Brain Network Dynamics of Local and Global Predictive Processing in Aging.

Code ↔ Paper

30 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 30 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Methods › Statistical Analysis of ERF Responses ↔ Main_Analysis.m, lines 935–975 · score 0.88 · 0–800 ms, cluster forming threshold, error rate, maximum cluster, family, mixed
[2] § Methods › Statistical Analysis of ERF Responses ↔ Analysis_Group_and_Condition_Seperated_Data.m, lines 1229–1270 · score 0.88 · 0–800 ms, cluster forming threshold, error rate, maximum cluster, family, mixed
[3] § Methods › Investigating BROAD‐NESS Networks in Group and Condition Separated Data ↔ Analysis_Group_and_Condition_Seperated_Data.m, lines 1229–1270 · score 0.85 · 0–800 ms, cluster forming threshold, maximum cluster, timepoint, principal component, Family
[4] § Methods › Investigating BROAD‐NESS Networks in Group and Condition Separated Data ↔ Main_Analysis.m, lines 935–975 · score 0.85 · 0–800 ms, cluster forming threshold, maximum cluster, timepoint, principal component, Family
[5] § Methods › Source Reconstruction ↔ sources_3D_plot_LBPD.m, lines 1–38 · score 0.73 · head model, MNI152 T1, MEG sensors, brain source, active, activity
[6] § Methods › Experimental Paradigm ↔ Experimental_Paradigm.py, lines 16–56 · score 0.72 · incongruent blocks, target tones, probability, randomized, deviant, paradigm
[7] § Results › Brain Network Modularity Analysis ↔ Main_Analysis.m, lines 5227–5348 · score 0.71 · Anderson Darling, mixed ANOVA, Bonferroni correction, interaction, Older, Global
[8] § Methods › Phase Space and Recurrence Quantification Analysis of PCA‐Derived Networks ↔ Main_Analysis.m, lines 3935–4041 · score 0.69 · Benjamini Hochberg, random intercept, Satterthwaite, FDR, metric, interaction
[9] § Methods › Source Reconstruction ↔ MEG_SR_Beam_LBPD.m, lines 1–96 · score 0.69 · single shell, MNI152 T1, MEG sensors, brain source, model, weighted
[10] § Methods › Experimental Paradigm ↔ Experimental_Paradigm.py, lines 16–56 · score 0.68 · incongruent blocks, target tone, sound, sequences, stimuli, Paradigm
[11] § Methods › MEG Data Pre‐Processing ↔ BroadNess_APR2020.m, lines 26–63 · score 0.67 · HPI coils, movement compensation, raw, MaxFilter, MEG
[12] § Methods › Source Reconstruction ↔ MEG_SR_Beam_LBPD.m, lines 1–96 · score 0.67 · FieldTrip, beamforming algorithms, house, OSL, SPM, model
[13] § Methods › MEG Data Pre‐Processing ↔ Preprocessing_SourceReconstruction_BROADNESSHalfSplit.m, lines 6–53 · score 0.66 · HPI coils, movement compensation, raw, MaxFilter, MEG
[14] § Methods › Neural Data Acquisition ↔ MEG_sensors_MCS_plottingclusters_LBPD_D.m, lines 58–128 · score 0.65 · Elekta Neuromag TRIUX, pre processing, positions, MEG
[15] § Methods › Source Reconstruction ↔ sources_3D_plot_LBPD.m, lines 1–38 · score 0.60 · FieldTrip, neural signals, house, head, activity, model
[16] § Results › Deriving Brain Networks via PCA ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_NetworkEstimation.m, lines 1–84 · score 0.60 · broadband brain networks, spatial activation pattern, brain voxels, eigenvalue, eigenvector, PCs
[17] § Methods › Principal Component Analysis (PCA) ↔ PCA_LBPD.m, lines 80–148 · score 0.60 · maximum variance, covariance matrix, brain sources, eigenvalues, eigenvectors, PCA
[18] § Methods › Phase Space and Recurrence Quantification Analysis of PCA‐Derived Networks ↔ Main_Analysis.m, lines 2470–2612 · score 0.60 · Euclidean distance, phase space coordinates, shuffled, trajectory, younger, permutation
[19] § Results › Overview of the Experimental Design and MEG Source Reconstruction ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_NetworkEstimation.m, lines 1–84 · score 0.59 · spatial activation patterns, variance explained, brain voxels, BROAD NESS, occurrences, eigenvalues
[20] § Methods › Principal Component Analysis (PCA) ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_NetworkEstimation.m, lines 175–228 · score 0.59 · maximum variance, covariance matrix, brain sources, eigenvalues, eigenvectors, dimensionality
[21] § Methods › MEG Data Pre‐Processing ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_Visualizer.m, lines 1–121 · score 0.59 · Brain Activity, FSL, Centre, Human, Oxford, Mapping
[22] § Results › Spatial Gradient Embedding of BROAD‐NESS‐Derived Network Topographies ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_SpatialActivationClustering.m, lines 104–178 · score 0.58 · spatial activation patterns, Brain template, clustering solution, silhouette, embedding, optimal
[23] § Methods › Brain Network Modularity Analysis ↔ Main_Analysis.m, lines 5227–5348 · score 0.57 · mixed ANOVA, Bonferroni corrected, older, global
[24] § Methods › Spatial Gradient Embedding and Clustering Analysis of Network Topographies ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_Visualizer.m, lines 1–121 · score 0.57 · network topographies, BROAD NESS, uncover, map, quantify, brain networks
[25] § Results › Spatial Gradient Embedding of BROAD‐NESS‐Derived Network Topographies ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_SpatialActivationClustering.m, lines 104–178 · score 0.57 · brain templates, cluster solution, silhouette, embedded, repetitions, optimal
[26] § Methods › Investigating BROAD‐NESS Networks in Group and Condition Separated Data ↔ BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_EffectiveDimensionality.m, the whole file · a weak match · score 0.53 · effective dimensionality, variance explained, BROAD NESS, principal components, eigenspectrum, brain network
[27] § Methods › Principal Component Analysis (PCA) ↔ PCA_LBPD.m, lines 80–148 · score 0.52 · diagonal matrix, variance explained, eigenvalues, eigenvector, component, PCA
[28] § Methods › Source Reconstruction ↔ MEG_SR_Beam_LBPD.m, lines 421–531 · score 0.52 · covariance matrix, MEG sensors, concatenating, dipole, weights, signal
[29] § Methods › Phase Space and Recurrence Quantification Analysis of PCA‐Derived Networks ↔ Main_Analysis.m, lines 2614–2662 · score 0.51 · Euclidean distance, older participants, phase space, trajectories, age
[30] § Methods › Source Reconstruction ↔ MEG_SR_Beam_LBPD.m, lines 288–357 · score 0.50 · leadfield model, MEG sensors, dipole, beamforming

