OSCR

Mapping Higher-Order Topology in OCD Brain Networks with Hodge Laplacian

Code ↔ Paper

7 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 7 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and Methods › Characterization of OCD brain networks with the Hodge Laplacian ↔ Scripts.zip/Visualization/ClusterHeatMap.py, lines 73–140 · score 0.76 · cosine distances, agglomerative clustering, linkage, metrics, transformed, threshold
  2. [2] § Materials and Methods › Persistent homology and graph filtration ↔ Scripts.zip/Analysis/MainAnalysis.m, lines 9–23 · score 0.75 · birth death decomposition, maximum spanning tree, Graph filtration, MST, connected, edges
  3. [3] § Materials and Methods › Characterization of OCD brain networks with the Hodge Laplacian ↔ Scripts.zip/Analysis/MainAnalysis.m, lines 80–123 · score 0.69 · possible edges, cycle coefficient, FC matrix, cycle basis, vector
  4. [4] § Materials and Methods › Algebraic representation for 1-cycles and statistical analysis ↔ Scripts.zip/Analysis/MainAnalysis.m, lines 80–123 · score 0.68 · upper triangle, cycle coefficients, FC matrix, cycle basis, edge
  5. [5] § Results › Similar yet different functional profiles for 1-cycles in different subgroups ↔ Scripts.zip/Analysis/NotePermu.m, the whole file · a weak match · score 0.61 · onset OCD, early onset, Adult, ages, HC
  6. [6] § Materials and Methods › Characterization of OCD brain networks with the Hodge Laplacian ↔ Scripts.zip/Analysis/MainAnalysis.m, lines 125–233 · score 0.52 · Freedman Lane permutation, FWER, Cohen, model, OCD, cycle
  7. [7] § Materials and Methods › Algebraic representation for 1-cycles and statistical analysis ↔ Scripts.zip/Analysis/MainAnalysis.m, lines 25–78 · score 0.50 · zero eigenvalue, L1, eigenvector, loops, Laplacian, matrix

