OSCR

OFC-induced network modularity improves positive symptoms and attentional alertness in schizophrenia: a combined rTMS-fMRI study.

Code ↔ Paper

9 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 9 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Neuroimaging › Community detection-based interregional integration ↔ HelperFunctions/multiord_f.m, the whole file · a weak match · score 0.74 · multilayer network, resolution parameter, multilayer community, smaller, Louvain, adjacent
  2. [2] § Methods › Neuroimaging › Community detection-based interregional integration ↔ HelperFunctions/multicat_f.m, the whole file · a weak match · score 0.72 · multilayer network, resolution parameter, multilayer community, smaller, Louvain, adjacent
  3. [3] § Results › Genetic associations with brain network dynamics ↔ ABAnnotate.m, lines 221–304 · score 0.60 · Gene Ontology, biological process, enrichment, GO, Atlas, mapping
  4. [4] § Methods › Stimulation protocol ↔ scripts_import/import_BrainSpan_ABAEnrichment.m, the whole file · a weak match · score 0.59 · orbital frontal cortex, motor, superiorly, OFC
  5. [5] § Methods › Neuroimaging › Community detection-based interregional integration ↔ HelperFunctions/multiord.m, the whole file · a weak match · score 0.56 · multilayer community detection, quality function, heuristic, modularity
  6. [6] § Methods › Neuroimaging › Community detection-based interregional integration ↔ HelperFunctions/multicat_f.m, the whole file · a weak match · score 0.56 · multilayer community detection, multilayer modularity, heuristic, quality
  7. [7] § Methods › Relationship between brain integration and genetics ↔ scripts/generate_category_nulls.m, the whole file · a weak match · score 0.53 · category score, ensemble, GCEA, phenotypes, genes, map
  8. [8] § Results › Genetic associations with brain network dynamics ↔ scripts_import/import_disgenet_datasets.m, the whole file · a weak match · score 0.52 · disGeNET, semantic, molecular, disease, cell, component
  9. [9] § Methods › Relationship between brain integration and genetics ↔ ABAnnotate.m, lines 221–304 · score 0.52 · category enrichment, ensemble, GCEA, phenotypes, genes, map

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 131 lines · 5.1 KB · BSD-2-Clause · 2 matches

