Thalamic organization differentially contributes to clinical conditions in epilepsy.

Code ↔ Paper

8 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 8 matches · 3 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted

[1] § Materials and methods › Resting-state fMRI measures ↔ Coinfluence_Maps_Analysis.m, lines 71 to 98 · score 0.75 · LocE, PartC, ReHo, redundancy, focal, recruitment · Report a mismatch
[2] § Materials and methods › Resting-state fMRI measures ↔ Measurements/efficiency_wei.m, lines 1 to 106 · score 0.72 · clustering coefficient, adjacency matrices, local efficiency, algorithm, node, network · Report a mismatch
[3] § Materials and methods › Resting-state fMRI measures ↔ Coinfluence_Maps_Analysis.m, lines 71 to 98 · score 0.70 · LocE, PartC, ReHo, recruitment, DC, MC · Report a mismatch
[4] § Materials and methods › Resting-state fMRI measures ↔ Energy Efficiency/pipelineCompareStaticDynamicEnergy.m, the whole file · a weak match · score 0.67 · control nodes, functional connectivity, static, energy, dynamic, window · Report a mismatch
[5] § Materials and methods › Resting-state fMRI measures ↔ Measurements/regional_activation_energy.m, the whole file · a weak match · score 0.57 · regional activation energy, efficiency, brain, matrix, connectome · Report a mismatch
[6] § Materials and methods › Resting-state fMRI measures ↔ Measurements/regional_activation_energy.m, the whole file · a weak match · score 0.53 · Regional activation energy, brain · Report a mismatch
[7] § Materials and methods › Statistics and reproducibility › Statistical determination of co-influences across TLE clinical “conditions” ↔ permutools/booteffectsize.m, lines 1 to 60 · score 0.52 · pooled standard deviation, confidence intervals, Hedges, Permutools · Report a mismatch
[8] § Materials and methods › Data preprocessing ↔ Coinfluence_Maps_Analysis.m, lines 103 to 220 · score 0.51 · head motion, atlas, AAL, parcel, metrics, pre · Report a mismatch

