OSCR

How Low-Frequency Neural Activity Structures Language in Time.

Code ↔ Paper

7 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 7 matches
  1. [1] § MATERIALS AND METHODS › Source-Level Analysis ↔ MATLAB/tcrc_sourceReconstruction_fixedOri.m, lines 142–232 · score 0.95 · unit noise gain, depth bias, Hilbert transformed, source reconstruction, source space, LCMV
  2. [2] § MATERIALS AND METHODS › Source-Level Analysis ↔ MATLAB/tcrc_sourceStatistic_templategrid.m, lines 72–93 · score 0.79 · MNI space, individual space, individual MRI, source reconstruction, warped, template
  3. [3] § MATERIALS AND METHODS › Sensor-Level Analysis ↔ MATLAB/tcrc_sourceReconstruction_fixedOri.m, lines 142–232 · score 0.70 · inter trial phase, Hilbert transform, coherence, activity, filtered, window
  4. [4] § MATERIALS AND METHODS › Electrophysiological Analysis ↔ MATLAB/tcrc_artefacts.m, lines 1–48 · score 0.68 · SQUID jump, artifact rejection, segmented, rejected, channel, FieldTrip
  5. [5] § MATERIALS AND METHODS › Sensor-Level Analysis ↔ R/tcrc_regression_ITPC_ERF.Rmd, lines 76–167 · score 0.64 · baseline model, Bonferroni corrected, intercepts, mixed, timepoint, magnetometers
  6. [6] § MATERIALS AND METHODS › Source-Level Analysis ↔ MATLAB/tcrc_prepareSouce.m, lines 162–189 · score 0.60 · head surface, MNE, vertices, BEMs, MEG
  7. [7] § MATERIALS AND METHODS › Sensor-Level Analysis ↔ MATLAB/tcrc_itpc_delta.m, lines 63–90 · score 0.53 · inter trial phase, coherence, signal, duration, ITPC

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 232 lines · 9.6 KB · no license · 2 matches