Paper

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The authors' code

MATLAB · 5,427 lines · 202 KB · no license · 7 matches

The registry keeps no copy of this file: its repository has no license, so its authors keep all their rights to it. Your browser shows it from its source, with JavaScript.

It can be read at the source: Main_Analysis.m.

Overview

Authors: Mathias Houe Andersen^1,2,3, Gemma Fernández‐Rubio¹, David R Quiroga‐Martinez^1,4, Mattia Rosso^1,5, Mathias Klarlund¹, Kit Melissa Larsen², Hartwig Roman Siebner^2,3,6, Morten L Kringelbach^1,7,8, Peter Vuust¹, Leonardo Bonetti^1,7,8

ORCID iDs: Mathias Houe Andersen, Gemma Fernández‐Rubio, David R Quiroga‐Martinez, Mattia Rosso, Kit Melissa Larsen, Hartwig Roman Siebner, Morten L Kringelbach, Leonardo Bonetti

Institutions: University of Copenhagen (Denmark); Hvidovre Hospital (Denmark); Royal Academy of Music (Denmark); Ghent University (Belgium); Bispebjerg Hospital (Denmark); Copenhagen University Hospital (Denmark); University of Oxford (United Kingdom)

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), article e77857

Dates: received 27 April 2026; accepted 11 September 2026; published online 27 September 2026; in print September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/advs.77857 · PMID 42801541 · PMCID PMC13616317 · OpenAlex W7214554861

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), cognitive (subfield)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Graphs, Physiology & signal measures

Keywords: aging, brain networks, phase space, predictive coding, principal component analysis (PCA), recurrence quantification analysis (RQA)

Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Independent Research Fund Denmark (10.46540/5253-00003B, 1029-00007B); Carlsberg Foundation (CF20-0239, CF23-1491); Danish National Research Foundation (DNRF117); Lundbeck Foundation (R515-2025-684, R469-2024-1573); Købmand herman Sallings Fond; Pettit and Carlsberg Foundations; Nordic Mensa Fund; Center for Music in the Brain, Linacre College of the University of Oxford

Citations: not cited yet (Europe PMC); 109 references in the paper

Abstract

Cognitive aging is widely associated with a progressive weakening of neural responses associated with predictive brain mechanisms. This view is supported by decades of electrophysiological studies reporting attenuated mismatch responses in older adults. Yet the literature remains inconsistent, suggesting that aging may not uniformly attenuate such responses. One possibility is that aging exerts differential effects depending on task demands. Here we aim to separate whole‐brain networks underlying deviance processing in source‐reconstructed magnetoencephalography (MEG) data from 37 younger and 40 older adults performing the auditory local–global paradigm. Network decomposition revealed three temporally overlapping subsystems. Aging exerted selective effects across these networks. Early sensory deviance responses were enhanced within a network recruiting auditory cortices and medial cingulate regions, whereas later cognitive processes were attenuated in older adults. The level of multivariate recurrence across these networks was preserved with aging, while the processing of sensory violations induced more recurrence and less divergence relative to pattern‐based violations in both groups. This age‐related increase in sensory‐related mismatch responses challenges the prevailing view that sensory deviance processing simply declines with age. These results suggest that in aging, neural responses may be differentially distributed across distinct neural systems, amplifying sensory‐based processes while weakening cognitively demanding mechanisms.