Paper

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The authors' code

MATLAB · 233 lines · 8.1 KB · CC-BY-4.0 · 5 matches

  1. % Load the Gaussian filtered FC matrices
  2. % FC_all = load('.../FC_filtered.mat');
  3. average_conn_matrix = squeeze(mean(FC_all.FCE, 1));
  4. % Load saved 1-Cycle vectors & cycle coefficient to save time for subgroups
  5. % load('/Results/kCycleVect.mat');
  6. % load('/Results/Alpha.mat');
  7. demographics = readtable('.../DemTable.xlsx');
  9. %% Birth-Death Decomposition
  10. N = length(average_conn_matrix);
  11. G1 = graph(average_conn_matrix, 'upper', 'omitselfloops');
  13. % Birth Set: Maximum spanning tree
  14. g = graph(-average_conn_matrix, 'upper', 'omitselfloops');
  15. gTree = minspantree(g);
  16. gTreeMtx = gTree.Edges{:, :};
  17. gTreeMtx(:, 3) = gTreeMtx(:, 3) * -1;
  18. Wb = sortrows(gTreeMtx, 3);
  20. % Death Set: All other edges outside the MST
  21. deathMtx1 = rmedge(G1, Wb(:, 1), Wb(:, 2)).Edges{:, :};
  22. Wd = sortrows(deathMtx1, 3); % Sort the edges in ascending order -> graph filtration
  23. nkcycles = size(Wd,1);
  25. %% Graph Filtration: loop through all the edges in death set to form a 1-cycle
  26. kCycleVect = cell(1, nkcycles);
  27. upper_tri_indices = triu(true(N), 1);
  29. for cid = 1:nkcycles
  30. % Add one edge back to the tree each time, then form a cycle
  31. incdt = Wd(cid,:);
  32. MSTmat = [Wb; incdt];
  33. adjMat = zeros(N,N);
  34. for i = 1:size(MSTmat, 1)
  35. k = MSTmat(i,1);
  36. l = MSTmat(i,2);
  37. adjMat(k,l) = MSTmat(i,3);
  38. adjMat(l,k) = MSTmat(i,3);
  39. end
  41. % Form k-skeletons (1 for nodes and 2 for edges)
  42. kSkeleton2{1} = (1:N)';
  43. kSkeleton2{2} = sortrows(MSTmat(:, 1:2));
  45. % Create boundary matrix (only B1 is required here)
  46. num_nodes = size(kSkeleton{1}, 1);
  47. num_edges = size(kSkeleton{2}, 1);
  48. B1 = zeros(num_nodes, num_edges);
  50. for ii = 1:num_edges
  51. edge = kSkeleton{2}(ii, :);
  52. B1(edge(1), ii) = -1;
  53. B1(edge(2), ii) = 1;
  54. end
  56. % Laplacian L1 and its zero eigenvector
  57. Laplacemat = B1' * B1;
  58. [evec, evalue] = eig(Laplacemat);
  59. eigval = diag(evalue);
  60. % MATLAB only provides small non-zero eigenvalue instead of 0
  61. zero_eig_indices = find(abs(eigval) < 1e-6);
  62. EigVector = evec(:, zero_eig_indices);
  64. % Create and store the 1-cycles according to 0-eigenvector
  65. adjmat = zeros(N,N);
  66. if ~isempty(EigVector)
  67. cycle_vector = EigVector(:,1);
  68. for i = 1:length(cycle_vector)
  69. if abs(cycle_vector(i)) > 1e-6
  70. k = kSkeleton{2}(i,1);
  71. l = kSkeleton{2}(i,2);
  72. adjmat(k,l) = cycle_vector(i);
  73. adjmat(l,k) = cycle_vector(i);
  74. end
  75. end
  76. end
  77. kCycleVect{cid} = adjmat(upper_tri_indices);
  78. end
  80. %% Calculation of 1-cycle coefficient
  81. % Input: all FC matrices
  82. % For subgroups, the coefficients will be base on the entire common 1-cycle
  83. % basis, thus no need for another round of calculation
  84. Q = numel(kCycleVect); % Number of 1-cycles
  85. q = numel(kCycleVect{1}); % Number of all possible edges in upper triangle
  87. % Calculate common 1-cycle basis Φ
  88. % Use sparse matrix to save memory
  89. nnz_est = 0; for t=1:Q, nnz_est = nnz_est + nnz(kCycleVect{t}); end
  90. Phi = spalloc(q, Q, nnz_est);
  91. for t = 1:Q
  92. v = kCycleVect{t};
  93. Phi(:, t) = sparse(v);
  94. end
  96. % Convert FC matrix into flattened upper triangle vector
  97. vecUpper = @(A) A(triu(true(size(A)),1));
  98. MSTmask = logical(full(vecUpper(adjacency(gTree))));
  100. % Sparse QR decomposition
  101. [Qf, Rf] = qr(Phi, 0);
  102. QfT = Qf';
  104. Alpha = zeros(Q, N, 'single');
  105. % Alpha_MST = zeros(Q, N, 'single');
  106. % Alpha_Extra = zeros(Q, N, 'single');
  108. for s = 1:N
  109. W = squeeze(FC_all.FCE(s,:,:));
  110. W = (W+W')/2; W(1:size(W,1)+1:end) = 0;
  111. w = vecUpper(W);
  112. a = Rf \ (QfT * w);
  113. Alpha(:,s) = single(a);
  114. % Decomposition of 1-cycle coefficient
  115. w_MST = w;
  116. % w_Ext = w;
  117. w_MST(~MSTmask) = 0;
  118. % w_Ext(MSTmask) = 0;
  119. % a_MST = Rf \ (QfT * w_MST);
  120. a_Ext = Rf \ (QfT * (w - w_MST));
  121. Alpha_MST(:,s) = single(a_MST);
  122. Alpha_Extra(:,s) = single(a_Ext);
  123. end
  125. %% Topological inference
  126. % Group comparison for finding discriminating 1-cycles
