_pos);
  247. for i_com = 1:T_n_com_pos
  248. T_ind_subn_pos = find(T_ci_pos == i_com);
  250. T_subn_pos = T_suprathres_pos(T_ind_subn_pos,T_ind_subn_pos);
  251. T_subn_pos = triu(T_subn_pos); % to support functional network analysis, that is, allow within-network self-connections
  252. T_size_pos = sum(T_subn_pos(:)); % to support functional network analysis, that is, allow within-network self-connections
  254. T_subn_pos(T_subn_pos~=0) = (T_size_pos^E)*(T_thr_pos(i_thr)^H);
  255. T_subn_pos = T_subn_pos + triu(T_subn_pos,1)';
  256. TFNBS_t_score_i_post(T_ind_subn_pos,T_ind_subn_pos,i_thr) = T_subn_pos;
  257. end
  258. end
  260. TFNBS_t_score(:,:,i_post) = sum(TFNBS_t_score_i_post,3);
  262. % only negative
  263. elseif max(Post_Tvec(i_post,:)) <= 0
  264. T_thr_neg = -linspace(0,-min(Post_Tvec(i_post,:)),101);
  266. for i_thr = 1:101
  267. T_suprathres_neg = Tmat(:,:,i_post);
  268. T_suprathres_neg(T_suprathres_neg <= T_thr_neg(i_thr)) = nan;
  269. T_suprathres_neg(~isnan(T_suprathres_neg)) = 0;
  270. T_suprathres_neg(isnan(T_suprathres_neg)) = 1;
  272. [T_ci_neg, T_n_node_neg] = components(sparse(T_suprathres_neg));
  273. T_n_com_neg = length(T_n_node_neg);
  275. for i_com = 1:T_n_com_neg
  276. T_ind_subn_neg = find(T_ci_neg == i_com);
  278. T_subn_neg = T_suprathres_neg(T_ind_subn_neg,T_ind_subn_neg);
  279. T_subn_neg = triu(T_subn_neg); % to support functional network analysis, that is, allow within-network self-connections
  280. T_size_neg = sum(T_subn_neg(:)); % to support functional network analysis, that is, allow within-network self-connections
  282. T_subn_neg(T_subn_neg~=0) = -(T_size_neg^E)*(T_thr_neg(i_thr)^H);
  283. T_subn_neg = T_subn_neg + triu(T_subn_neg,1)';
  284. TFNBS_t_score_i_post(T_ind_subn_neg,T_ind_subn_neg,i_thr) = T_subn_neg;
  285. end
  286. end
  288. TFNBS_t_score(:,:,i_post) = sum(TFNBS_t_score_i_post,3);
  290. else
  291. T_thr_pos = linspace(0,max(Post_Tvec(i_post,:)),101);
  292. T_thr_neg = -linspace(0,-min(Post_Tvec(i_post,:)),101);
  294. for i_thr = 1:101
  295. % positive
  296. T_suprathres_pos = Tmat(:,:,i_post);
  297. T_suprathres_pos(T_suprathres_pos >= T_thr_pos(i_thr)) = nan;
  298. T_suprathres_pos(~isnan(T_suprathres_pos)) = 0;
  299. T_suprathres_pos(isnan(T_suprathres_pos)) = 1;
  301. [T_ci_pos, T_n_node_pos] = components(sparse(T_suprathres_pos));
  302. T_n_com_pos = length(T_n_node_pos);
  304. Tmp_pos = zeros(size(Mask_net));
  305. for i_com = 1:T_n_com_pos
  306. T_ind_subn_pos = find(T_ci_pos == i_com);
  308. T_subn_pos = T_suprathres_pos(T_ind_subn_pos,T_ind_subn_pos);
  309. T_subn_pos = triu(T_subn_pos); % to support functional network analysis, that is, allow within-network self-connections
  310. T_size_pos = sum(T_subn_pos(:)); % to support functional network analysis, that is, allow within-network self-connections
  312. T_subn_pos(T_subn_pos~=0) = (T_size_pos^E)*(T_thr_pos(i_thr)^H);
  313. T_subn_pos = T_subn_pos + triu(T_subn_pos,1)';
  314. Tmp_pos(T_ind_subn_pos,T_ind_subn_pos) = T_subn_pos;
  315. end
  317. % negative
