OSCR

How the influence of cingulate-lingual interactions on event segmentation changes from early to late adolescence.

Code ↔ Paper

10 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 10 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › EEG recording and preprocessing ↔ 03_code_for_analysis.zip/A1_step01_detect_flat_channels.m, lines 77–113 · score 0.90 · flat channels, BrainAmp, removed channels, reference electrode, channel locations, amplifier
  2. [2] § Methods › EEG recording and preprocessing ↔ 03_code_for_analysis.zip/A3_step03_final_export.m, lines 236–275 · score 0.83 · clean rawdata, high variance, outlier channels, PREP, noisy, rejection
  3. [3] § Methods › Statistical analyses ↔ 03_code_for_analysis.zip/C2_Mixed-effects logistic regression.R, the whole file · a weak match · score 0.69 · random intercept, logistic regression, glmer, binomial, mixed, coefficient
  4. [4] § Methods › Beamforming analysis ↔ 03_code_for_analysis.zip/E3_DBSCAN.m, lines 1–21 · score 0.65 · DICS beamformer, epsilon, edge, atlas, DBSCAN, neighboring
  5. [5] § Methods › Beamforming analysis ↔ 03_code_for_analysis.zip/E1_DICS.m, lines 109–153 · score 0.64 · spatial filters, Density, DICS, noise, cross, FieldTrip
  6. [6] § Methods › Connectivity analysis ↔ 03_code_for_analysis.zip/dependencies/nCREANN2024_06.m, lines 150–204 · score 0.60 · network parameters, hidden neurons, trained, validation, coefficient, error
  7. [7] § Methods › Connectivity analysis ↔ 03_code_for_analysis.zip/dependencies/mlp_batch2024_06.m, the whole file · a weak match · score 0.57 · activation functions, hidden neurons, residual, training, predict
  8. [8] § Methods › Time–frequency decomposition and cluster-based permutation testing ↔ 03_code_for_analysis.zip/D1_Segmentation_incl_virtualmarkers.m, lines 276–311 · score 0.52 · frequency resolution, padding, width, wavelet, FieldTrip, 30 Hz
  9. [9] § Methods › Connectivity analysis ↔ 03_code_for_analysis.zip/dependencies/mlp_batch2024_06.m, the whole file · a weak match · score 0.52 · hidden neurons, trained, validation, coefficient, error
  10. [10] § Methods › Beamforming analysis ↔ 03_code_for_analysis.zip/F1_LCMV.m, lines 160–206 · score 0.51 · covariance, LCMV, locked, matrix, filter, voxel

