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Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Multivariate pattern analyses ↔ 5t8gu/mahal_theta_kfold_basis_b.m, the whole file · a weak match · score 0.78 · covariance matrix, random subsampling, cross validated, equalize, decoding accuracy, cosine
  2. [2] § Materials and Methods › Multivariate pattern analyses ↔ 5t8gu/mahal_func_theta_kfold_b.m, lines 1–63 · score 0.75 · fold cross validation, covariance matrix, equalize, decoding accuracy, cosine, subsampling
  3. [3] § Materials and Methods › Multivariate pattern analyses › Alpha power time-course decoding ↔ 5t8gu/mahal_theta_kfold_basis_b.m, the whole file · a weak match · score 0.72 · cosine weighted, cross validation, smoothed, Decoding accuracy, covariance, Mahalanobis
  4. [4] § Materials and Methods › Multivariate pattern analyses › Alpha power time-course decoding ↔ 5t8gu/mahal_func_theta_kfold_b.m, lines 1–63 · score 0.67 · cosine weighted, cross validation, Decoding accuracy, covariance, Mahalanobis, vector
  5. [5] § Materials and Methods › EEG acquisition and preprocessing ↔ xzhyf/Figure_4E_4F.m, lines 2–86 · score 0.58 · EEGLAB, FieldTrip, 1200 ms, filtering, channels, 0.1 Hz
  6. [6] § Materials and Methods › EEG acquisition and preprocessing ↔ xzhyf/Figure_4E_4F.m, lines 2–86 · score 0.54 · Evoked alpha, EEGLAB, filtered, envelope, band, 12 Hz
  7. [7] § Materials and Methods › Multivariate pattern analyses ↔ xzhyf/Figure_3A_3B.m, lines 4–60 · score 0.50 · cross validated, orientation space, channel, 200 ms, window, binning

Paper

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

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The authors' code

MATLAB · 81 lines · 3.4 KB · no license · 2 matches

  1. function [distance_cos,distances] = mahal_theta_kfold_basis_b(data,theta,n_folds,data_trn)
  2. % orientation reconstruction using cross-validation, balanced
  3. % training set through subsampling
  4. %% input
  6. % data format is trial by channel by time
  8. % theta is vector with angles in radians for each trial (must comprise the
  9. % whole circle, thus, for orientation data, which is only 180 degrees, make
  10. % sure to multiply by 2). This function assumes a finite number of unique
  11. % angles
  13. % "n_folds" is the number of folds for training and testing
  15. %% output
  16. % output is trial by time, to summarize average over trials
  18. % distance_cos is a measure of decoding accuracy, cosine weighted distances
  19. % of pattern-difference between trials of increasinglt dissimilar
  20. % orientations
  22. % distances is the ordered mean-centred distances
  23. %%
  24. theta=circ_dist(theta,0); % to make makre that orientations are centered around 0
  26. u_theta=unique(theta);
  28. train_partitions = cvpartition(theta,'KFold',n_folds); % split data n times using Kfold
  29. distances=nan(length(u_theta),size(data,1),size(data,3)); % prepare for output
  31. theta_dist=circ_dist2(u_theta',theta)';
  33. % if no optional training data from different period in trial is provided,
  34. % then test and training data are the same
  35. if nargin==3
  36. data_trn=data;
  37. end
  39. for tst=1:n_folds % run for each fold
  40. trn_ind = training(train_partitions,tst); % get training trial rows
  41. tst_ind = test(train_partitions,tst); % get test trial rows
  42. trn_dat = data_trn(trn_ind,:,:); % isolate training data
  43. tst_dat = data(tst_ind,:,:); % isolate test data
  44. trn_theta =theta(trn_ind);
  45. m=double(nan(length(u_theta),size(data,2),size(data,3)));
  46. n_conds = [u_theta,histc(trn_theta,u_theta)]; % get number of trials in each condition
  47. m_temp=(nan(length(u_theta),size(data,2),size(data,3)));
  49. % random subsampling to equalize number of trials of each condition
  50. for c=1:length(u_theta)
  51. temp1=trn_dat(trn_theta==u_theta(c),:,:);
  52. ind=randsample(1:size(temp1,1),min(n_conds(:,2)));
  53. m_temp(c,:,:)=mean(temp1(ind,:,:),1);
  54. end
  56. % smooth over orientations in the training data using a predetermined
  57. % basis set
  58. cosfun = @(theta,mu)((0.5 + 0.5.*cos((theta-mu))).^(length(u_theta)-1));
  59. for c=1:length(unique(u_theta))
  60. m(c,:,:)=sum(bsxfun(@times,m_temp(:,:,:),cosfun(u_theta,u_theta(c))))./sum(cosfun(u_theta,u_theta(c)));
  61. end
  63. for t=1:size(data,3) % decode at each time-point
  64. if ~isnan(trn_dat(:,:,t))
  65. % compute pair-wise mahalabonis distance between test-trials
  66. % and averaged training data, using the covariance matrix
  67. % computed from the training data
  68. temp=pdist2(squeeze(m(:,:,t)), squeeze(tst_dat(:,:,t)),'mahalanobis',covdiag(trn_dat(:,:,t)));
  69. distances(:,tst_ind,t)=temp;
  70. end
  71. end
  72. end
  73. distance_cos=-mean(bsxfun(@times,cos(theta_dist)',distances),1); % take cosine-weigthed mean of distances
  74. % reorder distances so that same condition distance is in the middle
  75. for c=1:length(u_theta)
  76. temp=round(circ_dist(u_theta,u_theta(c)),4);
  77. temp(temp==round(pi,4))=round(-pi,4);
  78. [~,i]=sort(temp);
  79. distances(:,theta==u_theta(c),:)=distances(i,theta==u_theta(c),:);
  80. end
  81. distances=-bsxfun(@minus,distances,mean(distances,1)); % mean-centre distances