  127. % Exclude centers with less then 10 sub per group (for subgroup analysis)
  128. % For subgroup analysis selection, scripts are stored in NotePermu.m
  129. HC_ind = (demographics.Diag == 2);
  130. OCD_ind = (demographics.Diag == 1);
  131. Sel_ind = find(HC_ind | OCD_ind);
  132. FC_sel = FC_all.FCE(Sel_ind, :, :);
  134. dem_sel = demographics(Sel_ind, :);
  135. Alpha_sel = Alpha(Sel_ind,:);
  136. sites = string(dem_sel.Center(:));
  137. groups = dem_sel.Diag(:) == 1; % true=OCD, false=HC
  138. [site_levels, ~, g] = unique(sites);
  139. S = numel(site_levels);
  140. cntG = accumarray([g, ones(numel(g),1)], groups, [S,1], @sum, 0);
  141. cntH = accumarray([g, ones(numel(g),1)], ~groups, [S,1], @sum, 0);
  143. % Keep sites where both OCD and HC have > 10 subjects
  144. keep_site = (cntG >= 10) & (cntH >= 10);
  145. keep_mask = keep_site(g);
  147. % Get covariences after subject selection
  148. FC_sel = FC_sel(keep_mask, :, :);
  149. mSel = size(FC_sel, 1);
  150. dem_sel = dem_sel(keep_mask, :);
  151. grp = dem_sel.Diag(:) - 1; % grp: 1=OCD，0=HC
  152. sex = dem_sel.Sex(:) - 1;
  153. age = dem_sel.Age(:);
  154. FD_mean = dem_sel.FD_mean(:);
  156. Alpha_sel = Alpha_sel(keep_mask, :);
  157. sites = sites(keep_mask);
  158. hc_cnt = numel(grp(grp == 1));
  159. fprintf('Number of HC: %04d;\nNumber of OCD: %04d \n', hc_cnt, numel(grp)-hc_cnt);
  161. % Create new category variable with the included sites only
  162. sites_cat = categorical(sites);
  163. Snew = numel(categories(sites_cat));
  165. % Freedman–Lane Permutation
  166. % Design matrices Xnuis / Xfull / Xred
  167. % Sites effect dummy (remove baseline column to avoid colinearity)
  168. Sdummy = dummyvar(sites_cat);
  169. Sdummy = Sdummy(:, 2:end);
  171. Xnuis = [ones(size(Alpha_sel,1),1), zscore(age), sex, zscore(FD_mean), Sdummy];
  172. Xfull = [Xnuis, grp(:)]; % Full model (with groups)
  173. Xred = Xnuis; % Simplified model (only convariences)
  175. N_sub = size(Alpha_sel,1);
  176. H0 = Xred / (Xred' * Xred) * Xred'; M0 = eye(N_sub) - H0;
  177. XtX_inv = inv(Xfull' * Xfull);
  178. gRow = size(Xfull,2);
  180. % Observed t & maxT
  181. beta_full = Xfull \ Alpha_sel;
  182. res_full = Alpha_sel - Xfull * beta_full;
  183. se2 = sum(res_full.^2, 1) ./ (N_sub - size(Xfull,2));
  184. se_beta = sqrt(se2 .* XtX_inv(gRow, gRow));
  185. t_obs = beta_full(gRow, :) ./ se_beta;
  186. T_obs = max(abs(t_obs));
  188. % Indexing sites for site-restricted permutation
  189. sitess = categorical(sites(:));
  190. p_full = size(Xfull,2);
  191. [levs,~,g2] = unique(sites_cat);
  192. idxBySite = arrayfun(@(s) find(g2==s), 1:numel(levs), 'UniformOutput', false);
  194. % Permutation
  195. rng(2025,'twister');
  196. nperm = 50000; TPerm = zeros(nperm,1);
  197. track_interval = 50; pTrace = zeros(floor(nperm / track_interval), 1);
  198. report_counter = 1;
  199. for b = 1:nperm
  200. R0 = M0 * Alpha_sel;
  201. R0p = zeros(size(R0));
  202. for s = 1:numel(idxBySite)
  203. idx = idxBySite{s};
  204. R0p(idx,:) = R0(idx(randperm(numel(idx))), :); % Permutation within sites
  205. end
  206. Ystar = H0 * Alpha_sel + R0p;
  207. beta_b = Xfull \ Ystar;
  208. res_b = Ystar - Xfull * beta_b;
  209. se2_b = sum(res_b.^2, 1) ./ (N_sub - size(Xfull,2));
  210. t_b = beta_b(gRow, :) ./ sqrt(se2_b .* XtX_inv(gRow,gRow));
  211. TPerm(b) = max(abs(t_b));
  212. if mod(b, track_interval) == 0
  213. current_p_val = (1 + sum(TPerm(1:b) >= T_obs)) / (1 + b);
  214. pTrace(report_counter) = current_p_val;
  215. report_counter = report_counter + 1;
  216. fprintf(' Permutation %d / %d: current P = %.4f\n', b, nperm, current_p_val);
  217. end
  218. end
  219. pval_global = (1 + sum(TPerm >= T_obs)) / (1 + b);
  220. fprintf('Freedman–Lane maxT (after site filtering): T=%.4f, p=%.3g\n', T_obs, pval_global);
  222. % FWER corrected p-val for every cycle
  223. pval_max = arrayfun(@(x) (1 + sum(TPerm >= x)) / (1 + b), abs(t_obs));
  224. sig_ind = find(pval_max(:) < 0.05);
  226. % Cohen's d value for every cycle
  227. d_adj = beta_full(gRow, :) ./ sqrt(se2);
  229. R2_partial = (t_obs.^2) ./ (t_obs.^2 + N_sub - size(Xfull, 2));
  231. T = table(sig_ind(:), t_obs(sig_ind).', abs(t_obs(sig_ind).'), pval_max(sig_ind).', d_adj(sig_ind).', R2_partial(sig_ind).', ...
  232. 'VariableNames', {'cycle_id', 't', 't_abs', 'pval_max', 'd_adj', 'R2_partial'});
  233. S = table2struct(T, 'ToScalar', true);

