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Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.

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5 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 5 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § MATERIALS AND METHODS › Derivation of group‐average networks and training of MS‐HBM for estimating individual‐specific networks ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step2_estimate_priors/CBIG_ArealMSHBM_dMSHBM_estimate_group_priors_parent.m, lines 1–60 · score 0.69 · von Mises Fisher, connectivity profiles, functional connectivity, clustered, trained
  2. [2] § MATERIALS AND METHODS › Derivation of group‐average networks and training of MS‐HBM for estimating individual‐specific networks ↔ stable_projects/brain_parcellation/Kong2022_ArealMSHBM/step2_estimate_priors/CBIG_ArealMSHBM_cMSHBM_estimate_group_priors_parent.m, lines 1–60 · score 0.69 · von Mises Fisher, connectivity profiles, functional connectivity, clustered, trained
  3. [3] § MATERIALS AND METHODS › Predicting language dominance using individual‐specific network topography ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/CBIG_MSHBM_Epilepsy_LI.m, the whole file · a weak match · score 0.66 · drug resistant epilepsy, right hemispheres, language network, fMRI, laterality, RH
  4. [4] § MATERIALS AND METHODS › Participants and datasets ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/CBIG_MSHBM_Epilepsy_LI.m, the whole file · a weak match · score 0.56 · MS HBM models, drug resistant epilepsy, language networks, fMRI, NIH
  5. [5] § MATERIALS AND METHODS › Participants and datasets ↔ stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/examples/CBIG_MSHBM_Epilepsy_wrapper.m, the whole file · a weak match · score 0.53 · MS HBM models, drug resistant epilepsy, Lang, rs, fMRI, NIH

Paper

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The authors' code

MATLAB · 130 lines · MIT · 2 matches

This file is not shown here: its repository is too large for the registry to keep the text of every file, and this one was left out.

It can be read at the source: stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy/CBIG_MSHBM_Epilepsy_LI.m.

Overview

Authors: Mervyn Jun Rui Lim1,2,3, Shaoshi Zhang1, Shreya Pande1, Aihuiping Xue1, Ru Kong1, Kareem Zaghloul2, Sara Inati2, B. T. Thomas Yeo1
ORCID iDs: Mervyn Jun Rui Lim, Shaoshi Zhang, Shreya Pande, Aihuiping Xue, Ru Kong, Kareem Zaghloul, Sara Inati, B. T. Thomas Yeo
  1. Computational Brain Imaging Group, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore
  2. National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda Maryland USA
  3. Division of Neurosurgery, Department of Surgery National University Hospital Singapore Singapore
Institutions: National Institutes of Health (United States); National University of Singapore (Singapore); National Institute of Neurological Disorders and Stroke (United States); National University Hospital (Singapore)
Journal: Epilepsia, volume 67, issue 9, pages 4649-4661
Dates: received 5 March 2026; accepted 19 May 2026; published online 8 June 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/epi.70323 · PMID 42257618 · PMCID PMC13592479 · OpenAlex W7163919583
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), epilepsy (population)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: functional magnetic resonance imaging, language lateralization, multisession hierarchical Bayesian model, precision functional mapping
MeSH: Drug Resistant Epilepsy*, Language*, Nerve Net*, Adult, Bayes Theorem, Brain Mapping, Connectome, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (NIH‐DIR ZIA NS009431‐05); NUS Yong Loo Lin School of Medicine (NUHSRO/2020/124/TMR/LOA); Singapore National Medical Research Council (NMRC) LCG (OFLCG19May‐0035); NMRC CTG‐IIT (CTGIIT23jan‐0001); NMRC OF‐IRG (OFIRG24jan‐0006, OFIRG24jul‐0049); NMRC STaR (STaR20nov‐0003); Singapore Ministry of Health (MOH) Centre Grant (CG21APR1009); United States National Institutes of Health (R01MH133334&2R01MH120080); Singapore National Research Foundation (NRF) Investigatorship (NRFI10‐2024‐0014)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

Objective: This study was undertaken to reliably estimate individual‐specific resting‐state cortical networks and determine whether language network topography can predict task‐based language dominance in drug‐resistant epilepsy.