Paper

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The authors' code

MATLAB · 126 lines · 5 KB · no license · 2 matches

  2. %%%%Early stopping point with pre-defined "Validation_max_fail"
  5. function [err W_IH W_HO Aa Bb]=mlp_batch2024_06(train_inp,train_out,hidden_neurons,weight_input_hidden,weight_hidden_output,a,b,epochs,eta,alpha,err,Validation_max_fail);
  6. output_neurons= min(size(train_out));%Output neuron's activation function is "y=g(v)=v"
  7. input_nodes = min(size(train_inp));%number of sources *lag
  9. %read how many patterns
  10. patterns = length(train_inp);
  13. %%%%======================= Devide Training set into 90% train and 10% validation set ================
  14. %%%%=======================[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio)
  16. %%%% case 1 ::: Random Division
  17. % [trainInd,valInd,testInd] = dividerand(patterns,0.9,0.1,0.0);
  19. % %%%% case 2 ::: Sequential Division ************ Better than Randomdivision
  20. % [trainInd,valInd,testInd] = divideblock(patterns,0.9,0.1,0.0);
  21. %
  22. % % %%%% case 3 ::: Multi-segment Sequentional Division
  23. Num_segmnt=4;%number of segments
  24. trainInd=[];valInd=[];
  25. Q_segmnt=floor(patterns/Num_segmnt);
  26. for segmnt=1:Num_segmnt
  27. [trainInd_segmnt,valInd_segmnt,testInd] = divideblock(Q_segmnt,0.9,0.1,0.0);
  28. trainInd=[trainInd,(segmnt-1)*Q_segmnt+trainInd_segmnt];
  29. valInd=[valInd,(segmnt-1)*Q_segmnt+valInd_segmnt];
  30. end
  31. trainInd_residual=Num_segmnt*Q_segmnt+1:patterns;%%the residual indexes of "patterns" and "Num_segmnt*floor(patterns/Num_segmnt)" is devoted to the training set
  32. trainInd=[trainInd,trainInd_residual];
  35. %%%%=======================
  36. train_train_inp=train_inp(:,trainInd);%%train inp set after divition into Train and Validation
  37. train_train_out=train_out(:,trainInd);%%train out set after divition into Train and Validation
  38. val_inp=train_inp(:,valInd);%%validation inp set after divition into Train and Validation
  39. val_out=train_out(:,valInd);%%validation out set after divition into Train and Validation
  41. %%% Learining
  42. patterns=length(train_train_inp);
  43. patterns_val=length(val_inp);
  45. % Variables to store the best weights and parameters
  46. W_IH = weight_input_hidden;
  47. W_HO = weight_hidden_output;
  48. Aa = a;
  49. Bb = b;
  50. best_val_err = inf;
  51. val_fail_count = 0;
  53. %initial del
  54. del_HO{1}=zeros(size(weight_hidden_output));%size(hidden_neurons*Output_neurons)
  55. del_IH{1}=zeros(size(weight_input_hidden'));%size (hidden_neurons*inut_nodes)
  56. del_a{1}=zeros(size(a'));%size(hidden_neurons*1)
  57. del_b{1}=zeros(size(b'));%size(hidden_neurons*1)
  58. %do a number of itteration
  59. for iter = 2:epochs
  60. %I dont randomize patnum
  61. %%%%Initializing.....
  63. alr=eta(iter);%learning rate for I/H weigths
  64. blr = alr / 10;%learning rate for H/O weigths
  65. this_pat = train_train_inp;%size (inut_nodes*patterns)
  67. v=weight_input_hidden'*this_pat;%v=(W_IH)'*X;size(hidden_neurons*pattern)
  68. z = a'.*tanh(b'.*v);%hidden neuron activation (z=a*tanh(b*v));size(hidden_neurons*pattern)
  69. y_out=weight_hidden_output'*z;%output neuron activation ;size(Output_neurons*pattern)
  70. error = train_train_out-y_out;%current error for this pattern ;size(Output_neurons*pattern)
  71. % adjust weight hidden - output
  72. del_HO{iter}=(-blr)*-1/patterns*z*error'+alpha*del_HO{iter-1};%size(hidden_neurons*Output_neurons)
  73. % adjust the weights input - hidden
  74. delta=-weight_hidden_output*error;%size(hidden_neurons*pattern)
  75. del_IH{iter}=(-alr)*1/patterns*(delta.*(a'.*b'.*(1-(tanh(b'.*v).^2))))*this_pat'+alpha*(del_IH{iter-1});%size (hidden_neurons*inut_nodes)
  76. % adjust the coefficients of hidden neuron's activation function
  77. del_a{iter}=(-alr/5)*mean(delta.*tanh(b'.*v),2)+alpha*(del_a{iter-1});%do summation for all observation (patterns); size(hidden_neurons*1)
  78. del_b{iter}=(-alr/5)*mean(delta.*(a'.*v.* (1-(tanh(b'.*v).^2))),2)+alpha*(del_b{iter-1});%do summation for all observation (patterns); size(hidden_neurons*1)
  81. % Update parameters
  82. weight_hidden_output = weight_hidden_output+del_HO{iter};
  83. weight_input_hidden = weight_input_hidden +(del_IH{iter})';
  84. a=a+(del_a{iter})';
  85. b=b+(del_b{iter})';
  88. % err(:,iter) = sqrt(mean(mean(error.^2,1),2)); % overall training error at end of each itteration
  89. err(iter) = sqrt(mean((error).^2, 'all'));
  90. % -- another itteration finished
  92. if err(iter) < err(iter-1)
  93. eta(iter+1)=1.25*eta(iter);
  95. else
  96. eta(iter+1)=0.75*eta(iter);
  97. end
  98. if eta(iter+1) < 1e-3
  99. eta(iter+1)=eta(iter);
  100. end
  101. %%%############################% overall validation error at end of each itteration
  103. v_val=weight_input_hidden'*val_inp;
  104. z_val=a'.*tanh(b'.*v_val);
  105. pred_val=weight_hidden_output'*z_val;
  106. error_val=val_out-pred_val;
  107. err_val(iter) = sqrt(mean((error_val).^2, 'all'));
  110. % Check for early stopping condition
  111. if err_val(iter) < best_val_err
  112. best_val_err = err_val(iter);
  113. W_HO=weight_hidden_output;
  114. W_IH=weight_input_hidden;
  115. Aa=a;
  116. Bb=b;
  117. val_fail_count = 0;
  118. else
  119. val_fail_count = val_fail_count + 1;
  120. if val_fail_count >= Validation_max_fail
  121. break;
  122. end
  123. end
  126. end