  1. %% TCRC perform source reconstruction
  3. clear; close all;
  4. restoredefaultpath;
  5. %addpath '/Fieldtrip/fieldtrip-20210807/';
  6. addpath '/Fieldtrip/fieldtrip-20230125/';
  7. ft_defaults
  8. addpath '/Fieldtrip/fieldtrip-20230125/external/mne/';
  9. addpath '/Fieldtrip/fieldtrip-20230125/external/freesurfer/';
  11. subjects = table2cell(readtable('/tcrc/cfg/list_of_subjects_all.lst', 'FileType', 'text', 'ReadVariableNames', false));
  12. blocks = {'2' '3' '4' '5' '6' '7'}; % only experimental blocks
  13. rawDIR = '/tcrc/';
  14. proDIR = '/prepro/';
  16. % load ITPC Stats
  17. load([proDIR 'stats/ITPCStats_0.2_4.mat']);
  18. % get on and offset time across both clusters
  19. onOff = ITPCStats.time([find(any(ITPCStats.mask),1,'first') find(any(ITPCStats.mask),1,'last')]);
  21. for s = 1:length(subjects)
  22. SUBJECT = subjects{s};
  24. %% directories
  25. % input
  26. headmodelFILE = [proDIR SUBJECT filesep SUBJECT '-headmodel.mat'];
  27. sourcespaceFILE = [proDIR SUBJECT filesep SUBJECT '-sourcespace.mat'];
  28. realignedmriFILE = [proDIR SUBJECT filesep SUBJECT '-realignedMRI.mat'];
  29. mnePath = [rawDIR 'freesurfer/' SUBJECT '/surf/'];
  30. concatFILE = [proDIR SUBJECT filesep SUBJECT '_concat_0.2_30_7.mat'];
  31. artfFILE = [proDIR SUBJECT filesep SUBJECT '_rejectedTrials_0.2_30_7_manualRemoval_2.mat'];
  32. templategridFILE = [proDIR SUBJECT filesep SUBJECT '-templateGRID_5mm.mat'];
  34. % output
  35. sourceITPCFILE = [proDIR SUBJECT filesep SUBJECT '_sourceITPC_cluster_2s_lambda5_fixedOri_noNormal_ung_templateGrid_5mm.mat']; % itpc
  37. % loading
  38. load(headmodelFILE); % headmodel
  39. load(sourcespaceFILE); % sourcespace
  40. load(realignedmriFILE); % realignedmri
  41. load(templategridFILE); % templategrid
  42. disp('Loading headmodel, sourcespace and aligned MRI.')
  44. %% Prepare data
  45. load(concatFILE); % concatenated data
  47. % downsample
  48. cfg = [];
  49. cfg.resamplefs = 100;
  50. downDATA = ft_resampledata(cfg, concatDATA);
  52. % remove trials maked as artf
  53. load(artfFILE);
  54. fullTrials = 1:length(downDATA.trialinfo);
  55. fullTrials(trialIdx_art) = [];
  56. cfg = [];
  57. cfg.trials = fullTrials;
  58. downDATA = ft_selectdata(cfg, downDATA);
  60. % removed Trials across all blocks
  61. removedFILE = [proDIR SUBJECT filesep SUBJECT '_removedTrials_acrossBlocks.mat'];
  62. load(removedFILE, 'allTrials');
  64. % low-pass filter at 4 Hz
  65. srate = downDATA.fsample;
  66. for t = 1:length(downDATA.trial)
  67. % Padding with reflection
  68. len = length(downDATA.trial{t});
  69. pad = fliplr(downDATA.trial{t});
  70. pad_data = [pad downDATA.trial{t} pad];
  72. [b,a] = butter(8, 4/(srate/2), 'low');
  73. lp_data = filtfilt(b, a, pad_data')';
  74. downDATA.trial{t} = lp_data(:, 1+len:end-len);
  75. end
  77. %% Compute solution for each block individually
  78. activity_across_blocks = cell(6,1);
  80. for b = 1:6
  81. BLOCK = blocks{b};
  83. % output
  84. leadfieldFILE = [proDIR SUBJECT filesep SUBJECT BLOCK '-leadfield_noNormal_templateGrid_5mm.mat'];
  86. % sensors
  87. sensorFILE = [rawDIR SUBJECT filesep SUBJECT BLOCK '_ts.fif'];
  88. sens = ft_read_sens(sensorFILE, 'senstype', 'meg');
  89. sens = ft_convert_units(sens, 'cm');
  91. %% Compute forward solution in Fieldtrip - Sensors for each block individually
  93. % plotting as test
  94. % figure;
  95. % ft_plot_headmodel(headmodel, 'edgecolor', 'none', 'facecolor', 'cortex'); alpha 0.5;
  96. % ft_plot_sens(sens, 'coilshape', 'point', 'style', 'r.');
  97. % ft_plot_mesh(templategrid.pos(templategrid.inside,:));
  98. % view([0 90 0]);
  100. % needs sourcespace & volume conductor & timelock data
  101. % make leadfield for magnetometers only
  102. if ~exist(leadfieldFILE, 'file')
  103. cfg = [];
  104. cfg.grad = sens; %data.grad
  105. cfg.channel = {'*1'}; % magnetometers only
  106. cfg.sourcemodel = templategrid; %sourcespace;
  107. cfg.headmodel = headmodel;
  108. cfg.method = 'singleshell';
  109. cfg.normalize = 'no'; % EITHER leadfield normalisation OR beamformer; to remove depth bias (Q in eq. 27 of van Veen et al, 1997)
  110. leadfield = ft_prepare_leadfield(cfg);
  112. % Save Leadfield
  113. save(leadfieldFILE, 'leadfield');
  114. else
  115. load(leadfieldFILE) % load if exists
  116. disp('Loading existing leadfield file.')
  117. end
  119. % make a figure of the single subject headmodel, and grid positions
