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Distributed cortical network dynamics of binocular convergent eye movements in humans.

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.

The 3 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS › CCC ↔ LoadParcellatedDataInMatlab_Example_cortexonly.m, the whole file · a weak match · score 0.70 · hemispheres separately, network assignment, cortical parcel, fMRI, MNI, atlas
  2. [2] § RESULTS › Distributed Network-Level Processes in Shaping Local Activations ↔ LoadParcellatedDataInMatlab_Example.m, the whole file · a weak match · score 0.60 · Cole Anticevic, fMRI, functionally connected, partition, parcellations, atlas
  3. [3] § METHODS › CCC ↔ LoadParcellatedDataInMatlab_Example.m, the whole file · a weak match · score 0.55 · network assignment, fMRI, MNI, partition, correlation, atlas

Paper

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

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The authors' code

MATLAB · 31 lines · 1.3 KB · other · 2 matches

  1. %Make sure to have this in your shell path:
  2. %wb_command
  4. addpath('code/')
  5. addpath('code/gifti-1.6/')
  7. %Setting the parcel files to be the 718 parcels (cortical + subcortical)
  8. parcelCIFTIFile='CortexSubcortex_ColeAnticevic_NetPartition_wSubcorGSR_parcels_LR.dlabel.nii';
  10. parcelTSFilename='Output_Atlas_CortSubcort.Parcels.LR.ptseries.nii';
  12. %Set this to be your input fMRI data CIFTI file
  13. inputFile='HCPS1200MSMAll/100206/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_Atlas_MSMAll.dtseries.nii';
  15. eval(['!wb_command -cifti-parcellate ' inputFile ' ' parcelCIFTIFile ' COLUMN ' parcelTSFilename ' -method MEAN'])
  17. %Load parcellated data (requires the ciftiopen function from the HCP website, FieldTrip)
  18. LR_dat = ciftiopen(parcelTSFilename,'wb_command');
  20. NUMPARCELS=718;
  21. tseriesMatSubj=LR_dat.cdata;
  23. %Loading other relevant files
  24. load('cortex_subcortex_community_order.mat');
  25. netorder=readtable('network_labelfile.txt','ReadVariableNames',false);
  26. netassignments=table2array(readtable('cortex_subcortex_parcel_network_assignments.txt','ReadVariableNames',false));
  28. %Computing Pearson correlation-based functional connectivity and vizualizing the data (assuming preprocessing has already been done)
  29. FCmat=corrcoef(tseriesMatSubj');
  30. FCmat_sorted=FCmat(indsort,indsort);
  31. figure;imagesc(FCmat_sorted)

LoadParcellatedDataInMatlab_Example.m at commit e4ea9fd, under other · at the source

Overview

Authors: Farzin Hajebrahimi1, Suril Gohel2, Michael W Cole3, Tara L Alvarez1
ORCID iDs: Farzin Hajebrahimi
  1. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, United States
  2. Department of Health Informatics, Rutgers University School of Health Professions, Newark, New Jersey, United States
  3. Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States
Institutions: New Jersey Institute of Technology (United States); Rutgers, The State University of New Jersey (United States); Rutgers Health (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 531-566
Dates: received 9 October 2025; accepted 25 January 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.549 · PMID 42039099 · PMCID PMC13108499 · OpenAlex W7128162472
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Cortical network, Network dynamics, Distributed networks, Binocular vision, Convergent eye movements
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Eye Institute (R01 EY023261, NEI-R01EY023261)
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Neuroimaging studies in humans have localized brain functions to specific brain regions, but a recent shift toward distributed network-based models of brain function promises deeper insights into the network processes that generate brain functionality. Resting-state functional connectivity provides a rich mapping of the brain’s network architecture, linking with both underlying structure and task-evoked responses across the whole brain. In this study, we utilized a model based on propagation of task-evoked activations over resting-state functional connectivity networks to identify cortical contributions to localized functional brain activations associated with binocular convergent eye movements. Binocular vision is crucial for daily routine activities, with its impairment leading to significant challenges in daily life. The distributed network-level mechanisms of binocular convergent eye movements remain unknown. Results showed that mapping activity flow over brain connections accurately generated brain activations associated with convergent eye movements, which were distinct from those observed during control tasks. The visual and dorsal attention networks dominated the propagation of activations through resting-state connections during convergent eye movements. In conclusion, highly distributed network pathways are involved in convergent eye movements, with some pathways contributing much more than others, providing important implications for future clinical models of binocular dysfunction.

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.

colelab.github.io/actflowtoolbox

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Code Availability STATEMENT”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: colelab.github.io/ActflowToolbox/

colelab.github.io/actflowcategories

License: none: the authors keep all their rights
State: the link is dead, verified on 29 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Code Availability STATEMENT”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link is dead (HTTP 404)
  • 29 September 2026: the link is dead (HTTP 404)
At the source: colelab.github.io/ActFlowCategories

ColeLab/ColeAnticevicNetPartition

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e4ea9fd709ead8616843924b717c6abce62ea05c, 23 June 2026
Languages: MATLAB (28), Python (2), C (2), Shell (1)
Size: 124 files, 33 scripts
Software Heritage: not archived
Found in: “Code Availability STATEMENT”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Connectome Workbench (3 files), cifti-matlab (2 files), Matplotlib (2 files), NiBabel (2 files), NumPy (2 files), GIfTI library for MATLAB (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
35 files
At the source: github.com/ColeLab/ColeAnticevicNetPartition

Code Availability STATEMENT

Code to conduct activity flow mapping and the subsequent statistics is publicly available via the Brain Activity Flow (“Actflow”) Toolbox (https://colelab.github.io/ActflowToolbox/). The code used in this study is leveraged from the code originally available via https://colelab.github.io/ActFlowCategories from Cocuzza et al. (2024) and modified for the present study. Connectome workbench is publicly available via https://www.humanconnectome.org/software/connectome-workbench. The CAB-NP is publicly available via https://github.com/ColeLab/ColeAnticevicNetPartition.

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;
  • 33 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);
  • 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 StatemenT

The data used in this study are a part of a randomized controlled clinical trial, and the public repositories are not publicly available currently. The Institutional Review Board during data collection did not ask participants to post nonidentifiable information publicly; hence, data will be shared upon request.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 1 funder, 84 references.

Cite

This paper

Hajebrahimi, F., Gohel, S., Cole, M. W., & Alvarez, T. L. (2026). Distributed cortical network dynamics of binocular convergent eye movements in humans. Network neuroscience (Cambridge, Mass.), 10(2), 531-566. https://doi.org/10.1162/netn.a.549

BibTeX

@article{hajebrahimi2026distributed,
author = {Hajebrahimi, Farzin and Gohel, Suril and Cole, Michael W and Alvarez, Tara L},
title = {{Distributed cortical network dynamics of binocular convergent eye movements in humans}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {531--566},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.549},
url = {https://doi.org/10.1162/netn.a.549},
pmid = {42039099},
pmcid = {PMC13108499}
}

RIS

TY - JOUR
AU - Hajebrahimi, Farzin
AU - Gohel, Suril
AU - Cole, Michael W
AU - Alvarez, Tara L
TI - Distributed cortical network dynamics of binocular convergent eye movements in humans
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 531
EP - 566
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.549
UR - https://doi.org/10.1162/netn.a.549
LA - en
ER -

CSL-JSON

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

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