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MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging.

Code ↔ Paper

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The 2 matches
  1. [1] § Materials and methods › Statistics › PLS inference ↔ myPLS_functions/myPLS_bootstrapping.m, lines 1–57 · score 0.77 · confidence interval, standard deviation, covariance matrix, Bootstrap Sampling, latent component, saliences
  2. [2] § Materials and methods › Statistics › PLS inference ↔ myPLS_functions/myPLS_analysis.m, lines 1–75 · score 0.76 · confidence interval, standard deviation, covariance matrix, latent component, Bootstrap Sampling, saliences

Paper

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The authors' code

MATLAB · 157 lines · 6.3 KB · GPL-2.0 · 1 match

  1. function boot_results = myPLS_bootstrapping(X0,Y0,U,V,S,grouping,pls_opts)
  3. % This function computes bootstrap resampling with replacement on X and Y,
  4. % with option to either ignore or account for groups (e.g. diagnosis).
  5. % The PLS analysis is re-computed for each bootstrap sample.
  6. %
  7. % Inputs:
  8. % - X0 : N x M matrix, N is #subjects, M is #imaging,
  9. % brain data (not normalized!)
  10. % - Y0 : N x B matrix, B is #behaviors/design scores,
  11. % behavior/design data (not normalized!)
  12. % - U : B x L matrix, L is #latent components (LCs),
  13. % behavior/design saliences
  14. % - V : B x L matrix, imaging saliences
  15. % - S : L x L matrix, singular values (diagonal matrix)
  16. % - grouping : N x 1 vector, subject grouping (e.g. diagnosis)
  17. % e.g. [1,1,2] = subjects 1 and 2 belong to group 1,
  18. % subject 3 belongs to group 2
  19. % - pls_opts : options for the PLS analysis
  20. % Necessary fields for this function:
  21. % - .nBootstraps : number of bootstrap samples
  22. % - .grouped_PLS : binary variable indicating if groups
  23. % should be considered when computing R
  24. % 0 = PLS will computed over all subjects
  25. % 1 = R will be constructed by concatenating group-wise
  26. % covariance matrices (as in conventional behavior PLS)
  27. % - .grouped_boot : binary variable indicating if groups should be
  28. % considered during bootstrapping
  29. % 0 = bootstrapping ignoring grouping
  30. % 1 = bootstrapping within group
  31. % - .boot_procrustes_mod : mode for bootstrapping procrustes transform
  32. % 1 = standard (rotation computed only on U)
  33. % 2 = average rotation of U and V
  34. % - .save_boot_resampling: indicator whether to save bootstrap
  35. % resampling data or not
  36. % 0 = no saving of bootstrapping resampling data
  37. % 1 = save bootstrapping resampling data
  38. % - .normalization_img : normalization options for imaging data
  39. % - .normalization_behav : normalization options for behavior/design data
  40. % 0 = no normalization
  41. % 1 = zscore across all subjects
  42. % 2 = zscore within groups (default)
  43. % 3 = std normalization (no centering) across subjects
  44. % 4 = std normalization (no centering) within groups
  45. %
  46. % Outputs:
  47. % - boot_results : struct containing all results from bootstrapping
  48. % - .Ub_vect : B x S x P matrix, S is #LCs, P is #bootstrap samples,
  49. % bootstrapped behavior saliences for all LCs
  50. % - .Vb_vect : M x S xP matrix, bootstrapped brain saliences for all LCs
  51. % - .Lxb,.Lyb,.LC_img_loadings_boot,.LC_behav_loadings_boot :
  52. % bootstrapping scores (see myPLS_get_PLSscores for
  53. % details)
  54. % - .*_mean : mean of bootstrapping distributions
  55. % - .*_std : standard deviation of bootstrapping distributions
  56. % - .*_lB : lower bound of 95% confidence interval of bootstrapping distributions
  57. % - .*_uB : upper bound of 95% confidence interval of bootstrapping distributions
  59. % Set up random number generator
  60. rng(1);
  62. % Check that dimensions of X & Y are correct
  63. if(size(X0,1) ~= size(Y0,1))
  64. error('Input arguments X and Y should have the same number of rows');
  65. end
  67. nBehav = size(Y0,2);
  68. nGroups = size(unique(grouping),1);
  71. disp('... Bootstrapping ...')
  73. % Compute the bootstrapping orders (under consideration of the grouping, if asked for)
  74. all_boot_orders = myPLS_get_boot_orders(pls_opts.nBootstraps,grouping,pls_opts.grouped_boot);
  76. % Run PLS in each bootstrap sample
  77. for iB = 1:pls_opts.nBootstraps
  79. % Resampling X
  80. Xb = X0(all_boot_orders(:,iB),:);
  81. Xb = myPLS_norm(Xb,grouping,pls_opts.normalization_img);
  83. % Resampling Y
  84. Yb = Y0(all_boot_orders(:,iB),:);
  85. Yb = myPLS_norm(Yb,grouping,pls_opts.normalization_behav);
  87. % Generate cross-covariance matrix between resampled X and Y
  88. Rb = myPLS_cov(Xb,Yb,grouping,pls_opts.grouped_PLS);
  90. % Singular value decomposition of Rb
  91. [Ub,Sb,Vb] = svd(Rb,'econ');
  93. % Procrustas transform (correction for axis rotation/reflection)
  94. switch pls_opts.boot_procrustes_mod
  95. case 1
  96. % Computed on U only
  97. rotatemat_full = rri_bootprocrust(U, Ub);
  99. % Rotate and re-scale Ub and Vb
  100. Vb = Vb * Sb * rotatemat_full;
  101. Ub = Ub * Sb * rotatemat_full;
  103. Vb = Vb./repmat(diag(S)',size(Vb,1),1);
  104. Ub = Ub./repmat(diag(S)',size(Ub,1),1);
  105. case 2
  106. % Computed on both U and V
  107. rotatemat1 = rri_bootprocrust(U, Ub);
  108. rotatemat2 = rri_bootprocrust(V, Vb);
  110. % Full rotation
  111. rotatemat_full = (rotatemat1 + rotatemat2)/2;
  113. % Apply full rotation to Vb and Ub
  114. Vb = Vb * rotatemat_full;
  115. Ub = Ub * rotatemat_full;
  117. otherwise
  118. error('invalid value in pls_opts.boot_procrustes_mod!');
  119. end
  121. % Vectors with all bootstrap samples -> needed for percentile computation
  122. Ub_vect(:,:,iB) = Ub;
  123. Vb_vect(:,:,iB) = Vb;
  125. % Compute bootstrapping PLS scores
  126. [Lxb,Lyb,corr_Lxb_Xb,corr_Lyb_Yb,corr_Lxb_Yb,corr_Lyb_Xb] = ...
  127. myPLS_get_PLS_scores_loadings(Xb,Yb,Vb,Ub,grouping,pls_opts);
  129. boot_results.Lxb(:,:,iB) = Lxb;
  130. boot_results.Lyb(:,:,iB) = Lyb;
  131. boot_results.LC_img_loadings_boot(:,:,iB) = corr_Lxb_Xb;
  132. boot_results.LC_behav_loadings_boot(:,:,iB) = corr_Lyb_Yb;
  134. end
  136. % Compute bootstrapping statistics
  137. boot_stats = myPLS_bootstrap_stats(Ub_vect,Vb_vect,boot_results);
  139. % Save all the statistics fields in the boot_results (I am coding it like
  140. % this to facilitate adding more stats
  141. fN = fieldnames(boot_stats);
  142. for iF = 1:length(fN)
  143. boot_results.(fN{iF}) = boot_stats.(fN{iF});
  144. end
  146. % Save bootstrapping resampling data if asked for
  147. if pls_opts.save_boot_resampling
  148. boot_results.Ub_vect = Ub_vect;
  149. boot_results.Vb_vect = Vb_vect;
  150. else
  151. boot_results = rmfield(boot_results,'LC_img_loadings_boot');
  152. boot_results = rmfield(boot_results,'LC_behav_loadings_boot');
  153. end
  156. if mod(iB,200); fprintf('\n'); end
  157. disp(' ')