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

Overview

Authors: Hanyang Ruan1,2, Moo K. Chung3, Willem B. Bruin4,5,6, Nadža Džinalija4,5,6, Yoshinari Abe7,8, Pino Alonso9,10,11, Alan Anticevic12, Srinivas Balachander13, Marcelo C. Batistuzzo14,15, Francesco Benedetti16,17, Sara Bertolín9,10,11, Silvia Brem18,19, Youngsun Cho12, Federica Colombo16,17, Beatriz Couto20,21,22, Goi Khia Eng23,24, Sónia Ferreira20,21,22, Jamie D. Feusner25,26,27, Rachael G. Grazioplene12, Patricia Gruner12
and 60 other authorsKristen Hagen28,29,30, Bjarne Hansen28,31, Yoshiyuki Hirano32,33, Marcelo Q. Hoexter14, Jonathan Ipser34, Fern Jaspers-Fayer35, Minah Kim36,37,38, Jun Soo Kwon38,39,40, Luisa Lazaro10,41,42, Chiang-Shan R. Li12, Christine Lochner43, Rachel Marsh44, Ignacio Martínez-Zalacaín9,45, Jose M. Menchón9,10,11, Pedro S. Moreira20,21,46, Pedro Morgado20,21,22, Emma Muñoz47, Akiko Nakagawa32, Janardhanan C. Narayanaswamy13,48,49, Erika L. Nurmi50, Joseph O’Neill50, Jose C. Pariente47, John C. Piacentini50, Maria Picó-Pérez20,51, Fabrizio Piras52, Federica Piras52, Christopher Pittenger53,54, Janardhan Y. C. Reddy13, Daniela Rodriguez-Manrique1,2,55, Yuki Sakai7,56,57, Joao R. Sato58, Eiji Shimizu32,33, Venkataram Shivakumar59, Helen B. Simpson60, Carles Soriano-Mas9,10,61, Nuno Sousa20,21,22, Emily R. Stern23,24,62, Evelyn Stewart35, Philip R. Szeszko63,64, Sophia I. Thomopoulos65, Anders L. Thorsen28,31, Benedetta Vai16,17, Anouk van der Straten5,66, Ysbrand D. van der Werf4,6, Wieke van Leeuwen67, Hein van Marle5,68, Guido van Wingen5,6, Daniela Vecchio52, Ganesan Venkatasubramanian13, Chris Vriend6,69,70, Susanne Walitza18,19, Zhen Wang71, Tokiko Yoshida33,72, Je-Yeon Yun73,74, Qing Zhao71, ENIGMA OCD Working Group, Paul M. Thompson65, Dan J. Stein75, Odile A. van den Heuvel4,5,6, Kathrin Koch1,2
ORCID iDs: Hanyang Ruan, Willem B. Bruin, Nadža Džinalija, Yoshinari Abe, Pino Alonso, Srinivas Balachander, Marcelo C. Batistuzzo, Francesco Benedetti, Sara Bertolín, Silvia Brem, Youngsun Cho, Federica Colombo, Goi Khia Eng, Sónia Ferreira, Jamie D. Feusner, Patricia Gruner, Kristen Hagen, Yoshiyuki Hirano, Marcelo Q. Hoexter, Jonathan Ipser, Fern Jaspers-Fayer, Minah Kim, Jun Soo Kwon, Luisa Lazaro, Chiang-Shan R. Li, Christine Lochner, Rachel Marsh, Ignacio Martínez-Zalacaín, Jose M. Menchón, Pedro S. Moreira, Pedro Morgado, Emma Muñoz, Erika L. Nurmi, Joseph O’Neill, Jose C. Pariente, John C. Piacentini, Maria Picó-Pérez, Fabrizio Piras, Federica Piras, Christopher Pittenger, Janardhan Y. C. Reddy, Daniela Rodriguez-Manrique, Yuki Sakai, Eiji Shimizu, Venkataram Shivakumar, Helen B. Simpson, Carles Soriano-Mas, Nuno Sousa, Emily R. Stern, Evelyn Stewart, Philip R. Szeszko, Sophia I. Thomopoulos, Anders L. Thorsen, Benedetta Vai, Anouk van der Straten, Ysbrand D. van der Werf, Wieke van Leeuwen, Hein van Marle, Guido van Wingen, Daniela Vecchio, Ganesan Venkatasubramanian, Chris Vriend, Susanne Walitza, Zhen Wang, Tokiko Yoshida, Je-Yeon Yun, Qing Zhao, Paul M. Thompson, Dan J. Stein, Odile A. van den Heuvel, Kathrin Koch
75 affiliations
  1. Department of Neuroradiology, TUM University Hospital, School of Medicine and Health, Technical University of Munich (TUM), Munich, Germany
  2. School of Medicine and Health, TUM-NIC Neuroimaging Center, Technical University of Munich, Munich, Germany
  3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
  4. Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Amsterdam, the Netherlands
  5. Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, the Netherlands
  6. Compulsivity, Impulsivity and Attention program, Amsterdam Neuroscience, Amsterdam, the Netherlands
  7. Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
  8. Sugimoto Psychiatric Clinic, Kyoto, Japan
  9. Bellvitge Biomedical Research Insitute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
  10. CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
  11. Department of Clinical Science, Faculty of Medicine, University of Barcelona, Barcelona, Spain
  12. Department of Psychiatry, Yale University, New Haven, CT, USA