  1. function [B,twom] = multicat_f(A,gamma,omega)
  2. %MULTICAT_F returns multilayer Newman-Girvan modularity matrix for unordered undirected layers, function handle version
  3. %
  4. % Version: 2.2.0
  5. % Date: Thu 11 Jul 2019 12:25:42 CEST
  6. %
  7. % Input: A: Cell array of NxN adjacency matrices for each layer of an
  8. % unordered multilayer undirected network
  9. % gamma: intralayer resolution parameter
  10. % omega: interlayer coupling strength
  11. %
  12. % Output: B: [NxT]x[NxT] function handle where B(i) returns the ith column
  13. % of the flattened modularity tensor for the
  14. % multilayer network with uniform categorical coupling (T is
  15. % the number of layers of the network)
  16. % twom: normalisation constant
  17. %
  18. % Example of usage: [B,twom]=multicat_f(A,gamma,omega);
  19. % [S,Q]= genlouvain(B); % see iterated_genlouvain.m and
  20. % postprocess_categorical_multilayer.m for how to improve output
  21. % multilayer partition
  22. % Q=Q/twom;
  23. % S=reshape(S,N,T);
  24. %
  25. % [B,twom] = MULTICAT(A,GAMMA, OMEGA) with A a cell array of square
  26. % symmetric matrices of equal size each representing an undirected network
  27. % "layer" computes the multilayer modularity matrix using the quality
  28. % function described in Mucha et al. 2010, with intralayer resolution
  29. % parameter GAMMA, and with interlayer coupling OMEGA connecting
  30. % all-to-all categorical layers. Once the mulilayer modularity matrix is
  31. % computed, optimization can be performed by the generalized Louvain code
  32. % GENLOUVAIN or ITERATED_GENLOUVAIN. The output B can be used with other
  33. % heuristics, provided the same mapping is used to go from the multilayer
  34. % tensor to the multilayer flattened matrix. That is, the node-layer tuple
  35. % (i,s) is mapped to i + (s-1)*N. [Note that we can define a mapping between
  36. % a multilayer partition S_m stored as an N by T matrix and the corresponding
  37. % flattened partition S stored as an NT by 1 vector. In particular
  38. % S_m = reshape(S,N,T) and S = S_m(:).]
  39. %
  40. % See also
  41. % genlouvain heuristics: GENLOUVAIN, ITERATED_GENLOUVAIN
  42. % multilayer wrappers: MULTICATF, MULTIORD, MULTIORDF
  43. % other heuristics: SPECTRAL23
  44. % Kernighan-Lin improvement: KLNB
  45. %
  46. % Notes:
  47. % The matrices in the cell array A are assumed to be symmetric, square,
  48. % and of equal size. These assumptions are not checked here.
  49. %
  50. % For smaller systems, it is potentially more efficient (and easier) to
  51. % directly use the sparse quality/modularity matrix B, as in MULTICAT.
  52. %
  53. % This code serves as a template and can be modified for situations
  54. % with other wrinkles (e.g., different intralayer null models,
  55. % different numbers of nodes from layer-to-layer, or systems which are
  56. % both multiplex and longitudinal). That is, this code is only a
  57. % starting point; it is by no means exhaustive.
  58. %
  59. % By using this code, the user implicitly acknowledges that the authors
  60. % accept no liability associated with that use. (What are you doing
  61. % with it anyway that might cause there to be a potential liability?!?)
  62. %
  63. % References:
  64. % Blondel, Vincent D., Jean-Loup Guillaume, Renaud Lambiotte, and
  65. % Etienne Lefebvre, "Fast unfolding of communities in large networks,"
  66. % Journal of Statistical Mechanics: Theory and Experiment, P10008
  67. % (2008).
  68. %
  69. % Fortunato, Santo, "Community detection in graphs," Physics Reports
  70. % 486, 75-174 (2010).
  71. %
  72. % Good, Benjamin H., Yves-Alexandre de Montjoye, and Aaron Clauset,
  73. % "Performance of modularity maximization in practical contexts,"
  74. % Physical Review E 81, 046106 (2010).
  75. %
  76. % Newman, Mark E. J. and Michelle Girvan. "Finding and Evaluating
  77. % Community Structure in Networks", Physical Review E 69, 026113 (2004).
  78. %
  79. % Mucha, Peter J., Thomas Richardson, Kevin Macon, Mason A. Porter, and
  80. % Jukka-Pekka Onnela. "Community Structure in Time-Dependent,
  81. % Multiscale, and Multiplex Networks," Science 328, 876-878 (2010).
  82. %
  83. % Porter, M. A., J. P. Onnela, and P. J. Mucha, "Communities in
  84. % networks," Notices of the American Mathematical Society 56, 1082-1097
  85. % & 1164-1166 (2009).
  86. %
  87. % Acknowledgments:
  88. % Thank you to Dani Bassett, Jesse Blocher, Bruce Rogers, and Simi Wang
  89. % for their collaborative help which led to significant cleaning up
  90. % of earlier versions of our multilayer community detection codes.
  93. if nargin<2||isempty(gamma)
  94. gamma=1;
  95. end
  97. if nargin<3||isempty(omega)
  98. omega=1;
  99. end
  102. N=length(A{1});
  103. T=length(A);
  105. if length(gamma)==1
  106. gamma=repmat(gamma,T,1);
  107. end
  109. ii=[]; jj=[]; vv=[];
  110. ki=[]; kj=[]; kv=[];
  111. twom=0;
  112. for s=1:T
  113. indx=[1:N]'+(s-1)*N;
  114. [i,j,v]=find(A{s});
  115. ii=[ii;indx(i)]; jj=[jj;indx(j)]; vv=[vv;v];
  116. k=sum(A{s});
  117. mm=sum(k);
  118. ki=[ki;indx];
  119. kj=[kj;ones(N,1)*s];
  120. kv=[kv;k(:)./mm];
  121. twom=twom+sum(k);
  122. end
  123. AA = sparse(ii,jj,vv,N*T,N*T);
  124. K=sparse(ki,kj,kv,N*T,T);
  125. clear ii jj vv ki kj kv
  126. kvec = full(sum(AA));
  127. all2all = N*[(-T+1):-1,1:(T-1)];
  128. AA = AA + omega*spdiags(ones(N*T,2*T-2),all2all,N*T,N*T);
  129. B = @(i) AA(:,i) - gamma(ceil(i/(N+eps)))*K(:,ceil(i/(N+eps)))*kvec(i);
  130. twom=twom+2*N*(T-1)*T*omega;
  131. end