myPLS_bootstrapping.m at commit 351b892, under GPL-2.0 · at the source

Overview

Authors: Clément Guichet1, Sylvain Harquel1, Raouf Zouglech1, Camille Lemaire2, Émilie Cousin1, Vincent Auboiroux3, Aurélie Campagne1, Monica Baciu1,4
  1. Université Grenoble Alpes, CNRS LPNC UMR 5105, Grenoble, France
  2. Department of Neurorehabilitation, CHU Grenoble Alpes, Grenoble, France
  3. Université Grenoble Alpes, CEA LETI, Grenoble, France
  4. Department of Neurology, CHU Grenoble Alpes Université Grenoble Alpes, Grenoble, France
Institutions: Centre National de la Recherche Scientifique (France); Laboratoire de Psychologie et NeuroCognition (France); Université Grenoble Alpes (France); Centre Hospitalier Universitaire de Grenoble (France); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Laboratoire d'Électronique des Technologies de l'Information (France)
Journal: Frontiers in computational neuroscience, volume 20, article 1816522
Dates: received 24 February 2026; accepted 11 June 2026; published online 1 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fncom.2026.1816522 · PMID 42460388 · PMCID PMC13369244 · OpenAlex W7166846679
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), healthy (population), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: brain state dynamics, cognition, healthy aging, hidden markov model, language, MEG, predictive processing, SENECA
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Université Grenoble Alpes (ANR-15-IDEX-02); Agence Nationale de la Recherche (IDEX-02, ANR‐15‐IDEX‐02, 15-IDEX-02)
Citations: not cited yet (Europe PMC); 116 references in the paper