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 30 matches between paragraphs and lines of code.

leonardob92/LBPD-1.0

License: GPL-3.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 17ce460e8036eb77f2d81dc2fa0f24c0a8ecb6b1, 2 June 2025

Languages: MATLAB (166), Java (1)

Size: 201 files, 167 scripts

Software Heritage: not archived

Found in: the text, “MEG Data Pre‐Processing”

Holds: README, license file

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

Tools: OHBA Software Library (OSL) (32 files), SPM (19 files), Statistics and Machine Learning Toolbox (18 files), Tools for NIfTI and ANALYZE image (MATLAB) (15 files), FieldTrip (10 files), Brain Connectivity Toolbox (1 file), export_fig (1 file), FSL (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), Connectome Workbench (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

168 files

BrainSources_MonteCarlosim_3D_LBPD_D.m — MATLAB, 208 lines
Cluster_Aarhus_ParallelComputing/Cov_GenEig.m — MATLAB, 81 lines
Cluster_Aarhus_ParallelComputing/GED_AvgSingleCovs.m — MATLAB, 88 lines
Cluster_Aarhus_ParallelComputing/GED_SingleSubjects.m — MATLAB, 72 lines
Cluster_Aarhus_ParallelComputing/InducedResponses_Morlet_ROIs_AarhusClust.m — MATLAB, 69 lines
Cluster_Aarhus_ParallelComputing/Induced_Resp_SingleSub_AAL_Coordsj_AarhusClust.m — MATLAB, 84 lines
Cluster_Aarhus_ParallelComputing/Induced_Resp_SingleSubj_AarhusClust.m — MATLAB, 77 lines
Cluster_Aarhus_ParallelComputing/RSA_APR2020_All.m — MATLAB, 62 lines
Cluster_Aarhus_ParallelComputing/Replay_Long.m — MATLAB, 102 lines
Cluster_Aarhus_ParallelComputing/Replay_TG_Plot_Stats.m — MATLAB, 104 lines
Cluster_Aarhus_ParallelComputing/Replay_TG_Plot_Stats_2Groups.m — MATLAB, 100 lines
Cluster_Aarhus_ParallelComputing/StandardErrorGaussian.m — MATLAB, 17 lines
Cluster_Aarhus_ParallelComputing/averaging_TRF.m — MATLAB, 30 lines
Cluster_Aarhus_ParallelComputing/cluster_Dlabel.m — MATLAB, 30 lines
Cluster_Aarhus_ParallelComputing/cluster_africa.m — MATLAB, 13 lines
Cluster_Aarhus_ParallelComputing/cluster_beamfirstlevel.m — MATLAB, 13 lines
Cluster_Aarhus_ParallelComputing/cluster_beamforming.m — MATLAB, 13 lines
Cluster_Aarhus_ParallelComputing/cluster_beamforming_script.m — MATLAB, 136 lines
Cluster_Aarhus_ParallelComputing/cluster_beamgrouplevel.m — MATLAB, 16 lines
Cluster_Aarhus_ParallelComputing/cluster_beamsubjlevel.m — MATLAB, 13 lines
Cluster_Aarhus_ParallelComputing/cluster_epoch.m — MATLAB, 12 lines
Cluster_Aarhus_ParallelComputing/cluster_epoch_osl.m — MATLAB, 22 lines
Cluster_Aarhus_ParallelComputing/cluster_merging.m — MATLAB, 11 lines
Cluster_Aarhus_ParallelComputing/cluster_oat_save_nii_stats.m — MATLAB, 12 lines
Cluster_Aarhus_ParallelComputing/cluster_rembadcomp.m — MATLAB, 18 lines
Cluster_Aarhus_ParallelComputing/cluster_spmobject.m — MATLAB, 11 lines
Cluster_Aarhus_ParallelComputing/cluster_subjlevel.m — MATLAB, 13 lines
Cluster_Aarhus_ParallelComputing/clusterbasedpermutation_osl.m — MATLAB, 12 lines
Cluster_Aarhus_ParallelComputing/combining_planar_cluster.m — MATLAB, 15 lines
Cluster_Aarhus_ParallelComputing/coregfunc.m — MATLAB, 24 lines
Cluster_Aarhus_ParallelComputing/coregfuncp.m — MATLAB, 30 lines
Cluster_Aarhus_ParallelComputing/coregscript.m — MATLAB, 60 lines
Cluster_Aarhus_ParallelComputing/decoding.m — MATLAB, 44 lines
Cluster_Aarhus_ParallelComputing/downsampling.m — MATLAB, 15 lines
Cluster_Aarhus_ParallelComputing/filtering.m — MATLAB, 15 lines
Cluster_Aarhus_ParallelComputing/mean_D.m — MATLAB, 10 lines
Cluster_Aarhus_ParallelComputing/mean_dimensionalityreductionfromvoxels.m — MATLAB, 161 lines
Cluster_Aarhus_ParallelComputing/red_dim.m — MATLAB, 16 lines