Paper

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The authors' code

MATLAB · 264 lines · 10 KB · no license · 3 matches

%% =========================================================================
% Coinfluence Maps Analysis Pipeline
% Temporal Lobe Epilepsy – Multicondition Effect-Size Mapping
% Author: Qirui Zhang
%
% Requirements:
% - PERMUTOOLS: https://github.com/mickcrosse/PERMUTOOLS
% - SurfStat: https://math.mcgill.ca/keith/surfstat/
%
% Input:
% - TLE_Pre_Post_Dataset_Anonymous (Excel)
% - measures.mat (contains Data_pre, Data_post, Data_delta)
% - AAL atlas NIfTI
% =========================================================================
clear; clc;
%% -------------------------------------------------------------------------
% 0. Paths & Output Directories
%% -------------------------------------------------------------------------
result_dir = './Coinfluence_Maps/';
dir_each = fullfile(result_dir, 'condition_level_maps');
dir_summary = fullfile(result_dir, 'summary_maps');
if ~exist(result_dir, 'dir'); mkdir(result_dir); end
if ~exist(dir_each, 'dir'); mkdir(dir_each); end
if ~exist(dir_summary, 'dir'); mkdir(dir_summary); end
%% -------------------------------------------------------------------------
% 1. Load Subject Information
%% -------------------------------------------------------------------------
[NUM,TXT,RAW] = xlsread('./TLE_Pre_Post_Dataset_Anonymous','include_pre');
SUB = unique(RAW(2:end,1)); % subject ID
group = RAW(2:end,6); % overall group
group_TLE = RAW(2:end,7); % TLE vs HC
age = cell2mat(RAW(2:end,9));
sex = RAW(2:end,3);
hm = cell2mat(RAW(2:end,2)); % head motion
outcome = RAW(2:end,20); % Engel (G1 poor, G2 good)
pathology = RAW(2:end,19); % HS vs non-HS
focality = RAW(2:end,17); % focal vs FBTCS
ALL_index = 1:length(SUB);
TLE_index = find(strcmp(group_TLE,'G1'));
HC_index = find(strcmp(group_TLE,'G2'));
good_idx = find(strcmp(outcome,'G2'));
poor_idx = find(strcmp(outcome,'G1'));
%% -------------------------------------------------------------------------
% 2. Load imaging measures (structures)
%% -------------------------------------------------------------------------
load('./measures.mat'); % Data_pre, Data_post, Data_delta (each has .AAL and .combine)
%% -------------------------------------------------------------------------
% 3. Atlas information
%% -------------------------------------------------------------------------
atlas_nii = './aal193_229_193.nii';
aal_order = 1:116;
exclude_ROI = load('.\exculde_ROI_FILP.txt');
%% -------------------------------------------------------------------------
% 4. Define Measures & Clinical Conditions
%% -------------------------------------------------------------------------
% Nine non-redundant imaging metrics
measure_list = {'ALFF','ReHo','GM','Intergration','Recruitment','DC','LocE','PartC','MC'};
% Six conditions
condition_labels = { ...
'P1_TLE_vs_HC', ... % pre-surgical organization: TLE vs HC
'P2_Post_vs_Pre', ... % post-surgical organization: post vs pre
'Outcome_Poor_vs_Good', ... % seizure outcome
'Seizure_Focality', ... % focal vs FBTCS
'Pathology', ... % HS vs non-HS
'Laterality'}; % seizure onset lateralization
condition_var = { ...
'group_TLE', ... % P1: TLE vs HC
'group_TLE', ... % P2: paired TLE pre/post
'outcome', ... % poor vs good
'focality', ... % focal vs FBTCS
'pathology', ... % HS vs non-HS
'group'}; % left vs right
condition_index = { ...
ALL_index, ... % P1: all (TLE+HC)
TLE_index, ... % P2: only TLE with pre/post
TLE_index, ... % outcome: TLE only
TLE_index, ... % focality: TLE only
TLE_index, ... % pathology: TLE only
TLE_index}; % lateralization: TLE only
%% -------------------------------------------------------------------------
% 5. Main Loop – Condition × Measure
%% -------------------------------------------------------------------------
n_roi = 116;
n_cond = numel(condition_labels);
n_measure = numel(measure_list);
D_all = zeros(n_roi, n_cond, n_measure);
P_all = zeros(n_roi, n_cond, n_measure);
for ci = 1:n_cond
idx_cond = condition_index{ci};
% covariates for current condition
ageI = age(idx_cond);
sexI = sex(idx_cond);
hmI = hm(idx_cond);
AgeTerm = term(ageI);
SexTerm = term(sexI);
HMTerm = term(hmI);
cond_raw = eval(condition_var{ci});
cond_raw = cond_raw(idx_cond);
CondTerm = term(cond_raw);
% for independent-sample comparisons, define G1/G2
