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Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation.

Code ↔ Paper

3 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.

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  1. [1] § STAR★Methods › Method details › rs-fMRI preprocessing and functional connectome construction ↔ stable_projects/brain_parcellation/Xue2021_IndCerebellum/CBIG_IndCBM_compute_vol2surf_fc.m, lines 1–85 · score 0.56 · cerebral cortex, functional connectivity matrix, parcellated, Brain
  2. [2] § STAR★Methods › Quantification and statistical analysis › Statistical analysis ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/palm_defaults.m, the whole file · a weak match · score 0.55 · Software Foundation, linear models, squared, Pearson, FDR, clustering
  3. [3] § STAR★Methods › Quantification and statistical analysis › Statistical analysis ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/palm_quickperms.m, the whole file · a weak match · score 0.52 · Software Foundation, linear models, variance, MATLAB, variables

Paper

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The authors' code

MATLAB · 164 lines · 6.1 KB · MIT · 1 match

  1. function vol2surf_fc = CBIG_IndCBM_compute_vol2surf_fc(lh_surf_file, rh_surf_file, vol_file, template_file, output_file)
  3. % vol2surf_fc = CBIG_IndCBM_compute_vol2surf_fc(lh_surf_file, rh_surf_file, vol_file, template_file, output_file)
  4. %
  5. % This function computes the functional connectivity between the cerebellum
  6. % in the volume and the cerebral cortex on the surface. The resolution
  7. % should match your created cifti template. For template details see
  8. % CBIG_IndCBM_create_template.sh.
  9. %
  10. % Input:
  11. %
  12. % - lh_surf_file:
  13. % One single run: path of the lh surface time series (on surface)
  14. % Example: CBIG_CODE_DIR/data/example_data/CoRR_HNU/subj01/ ...
  15. % subj01_sess1/surf/lh.subj01_sess1_bld002_rest_skip4_stc_mc_ ...
  16. % residc_interp_FDRMS0.2_DVARS50_bp_0.009_0.08_fs6_sm6_fs5.nii.gz
  17. % Multiple runs: text file including all runs. Each row is the
  18. % file path of a run.
  19. %
  20. % - rh_surf_file:
  21. % One single run: path of the rh surface time series (on surface)
  22. % Example: CBIG_CODE_DIR/data/example_data/CoRR_HNU/subj01/ ...
  23. % subj01_sess1/surf/rh.subj01_sess1_bld002_rest_skip4_stc_mc_ ...
  24. % residc_interp_FDRMS0.2_DVARS50_bp_0.009_0.08_fs6_sm6_fs5.nii.gz
  25. % Multiple runs: text file including all runs. Each row is the
  26. % file path of a run.
  27. %
  28. % - vol_file:
  29. % One single run: path of the volume time series.
  30. % Example: CBIG_CODE_DIR/stable_projects/brain_parcellation/ ...
  31. % Xue2021_IndCerebellum/examples/input/vol/sub1/ ...
  32. % sub1_sess1_vol_4mm.nii.gz
  33. % Multiple runs: text file including all runs. Each row is the
  34. % file path of a run.
  35. %
  36. % - template_file:
  37. % Cifti template dscalar file specifying the cerebellar voxels.
  38. % Please create this file before you run this function. See
  39. % CBIG_IndCBM_create_template.sh to create this template file.
  40. % Example: ./examples/example_files/Sub1_fsaverage5_cerebellum_template.dscalar.nii
  41. %
  42. % Optional input:
  43. %
  44. % - output_file:
  45. % Path of output file. If given, 'vol2surf_fc' will be saved.
  46. %
  47. % Output:
  48. %
  49. % - vol2surf_fc:
  50. % M x N functional connectivity matrix.
  51. % M: cerebellar voxels, same order with the cifti template.
  52. % N: cerebral cortical vertices, lh and rh concatenated.
  53. %
  54. % Example:
  55. % vol2surf_fc = CBIG_IndCBM_compute_vol2surf_fc('proj/lh_run1.nii.gz', 'proj/rh_run1.nii.gz', ...
  56. % 'proj/vol_run1.nii.gz', 'proj/template.dscalar.nii')
  57. % CBIG_IndCBM_compute_vol2surf_fc('proj/lh_list.txt', 'proj/rh_list.txt', 'proj/vol_list.txt', ...
  58. % 'proj/template.dscalar.nii', 'proj/sub1_fc')