multicat_f.m at commit 0fb0aa8, under BSD-2-Clause · at the source

Overview

Authors: Ningning Zeng1,2, Min Wang3,4, Hui Zheng5, Xiong Jiao5,6, Ziliang Wang7, Kexu Zhang8, Katharina S. Goerlich2, André Aleman9, Jijun Wang5,10,11, Qiang Hu1,12
ORCID iDs: Ningning Zeng, Min Wang, Hui Zheng, Xiong Jiao, Kexu Zhang, Katharina S. Goerlich, Jijun Wang, Qiang Hu
  1. Neuroregulation Center, Wuhu Hospital of Anding Hospital (The Fourth People’s Hospital of Wuhu),Wuhu, China
  2. University Medical Center Groningen, University of Groningen,Groningen, Netherlands
  3. Department of Psychology, Ningbo University,Ningbo, China
  4. Department of Psychology, School of humanities and social sciences, University of Science and Technology of China,Hefei, China
  5. Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine,Shanghai, China
  6. Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University,Shanghai, China
  7. Department of Psychiatry, Zhenjiang Mental Health Center,Zhenjiang, China
  8. Department of psychiatry, Shandong Daizhuang Hospital, Jining, China
  9. Faculty of Psychology and Neuroscience, Maastricht University,Maastricht, Netherlands
  10. Mental and Psychological Rehabilitation Research Center, Center of Yuanshen Rehabilitation Institute, Shanghai Jiao Tong University School of Medicine,Shanghai, China
  11. Nantong Fourth People’s Hospital & Nantong Brain Hospital,Nantong, China
  12. Department of Psychiatry, School of Medicine, Jiangsu University,Zhenjiang, China
Institutions: The Fourth People's Hospital (China); University of Groningen (Netherlands); Ningbo University (China); University of Science and Technology of China (China); Shanghai Jiao Tong University (China); Department of Psychiatry, Zhenjiang Mental Health Center,Zhenjiang, China; Maastricht University (Netherlands); Jiangsu University (China)
Journal: Nature communications, volume 17, issue 1, article 7010
Dates: received 22 May 2025; accepted 23 April 2026; published online 30 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72917-4 · PMID 42218138 · PMCID PMC13392451 · OpenAlex W7162860367
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other (modality), human (organism), schizophrenia / psychosis (population)
Methods: Spectral & time-frequency, Connectivity, Statistics, Graphs, Machine learning, fMRI & imaging
Keywords: Predictive markers, Randomized controlled trials, Schizophrenia
MeSH: Attention*, Prefrontal Cortex*, Schizophrenia*, Transcranial Magnetic Stimulation*, Adult, Cognition, Double-Blind Method, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: Jiangsu University (JDYY2023088); National Natural Science Foundation of China (BK20240506); Jiangsu Provincial Commission of Health and Family Planning (H2023036)
Citations: cited by 1 paper (Europe PMC); 56 references in the paper

Abstract

Repetitive Transcranial Magnetic Stimulation (rTMS) targeting the orbitofrontal cortex (OFC) has emerged as a promisingerapeutic option for drug-naïve people with schizophrenia (SCZ). However, the putative underlying mechanisms of OFC-induced physiological effects remain unknown. In this completed randomized, double-blind, placebo-controlled trial (ChiCTR2000041106), we delivered 4 weeks of low-frequency rTMS to the right OFC in SCZ, with participants receiving either active or sham stimulation, and followed a network neuroscience framework to explore the alteration of dynamic modularity induced by the OFC. The trial met its pre-specificized primary endpoint following active treatment. Neuroimaging analysis reported here were secondary outcomes. We found that the modularization between OFC and the default mode network (DMN) across time windows supported improvements in symptoms and cognitive function. This dynamics pattern was spatially constrained, with stronger rTMS modulation observed in DMN regions centered on the ventromedial prefrontal cortex (vmPFC). The spatial topography of this pattern was correlated with the expression of schizophrenia-related genes and markers of excitatory neurotransmission, supporting its biological relevance. Crucially, such cascade of physiological effects was specifically linked to improvements in cognitive attention/vigilance and were modulated by their positive symptoms. Exploratory analyses showed that OFC-induced modularity weakened the DMN’s causality over the downstream attention network. These findings reveal the important role for the dynamic modularity of the OFC as an “intermediate phenotype” mediating the pathway from genetic variation to behavioral manifestations, highlighting the potential of low-frequency stimulation of the OFC as a therapeutic strategy for specific subgroups of SCZ, especially those with positive symptoms and attention deficits.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