G1_idx = find(strcmp(cond_raw,'G1'));
if ci ~= 2 % P2 is paired, no G2
G2_idx = find(strcmp(cond_raw,'G2'));
end
for mi = 1:n_measure
metric_name = measure_list{mi};
% -----------------------------------------------------------------
% 5.1 Select data matrix used in linear model
% P2(Post_vs_Pre) uses Data_delta, others use Data_pre
% -----------------------------------------------------------------
if ci == 2
data_model = Data_delta.AAL.(metric_name); % 116 × subj
else
data_model = Data_pre.AAL.(metric_name); % 116 × subj
end
data_model = data_model(:, idx_cond); % 116 × nCondSubj
% -----------------------------------------------------------------
% 5.2 Fit linear model and compute t-map
% -----------------------------------------------------------------
if ci == 2
% Paired design (post – pre)
M = CondTerm; % dummy term for completeness
slm = SurfStatLinMod(data_model', M);
slmp = SurfStatT(slm, CondTerm); % not actually used for contrast coding
% ---- effect size: post vs pre (paired) ----
data_pre_roi = Data_pre.AAL.(metric_name);
data_post_roi = Data_post.AAL.(metric_name);
data_pre_roi = data_pre_roi(:, idx_cond);
data_post_roi = data_post_roi(:, idx_cond);
data_post_roi(exclude_ROI==1,:) = 0;
data_pre_roi(exclude_ROI==1,:) = 0;
[d, d_ci] = booteffectsize( ...
data_post_roi', data_pre_roi', ...
'effect','cohen','paired',1,'correct',1);
[~, p_val] = ttest(data_post_roi', data_pre_roi');
% mask out resected temporal parcels
slmp.t(exclude_ROI==1) = 0;
slmp.p = p_val(:)';
slmp.p(exclude_ROI==1) = 1;
else
% Independent-sample tests with covariates
M_full = 1 + CondTerm + AgeTerm + SexTerm + HMTerm;
slm = SurfStatLinMod(data_model', M_full);
slmp = SurfStatT(slm, CondTerm.G1 - CondTerm.G2);
% residuals after regressing out age, sex, head motion
M_cov = 1 + AgeTerm + SexTerm + HMTerm;
slm_cov = SurfStatLinMod(data_model', M_cov);
residuals = data_model' - slm_cov.X * slm_cov.coef;
res_G1 = residuals(G1_idx,:);
res_G2 = residuals(G2_idx,:);
[d, d_ci] = booteffectsize( ...
res_G1, res_G2, ...
'effect','cohen','paired',0,'correct',1);
slmp.p = 2 * (1 - tcdf(abs(slmp.t), slmp.df));
end
slmp.d = d;
slmp.d_ci = d_ci;
% -----------------------------------------------------------------
% 5.3 Save NIfTI and .mat for this condition × metric
% -----------------------------------------------------------------
out_nii = fullfile(dir_each, ...
sprintf('%s_%s_d.nii', condition_labels{ci}, metric_name));
parcel_to_volume(slmp.d, atlas_nii, out_nii, aal_order);
out_mat = fullfile(dir_each, ...
sprintf('%s_%s_stats.mat', condition_labels{ci}, metric_name));
save(out_mat, 'slmp');
% collect into big arrays for summary maps
D_all(:, ci, mi) = slmp.d(:);
P_all(:, ci, mi) = slmp.p(:);
end
end
save(fullfile(result_dir, 'D_all.mat'), 'D_all', 'P_all');
%% -------------------------------------------------------------------------
% 6. Summary Maps – Mean Absolute Effect Size Across Measures
% --------------------------------------------------------------------------
% 6.1 All 6 conditions × 9 measures
D_flat = reshape(D_all, [n_roi, numel(D_all)/n_roi]);
mean_d_all = mean(abs(D_flat), 2, 'omitnan');
parcel_to_volume(mean_d_all, atlas_nii, ...
fullfile(dir_summary, 'Mean_d_all_conditions.nii'), aal_order);
% 6.2 Pre-surgical conditions only (exclude P2 Post_vs_Pre)
D_sel = D_all(:, [1 3 4 5 6], :); % 5 conditions × 9 measures
D_sel_flat = reshape(D_sel, [n_roi, numel(D_sel)/n_roi]);
mean_d_pre = mean(abs(D_sel_flat), 2, 'omitnan');
parcel_to_volume(mean_d_pre, atlas_nii, ...
fullfile(dir_summary, 'Mean_d_pre_surgical_conditions.nii'), aal_order);
% 6.3 Condition-specific summary maps – Mean |d| across metrics (per condition)
% Naming convention for each clinical condition (used in output filenames)
condition_type_name = {'P1_TLE_HC', ...
'P2_Post_Pre', ...
'Outcome_poor_good', ...
'Seizure_Focality_FBTCS+_FBTCS-', ...
'Pathology_HS_nHS', ...
'Laterality_Left_Right'};
mean_d_each = zeros(n_roi, n_cond);
for ci = 1:n_cond
% Mean absolute effect size across all 9 metrics for this condition
mean_d_each(:, ci) = mean(abs(D_all(:, ci, :)), 3, 'omitnan');
% Save condition-specific mean |d| map to summary_maps/
out_nii = fullfile(dir_summary, ...
sprintf('%s_Mean_d.nii', condition_type_name{ci}));
parcel_to_volume(mean_d_each(:, ci), atlas_nii, out_nii, aal_order);
end