  59. %
  60. % Written by XUE Aihuiping and CBIG under MIT license: https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
  62. if(contains(vol_file,'.nii')) % Single run
  63. vol_list{1} = vol_file;
  64. lh_surf_list{1} = lh_surf_file;
  65. rh_surf_list{1} = rh_surf_file;
  66. else % Multiple runs
  67. fid = fopen(vol_file);
  68. vol_list = textscan(fid, '%s');
  69. vol_list = vol_list{1};
  70. fclose(fid);
  71. fid = fopen(lh_surf_file);
  72. lh_surf_list = textscan(fid, '%s');
  73. lh_surf_list = lh_surf_list{1};
  74. fclose(fid);
  75. fid = fopen(rh_surf_file);
  76. rh_surf_list = textscan(fid, '%s');
  77. rh_surf_list = rh_surf_list{1};
  78. fclose(fid);
  79. end
  81. N = length(vol_list);
  82. if(~isequal(N, length(lh_surf_list)) || ~isequal(N, length(rh_surf_list)))
  83. error('List lengths for vol, lh and rh are not consistent.');
  84. end
  85. disp([num2str(N) ' runs in total.']);
  87. % Find cerebellum structure
  88. cifti = ft_read_cifti(template_file);
  89. for i = 1:length(cifti.brainstructurelabel)
  90. if(strcmp(cifti.brainstructurelabel{i}, 'CEREBELLUM_LEFT'))
  91. cbm(1) = i;
  92. elseif(strcmp(cifti.brainstructurelabel{i}, 'CEREBELLUM_RIGHT'))
  93. cbm(2) = i;
  94. end
  95. end
  97. % Read ras information from template and convert to matlab index
  98. vol = MRIread(vol_list{1});
  99. lh_cbm_ras = cifti.pos(cifti.brainstructure == cbm(1), :);
  100. lh_cbm_mask = zeros(size(vol.vol, 2), size(vol.vol,1), size(vol.vol, 3));
  101. v_num = length(lh_cbm_ras);
  102. for i = 1:v_num
  103. ras = lh_cbm_ras(i,:)';
  104. vox = CBIG_ConvertRas2Vox(ras, vol.vox2ras);
  105. vox = ceil(vox([2 1 3]));
  106. lh_cbm_mask(vox(2), vox(1), vox(3)) = cbm(1);
  107. end
  108. rh_cbm_ras = cifti.pos(cifti.brainstructure == cbm(2), :);
  109. rh_cbm_mask = zeros(size(vol.vol, 2), size(vol.vol, 1), size(vol.vol, 3));
  110. v_num = length(rh_cbm_ras);
  111. for i = 1:v_num
  112. ras = rh_cbm_ras(i,:)';
  113. vox = CBIG_ConvertRas2Vox(ras, vol.vox2ras);
  114. vox = ceil(vox([2 1 3]));
  115. rh_cbm_mask(vox(2), vox(1), vox(3)) = cbm(2);
  116. end
  117. lh_vol_index = find(lh_cbm_mask == cbm(1));
  118. rh_vol_index = find(rh_cbm_mask == cbm(2));
  119. vol_index = [lh_vol_index; rh_vol_index];
  120. disp(['Left hemisphere of cerebellum: ' num2str(length(lh_vol_index)) ' voxels.']);
  121. disp(['Right hemisphere of cerebellum: ' num2str(length(rh_vol_index)) ' voxels.']);
  123. disp(['Computing vol to surf... ']);
  124. for i =1:N
  125. disp(['Run: ' num2str(i)]);
  126. disp(['Reading lh surface data: ' lh_surf_list{i}]);
  127. lh = MRIread(lh_surf_list{i});
  128. lh_data = reshape(lh.vol, lh.nvoxels, lh.nframes);
  129. disp(['Reading rh surface data: ' rh_surf_list{i}]);
  130. rh = MRIread(rh_surf_list{i});
  131. rh_data = reshape(rh.vol, rh.nvoxels, rh.nframes);
  132. surf_data = [lh_data; rh_data]';
  133. clear lh rh lh_data rh_data
  134. disp(['Reading volume data: ' vol_list{i}]);
  135. if(i ~= 1)
  136. vol = MRIread(vol_list{i});
  137. end
  138. vol_data = permute(vol.vol, [2,1,3,4]);
  139. vol_data = reshape(vol_data, vol.nvoxels, vol.nframes);
  140. clear vol
  141. vol_data = vol_data(vol_index, :);
  142. vol_data = vol_data';
  143. disp('Computing correlation...');
  144. r_vol2surf = single(CBIG_corr(vol_data, surf_data));
  145. clear surf_data vol_data
  146. z_vol2surf = CBIG_StableAtanh(r_vol2surf);
  147. clear r_vol2surf
  148. if(i == 1)
  149. vol2surf_fc = z_vol2surf;
  150. else
  151. vol2surf_fc = vol2surf_fc + z_vol2surf;
  152. end
  153. clear z_vol2surf
  154. end
  155. vol2surf_fc = vol2surf_fc / N;
  157. % Save 'vol2surf' if 'output_name' is given
  158. if(nargin==5)
  159. disp(['Saving correlation: ' output_file]);
  160. save(output_file, 'vol2surf_fc', '-v7.3');
  161. disp(['Correlation saved at: ' output_file]);
  162. end
  164. end