  13. OCD clinic, Department of Psychiatry, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
  14. Department of Psychiatry, University of Sao Paulo School of Medicine, Sao Paulo, Brazil
  15. Department of Methods and Techniques in Psychology, Pontifical Catholic University, Sao Paulo, Brazil
  16. Psychiatry & Clinical Psychobiology, Division of Neuroscience, IRCCS Scientific Institute Ospedale San Raffaele, Milano, Italy
  17. Vita-Salute San Raffaele University, Milano, Italy
  18. Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland
  19. Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland
  20. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
  21. ICVS/3B’s, PT Government Associate Laboratory, Braga/Guimarães, Portugal
  22. Clinical Academic Center - Braga, Braga, Portugal
  23. Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
  24. Clinical Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
  25. Department of Psychiatry, Division of Neurosciences and Clinical Translation, University of Toronto, Toronto, ON, Canada
  26. Centre for Addiction and Mental Health, Toronto, ON, Canada
  27. Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
  28. Bergen Center for Brain Plasticity, Haukeland University Hospital, Bergen, Norway
  29. Molde Hospital, Møre og Romsdal Hospital Trust, Molde, Norway
  30. Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
  31. Center for Crisis Psychology, University of Bergen, Bergen, Norway
  32. Research Center for Child Mental Development, Chiba University, Chiba, Japan
  33. United Graduate School of Child Development, The University of Osaka, Suita, Japan
  34. Department of Psychiatry and Mental Health and Neuroscience Institute, Brain Behaviour Unit, University of Cape Town, Cape Town, South Africa
  35. Department of Psychiatry, University of British Columbia, Vancouver, Canada
  36. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
  37. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea
  38. Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
  39. Department of Psychiatry, Hanyang University College of Medicine, Seoul, Republic of Korea
  40. Department of Psychiatry, Hanyang University Hospital, Seoul, Republic of Korea
  41. Department of Child and Adolescent Psychiatry and Psychology, Hospital Clinic of Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
  42. Department of Medicine, University of Barcelona, Spain
  43. SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Stellenbosch, South Africa
  44. Columbia University Medical College, Columbia University, New York, NY, USA
  45. Department of Radiology, Bellvitge University Hospital, Barcelona, Spain
  46. Psychological Neuroscience Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
  47. Magnetic Resonance Image Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
  48. Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, Australia
  49. Monash Health, Melbourne, Australia
  50. Division of Child and Adolescent Psychiatry, Jane & Terry Semel Institute For Neurosciences, University of California, Los Angeles, CA, USA
  51. Departamento de Psicología Básica, Clínica y Psicobiología, Universitat Jaume I, Castellon de la Plana, Spain
  52. Neuropsychiatry Laboratory, Department of Clinical Neuroscience and Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italy
  53. Departments of Psychiatry, Neuroscience, Psychology, and Yale Child Study Center, Yale University, New Haven, CT, USA
  54. Center for Brain and Mind Health, Yale University School of Medicine, New Haven, CT, USA
  55. Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Munich, Germany
  56. ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan
  57. XNef Inc
  58. Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil
  59. Department of Integrative Medicine, National Institute of Mental Health And Neurosciences (NIMHANS), Bangalore, India
  60. Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
  61. Department of Social Psychology and Quantitative Psychology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
  62. Neuroscience Institute, New York University School of Medicine, New York, NY, USA