LeonDLotter/ABAnnotate

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2e32664c29341cbf7f43d7235bd4a95beb4adc2d, 30 May 2025
Languages: MATLAB (36), Jupyter (1), Python (1)
Size: 79 files, 38 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, documentation, 3 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration
Tools: SPM (4 files), Statistics and Machine Learning Toolbox (2 files), abagen (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file), Matplotlib (1 file), Nilearn (1 file), NumPy (1 file), pandas (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
40 files
At the source: github.com/LeonDLotter/ABAnnotate

GenLouvain/GenLouvain

License: BSD-2-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0fb0aa8bccae4ffb5a840c8f68f8dd48f1293de2, 14 January 2024
Languages: MATLAB (32), C++ (5), C/C++ (4), C (3)
Size: 61 files, 44 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
46 files
At the source: github.com/GenLouvain/GenLouvain

hiroyuki-kasai/NMFLibrary

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: ed44132dfe1b5495df685006b42259f0bd16bea3, 26 July 2022
Languages: MATLAB (242), Python (1)
Size: 298 files, 243 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Image Processing Toolbox (2 files), Optimization Toolbox (1 file), Signal Processing Toolbox (1 file), Matplotlib (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
245 files
At the source: github.com/hiroyuki-kasai/NMFLibrary

Code availability

Data post-processing was done in MATLAB. The dynamic community detection code is publicly available at https://github.com/GenLouvain/GenLouvain. The NMF code is publicly available at https://github.com/hiroyuki-kasai/NMFLibrary. The genotyping code is publicly available at https://github.com/LeonDLotter/ABAnnotate. Granger causality analysis used the dynamicBC toolbox. All statistical analyses were performed in R. Visualization was performed using BrainNet Viewer (https://www.nitrc.org/projects/bnv) and MRIcroGL (https://www.nitrc.org/projects/mricrogl/).

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

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 325 scripts, each with its path and the digest of its content;
  • 9 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 dataset includes self-reported demographics, clinical assessments, neurocognitive measures, and functional neuroimaging data from individuals with first-episode psychosis. Although all identifying information has been removed, there remains a minimal risk of re-identification due to rare individual characteristics. To protect participant anonymity, the raw data are protected and are not available due to data privacy laws. The data are available upon request with a signed data-sharing agreement that ensures secure handling and storage in line with our protocol. Requests can be directed to the corresponding author and will be addressed promptly. Access is limited to qualified researchers at recognized academic or medical institutions for non-commercial scientific purposes. Requests will normally be acknowledged within 2 weeks, with access granted within approximately 6–8 weeks. Besides, for all reported figures and table. Source data are provided with this paper.

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

Versions

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Version 2, 28 September 2026

  • Funding: added Jiangsu University: JDYY2023088; Natural Science Foundation of Jiangsu Province: BK20240506; Jiangsu Provincial Commission of Health and Family Planning: H2023036

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 3 keywords, 12 MeSH terms, 56 references.

Cite

This paper

Zeng, N., Wang, M., Zheng, H., Jiao, X., Wang, Z., Zhang, K., Goerlich, K. S., Aleman, A., Wang, J., & Hu, Q. (2026). OFC-induced network modularity improves positive symptoms and attentional alertness in schizophrenia: a combined rTMS-fMRI study. Nature communications, 17(1), 7010. https://doi.org/10.1038/s41467-026-72917-4

BibTeX

@article{zeng2026ofc,
author = {Zeng, Ningning and Wang, Min and Zheng, Hui and Jiao, Xiong and Wang, Ziliang and Zhang, Kexu and Goerlich, Katharina S. and Aleman, André and Wang, Jijun and Hu, Qiang},
title = {{OFC-induced network modularity improves positive symptoms and attentional alertness in schizophrenia: a combined rTMS-fMRI study}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {7010},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72917-4},
url = {https://doi.org/10.1038/s41467-026-72917-4},
pmid = {42218138},
pmcid = {PMC13392451}
}

RIS

TY - JOUR
AU - Zeng, Ningning
AU - Wang, Min
AU - Zheng, Hui
AU - Jiao, Xiong
AU - Wang, Ziliang
AU - Zhang, Kexu
AU - Goerlich, Katharina S.
AU - Aleman, André
AU - Wang, Jijun
AU - Hu, Qiang
TI - OFC-induced network modularity improves positive symptoms and attentional alertness in schizophrenia: a combined rTMS-fMRI study
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/30
VL - 17
IS - 1
SP - 7010
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72917-4
UR - https://doi.org/10.1038/s41467-026-72917-4
LA - en
ER -

CSL-JSON

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"container-title": "Nature communications",
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