CBIG_IndCBM_compute_vol2surf_fc.m at commit 35b5664, under MIT · at the source

Overview

Authors: Chao Tao1,2,3, Shunshun Cui1,2,3, Yuhao Shen1,2,3, Yan Cheng1,2,3, Jiajia Zhu1,2,3, Yongqiang Yu1,2,3
  1. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
  2. Research Center of Clinical Medical Imaging, Hefei, Anhui Province 230032, China
  3. Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei 230032, China
Institutions: First Affiliated Hospital of Anhui Medical University (China)
Journal: iScience, volume 29, issue 8, article 116903
Dates: received 17 December 2025; accepted 7 July 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116903 · PMID 42666734 · PMCID PMC13523837 · OpenAlex W7170054038
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Machine learning
Keywords: resting-state functional connectivity, task-evoked neural activation, brain connectome, functional magnetic resonance imaging, cognition
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Anhui Provincial Natural Science Foundation; National Natural Science Foundation of China
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

Elucidating how resting-state functional connectivity relates to task-evoked neural activation is an important topic in systems neuroscience. We analyzed task-based and resting-state functional magnetic resonance imaging data from 1,005 participants from the Human Connectome Project. On the basis of connectome-constrained predictive modeling, we calculated a neural activation constraint index (NACI) to assess the extent to which intrinsic functional connectome architecture constrains task-evoked neural activation. NACIs showed task-dependent variations, indicating differential constraint effects of the intrinsic functional connectome across distinct tasks. Spectral clustering based on the NACI classified participants into the high- and low-constraint groups. The high-constraint group exhibited superior cognitive functions in several domains and better performance across multiple tasks. Higher NACIs from working memory, language, and relational tasks correlated with greater cognitive functions and better task performance. These findings support the neurobiological and behavioral relevance of NACI and suggest its utility for characterizing individual differences in functional brain organization.

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 3 matches between paragraphs and lines of code.

1421828675-arch/NACI-spectral-clustering-HCP

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f0c85f2e3c07cfd0fb100512ca5a678ae13eda40, 4 May 2026
Languages: MATLAB (4)
Size: 7 files, 4 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, 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
5 files
At the source: github.com/1421828675-arch/NACI-spectral-clustering-HCP

ThomasYeoLab/CBIG

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (93 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/CBIG

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

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,002 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 and code availability

This study uses publicly available data from the HCP, which can be accessed at https://www.humanconnectome.org/.

The MATLAB code used for NACI calculation, spectral clustering, robustness analyses, and cluster consistency analyses is publicly available at https://github.com/1421828675-arch/NACI-spectral-clustering-HCP.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon reasonable request.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 2 funders, 56 references.

Cite

This paper

Tao, C., Cui, S., Shen, Y., Cheng, Y., Zhu, J., & Yu, Y. (2026). Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation. iScience, 29(8), 116903. https://doi.org/10.1016/j.isci.2026.116903

BibTeX

@article{tao2026neurobiological,
author = {Tao, Chao and Cui, Shunshun and Shen, Yuhao and Cheng, Yan and Zhu, Jiajia and Yu, Yongqiang},
title = {{Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {116903},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116903},
url = {https://doi.org/10.1016/j.isci.2026.116903},
pmid = {42666734},
pmcid = {PMC13523837}
}

RIS

TY - JOUR
AU - Tao, Chao
AU - Cui, Shunshun
AU - Shen, Yuhao
AU - Cheng, Yan
AU - Zhu, Jiajia
AU - Yu, Yongqiang
TI - Neurobiological and behavioral relevance of intrinsic functional connectome constraints on task-evoked neural activation
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/22
VL - 29
IS - 8
SP - 116903
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116903
UR - https://doi.org/10.1016/j.isci.2026.116903
LA - en
ER -

CSL-JSON

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