Coinfluence_Maps_Analysis.m, no license · at the source

Overview

Authors: Qirui Zhang¹, Sam S. Javidi¹, Ankeeta Ankeeta¹, Michael R. Sperling¹, Zhiqiang Zhang², Joseph I. Tracy¹

ORCID iDs: Qirui Zhang, Michael R. Sperling, Zhiqiang Zhang, Joseph I. Tracy

Institutions: Thomas Jefferson University (United States); Nanjing University (China)

Journal: Communications medicine, volume 6, issue 1, article 281

Dates: received 7 July 2025; accepted 10 March 2026; published online 24 March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s43856-026-01530-9 · PMID 41876841 · PMCID PMC13183871 · OpenAlex W7140193856

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: structural MRI / diffusion (modality), human (organism), epilepsy (population), clinical / translational (subfield)

Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Graphs, fMRI & imaging, Smoothing, state filtering, decompositions

Keywords: Epilepsy, Cognitive neuroscience

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Epilepsy Society; National Institutes of Health

Citations: cited by 1 paper (Europe PMC); 67 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

OSF 49xde

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Languages: MATLAB (2)

Size: 9 files, 2 scripts

Software Heritage: not checked

Found in: “Code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Image Processing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

2 files

Coinfluence_Maps_Analysis.m, MATLAB, 264 lines, 3 matches
parcel_to_volume.m, MATLAB, 25 lines

At the source: osf.io/49xde/

xiaosonghe/DynamicFunctionalConnectivity

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 79bda4f73695624314a4a9699856e20ac551ef25, 2 May 2020

Languages: MATLAB (8)

Size: 9 files, 8 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Wavelet Toolbox (3 files), SPM (3 files), Brain Connectivity Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

9 files

S1CreateIndividualROIs.m, MATLAB, 35 lines
S2ROISignalExtract.m, MATLAB, 18 lines
S2tLIofActivation.m, MATLAB, 171 lines
S2tWaveletDecompose.m, MATLAB, 22 lines
S3CommunityMatrix.m, MATLAB, 68 lines
S4ModularityMeasures.m, MATLAB, 23 lines
S5RandomCommunity.m, MATLAB, 152 lines
S6NullModelMeasures.m, MATLAB, 45 lines
README.md, Text, 13 lines

At the source: github.com/xiaosonghe/DynamicFunctionalConnectivity

brain-intelligence-lab/functional_controllability

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 323786d6f1249205bed2a3614818dc646a5ae1d5, 2 July 2022

Languages: MATLAB (17), Python (2)

Size: 27 files, 19 scripts

Software Heritage: 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), NumPy (2 files), scikit-learn (2 files), SciPy (2 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