Cluster_Aarhus_ParallelComputing/remove_bad_components_l.m — MATLAB, 28 lines
Cluster_Aarhus_ParallelComputing/sensor_average.m — MATLAB, 16 lines
Cluster_Aarhus_ParallelComputing/time_frequency_MEGsensors_dim.m — MATLAB, 24 lines
DTI_GT_MCS.m — MATLAB, 128 lines
DTI_cluster_perm_2groups_LBPD.m — MATLAB, 312 lines
Epoch_singletone_fl_F3.m — MATLAB, 115 lines
External/altmany-export_fig-412662f/ImageSelection.java — Java, 38 lines
External/altmany-export_fig-412662f/append_pdfs.m — MATLAB, 124 lines
External/altmany-export_fig-412662f/copyfig.m — MATLAB, 59 lines
External/altmany-export_fig-412662f/crop_borders.m — MATLAB, 157 lines
External/altmany-export_fig-412662f/eps2pdf.m — MATLAB, 200 lines
External/altmany-export_fig-412662f/export_fig.m — MATLAB, 1,520 lines
External/altmany-export_fig-412662f/fix_lines.m — MATLAB, 151 lines
External/altmany-export_fig-412662f/ghostscript.m — MATLAB, 196 lines
External/altmany-export_fig-412662f/im2gif.m — MATLAB, 201 lines
External/altmany-export_fig-412662f/isolate_axes.m — MATLAB, 130 lines
External/altmany-export_fig-412662f/pdf2eps.m — MATLAB, 55 lines
External/altmany-export_fig-412662f/pdftops.m — MATLAB, 168 lines
External/altmany-export_fig-412662f/print2array.m — MATLAB, 261 lines
External/altmany-export_fig-412662f/print2eps.m — MATLAB, 605 lines
External/altmany-export_fig-412662f/read_write_entire_textfile.m — MATLAB, 37 lines
External/altmany-export_fig-412662f/user_string.m — MATLAB, 111 lines
External/altmany-export_fig-412662f/using_hg2.m — MATLAB, 36 lines
External/bluewhitered_PD.m — MATLAB, 141 lines
External/bwconncomp.m — MATLAB, 148 lines
External/creatingAALnifti/create_AALnifti.m — MATLAB, 35 lines
External/creatingAALnifti/createniftis.m — MATLAB, 14 lines
External/creatingAALnifti/load_nii.m — MATLAB, 198 lines
External/creatingAALnifti/load_nii_ext.m — MATLAB, 148 lines
External/creatingAALnifti/load_nii_hdr.m — MATLAB, 280 lines
External/creatingAALnifti/load_nii_img.m — MATLAB, 392 lines
External/creatingAALnifti/save_nii.m — MATLAB, 286 lines
External/creatingAALnifti/save_nii_ext.m — MATLAB, 38 lines
External/creatingAALnifti/save_nii_hdr.m — MATLAB, 227 lines
External/creatingAALnifti/xform_nii.m — MATLAB, 520 lines
External/findND.m — MATLAB, 131 lines
External/ft_topoplotER.m — MATLAB, 208 lines
External/giftools/gifread.m — MATLAB, 207 lines
External/giftools/gifwrite.m — MATLAB, 311 lines
External/giftools/imcast.m — MATLAB, 145 lines
External/giftools/imrange.m — MATLAB, 27 lines
External/islands.m — MATLAB, 229 lines
External/modularity_und.m — MATLAB, 122 lines
External/morlet_transform.m — MATLAB, 107 lines
External/morlet_wavelet.m — MATLAB, 21 lines
External/osl_spinning_brain.m — MATLAB, 88 lines
External/parcellation.m — MATLAB, 542 lines
External/permtestDimitrios/fdr.m — MATLAB, 134 lines
External/permtestDimitrios/pl_cellmean.m — MATLAB, 44 lines
External/permtestDimitrios/pl_cellmean2.m — MATLAB, 30 lines
External/permtestDimitrios/pl_cellslice.m — MATLAB, 16 lines
External/permtestDimitrios/pl_celltstat.m — MATLAB, 65 lines
External/permtestDimitrios/pl_celltstat2_equalvar.m — MATLAB, 47 lines
External/permtestDimitrios/pl_celltstat2_unequalvar.m — MATLAB, 48 lines
External/permtestDimitrios/pl_conncomp.m — MATLAB, 131 lines
External/permtestDimitrios/pl_inputparser.m — MATLAB, 79 lines
External/permtestDimitrios/pl_mat2cell.m — MATLAB, 23 lines
External/permtestDimitrios/pl_permalphamap.m — MATLAB, 74 lines
External/permtestDimitrios/pl_permalphamap2.m — MATLAB, 86 lines
External/permtestDimitrios/pl_permtest.m — MATLAB, 140 lines
External/permtestDimitrios/pl_permtest2.m — MATLAB, 147 lines