mahal_theta_kfold_basis_b.m, no license · at the source

Overview

Authors: Yuanyuan Weng1, Jelmer P. Borst2, Elkan G. Akyürek1
ORCID iDs: Yuanyuan Weng, Jelmer P. Borst, Elkan G. Akyürek
  1. Department of Experimental Psychology, University of Groningen, Groningen, The Netherlands
  2. Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands
Institutions: University of Groningen (Netherlands)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1199
Dates: received 16 October 2025; accepted 11 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1199 · PMID 41958633 · PMCID PMC13058851 · OpenAlex W7136454982
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, Physiology & signal measures
Keywords: working memory, activity-silent maintenance, EEG, alpha oscillations
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: China Scholarship Council (202106990030)
Citations: not cited yet (Europe PMC); 70 references in the paper

Abstract

Recent theory on the neural basis of working memory (WM) has attributed an important role to “activity-silent” or -quiescent mechanisms, suggesting that sustained neural activity might not be essential in the retention of information. This idea has been challenged by reports of ongoing neural activity in the alpha band during WM maintenance, however. The precise role of these alpha oscillations is unclear: Do they reflect attentional prioritization of stored information, or do they serve as a general maintenance mechanism, for instance to periodically refresh synaptic traces? To address this, we designed a visual WM task involving two memory items, one of which was prioritized by being tested first for recall. The task included both short (1 second) and long (3 seconds) delay intervals between encoding and retrieval. The long delay condition allowed us to test whether the alpha-based decoding effects persist beyond the early delay period, thereby putting accounts that attribute alpha activity to generic maintenance processes to the test. Time-resolved decoding analyses revealed that both tested-first and tested-second items were initially decodable following stimulus presentation. However, only the tested-first item exhibited sustained decodability throughout the delay, particularly in the long delay condition, where it transitioned into a stable coding scheme. This prolonged representation was selectively supported by induced alpha power, which reliably tracked the prioritized tested-first item, but not the deprioritized tested-second item. Impulse-based decoding further confirmed this asymmetry, showing a selective increase in readout for the tested-second item only when it became immediately task relevant. Together, these findings suggest that sustained alpha-band activity primarily reflects attentional prioritization, rather than general memory maintenance. Unattended, deprioritized items appear to transition into an activity-quiescent state, consistent with models of synaptic storage in WM.

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 dtknz

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (59)
Size: 100 files, 59 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CircStat (40 files), Statistics and Machine Learning Toolbox (24 files), FieldTrip (10 files), Image Processing Toolbox (6 files), EEGLAB (5 files), Signal Processing Toolbox (5 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
59 files
At the source: osf.io/dtknz

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;
  • 59 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

No dataset and no data link were found in the paper.

Data and Code Availability

All data, analysis scripts, and results supporting this study are publicly available at the Open Science Framework (OSF): https://osf.io/dtknz.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 4 keywords, 1 funder, 69 references.

Cite

This paper

Weng, Y., Borst, J. P., & Akyürek, E. G. (2026). Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1199. https://doi.org/10.1162/imag.a.1199

BibTeX

@article{weng2026sustained,
author = {Weng, Yuanyuan and Borst, Jelmer P. and Akyürek, Elkan G.},
title = {{Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1199},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1199},
url = {https://doi.org/10.1162/imag.a.1199},
pmid = {41958633},
pmcid = {PMC13058851}
}

RIS

TY - JOUR
AU - Weng, Yuanyuan
AU - Borst, Jelmer P.
AU - Akyürek, Elkan G.
TI - Sustained alpha oscillations serve attentional prioritization in working memory, not maintenance
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/07
VL - 4
SP - IMAG.a.1199
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1199
UR - https://doi.org/10.1162/imag.a.1199
LA - en
ER -

CSL-JSON

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

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