  63. Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, NY, USA
  64. Mental Illness Research, Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
  65. Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
  66. Levvel, Amsterdam, the Netherlands
  67. Department of Psychiatry, Arkin Mental Health Center, Amsterdam, the Netherlands
  68. Mood, Anxiety, Psychosis, Stress and Sleep program, Amsterdam Neuroscience, Amsterdam, the Netherlands
  69. Department of Anatomy & Neurosciences, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
  70. Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
  71. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, P. R. China
  72. Cognitive Behavioral Therapy Center, Chiba University Hospital, Chiba, Japan
  73. Department of Psychiatry, Seoul National University Hospital, Seoul, Republic of Korea
  74. Yeongeon Student Support Center, Seoul National University College of Medicine, Seoul, Republic of Korea
  75. SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Neuroscience Institute, University of Cape Town, Cape Town, South Africa
Institutions: Technical University of Munich (Germany); University of Wisconsin–Madison (United States); Amsterdam University Medical Centers (Netherlands); Amsterdam Neuroscience (Netherlands); Kyoto Prefectural University of Medicine (Japan); Institut d'Investigació Biomédica de Bellvitge (Spain); Bellvitge University Hospital (Spain); Centro de Investigación Biomédica en Red de Salud Mental (Spain); Instituto de Salud Carlos III (Spain); Universitat de Barcelona (Spain); Yale University (United States); National Institute of Mental Health and Neurosciences (India); Universidade de São Paulo (Brazil); Pontifícia Universidade Católica de São Paulo (Brazil); IRCCS Ospedale San Raffaele (Italy); Vita-Salute San Raffaele University (Italy); Psychiatrische Universitätsklinik Zürich (Switzerland); University of Zurich (Switzerland); ETH Zurich (Switzerland); University of Minho (Portugal); Clinical Academic Center of Braga (Portugal); New York University (United States); Nathan Kline Institute for Psychiatric Research (United States); University of Toronto (Canada); Centre for Addiction and Mental Health (Canada); Karolinska Institutet (Sweden); Haukeland University Hospital (Norway); Molde Hospital (Norway); Norwegian University of Science and Technology (Norway); University of Bergen (Norway); Chiba University (Japan); The University of Osaka (Japan); University of Cape Town (South Africa); University of British Columbia (Canada); Seoul National University (South Korea); Seoul National University Hospital (South Korea); Hanyang University (South Korea); Hanyang University Seoul Hospital (South Korea); Stellenbosch University (South Africa); Columbia University (United States); Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (Spain); Monash University (Australia); Monash Health (Australia); University of California, Los Angeles (United States); Universitat Jaume I (Spain); Fondazione Santa Lucia (Italy); Ludwig-Maximilians-Universität München (Germany); Universidade Federal do ABC (Brazil); Columbia University Irving Medical Center (United States); Icahn School of Medicine at Mount Sinai (United States); Mental Illness Research, Education and Clinical Centers (United States); James J. Peters VA Medical Center (United States); University of Southern California (United States); Levvel (Netherlands); Arkin (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Shanghai Mental Health Center (China); Shanghai Jiao Tong University (China); Chiba University Hospital (Japan)
Journal: bioRxiv (preprint)
Dates: published online 6 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.64898/2026.03.04.709586 · OpenAlex W7134107076
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), systems (subfield)
Methods: Connectivity, Statistics, Machine learning, Smoothing, state filtering, decompositions, fMRI & imaging
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (165.610.002, 016.156.318); Swiss National Science Foundation (320030, 130237)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