  318. T_suprathres_neg = Tmat(:,:,i_post);
  319. T_suprathres_neg(T_suprathres_neg <= T_thr_neg(i_thr)) = nan;
  320. T_suprathres_neg(~isnan(T_suprathres_neg)) = 0;
  321. T_suprathres_neg(isnan(T_suprathres_neg)) = 1;
  323. [T_ci_neg, T_n_node_neg] = components(sparse(T_suprathres_neg));
  324. T_n_com_neg = length(T_n_node_neg);
  326. Tmp_neg = zeros(size(Mask_net));
  327. for i_com = 1:T_n_com_neg
  328. T_ind_subn_neg = find(T_ci_neg == i_com);
  330. T_subn_neg = T_suprathres_neg(T_ind_subn_neg,T_ind_subn_neg);
  331. T_subn_neg = triu(T_subn_neg); % to support functional network analysis, that is, allow within-network self-connections
  332. T_size_neg = sum(T_subn_neg(:)); % to support functional network analysis, that is, allow within-network self-connections
  334. T_subn_neg(T_subn_neg~=0) = -(T_size_neg^E)*(T_thr_neg(i_thr)^H);
  335. T_subn_neg = T_subn_neg + triu(T_subn_neg,1)';
  336. Tmp_neg(T_ind_subn_neg,T_ind_subn_neg) = T_subn_neg;
  337. end
  339. TFNBS_t_score_i_post(:,:,i_thr) = Tmp_pos + Tmp_neg;
  340. end
  342. TFNBS_t_score(:,:,i_post) = sum(TFNBS_t_score_i_post,3);
  343. end
  344. end
  346. %% permutation test
  347. Data = cell2mat(Data);
  349. Ind_start = zeros(N_group,1);
  350. Ind_start(1) = 1;
  351. for i_group = 1:N_group-1
  352. Ind_start(i_group+1) = sum(N_sub(1:i_group))+1;
  353. end
  355. max_TFNBS_f_score_rand = zeros(M,1);
  356. TFNBS_t_score_rand = zeros([size(Mask_net),size(Post_Tvec,1),M]);
  358. for i_per = 1:M
  359. Ind_rand = randperm(sum(N_sub));
  360. Data_rand = cell(N_group, 1);
  362. for i_group = 1:N_group
  363. Data_rand{i_group} = Data(Ind_rand(Ind_start(i_group):sum(N_sub(1:i_group))),:);
  364. end
  366. if nargin == 8
  367. [Fvec_rand,~,~,Post_Tvec_rand,~,~] = gretna_ancova1(Data_rand,Cova);
  368. else
  369. [Fvec_rand,~,~,Post_Tvec_rand,~,~] = gretna_ancova1(Data_rand);
  370. end
  372. % F test
  373. Fmat_rand = zeros(size(Mask_net));
  374. Fmat_rand(Ind_mask) = Fvec_rand;
  375. Fmat_rand = Fmat_rand + triu(Fmat_rand,1)'; % to support functional network analysis, that is, allow within-network self-connections
  376. Fmat_rand(isnan(Fmat_rand)) = 0;
  378. F_thr_rand = linspace(0, max(Fvec_rand), 101);
  379. TFNBS_f_score_rand = zeros([size(Mask_net),101]);
  381. for i_thr = 1:101
  382. F_suprathres_rand = Fmat_rand;
  383. F_suprathres_rand(F_suprathres_rand >= F_thr_rand(i_thr)) = nan;
  384. F_suprathres_rand(~isnan(F_suprathres_rand)) = 0;
  385. F_suprathres_rand(isnan(F_suprathres_rand)) = 1;
  387. [F_ci_rand, F_n_node_rand] = components(sparse(F_suprathres_rand));
  388. F_n_com_rand = length(F_n_node_rand);
  390. for i_com = 1:F_n_com_rand
  391. F_ind_subn_rand = find(F_ci_rand == i_com);
  393. F_subn_rand = F_suprathres_rand(F_ind_subn_rand,F_ind_subn_rand);
  394. F_subn_rand = triu(F_subn_rand); % to support functional network analysis, that is, allow within-network self-connections
  395. F_size_rand = sum(F_subn_rand(:)); % to support functional network analysis, that is, allow within-network self-connections