External/permtestDimitrios/pl_permtestcluster.m — MATLAB, 237 lines
External/permtestDimitrios/pl_permtestcluster2.m — MATLAB, 249 lines
External/permtestDimitrios/pl_sliceblocks.m — MATLAB, 16 lines
External/remove_source_leakage_b.m — MATLAB, 135 lines
External/rm_raincloud2.m — MATLAB, 196 lines
External/schemaball-master/schemaball.m — MATLAB, 243 lines
External/schemaball-master/schemaballUnit.m — MATLAB, 44 lines
External/smooth_vol_osl.m — MATLAB, 27 lines
External/topoplot_common.m — MATLAB, 883 lines
External/violin.m — MATLAB, 266 lines
Extract_BrainCluster_Information_3D_LBPD_D.m — MATLAB, 83 lines
Extract_MEGSensor_Information_LBPD_D.m — MATLAB, 64 lines
FC_Brain_Schemaball_plotting_LBPD_D.m — MATLAB, 181 lines
From3DNifti_OrMNICoords_2_CoordMatrix_8mm_LBPD_D.m — MATLAB, 118 lines
FromCoordMatrix_2_3DNifti_8mm_LBPD_D.m — MATLAB, 115 lines
FunctionalSpatialClustering_voxels2ROIs_LBPD_D.m — MATLAB, 534 lines
GT_modul_plot_LBPD.m — MATLAB, 206 lines
IFC_plotting_LBPD_D.m — MATLAB, 163 lines
InducedResponses_Morlet_Coords_AALROIs_LBPD_D.m — MATLAB, 199 lines
InducedResponses_Morlet_ROIs_LBPD_D.m — MATLAB, 135 lines
InducedResponses_Morlet_WholeBrain_LBPD_D.m — MATLAB, 93 lines
LBPD_startup_D.m — MATLAB, 48 lines
LF_3D_plot_LBPD.m — MATLAB, 174 lines
MEGSourceReconstruction_LeadFieldModel_Workshop.m — MATLAB, 119 lines
MEG_SR_Beam_LBPD.m — MATLAB, 910 lines, 4 matches
MEG_SR_Stats1_Fast_LBPD.m — MATLAB, 292 lines
MEG_SR_Stats_twogroups_LBPD.m — MATLAB, 115 lines
MEG_sensors_MCS_plottingclusters_LBPD_D.m — MATLAB, 238 lines, 1 match
MEG_sensors_MCS_reshapingdata_LBPD_D.m — MATLAB, 103 lines
MEG_sensors_MonteCarlosim_LBPD_D.m — MATLAB, 268 lines
MEG_sensors_combining_magclustsign_LBPD_D.m — MATLAB, 137 lines
MEG_sensors_plotting_ttest_LBPD_D.m — MATLAB, 839 lines
MEG_sensors_plotting_ttest_LBPD_D2.m — MATLAB, 920 lines
MEG_sensors_plotting_ttest_LBPD_D_marina.m — MATLAB, 767 lines
PCA_LBPD.m — MATLAB, 271 lines, 2 matches
ReadMe.m — MATLAB, 212 lines
STC_plottingtimeseries_LBPD.m — MATLAB, 155 lines
Workbench_Codes_LocalMac_Example.m — MATLAB, 47 lines
cluster_DTI_perm_2groups_1.m — MATLAB, 61 lines
cluster_DTI_perm_2groups_2.m — MATLAB, 53 lines
cluster_DTI_perm_2groups_3.m — MATLAB, 92 lines
create_HCP_MMP1_nifti_LBPD.m — MATLAB, 42 lines
degree_segregation_MCS_LBPD_D.m — MATLAB, 255 lines
diff_conditions_matr_couplingROIs_MCS_LBPD_D.m — MATLAB, 264 lines
diff_conditions_matr_degree_MCS_LBPD_D.m — MATLAB, 199 lines
extracting_data_LBPD_D.m — MATLAB, 50 lines
generalCoupling_prepareplotting_LBPD_D.m — MATLAB, 107 lines
islands3D_LBPD_D.m — MATLAB, 551 lines
oneD_MCS_LBPD_D.m — MATLAB, 136 lines
phasesynchrony_LBPD_D.m — MATLAB, 174 lines
plot_sensors_wavebis2.m — MATLAB, 699 lines
plot_wave_conditions.m — MATLAB, 153 lines
plotting_ROIs_horzbars_LBPD_D.m — MATLAB, 90 lines
preparing_baseline_from_restingstate_LBPD_D.m — MATLAB, 85 lines
ps_preparedata_spmobj_LBPD_D.m — MATLAB, 207 lines
ps_statistics_LBPD_D.m — MATLAB, 268 lines
schemaball_modularity_LBPD.m — MATLAB, 215 lines
signROIdegree_otherROI_prepareplotting_LBPD_D.m — MATLAB, 115 lines
signROIs_degree_connotherROIs_LBPD_D.m — MATLAB, 126 lines
sources_3D_plot_LBPD.m — MATLAB, 218 lines, 2 matches
static_FC_MEG_LBPD_D.m — MATLAB, 413 lines
twoD_MCS_LBPD_D.m — MATLAB, 175 lines
waveform_plotting_local_DEPRECATED.m — MATLAB, 158 lines
waveform_plotting_local_v2.m — MATLAB, 212 lines
waveform_plotting_local_v3.m — MATLAB, 279 lines
waveplot_groups_local.m — MATLAB, 189 lines
waveplot_groups_local_v2.m — MATLAB, 256 lines
waveplot_groups_server_LBPD.m — MATLAB, 180 lines
LICENSE — License, 674 lines