Abstract

Healthy aging is accompanied by subtle difficulties in language production. While behavioral and neuroimaging studies suggest that older adults rely on acute semantic access to maintain language abilities, the underlying neurophysiological mechanisms remain poorly understood. In particular, it is still unclear how large-scale brain dynamics reorganize to support naturalistic sentence generation with age. In this study, we investigated the spatiotemporal brain-state dynamics during covert sentence generation (GE2REC protocol) in younger and older adults using magnetoencephalography (MEG). Source-reconstructed MEG signals were analyzed using a Hidden Markov Model which identified five recurrent brain states, encompassing language-semantic, language-control, sensorimotor, and visual domains. Latent modeling was then used to relate the spectral and temporal properties of these brain states to age and language performance. Spectrally, older adults appear to redistribute oscillatory activity from sensorimotor-related states toward semantic-related states across alpha, beta, and low-gamma frequency bands. Temporally, older adults exhibit a more segmented processing sequence between semantic and sensorimotor processing which interfaces with visuo-posterior processing. These changes robustly covaried with age and better verbal fluency (semantic and lexical). Taken together, these results suggest that the older adult brain undergoes a coordinated time-frequency reorganization to support sentence production. Older individuals likely establish an embodied semantic strategy that involves a more segmented processing sequence during sentence production via visuo-posterior information processing. We speculate that this may help shape a resource-efficient, predictive route for complex cognition in older adulthood.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

MIPLabCH/myPLS

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 351b892883eab1b7afba236917f1b25e937880e7, 13 June 2022
Languages: MATLAB (35)
Size: 48 files, 35 scripts
Software Heritage: not archived
Found in: the end of the paper
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), SPM (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
37 files
At the source: github.com/MIPLabCH/myPLS

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 35 scripts, each with its path and the digest of its content;
  • 2 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 raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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 2, 28 September 2026

  • Funding: added Université Grenoble Alpes: ANR-15-IDEX-02; Agence Nationale de la Recherche: IDEX-02, ANR‐15‐IDEX‐02, 15-IDEX-02

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 8 keywords, 109 references.

Cite

This paper

Guichet, C., Harquel, S., Zouglech, R., Lemaire, C., Cousin, É., Auboiroux, V., Campagne, A., & Baciu, M. (2026). MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging. Frontiers in computational neuroscience, 20, 1816522. https://doi.org/10.3389/fncom.2026.1816522

BibTeX

@article{guichet2026meg,
author = {Guichet, Clément and Harquel, Sylvain and Zouglech, Raouf and Lemaire, Camille and Cousin, Émilie and Auboiroux, Vincent and Campagne, Aurélie and Baciu, Monica},
title = {{MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging}},
journal = {Frontiers in computational neuroscience},
year = {2026},
month = jul,
volume = {20},
pages = {1816522},
publisher = {Frontiers Media SA},
issn = {1662-5188},
doi = {10.3389/fncom.2026.1816522},
url = {https://doi.org/10.3389/fncom.2026.1816522},
pmid = {42460388},
pmcid = {PMC13369244}
}

RIS

TY - JOUR
AU - Guichet, Clément
AU - Harquel, Sylvain
AU - Zouglech, Raouf
AU - Lemaire, Camille
AU - Cousin, Émilie
AU - Auboiroux, Vincent
AU - Campagne, Aurélie
AU - Baciu, Monica
TI - MEG state dynamics of sentence generation: evidence for a compensatory segmentation mechanism in healthy aging
T2 - Frontiers in computational neuroscience
J2 - Front Comput Neurosci
PY - 2026
DA - 2026/07/01
VL - 20
SP - 1816522
SN - 1662-5188
PB - Frontiers Media SA
DO - 10.3389/fncom.2026.1816522
UR - https://doi.org/10.3389/fncom.2026.1816522
LA - en
ER -

CSL-JSON

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"author": [
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"given": "Clément"
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