mlp_batch2024_06.m, no license · at the source

Overview

Authors: Astrid Prochnow1, Xianzhen Zhou1, Foroogh Ghorbani1, Veit Roessner1,2, Bernhard Hommel3, Christian Beste1,2,3
  1. Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Schubertstrasse 42, 03107 Dresden, Germany
  2. German Center for Child and Adolescent Health (DZKJ), Partner Site Leipzig/Dresden, Dresden, Germany
  3. School of Psychology, Shandong Normal University, Jinan, China
Institutions: Technische Universität Dresden (Germany); Shandong Normal University (China)
Journal: Scientific reports, volume 16, issue 1, article 11377
Dates: received 28 October 2025; accepted 24 March 2026; published online 2 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-46182-w · PMID 41927775 · PMCID PMC13049087 · OpenAlex W7148434036
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Spectral & time-frequency, Source localization
Keywords: EEG, Beta frequency band, Brain connectivity, Beamforming, Event perception, Neuroscience, Psychology
MeSH: Gyrus Cinguli*, Adolescent, Brain Mapping, Electroencephalography, Female, Humans, Male (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Bundesministerium für Bildung und Forschung (01GL2405B); Technische Universität Dresden
Citations: not cited yet (Europe PMC); 83 references in the paper
Research resources: with MATLAB RRID:SCR_001622, RRID:SCR_001905, RRID:SCR_002521, RRID:SCR_004849, RRID:SCR_007292, RRID:SCR_016479

Abstract

Event segmentation, the process of dividing continuous experience into discrete events, plays a critical role in making sense of, and navigating one’s environment. This process is likely subject to maturational changes during adolescence, but the modulation of underlying neural processes is contentious. To gain novel insights into the neurophysiological development of event segmentation, we examined N = 72 adolescents (13.8 ± 1.7 years, 32 males) using EEG methods and focused on modulations of theta, alpha and beta band activity. Moreover, we examined how directed connectivity between functional neuroanatomical structures associated with beta band activity across age. Connectivity from the cingulate to the lingual gyrus within the beta frequency band were significantly related to the likelihood of event segmentation, as shown by regression and moderation analyses, whereas theta and alpha band activity yielded no significant effects. The moderation analysis indicated that in older adolescents, reduced connectivity from the cingulate to the lingual gyrus likely promotes a balance between using event schemata and perceptual input when forming and updating working event models. This pattern across adolescence suggests that as the brain matures, the balanced consideration of both perceptual inputs and established episodic traces becomes increasingly relevant in the context of event segmentation. This likely enhances the efficiency of event segmentation, thereby improving the ability to organize environmental information during adolescence.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-46182-w.

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 10 matches between paragraphs and lines of code.

OSF 4q7sc

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 7 files
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files), EEGLAB (4 files), Statistics and Machine Learning Toolbox (2 files), car (1 file), ggplot2 (1 file), ICLabel (1 file), lme4 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
29 files
At the source: osf.io/4q7sc/?view_only=df351aa23e6649fbaa4bfdb138a1182a

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

Tracing map

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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;
  • 29 scripts, each with its path and the digest of its content;
  • 10 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

Raw data and code used for the described analyses are available in OSF https://osf.io/4q7sc/?view_only=df351aa23e6649fbaa4bfdb138a1182a.

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 Bundesministerium für Bildung und Forschung: 01GL2405B; Technische Universität Dresden

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 7 MeSH terms, 74 references, 6 RRIDs.

Cite

This paper

Prochnow, A., Zhou, X., Ghorbani, F., Roessner, V., Hommel, B., & Beste, C. (2026). How the influence of cingulate-lingual interactions on event segmentation changes from early to late adolescence. Scientific reports, 16(1), 11377. https://doi.org/10.1038/s41598-026-46182-w

BibTeX

@article{prochnow2026how,
author = {Prochnow, Astrid and Zhou, Xianzhen and Ghorbani, Foroogh and Roessner, Veit and Hommel, Bernhard and Beste, Christian},
title = {{How the influence of cingulate-lingual interactions on event segmentation changes from early to late adolescence}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {11377},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-46182-w},
url = {https://doi.org/10.1038/s41598-026-46182-w},
pmid = {41927775},
pmcid = {PMC13049087}
}

RIS

TY - JOUR
AU - Prochnow, Astrid
AU - Zhou, Xianzhen
AU - Ghorbani, Foroogh
AU - Roessner, Veit
AU - Hommel, Bernhard
AU - Beste, Christian
TI - How the influence of cingulate-lingual interactions on event segmentation changes from early to late adolescence
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/02
VL - 16
IS - 1
SP - 11377
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-46182-w
UR - https://doi.org/10.1038/s41598-026-46182-w
LA - en
ER -

CSL-JSON

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