21 files

Energy Efficiency/calcu_S.m, MATLAB, 14 lines
Energy Efficiency/calcu_d.m, MATLAB, 20 lines
Energy Efficiency/diagW.m, MATLAB, 25 lines
Energy Efficiency/dynamicMiniEnergy.m, MATLAB, 37 lines
Energy Efficiency/dynamicMiniEnergyDisruptOrder.m, MATLAB, 52 lines
Energy Efficiency/functional2system.m, MATLAB, 26 lines
Energy Efficiency/gram_definite_integral.m, MATLAB, 16 lines
Energy Efficiency/gram_lyaplov.m, MATLAB, 18 lines
Energy Efficiency/pipelineCompareDynamicEnergyAfterShuffle.m, MATLAB, 59 lines
Energy Efficiency/pipelineCompareStaticDynamicEnergy.m, MATLAB, 67 lines, 1 match
Energy Efficiency/staticMiniEnergy.m, MATLAB, 23 lines
Measurements/ar_mls.m, MATLAB, 94 lines
Measurements/ave_control.m, MATLAB, 29 lines
Measurements/efficiency_wei.m, MATLAB, 138 lines, 1 match
Measurements/modal_control.m, MATLAB, 32 lines
Measurements/participation_coef.m, MATLAB, 49 lines
Measurements/regional_activation_energy.m, MATLAB, 23 lines, 2 matches
Prediction/kernel_ridge_predict.py, Python, 119 lines
Prediction/kernel_ridge_predict_combine.py, Python, 112 lines
LICENSE, License, 21 lines
README.md, Text, 14 lines

At the source: github.com/brain-intelligence-lab/functional_controllability

mickcrosse/PERMUTOOLS

License: BSD-3-Clause

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 3ef0667900e4ee93bb3b0543078c6dd2702c3ade, 21 August 2026

Languages: MATLAB (65)

Size: 79 files, 65 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, tests, documentation

Not found: CITATION.cff, environment file, continuous integration

Tools: Statistics and Machine Learning Toolbox (24 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

67 files

examples/run_booteffectsize_examples.m, MATLAB, 124 lines
examples/run_github_examples.m, MATLAB, 264 lines
examples/run_paper_examples.m, MATLAB, 79 lines
examples/run_permuanova1_examples.m, MATLAB, 95 lines
examples/run_permuanova2_examples.m, MATLAB, 123 lines
examples/run_permuanovan_examples.m, MATLAB, 130 lines
examples/run_permuchi2_examples.m, MATLAB, 154 lines
examples/run_permucorr_examples.m, MATLAB, 188 lines
examples/run_permuglm_examples.m, MATLAB, 237 lines
examples/run_permumanova1_examples.m, MATLAB, 113 lines
examples/run_permumanova2_examples.m, MATLAB, 123 lines
examples/run_permuranova1_examples.m, MATLAB, 106 lines
examples/run_permuranova2_examples.m, MATLAB, 130 lines
examples/run_permuregress_examples.m, MATLAB, 213 lines
examples/run_permuttest2_examples.m, MATLAB, 214 lines
examples/run_permuttest_examples.m, MATLAB, 198 lines
examples/run_permuvartest2_examples.m, MATLAB, 152 lines
examples/run_permuvartest_examples.m, MATLAB, 143 lines
examples/run_permuztest_examples.m, MATLAB, 163 lines
octave/booteffectsize.m, MATLAB, 449 lines
octave/permuanova1.m, MATLAB, 198 lines
octave/permuanova2.m, MATLAB, 306 lines
octave/permucorr.m, MATLAB, 264 lines
octave/permuttest.m, MATLAB, 268 lines
octave/permuttest2.m, MATLAB, 279 lines
octave/permuvartest.m, MATLAB, 222 lines
octave/permuvartest2.m, MATLAB, 232 lines
octave/permuztest.m, MATLAB, 186 lines
octave/test/test_permuanova1.m, MATLAB, 35 lines
octave/test/test_permuanova2.m, MATLAB, 35 lines
octave/test/test_permuttest.m, MATLAB, 32 lines
octave/test/test_permuttest2.m, MATLAB, 31 lines
octave/test/test_permuvartest2.m, MATLAB, 31 lines
octave/test/test_permuztest.m, MATLAB, 31 lines
octave/utils/ptpaircols.m, MATLAB, 36 lines
octave/utils/ptparsevarargin.m, MATLAB, 95 lines
octave/utils/ptvalidateparamin.m, MATLAB, 21 lines
octave/utils/ptvec2mat.m, MATLAB, 36 lines
permutools/booteffectsize.m, MATLAB, 445 lines, 1 match
permutools/permuanova1.m, MATLAB, 206 lines
permutools/permuanova2.m, MATLAB, 349 lines
permutools/permuanovan.m, MATLAB, 256 lines
permutools/permuchi2.m, MATLAB, 222 lines
permutools/permucorr.m, MATLAB, 343 lines
permutools/permuglm.m, MATLAB, 343 lines
permutools/permumanova1.m, MATLAB, 219 lines
permutools/permumanova2.m, MATLAB, 259 lines
permutools/permuranova1.m, MATLAB, 174 lines
permutools/permuranova2.m, MATLAB, 212 lines
permutools/permuregress.m, MATLAB, 252 lines
permutools/permuttest.m, MATLAB, 338 lines
permutools/permuttest2.m, MATLAB, 400 lines
permutools/permuvartest.m, MATLAB, 221 lines
permutools/permuvartest2.m, MATLAB, 341 lines
permutools/permuztest.m, MATLAB, 195 lines
permutools/test/test_permuanova1.m, MATLAB, 35 lines
permutools/test/test_permuanova2.m, MATLAB, 35 lines
permutools/test/test_permuttest.m, MATLAB, 32 lines
permutools/test/test_permuttest2.m, MATLAB, 31 lines
permutools/test/test_permuvartest2.m, MATLAB, 31 lines
permutools/test/test_permuztest.m, MATLAB, 31 lines
permutools/utils/ptpaircols.m, MATLAB, 36 lines
permutools/utils/ptparsevarargin.m, MATLAB, 89 lines
permutools/utils/ptvalidateparamin.m, MATLAB, 21 lines
permutools/utils/ptvec2mat.m, MATLAB, 36 lines
LICENSE, License, 29 lines
README.md, Text, 413 lines