Obsessive-compulsive disorder (OCD) is a disabling condition that is characterized by disruptions in distributed brain circuit dynamics. However, current network studies predominantly evaluate these circuits by measuring functional synchrony (connectivity) between pairs of regions of interest, potentially overlooking complex higher-order interactions. In this study, we applied a Hodge Laplacian topological framework to investigate these higher-order interactions in OCD. Using a large-scale resting-state fMRI dataset from the ENIGMA-OCD consortium (1,024 OCD patients and 1,028 healthy controls across 28 sites worldwide), we identified significant disruptions in topological loops spanning frontoparietal, default mode, and sensorimotor networks. Crucially, the edges constituting these abnormal loops largely lacked significant pairwise differences, highlighting higher-order multi-nodal disturbances. Subgroup analyses revealed that these disruptions were most pronounced in adult, medicated, and high-severity OCD patients. Our findings suggest that OCD pathology involves abnormal recurrent higher-order multiregion interactions, providing new insights into the brain’s functional organization and offering potential biomarkers for clinical application.

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 7 matches between paragraphs and lines of code.

Zenodo 18861978

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data and materials availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), NumPy (3 files), SciPy (3 files), Matplotlib (2 files), pandas (2 files), h5py (1 file), NetworkX (1 file), scikit-learn (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
7 files
At the source: doi.org/10.5281/zenodo.18861978

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;
  • 7 scripts, each with its path and the digest of its content;
  • 7 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 and materials availability

The full ENIGMA OCD data are not publicly available in a repository as they might contain information that could compromise the privacy of research participants. Access to these data may be requested through the ENIGMA OCD Working Group data access procedures and is subject to approval and completion of the required data-use agreements. More information about the ENIGMA OCD Working Group can be found here: https://enigma.ini.usc.edu/ongoing/enigma-ocd-working-group/. The source codes for the Hodge Laplacian analysis, sensitivity analysis, visualization, as well as non–participant-level outputs in this study are openly available on Zenodo at https://doi.org/10.5281/zenodo.18861978. All other data needed to evaluate the conclusions are available in the Article and/or Supplementary Information.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, dates, 80 authors, 2 funders, 64 references.

Cite

This paper

Ruan, H., Chung, M. K., Bruin, W. B., Džinalija, N., Abe, Y., Alonso, P., Anticevic, A., Balachander, S., Batistuzzo, M. C., Benedetti, F., Bertolín, S., Brem, S., Cho, Y., Colombo, F., Couto, B., Eng, G. K., Ferreira, S., Feusner, J. D., Grazioplene, R. G., . . . Koch, K. (2026). Mapping Higher-Order Topology in OCD Brain Networks with Hodge Laplacian. bioRxiv (preprint). https://doi.org/10.64898/2026.03.04.709586

BibTeX

@article{ruan2026mapping,
author = {Ruan, Hanyang and Chung, Moo K. and Bruin, Willem B. and Džinalija, Nadža and Abe, Yoshinari and Alonso, Pino and Anticevic, Alan and Balachander, Srinivas and Batistuzzo, Marcelo C. and Benedetti, Francesco and Bertolín, Sara and Brem, Silvia and Cho, Youngsun and Colombo, Federica and Couto, Beatriz and Eng, Goi Khia and Ferreira, Sónia and Feusner, Jamie D. and Grazioplene, Rachael G. and Gruner, Patricia and Hagen, Kristen and Hansen, Bjarne and Hirano, Yoshiyuki and Hoexter, Marcelo Q. and Ipser, Jonathan and Jaspers-Fayer, Fern and Kim, Minah and Kwon, Jun Soo and Lazaro, Luisa and Li, Chiang-Shan R. and Lochner, Christine and Marsh, Rachel and Martínez-Zalacaín, Ignacio and Menchón, Jose M. and Moreira, Pedro S. and Morgado, Pedro and Muñoz, Emma and Nakagawa, Akiko and Narayanaswamy, Janardhanan C. and Nurmi, Erika L. and O’Neill, Joseph and Pariente, Jose C. and Piacentini, John C. and Picó-Pérez, Maria and Piras, Fabrizio and Piras, Federica and Pittenger, Christopher and Reddy, Janardhan Y. C. and Rodriguez-Manrique, Daniela and Sakai, Yuki and Sato, Joao R. and Shimizu, Eiji and Shivakumar, Venkataram and Simpson, Helen B. and Soriano-Mas, Carles and Sousa, Nuno and Stern, Emily R. and Stewart, Evelyn and Szeszko, Philip R. and Thomopoulos, Sophia I. and Thorsen, Anders L. and Vai, Benedetta and van der Straten, Anouk and van der Werf, Ysbrand D. and van Leeuwen, Wieke and van Marle, Hein and van Wingen, Guido and Vecchio, Daniela and Venkatasubramanian, Ganesan and Vriend, Chris and Walitza, Susanne and Wang, Zhen and Yoshida, Tokiko and Yun, Je-Yeon and Zhao, Qing and {ENIGMA OCD Working Group} and Thompson, Paul M. and Stein, Dan J. and van den Heuvel, Odile A. and Koch, Kathrin},
title = {{Mapping Higher-Order Topology in OCD Brain Networks with Hodge Laplacian}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.04.709586},
url = {https://doi.org/10.64898/2026.03.04.709586}
}