  397. F_subn_rand(F_subn_rand~=0) = (F_size_rand^E)*(F_thr_rand(i_thr)^H);
  398. F_subn_rand = F_subn_rand + triu(F_subn_rand,1)';
  399. TFNBS_f_score_rand(F_ind_subn_rand,F_ind_subn_rand,i_thr) = F_subn_rand;
  400. end
  401. end
  403. TFNBS_f_score_rand = sum(TFNBS_f_score_rand,3);
  404. max_TFNBS_f_score_rand(i_per) = max(TFNBS_f_score_rand(:));
  406. % for post hoc T test
  407. for i_post = 1:size(Post_Tvec_rand,1)
  408. Tmat_rand = zeros(size(Mask_net));
  409. Tmat_rand(Ind_mask) = Post_Tvec_rand(i_post,:);
  410. Tmat_rand = Tmat_rand + triu(Tmat_rand,1)'; % to support functional network analysis, that is, allow within-network self-connections
  411. Tmat_rand(isnan(Tmat_rand)) = 0;
  412. TFNBS_t_score_ipost_rand = zeros([size(Mask_net),101]);
  414. % only positive
  415. if min(Post_Tvec_rand(i_post,:)) >= 0
  416. T_thr_pos_rand = linspace(0,max(Post_Tvec_rand(i_post,:)),101);
  418. for i_thr = 1:101
  419. T_suprathres_pos_rand = Tmat_rand;
  420. T_suprathres_pos_rand(T_suprathres_pos_rand >= T_thr_pos_rand(i_thr)) = nan;
  421. T_suprathres_pos_rand(~isnan(T_suprathres_pos_rand)) = 0;
  422. T_suprathres_pos_rand(isnan(T_suprathres_pos_rand)) = 1;
  424. [T_ci_pos_rand, T_n_node_pos_rand] = components(sparse(T_suprathres_pos_rand));
  425. T_n_com_pos_rand = length(T_n_node_pos_rand);
  427. for i_com = 1:T_n_com_pos_rand
  428. T_ind_subn_pos_rand = find(T_ci_pos_rand == i_com);
  430. T_subn_pos_rand = T_suprathres_pos_rand(T_ind_subn_pos_rand,T_ind_subn_pos_rand);
  431. T_subn_pos_rand = triu(T_subn_pos_rand); % to support functional network analysis, that is, allow within-network self-connections
  432. T_size_pos_rand = sum(T_subn_pos_rand(:)); % to support functional network analysis, that is, allow within-network self-connections
  434. T_subn_pos_rand(T_subn_pos_rand~=0) = (T_size_pos_rand^E)*(T_thr_pos_rand(i_thr)^H);
  435. T_subn_pos_rand = T_subn_pos_rand + triu(T_subn_pos_rand,1)';
  436. TFNBS_t_score_ipost_rand(T_ind_subn_pos_rand,T_ind_subn_pos_rand,i_thr) = T_subn_pos_rand;
  437. end
  438. end
  440. TFNBS_t_score_rand(:,:,i_post,i_per) = sum(TFNBS_t_score_ipost_rand,3);
  442. % only negative
  443. elseif max(Post_Tvec_rand(i_post,:)) <= 0
  444. T_thr_neg_rand = -linspace(0,-min(Post_Tvec_rand(i_post,:)),101);
  446. for i_thr = 1:101
  447. T_suprathres_neg_rand = Tmat_rand;
  448. T_suprathres_neg_rand(T_suprathres_neg_rand <= T_thr_neg_rand(i_thr)) = nan;
  449. T_suprathres_neg_rand(~isnan(T_suprathres_neg_rand)) = 0;
  450. T_suprathres_neg_rand(isnan(T_suprathres_neg_rand)) = 1;
  452. [T_ci_neg_rand, T_n_node_neg_rand] = components(sparse(T_suprathres_neg_rand));
  453. T_n_com_neg_rand = length(T_n_node_neg_rand);
  455. for i_com = 1:T_n_com_neg_rand
  456. T_ind_subn_neg_rand = find(T_ci_neg_rand == i_com);
  458. T_subn_neg_rand = T_suprathres_neg_rand(T_ind_subn_neg_rand,T_ind_subn_neg_rand);
  459. T_subn_neg_rand = triu(T_subn_neg_rand); % to support functional network analysis, that is, allow within-network self-connections