At the source: github.com/leonardob92/LBPD-1.0.git

leonardob92/broadness_meg_auditoryrecognition

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 03721089e8c826a929c21f4e8833d51b7348889c, 4 September 2026

Languages: MATLAB (62)

Size: 74 files, 62 scripts

Software Heritage: not archived

Found in: “Code Availability Statement”

Holds: README

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

Tools: Tools for NIfTI and ANALYZE image (MATLAB) (16 files), Statistics and Machine Learning Toolbox (6 files), Image Processing Toolbox (2 files), FSL (1 file), OHBA Software Library (OSL) (1 file), SPM (1 file), Violinplot-Matlab (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

63 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 0372108, when its fingerprint is the one OSCR verified. How this works.

AdvancedScience_Revision/BROADNESS_Plotting_LocalComputer_Revision_Part_1.m — MATLAB, 551 lines, shown from its source
AdvancedScience_Revision/BROADNESS_Plotting_LocalComputer_Revision_Part_2.m — MATLAB, 342 lines, shown from its source
AdvancedScience_Revision/BroadNess_AarhusServer_Revision_Part_1.m — MATLAB, 162 lines, shown from its source
AdvancedScience_Revision/BroadNess_AarhusServer_Revision_Part_2.m — MATLAB, 916 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Example.m — MATLAB, 403 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/affine.m — MATLAB, 554 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/bipolar.m — MATLAB, 94 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/bresenham_line3d.m — MATLAB, 189 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/clip_nii.m — MATLAB, 115 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/collapse_nii_scan.m — MATLAB, 260 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/expand_nii_scan.m — MATLAB, 48 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/extra_nii_hdr.m — MATLAB, 255 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/flip_lr.m — MATLAB, 84 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/get_nii_frame.m — MATLAB, 164 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_nii.m — MATLAB, 198 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_nii_ext.m — MATLAB, 207 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_nii_hdr.m — MATLAB, 280 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_nii_img.m — MATLAB, 392 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_untouch0_nii_hdr.m — MATLAB, 200 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_untouch_header_only.m — MATLAB, 187 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_untouch_nii.m — MATLAB, 191 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_untouch_nii_hdr.m — MATLAB, 217 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/load_untouch_nii_img.m — MATLAB, 468 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/make_ana.m — MATLAB, 210 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/make_nii.m — MATLAB, 256 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/mat_into_hdr.m — MATLAB, 83 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/pad_nii.m — MATLAB, 142 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/reslice_nii.m — MATLAB, 321 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_file_menu.m — MATLAB, 179 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_orient.m — MATLAB, 106 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_orient_ui.m — MATLAB, 251 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_select_file.m — MATLAB, 636 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_xhair.m — MATLAB, 92 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/rri_zoom_menu.m — MATLAB, 33 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_nii.m — MATLAB, 286 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_nii_ext.m — MATLAB, 38 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_nii_hdr.m — MATLAB, 227 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_untouch0_nii_hdr.m — MATLAB, 219 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_untouch_header_only.m — MATLAB, 71 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_untouch_nii.m — MATLAB, 232 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_untouch_nii_hdr.m — MATLAB, 207 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/save_untouch_slice.m — MATLAB, 580 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/unxform_nii.m — MATLAB, 40 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/verify_nii_ext.m — MATLAB, 45 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/view_nii.m — MATLAB, 4,873 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/view_nii_menu.m — MATLAB, 480 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/NIfTI_20140122/xform_nii.m — MATLAB, 521 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/dl.m — MATLAB, 151 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/entropy.m — MATLAB, 57 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/jadeR.m — MATLAB, 330 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_External/tt.m — MATLAB, 190 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_AlternativeNetworkEstimation_ICA.m — MATLAB, 205 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_EffectiveDimensionality.m — MATLAB, 65 lines, 1 match, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_NetworkEstimation.m — MATLAB, 320 lines, 3 matches, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_PhaseSpaceStatistics.m — MATLAB, 1,254 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_PhaseSpace_RQA.m — MATLAB, 610 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_Plot_ActivationTimeseries.m — MATLAB, 493 lines, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_SpatialActivationClustering.m — MATLAB, 667 lines, 2 matches, shown from its source
BROADNESS_Toolbox/BROADNESS_Functions/BROADNESS_Visualizer.m — MATLAB, 518 lines, 2 matches, shown from its source
BROADNESS_Toolbox/BROADNESS_Startup.m — MATLAB, 48 lines, shown from its source
BroadNess_APR2020.m — MATLAB, 856 lines, 1 match, shown from its source
PCA_UseMe.m — MATLAB, 23 lines, shown from its source
README.md — Text, 25 lines, shown from its source