RIS

TY - JOUR
AU - Ruan, Hanyang
AU - Chung, Moo K.
AU - Bruin, Willem B.
AU - Džinalija, Nadža
AU - Abe, Yoshinari
AU - Alonso, Pino
AU - Anticevic, Alan
AU - Balachander, Srinivas
AU - Batistuzzo, Marcelo C.
AU - Benedetti, Francesco
AU - Bertolín, Sara
AU - Brem, Silvia
AU - Cho, Youngsun
AU - Colombo, Federica
AU - Couto, Beatriz
AU - Eng, Goi Khia
AU - Ferreira, Sónia
AU - Feusner, Jamie D.
AU - Grazioplene, Rachael G.
AU - Gruner, Patricia
AU - Hagen, Kristen
AU - Hansen, Bjarne
AU - Hirano, Yoshiyuki
AU - Hoexter, Marcelo Q.
AU - Ipser, Jonathan
AU - Jaspers-Fayer, Fern
AU - Kim, Minah
AU - Kwon, Jun Soo
AU - Lazaro, Luisa
AU - Li, Chiang-Shan R.
AU - Lochner, Christine
AU - Marsh, Rachel
AU - Martínez-Zalacaín, Ignacio
AU - Menchón, Jose M.
AU - Moreira, Pedro S.
AU - Morgado, Pedro
AU - Muñoz, Emma
AU - Nakagawa, Akiko
AU - Narayanaswamy, Janardhanan C.
AU - Nurmi, Erika L.
AU - O’Neill, Joseph
AU - Pariente, Jose C.
AU - Piacentini, John C.
AU - Picó-Pérez, Maria
AU - Piras, Fabrizio
AU - Piras, Federica
AU - Pittenger, Christopher
AU - Reddy, Janardhan Y. C.
AU - Rodriguez-Manrique, Daniela
AU - Sakai, Yuki
AU - Sato, Joao R.
AU - Shimizu, Eiji
AU - Shivakumar, Venkataram
AU - Simpson, Helen B.
AU - Soriano-Mas, Carles
AU - Sousa, Nuno
AU - Stern, Emily R.
AU - Stewart, Evelyn
AU - Szeszko, Philip R.
AU - Thomopoulos, Sophia I.
AU - Thorsen, Anders L.
AU - Vai, Benedetta
AU - van der Straten, Anouk
AU - van der Werf, Ysbrand D.
AU - van Leeuwen, Wieke
AU - van Marle, Hein
AU - van Wingen, Guido
AU - Vecchio, Daniela
AU - Venkatasubramanian, Ganesan
AU - Vriend, Chris
AU - Walitza, Susanne
AU - Wang, Zhen
AU - Yoshida, Tokiko
AU - Yun, Je-Yeon
AU - Zhao, Qing
AU - ENIGMA OCD Working Group
AU - Thompson, Paul M.
AU - Stein, Dan J.
AU - van den Heuvel, Odile A.
AU - Koch, Kathrin
TI - Mapping Higher-Order Topology in OCD Brain Networks with Hodge Laplacian
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/06
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.04.709586
UR - https://doi.org/10.64898/2026.03.04.709586
LA - en
ER -

CSL-JSON

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