Methods: We utilized a multisession hierarchical Bayesian model (MS‐HBM) trained on drug‐resistant epilepsy patients to map high‐quality individual‐specific cortical networks in this population (n = 65) with only 6–24 min of resting‐state functional magnetic resonance imaging (fMRI). We compared the quality of networks to MS‐HBM models trained on healthy participants from the human connectome project (n = 40) and tested the generalizability of the model in an independent cohort of drug‐resistant epilepsy participants (n = 26). Resting‐state language network topography was then used to predict task‐based language dominance.

Results: Ninety‐one participants with drug‐resistant epilepsy (National Institutes of Health, n = 65; University of Iowa, n = 26) were included: 61 (67.0%) temporal lobe epilepsy, 29 (31.9%) extratemporal lobe epilepsy, and one (1.1%) undetermined seizure onset zone. The mean age was 33.0 ± 11.4 years, and 50 (54.9%) were male. There were 40 healthy participants with a mean age of 29.0 ± 4.0 years, and 16 (40.0%) were male. MS‐HBM trained on drug‐resistant epilepsy estimated individual‐specific networks that more accurately capture cortical functional organization than group‐average networks or MS‐HBM trained on healthy participants. The trained MS‐HBM model generalized to an independent cohort of drug‐resistant epilepsy participants with concurrent intracranial electrical stimulation and fMRI. Critically, cortical evoked fMRI activity aligned more closely with individual‐specific networks than with group‐average networks. Furthermore, individual‐specific language network topography significantly predicted task‐based language dominance, achieving high accuracy for left (area under the curve [AUC] = .82), bilateral (AUC = .72), and right (AUC = .83) dominance.

Significance: These results demonstrate that MS‐HBM captures functionally meaningful network reorganization in drug‐resistant epilepsy and enables accurate, individual‐level prediction of language lateralization, with direct implications for presurgical functional mapping.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

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 text, “CONCLUSIONS”
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/tree/master/stable_projects/brain_parcellation/Lim2026_MSHBM_epilepsy

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;
  • 1,998 scripts, each with its path and the digest of its content;
  • 5 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

Deidentified data from the National Institutes of Health will be made available upon reasonable request to . All other data used are open source and available at their respective sites.

Reproduced under the paper's license (CC BY-NC), 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 2, 28 September 2026

  • Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 12 MeSH terms, 9 funders, 55 references.

Cite

This paper

Lim, M. J. R., Zhang, S., Pande, S., Xue, A., Kong, R., Zaghloul, K., Inati, S., & Yeo, B. T. T. (2026). Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy. Epilepsia, 67(9), 4649-4661. https://doi.org/10.1002/epi.70323

BibTeX

@article{lim2026individual,
author = {Lim, Mervyn Jun Rui and Zhang, Shaoshi and Pande, Shreya and Xue, Aihuiping and Kong, Ru and Zaghloul, Kareem and Inati, Sara and Yeo, B. T. Thomas},
title = {{Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy}},
journal = {Epilepsia},
year = {2026},
month = jun,
volume = {67},
number = {9},
pages = {4649--4661},
publisher = {Wiley},
issn = {0013-9580},
doi = {10.1002/epi.70323},
url = {https://doi.org/10.1002/epi.70323},
pmid = {42257618},
pmcid = {PMC13592479}
}

RIS

TY - JOUR
AU - Lim, Mervyn Jun Rui
AU - Zhang, Shaoshi
AU - Pande, Shreya
AU - Xue, Aihuiping
AU - Kong, Ru
AU - Zaghloul, Kareem
AU - Inati, Sara
AU - Yeo, B. T. Thomas
TI - Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy
T2 - Epilepsia
J2 - Epilepsia
PY - 2026
DA - 2026/06/08
VL - 67
IS - 9
SP - 4649
EP - 4661
SN - 0013-9580
PB - Wiley
DO - 10.1002/epi.70323
UR - https://doi.org/10.1002/epi.70323
LA - en
ER -

CSL-JSON

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