At the source: github.com/leonardob92/BROADNESS_MEG_AuditoryRecognition/tree/main/BROADNESS_Toolbox

MathiasHoueAndersen/Predictive-Processing-In-Aging

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 0fbee79640fee8db582a9759101515734fde2732, 11 August 2026

Languages: MATLAB (5), Python (1)

Size: 8 files, 6 scripts

Software Heritage: not archived

Found in: “Code Availability Statement”

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

Tools: Statistics and Machine Learning Toolbox (3 files), Image Processing Toolbox (2 files), SPM (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), NumPy (1 file), PsychoPy (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

6 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 0fbee79, when its fingerprint is the one OSCR verified. How this works.

Analysis_Group_and_Condition_Seperated_Data.m — MATLAB, 2,103 lines, shown from its source
Experimental_Paradigm.py — Python, 293 lines, shown from its source
Main_Analysis.m — MATLAB, 5,427 lines, shown from its source
Plotting_of_Halfsplit_RobustnessCheck.m — MATLAB, 1,122 lines, shown from its source
Prepare_Nifti_files_for_Workbench.m — MATLAB, 65 lines, shown from its source
Sensor-level_and_Behavioural_RobustnessChecks.m — MATLAB, 1,808 lines, shown from its source

At the source: github.com/MathiasHoueAndersen/Predictive-Processing-In-Aging

leonardob92/BROADNESS_Aging_MMN_AdvancedScience

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 98fb36a11366f0ef71e6154f2236a5d29ee4d486, 22 June 2026

Languages: MATLAB (1)

Size: 2 files, 1 script

Software Heritage: not archived

Found in: “Code Availability Statement”

Holds: README

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

Tools: FSL (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), OHBA Software Library (OSL) (1 file), SPM (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

2 files, not copied: shown from their source

OSCR keeps no copy of these files: this repository has no license that allows it. The reader above shows each one from its source, fetched by your browser at commit 98fb36a, when its fingerprint is the one OSCR verified. How this works.

Preprocessing_SourceReconstruction_BROADNESSHalfSplit.m — MATLAB, 1,326 lines, 1 match, shown from its source
README.md — Text, 12 lines, shown from its source

At the source: github.com/leonardob92/BROADNESS_Aging_MMN_AdvancedScience.git

mathiashoueandersen/hierarchical-predictive-processing

License: none: the authors keep all their rights

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 0fbee79640fee8db582a9759101515734fde2732, 11 August 2026

Languages: MATLAB (5), Python (1)

Size: 8 files, 6 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

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

Tools: Statistics and Machine Learning Toolbox (3 files), Image Processing Toolbox (2 files), SPM (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (1 file), NumPy (1 file), PsychoPy (1 file)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

6 files, not copied: shown from their source

Analysis_Group_and_Condition_Seperated_Data.m — MATLAB, 2,103 lines, 2 matches, shown from its source
Experimental_Paradigm.py — Python, 293 lines, 2 matches, shown from its source
Main_Analysis.m — MATLAB, 5,427 lines, 7 matches, shown from its source
Plotting_of_Halfsplit_RobustnessCheck.m — MATLAB, 1,122 lines, shown from its source
Prepare_Nifti_files_for_Workbench.m — MATLAB, 65 lines, shown from its source
Sensor-level_and_Behavioural_RobustnessChecks.m — MATLAB, 1,808 lines, shown from its source

At the source: github.com/MathiasHoueAndersen/Hierarchical-Predictive-Processing/blob/main/Experimental_Paradigm.py

Code Availability Statement

The BROAD‐NESS toolbox is available at the following link and is required to run the data analysis pipeline: https://github.com/leonardob92/BROADNESS_MEG_AuditoryRecognition/tree/main/BROADNESS_Toolbox The main data analysis pipeline used in this study is available at the following link: https://github.com/MathiasHoueAndersen/Predictive‐Processing‐In‐Aging (https://github.com/MathiasHoueAndersen/Predictive-Processing-In-Aging) In‐house‐built code and functions used for the pre‐processing of MEG data in this study are part of the LBPD repository which is available at the following link: https://github.com/leonardob92/BROADNESS_Aging_MMN_AdvancedScience.git.