  460. T_size_neg_rand = sum(T_subn_neg_rand(:)); % to support functional network analysis, that is, allow within-network self-connections
  462. T_subn_neg_rand(T_subn_neg_rand~=0) = -(T_size_neg_rand^E)*(T_thr_neg_rand(i_thr)^H);
  463. T_subn_neg_rand = T_subn_neg_rand + triu(T_subn_neg_rand,1)';
  464. TFNBS_t_score_ipost_rand(T_ind_subn_neg_rand,T_ind_subn_neg_rand,i_thr) = T_subn_neg_rand;
  465. end
  466. end
  468. TFNBS_t_score_rand(:,:,i_post,i_per) = sum(TFNBS_t_score_ipost_rand,3);
  470. else
  471. T_thr_pos_rand = linspace(0,max(Post_Tvec_rand(i_post,:)),101);
  472. T_thr_neg_rand = -linspace(0,-min(Post_Tvec_rand(i_post,:)),101);
  474. for i_thr = 1:101
  475. % positive
  476. T_suprathres_pos_rand = Tmat_rand;
  477. T_suprathres_pos_rand(T_suprathres_pos_rand >= T_thr_pos_rand(i_thr)) = nan;
  478. T_suprathres_pos_rand(~isnan(T_suprathres_pos_rand)) = 0;
  479. T_suprathres_pos_rand(isnan(T_suprathres_pos_rand)) = 1;
  481. [T_ci_pos_rand, T_n_node_pos_rand] = components(sparse(T_suprathres_pos_rand));
  482. T_n_com_pos_rand = length(T_n_node_pos_rand);
  484. Tmp_pos_rand = zeros(size(Mask_net));
  485. for i_com = 1:T_n_com_pos_rand
  486. T_ind_subn_pos_rand = find(T_ci_pos_rand == i_com);
  488. T_subn_pos_rand = T_suprathres_pos_rand(T_ind_subn_pos_rand,T_ind_subn_pos_rand);
  489. T_subn_pos_rand = triu(T_subn_pos_rand); % to support functional network analysis, that is, allow within-network self-connections
  490. T_size_pos_rand = sum(T_subn_pos_rand(:)); % to support functional network analysis, that is, allow within-network self-connections
  492. T_subn_pos_rand(T_subn_pos_rand~=0) = (T_size_pos_rand^E)*(T_thr_pos_rand(i_thr)^H);
  493. T_subn_pos_rand = T_subn_pos_rand + triu(T_subn_pos_rand,1)';
  494. Tmp_pos_rand(T_ind_subn_pos_rand,T_ind_subn_pos_rand) = T_subn_pos_rand;
  495. end
  497. % negative
  498. T_suprathres_neg_rand = Tmat_rand;
  499. T_suprathres_neg_rand(T_suprathres_neg_rand <= T_thr_neg_rand(i_thr)) = nan;
  500. T_suprathres_neg_rand(~isnan(T_suprathres_neg_rand)) = 0;
  501. T_suprathres_neg_rand(isnan(T_suprathres_neg_rand)) = 1;
  503. [T_ci_neg_rand, T_n_node_neg_rand] = components(sparse(T_suprathres_neg_rand));
  504. T_n_com_neg_rand = length(T_n_node_neg_rand);
  506. Tmp_neg_rand = zeros(size(Mask_net));
  507. for i_com = 1:T_n_com_neg_rand
  508. T_ind_subn_neg_rand = find(T_ci_neg_rand == i_com);
  510. T_subn_neg_rand = T_suprathres_neg_rand(T_ind_subn_neg_rand,T_ind_subn_neg_rand);
  511. T_subn_neg_rand = triu(T_subn_neg_rand); % to support functional network analysis, that is, allow within-network self-connections
  512. T_size_neg_rand = sum(T_subn_neg_rand(:)); % to support functional network analysis, that is, allow within-network self-connections
  514. T_subn_neg_rand(T_subn_neg_rand~=0) = -(T_size_neg_rand^E)*(T_thr_neg_rand(i_thr)^H);
  515. T_subn_neg_rand = T_subn_neg_rand + triu(T_subn_neg_rand,1)';
  516. Tmp_neg_rand(T_ind_subn_neg_rand,T_ind_subn_neg_rand) = T_subn_neg_rand;
  517. end
  519. TFNBS_t_score_ipost_rand(:,:,i_thr) = Tmp_pos_rand + Tmp_neg_rand;
  520. end