  120. % figure;
  121. % ft_plot_headmodel(headmodel, 'edgecolor', 'none'); alpha 0.4;
  122. % ft_plot_mesh(leadfield.pos(leadfield.inside,:), 'facecolor', 'cortex'); hold off;
  123. % rotate3d
  125. %% Apply a common filter in beamforming - to make comparisons across conditions
  126. % Differences in source activity can then be ascribed to
  127. % differences in conditions, not to differences between the filters.
  129. % add condition to allTrials
  130. if length(allTrials) ~= length(downDATA.trialinfo)
  131. disp(['CAUTION: Something in the trials does not match for ' SUBJECT])
  132. pause;
  133. else
  134. allTrials(:,4) = downDATA.trialinfo;
  135. end
  137. % select trials for one block !
  138. cfg = [];
  139. cfg.trials = allTrials(find(allTrials(:,2) == b),3);
  140. trials = ft_selectdata(cfg, downDATA);
  142. %% (1) calculate the cross-spectral density matrix of the combined conditions
  144. % (1) GA ERF
  145. % https://brittas-summerofcode.blogspot.com/2017/08/the-hilbert-beamformer-pipeline_29.html
  146. cfg = [];
  147. % covariance required for later source localisation
  148. cfg.covariance = 'yes';
  149. cfg.covariancewindow = [onOff(1)-1 onOff(2)+1];
  150. cfg.latency = [onOff(1)-1 onOff(2)+1]; % define time window of interest based on ITPC
  151. timelock{s,b} = ft_timelockanalysis(cfg, trials);
  153. % (2) compute the spatial filters on GA
  154. % create spatial filter using the lcmv beamformer
  155. cfg = [];
  156. cfg.method = 'lcmv';
  157. cfg.sourcemodel = leadfield;
  158. cfg.headmodel = headmodel;
  159. cfg.lcmv.keepfilter = 'yes'; % keep spatial filter, can be used to reconstruct single trial time servies as virtual channel
  160. cfg.lcmv.fixedori = 'yes'; % project on axis of most variance
  161. cfg.lcmv.lambda = '5%'; % 1 or 5 % changes the filter
  162. cfg.lcmv.weightnorm = 'unitnoisegain'; % unit-noise-gain beamfomrmer (weight normalization) against depth bias
  163. sourceAll = ft_sourceanalysis(cfg, timelock{s,b});
  165. % (3) Hilbert transform raw data
  166. for t = 1:length(trials.trial)
  167. trials.trial{t} = hilbert(trials.trial{t}').'; % single-trial data in hilbert format
  168. end
  170. % (4) Beamforming: get single-trial source-space time courses
  171. % project all single trials through these filters
  173. beamformer = sourceAll.avg.filter;
  174. n_nodes = length(beamformer);
  175. activity_in_node = cell(1, length(trials.trial));
  177. % trial loop
  178. for t = 1:length(trials.trial)
  179. megtrldata = trials.trial{t}; % single-trial data
  180. datalen = size(megtrldata, 2); % number of data points
  181. trldata_in_node = zeros(n_nodes, datalen);
  182. % node loop
  183. for nodei = 1:n_nodes
  184. node = beamformer{nodei};
  185. mom = node*megtrldata;
  186. trldata_in_node(nodei,:) = mom;
  187. end
  188. activity_in_node{t} = trldata_in_node;
  189. fprintf('node time course: %s, trial %d/%d... \n', [SUBJECT BLOCK], t, length(trials.trial));
  190. end
  191. activity_across_blocks{b} = activity_in_node;
  192. end
  194. % (5) Compute ITPC for each level across blocks
  196. % combine activity across blocks
  197. trialSourceDATA = cat(2, activity_across_blocks{:});
  199. for duration = 1:7
  200. % select trials for duration
  201. durTrials = find(floor(allTrials(:,4)./10) == duration);
  202. itpc_data = trialSourceDATA(durTrials);
  204. % transform data dimensions
  205. tmp = cat(3, itpc_data{:}); % source x duration x trials
  206. fourierspctrm = permute(tmp, [3,1,2]); % trials x source x duration
  208. % put into fieldtrip structure
  209. itpc = [];
  210. itpc.pos = sourceAll.pos;
  211. itpc.inside = sourceAll.inside;
  212. itpc.time = trials.time{1};
  214. % compute inter-trial phase coherence (itpc)
  215. itpc.itpc = fourierspctrm./abs(fourierspctrm); % divide by amplitude
  216. itpc.itpc = sum(itpc.itpc,1); % sum angles
  217. itpc.itpc = abs(itpc.itpc)/size(fourierspctrm,1); % take the absolute value and normalize
  218. itpc.itpc = squeeze(itpc.itpc);
  219. itpc.avg = itpc.itpc;
  221. sourceITPC{s, duration} = itpc;
  223. clear itpc fourierspctrm tmp durTrials itpc_data;
  225. % plot itpc timecourse across conditions for random node
  226. % figure(s);
  227. % plot(sourceITPC{s, duration}.itpc(1,:)); hold on;
  228. end
  229. % save individual ITPC source
  230. indITPC = sourceITPC(s,:);
  231. save(sourceITPCFILE, 'indITPC');
  232. end