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:

5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
242 scripts, each with its path and the digest of its content;
30 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

Datasets cited

zenodo:18231641 — at Zenodo; found in “Data Availability Statement”

Data Availability Statement

The pre‐processed neuroimaging data generated in this study have been deposited in the Zenodo database and are publicly available: https://doi.org/10.5281/zenodo.18231641 [109]. The code used to present the stimuli of the experimental paradigm used in this study has been deposited on GitHub and is publicly available: https://github.com/MathiasHoueAndersen/Hierarchical‐Predictive‐Processing/blob/main/Experimental_Paradigm.py (https://github.com/MathiasHoueAndersen/Hierarchical-Predictive-Processing/blob/main/Experimental_Paradigm.py)

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, 28 September 2026: the first record

Recorded: type, language, journal, pages, dates, 10 authors, 6 keywords, 8 funders, 107 references.

Cite

This paper

Andersen, M. H., Fernández‐Rubio, G., Quiroga‐Martinez, D. R., Rosso, M., Klarlund, M., Larsen, K. M., Siebner, H. R., Kringelbach, M. L., Vuust, P., & Bonetti, L. (2026). Brain Network Dynamics of Local and Global Predictive Processing in Aging. Advanced science (Weinheim, Baden-Wurttemberg, Germany), e77857. https://doi.org/10.1002/advs.77857

BibTeX

@article{andersen2026brain, author = {Andersen, Mathias Houe and Fernández‐Rubio, Gemma and Quiroga‐Martinez, David R and Rosso, Mattia and Klarlund, Mathias and Larsen, Kit Melissa and Siebner, Hartwig Roman and Kringelbach, Morten L and Vuust, Peter and Bonetti, Leonardo}, title = {{Brain Network Dynamics of Local and Global Predictive Processing in Aging}}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, year = {2026}, month = sep, pages = {e77857}, publisher = {Wiley}, issn = {2198-3844}, doi = {10.1002/advs.77857}, url = {https://doi.org/10.1002/advs.77857}, pmid = {42801541}, pmcid = {PMC13616317} }

RIS

TY - JOUR AU - Andersen, Mathias Houe AU - Fernández‐Rubio, Gemma AU - Quiroga‐Martinez, David R AU - Rosso, Mattia AU - Klarlund, Mathias AU - Larsen, Kit Melissa AU - Siebner, Hartwig Roman AU - Kringelbach, Morten L AU - Vuust, Peter AU - Bonetti, Leonardo TI - Brain Network Dynamics of Local and Global Predictive Processing in Aging T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany) J2 - Adv Sci (Weinh) PY - 2026 DA - 2026/09/27 SP - e77857 SN - 2198-3844 PB - Wiley DO - 10.1002/advs.77857 UR - https://doi.org/10.1002/advs.77857 LA - en ER -

CSL-JSON

{ "id": "10.1002/advs.77857", "type": "article-journal", "title": "Brain Network Dynamics of Local and Global Predictive Processing in Aging", "container-title": "Advanced science (Weinheim, Baden-Wurttemberg, Germany)", "author": [ { "family": "Andersen", "given": "Mathias Houe" }, { "family": "Fernández‐Rubio", "given": "Gemma" }, { "family": "Quiroga‐Martinez", "given": "David R" }, { "family": "Rosso", "given": "Mattia" }, { "family": "Klarlund", "given": "Mathias" }, { "family": "Larsen", "given": "Kit Melissa" }, { "family": "Siebner", "given": "Hartwig Roman" }, { "family": "Kringelbach", "given": "Morten L" }, { "family": "Vuust", "given": "Peter" }, { "family": "Bonetti", "given": "Leonardo" } ], "container-title-short": "Adv Sci (Weinh)", "page": "e77857", "DOI": "10.1002/advs.77857", "PMID": "42801541", "PMCID": "PMC13616317", "ISSN": "2198-3844", "publisher": "Wiley", "URL": "https://doi.org/10.1002/advs.77857", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 27 ] ] } }

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Brain Network Dynamics of Local and Global Predictive Processing in Aging.

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Paper

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Abstract

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leonardob92/LBPD-1.0

leonardob92/broadness_meg_auditoryrecognition

MathiasHoueAndersen/Predictive-Processing-In-Aging

leonardob92/BROADNESS_Aging_MMN_AdvancedScience

mathiashoueandersen/hierarchical-predictive-processing

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