  522. TFNBS_t_score_rand(:,:,i_post,i_per) = sum(TFNBS_t_score_ipost_rand,3);
  523. end
  524. end
  526. fprintf('%d permutation test(s) have been done\n', i_per);
  528. end
  530. %% calculate p values
  531. % FWE-corrected p values for TFNBS_f_score
  532. TFNBS_f_pval = zeros(size(Mask_net));
  533. for i_edge = 1:length(Ind_mask)
  534. TFNBS_f_pval(Ind_mask(i_edge)) = sum(max_TFNBS_f_score_rand > TFNBS_f_score(Ind_mask(i_edge)))/(M+1);
  535. end
  536. TFNBS_f_pval = TFNBS_f_pval + triu(TFNBS_f_pval,1)'; % to support functional network analysis, that is, allow within-network self-connections
  538. % calculate uncorrected, two-tailed p value for post hoc TFNBS_t_score
  539. TFNBS_t_pval = zeros([size(Mask_net),size(Post_Tvec,1)]);
  540. for i_post = 1:size(Post_Tvec,1)
  541. TFNBS_t_pval_i_post = zeros(size(Mask_net));
  542. for i_edge = 1:length(Ind_mask)
  543. [Row,Col] = ind2sub(size(Mask_net),Ind_mask(i_edge));
  544. TFNBS_t_score_rand_i_post_i_edge = squeeze(TFNBS_t_score_rand(Row,Col,i_post,:));
  545. TFNBS_t_pval_i_post(Row,Col) = sum(abs(TFNBS_t_score_rand_i_post_i_edge)>abs(TFNBS_t_score(Row,Col,i_post)))/(M+1);
  546. end
  547. TFNBS_t_pval_i_post = TFNBS_t_pval_i_post + triu(TFNBS_t_pval_i_post,1)'; % to support functional network analysis, that is, allow within-network self-connections
  548. TFNBS_t_pval(:,:,i_post) = TFNBS_t_pval_i_post;
  549. end
  551. % determine edges showing significant group effect
  552. Ind_sig_edge = find(TFNBS_f_pval(Ind_mask) < Pthr);
  553. N_sig_edge = length(Ind_sig_edge);
  554. [Reg_sig_edge(:,1),Reg_sig_edge(:,2)] = ind2sub(size(Mask_net),Ind_mask(Ind_sig_edge));
  556. TFNBS_f_score_sig_edge = TFNBS_f_score(Ind_mask(Ind_sig_edge));
  557. TFNBS_f_pval_sig_edge = TFNBS_f_pval(Ind_mask(Ind_sig_edge));
  559. TFNBS_t_score_sig_edge = zeros(N_sig_edge,size(Post_Tvec,1));
  560. TFNBS_t_pval_sig_edge = zeros(N_sig_edge,size(Post_Tvec,1));
  561. for i_post = 1:size(Post_Tvec,1)
  562. TFNBS_t_score_i_post = TFNBS_t_score(:,:,i_post);
  563. TFNBS_t_pval_i_post = TFNBS_t_pval(:,:,i_post);
  564. for i_edge = 1:N_sig_edge
  565. TFNBS_t_score_sig_edge(:,i_post) = TFNBS_t_score_i_post(Ind_mask(Ind_sig_edge));
  566. TFNBS_t_pval_sig_edge(:,i_post) = TFNBS_t_pval_i_post(Ind_mask(Ind_sig_edge));
  567. end
  568. end
  570. TFNBS_Result.Fmat = Fmat;
  571. TFNBS_Result.P_Fmat = P_Fmat;
  572. TFNBS_Result.DF_Fmat = DF_Fmat;
  573. TFNBS_Result.Tmat = Tmat;
  574. TFNBS_Result.P_Tmat = P_Tmat;
  575. TFNBS_Result.DF_Tmat = DF_Tmat;
  577. TFNBS_Result.TFNBS_f_score = TFNBS_f_score;
  578. TFNBS_Result.TFNBS_f_pval = TFNBS_f_pval;
  579. TFNBS_Result.TFNBS_t_score = TFNBS_t_score;
  580. TFNBS_Result.TFNBS_t_pval = TFNBS_t_pval;
  582. TFNBS_Result.N_sig_edge = N_sig_edge;
  583. TFNBS_Result.Reg_sig_edge = Reg_sig_edge;
  584. TFNBS_Result.TFNBS_f_score_sig_edge = TFNBS_f_score_sig_edge;
  585. TFNBS_Result.TFNBS_f_pval_sig_edge = TFNBS_f_pval_sig_edge;
  586. TFNBS_Result.TFNBS_t_score_sig_edge = TFNBS_t_score_sig_edge;
  587. TFNBS_Result.TFNBS_t_pval_sig_edge = TFNBS_t_pval_sig_edge;
  589. return