tcrc_sourceReconstruction_fixedOri.m, no license · at the source

Overview

Authors: Lena Henke1, Burkhard Maess2, Lars Meyer1,3
ORCID iDs: Lena Henke, Burkhard Maess, Lars Meyer
  1. Max Planck Research Group Language Cycles, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  2. Methods and Development Group Brain Networks, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. Clinic for Phoniatrics and Pediatric Audiology, University Hospital Münster, Münster, Germany
Institutions: Max Planck Institute for Human Cognitive and Brain Sciences (Germany); University Hospital Münster (Germany)
Journal: Neurobiology of language (Cambridge, Mass.), volume 7, article NOL.a.249
Dates: received 25 August 2025; accepted 23 February 2026; published online 5 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/nol.a.249 · PMID 42137739 · PMCID PMC13171203 · OpenAlex W7133489750
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, Physiology & signal measures
Keywords: chunking, closure positive shift, delta-band oscillations, phase-locking, temporal constraint
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Max-Planck-Gesellschaft (Max Planck Society) (MPRG Language Cycles)
Citations: not cited yet (Europe PMC); 119 references in the paper

Abstract

The integration of sensory information in humans may be confined to a time window of 2–3 s. In language, this time window constrains the grouping of words into multi-word chunks, required for comprehension. Chunk boundaries are known to elicit a characteristic event-related brain potential, the Closure Positive Shift (CPS). The likelihood of a CPS increases with the duration of the chunk. In the frequency-domain, boundaries have been associated with neural oscillations in the delta band (<4 Hz). Here, we assessed whether the pace for chunking might be imposed by electrophysiological processing cycles of the brain with phase-locking of such activity underlying the CPS. We recorded participants’ magnetoencephalogram while they listened to globally ambiguous sentences allowing for two alternative ways of chunking. Chunking was not externally imposed, but the temporal limits of integration windows influenced chunk termination. Phase-locking of narrow-band low-frequency neural activity (i.e., <4 Hz) at the boundaries of multi-word chunks increased with sentence duration, and covaried with event-related fields. Behavioral data further indicate subtle interindividual differences in the duration of the integration time window. Source localization revealed neural generators in bilateral posterior temporal and right anterior regions. The brain appears to project duration-limited integration windows onto the incoming auditory speech signal, thus structuring language comprehension in time.

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 7 matches between paragraphs and lines of code.

OSF h3c9j

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (15), R (2)
Size: 18 files, 17 scripts
Software Heritage: not checked
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (13 files), Signal Processing Toolbox (4 files), ggplot2 (2 files), lme4 (2 files), tidyverse (2 files), easystats (1 file), emmeans (1 file), ggpubr (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
18 files
At the source: doi.org/10.17605/OSF.IO/H3C9J

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 7 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

Data availability statement

Raw data cannot be made publicly available due to ethical permissions and legal restrictions. Aggregated data to evaluate the conclusions in the paper are available on Zenodo (https://doi.org/10.5281/zenodo.18742644). Code is available in an OSF repository (https://doi.org/10.17605/OSF.IO/H3C9J).

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, volume, pages, dates, 3 authors, 5 keywords, 1 funder, 110 references.

Cite

This paper

Henke, L., Maess, B., & Meyer, L. (2026). How Low-Frequency Neural Activity Structures Language in Time. Neurobiology of language (Cambridge, Mass.), 7, NOL.a.249. https://doi.org/10.1162/nol.a.249

BibTeX

@article{henke2026how,
author = {Henke, Lena and Maess, Burkhard and Meyer, Lars},
title = {{How Low-Frequency Neural Activity Structures Language in Time}},
journal = {Neurobiology of language (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {7},
pages = {NOL.a.249},
publisher = {MIT Press},
issn = {2641-4368},
doi = {10.1162/nol.a.249},
url = {https://doi.org/10.1162/nol.a.249},
pmid = {42137739},
pmcid = {PMC13171203}
}

RIS

TY - JOUR
AU - Henke, Lena
AU - Maess, Burkhard
AU - Meyer, Lars
TI - How Low-Frequency Neural Activity Structures Language in Time
T2 - Neurobiology of language (Cambridge, Mass.)
J2 - Neurobiol Lang (Camb)
PY - 2026
DA - 2026/05/05
VL - 7
SP - NOL.a.249
SN - 2641-4368
PB - MIT Press
DO - 10.1162/nol.a.249
UR - https://doi.org/10.1162/nol.a.249
LA - en
ER -

CSL-JSON

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"container-title-short": "Neurobiol Lang (Camb)",
"volume": "7",
"page": "NOL.a.249",
"DOI": "10.1162/nol.a.249",
"PMID": "42137739",
"PMCID": "PMC13171203",
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