At the source: github.com/mickcrosse/PERMUTOOLS

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to the authors' code: brain-intelligence-lab/functional_controllability, mickcrosse/PERMUTOOLS, xiaosonghe/DynamicFunctionalConnectivity, OSF 49xde

Read it in the paper: doi.org/10.1038/s43856-026-01530-9.

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:

4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
94 scripts, each with its path and the digest of its content;
8 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 paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s43856-026-01530-9.

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, volume, issue, pages, dates, 6 authors, 2 keywords, 2 funders, 65 references.

Cite

This paper

Zhang, Q., Javidi, S. S., Ankeeta, A., Sperling, M. R., Zhang, Z., & Tracy, J. I. (2026). Thalamic organization differentially contributes to clinical conditions in epilepsy. Communications medicine, 6(1), 281. https://doi.org/10.1038/s43856-026-01530-9

BibTeX

@article{zhang2026thalamic, author = {Zhang, Qirui and Javidi, Sam S. and Ankeeta, Ankeeta and Sperling, Michael R. and Zhang, Zhiqiang and Tracy, Joseph I.}, title = {{Thalamic organization differentially contributes to clinical conditions in epilepsy}}, journal = {Communications medicine}, year = {2026}, month = mar, volume = {6}, number = {1}, pages = {281}, publisher = {Nature Publishing Group}, issn = {2730-664X}, doi = {10.1038/s43856-026-01530-9}, url = {https://doi.org/10.1038/s43856-026-01530-9}, pmid = {41876841}, pmcid = {PMC13183871} }

RIS

TY - JOUR AU - Zhang, Qirui AU - Javidi, Sam S. AU - Ankeeta, Ankeeta AU - Sperling, Michael R. AU - Zhang, Zhiqiang AU - Tracy, Joseph I. TI - Thalamic organization differentially contributes to clinical conditions in epilepsy T2 - Communications medicine J2 - Commun Med (Lond) PY - 2026 DA - 2026/03/24 VL - 6 IS - 1 SP - 281 SN - 2730-664X PB - Nature Publishing Group DO - 10.1038/s43856-026-01530-9 UR - https://doi.org/10.1038/s43856-026-01530-9 LA - en ER -