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

Overview

Authors: Jing Wang1, Jianshan Chen2,3, Ruilan Yang2, Junle Li1, Qiuxia Wu2, Jiaqi Sun2, Xiaofei Zhang2,3, Chanjuan Yang2, Jinhui Wang1,4,5,6,7, Liping Cao2,3
ORCID iDs: Jing Wang, Jinhui Wang, Liping Cao
  1. Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
  2. Department of Child and Adolescent Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, Guangzhou, China
  3. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
  4. Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
  5. Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
  6. Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
  7. Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China
Institutions: South China Normal University (China); Guangzhou Medical University (China)
Journal: Communications biology, volume 9, issue 1, article 1003
Dates: received 15 September 2025; accepted 29 April 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10237-5 · PMID 42115418 · PMCID PMC13389048 · OpenAlex W7160829493
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), depression (population), schizophrenia / psychosis (population), bipolar (population), developmental (subfield)
Methods: Statistics, Preprocessing, Connectivity, Graphs, fMRI & imaging
Keywords: Psychiatric disorders, Computational neuroscience
MeSH: Bipolar Disorder*, Brain*, Default Mode Network*, Major Depressive Disorder*, Schizophrenia*, Adolescent, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Bipolar Disorder and Treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82371538, 82472092)
Citations: cited by 1 paper (Europe PMC); 91 references in the paper

Abstract

The 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

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figshare 32049330

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (13)
Size: 13 files, 13 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files), SPM (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
13 files
At the source:

Code availability statement

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  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s42003-026-10237-5.

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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;
  • 13 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to a dataset: figshare 32034771
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s42003-026-10237-5.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 10 MeSH terms, 1 funder, 89 references.

Cite

This paper

Wang, J., Chen, J., Yang, R., Li, J., Wu, Q., Sun, J., Zhang, X., Yang, C., Wang, J., & Cao, L. (2026). Shared subcortical-default mode morphological dysconnectivity in adolescent bipolar disorder, major depressive disorder and schizophrenia. Communications biology, 9(1), 1003. https://doi.org/10.1038/s42003-026-10237-5

BibTeX

@article{wang2026shared,
author = {Wang, Jing and Chen, Jianshan and Yang, Ruilan and Li, Junle and Wu, Qiuxia and Sun, Jiaqi and Zhang, Xiaofei and Yang, Chanjuan and Wang, Jinhui and Cao, Liping},
title = {{Shared subcortical-default mode morphological dysconnectivity in adolescent bipolar disorder, major depressive disorder and schizophrenia}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1003},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10237-5},
url = {https://doi.org/10.1038/s42003-026-10237-5},
pmid = {42115418},
pmcid = {PMC13389048}
}

RIS

TY - JOUR
AU - Wang, Jing
AU - Chen, Jianshan
AU - Yang, Ruilan
AU - Li, Junle
AU - Wu, Qiuxia
AU - Sun, Jiaqi
AU - Zhang, Xiaofei
AU - Yang, Chanjuan
AU - Wang, Jinhui
AU - Cao, Liping
TI - Shared subcortical-default mode morphological dysconnectivity in adolescent bipolar disorder, major depressive disorder and schizophrenia
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/12
VL - 9
IS - 1
SP - 1003
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10237-5
UR - https://doi.org/10.1038/s42003-026-10237-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10237-5",
"type": "article-journal",
"title": "Shared subcortical-default mode morphological dysconnectivity in adolescent bipolar disorder, major depressive disorder and schizophrenia",
"container-title": "Communications biology",
"author": [
{
"family": "Wang",
"given": "Jing"
},
{
"family": "Chen",
"given": "Jianshan"
},
{
"family": "Yang",
"given": "Ruilan"
},
{
"family": "Li",
"given": "Junle"
},
{
"family": "Wu",
"given": "Qiuxia"
},
{
"family": "Sun",
"given": "Jiaqi"
},
{
"family": "Zhang",
"given": "Xiaofei"
},
{
"family": "Yang",
"given": "Chanjuan"
},
{
"family": "Wang",
"given": "Jinhui"
},
{
"family": "Cao",
"given": "Liping"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1003",
"DOI": "10.1038/s42003-026-10237-5",
"PMID": "42115418",
"PMCID": "PMC13389048",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10237-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
12
]
]
}
}

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