CSL-JSON

{ "id": "10.1038/s43856-026-01530-9", "type": "article-journal", "title": "Thalamic organization differentially contributes to clinical conditions in epilepsy", "container-title": "Communications medicine", "author": [ { "family": "Zhang", "given": "Qirui" }, { "family": "Javidi", "given": "Sam S." }, { "family": "Ankeeta", "given": "Ankeeta" }, { "family": "Sperling", "given": "Michael R." }, { "family": "Zhang", "given": "Zhiqiang" }, { "family": "Tracy", "given": "Joseph I." } ], "container-title-short": "Commun Med (Lond)", "volume": "6", "issue": "1", "page": "281", "DOI": "10.1038/s43856-026-01530-9", "PMID": "41876841", "PMCID": "PMC13183871", "ISSN": "2730-664X", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s43856-026-01530-9", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 24 ] ] } }

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

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Journal: Science advances
In common: NumPy, epilepsy, 8 references, author Michael R. Sperling
[2] doi:10.1038/s42003-024-05819-0 [code]: Macroscale intrinsic dynamics are associated with microcircuit function in focal and generalized epilepsies.
Journal: Communications biology
In common: SPM, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 3 other tools, epilepsy, 4 references, author Zhiqiang Zhang
[3] doi:10.1038/s42003-023-05075-8 [code]: Atypical functional connectivity hierarchy in Rolandic epilepsy.
Journal: Communications biology
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, scikit-learn, 2 other tools, epilepsy, 2 authors
[4] doi:10.1186/s12916-025-04099-7 [code]: Normative structural connectome constrains spreading transient brain activity in generalized epilepsy.
Journal: BMC medicine
In common: SPM, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 3 other tools, epilepsy, structural MRI / diffusion, 2 references, author Zhiqiang Zhang
[5] doi:10.1038/s41467-025-65042-1 [code]: Personalized biomarkers of multiscale functional alterations in temporal lobe epilepsy.
Journal: Nature communications
In common: Statistics and Machine Learning Toolbox, SciPy, NumPy, epilepsy, structural MRI / diffusion, clinical / translational, 4 references, author Zhiqiang Zhang
[6] doi:10.1038/s41467-025-57392-7 [code]: A generalized epilepsy network derived from brain abnormalities and deep brain stimulation.
Journal: Nature communications
In common: SPM, Image Processing Toolbox, Statistics and Machine Learning Toolbox, epilepsy, structural MRI / diffusion, 3 references, author Zhiqiang Zhang
[7] doi:10.1038/s42003-024-06726-0 [code]: Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy.
Journal: Communications biology
In common: Statistics and Machine Learning Toolbox, SciPy, NumPy, epilepsy, structural MRI / diffusion, 2 authors
[8] doi:10.1002/epi.70323 [code]: Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.
Journal: Epilepsia
In common: SPM, Image Processing Toolbox, Statistics and Machine Learning Toolbox, 3 other tools, epilepsy, 3 references
[9] doi:10.1038/s41598-022-27248-x [code]: Graph theoretical measures of fast ripple networks improve the accuracy of post-operative seizure outcome prediction.
Journal: Scientific reports
In common: Brain Connectivity Toolbox, Statistics and Machine Learning Toolbox, SciPy, 1 other tool, epilepsy, structural MRI / diffusion, clinical / translational, 1 reference, author Michael R. Sperling
[10] doi:10.1038/s41398-026-04025-2 [code]: Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.
Journal: Translational psychiatry
In common: Brain Connectivity Toolbox, SPM, Image Processing Toolbox, 4 other tools, 3 references

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Thalamic organization differentially contributes to clinical conditions in epilepsy.

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OSF 49xde

xiaosonghe/DynamicFunctionalConnectivity

brain-intelligence-lab/functional_controllability

mickcrosse/PERMUTOOLS

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