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Functionally distinct alpha components are differentially modulated by attention and affect behavior.

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  1. [1] § Materials & Methods › Univariate time–frequency analysis ↔ n39ht/er_computing_ica_timeseries_cue_complevel.m, the whole file · a weak match · score 0.55 · ft_combineplanar, taper, Hanning, FieldTrip, transformation

Paper

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The authors' code

MATLAB · 135 lines · 5.3 KB · no license · 1 match

  1. %% elie editing this to do a timecourse for each component
  2. clear
  3. cd /project/3035002.01/gamze/revision-scripts
  4. load taco_minus2sujlist.mat
  5. load /project/3035002.01/gamze/revisiondata/A1_A2_indx.mat
  7. for nsuj = 17:length(suj_list)
  9. subjectName = suj_list{nsuj};
  10. dir_data = ['/project/3035002.01/data/' subjectName '/ica/'];
  12. %load prerep allcue ICA data to identify a1 and a2 components
  13. fname = [dir_data, subjectName, '.cond.allcue.win.prerepwin.1s.ica.transform.mat'];
  14. load(fname)
  15. zvals = double(squeeze(sub_var.z_val_delay_fix));
  16. pvals = double(squeeze(sub_var.p_val_delay_fix));
  17. freqs = double(squeeze(sub_var.peak_frequency));
  19. %index for the original numbers of components corresponding to the
  20. %whole-trial data
  21. index_brain = sub_var.index_brain{1,1};
  23. %load whole trial ICA decomp
  24. fname2 = [dir_data, subjectName, '_firstcuelock_alltrials.ica.decomp.mat'];
  25. load(fname2) % brings comp. struct and data (channel)
  27. % numbers of compenents in the whole trial ICA data
  28. nIC = size(data.iclabel.classifications, 1);
  30. % take a1 & a2 components from the saved list
  31. idx_alpha1 = alpha1_mask{nsuj};
  32. idx_alpha2 = alpha2_mask{nsuj};
  34. %identify which original number of components are these
  35. alpha1_components = index_brain(idx_alpha1);
  36. alpha2_components = index_brain(idx_alpha2);
  39. % define the tfr cfg
  41. cfg_tfr = [];
  42. cfg_tfr.output = 'pow';
  43. cfg_tfr.method = 'mtmconvol';
  44. cfg_tfr.keeptrials = 'no';
  45. cfg_tfr.pad = 14;
  46. cfg_tfr.taper = 'hanning';
  47. cfg_tfr.foi = 6:1:13;
  48. cfg_tfr.t_ftimwin = 4 ./ cfg_tfr.foi;
  49. cfg_tfr.tapsmofrq = 0.2 .* cfg_tfr.foi;
  50. cfg_tfr.toi = -1:0.1:7;
  52. if ~isempty(alpha1_components)
  54. tfr_alpha1_pre = cell(1,numel(alpha1_components));
  55. tfr_alpha1_retro = cell(1,numel(alpha1_components));
  57. for ci=1:numel(alpha1_components)
  58. cfg = [];
  59. cfg.component = setdiff(1:nIC, alpha1_components(ci)); % remove everything except alpha1 comps
  60. data_alpha1 = ft_rejectcomponent(cfg, comp, data);
  62. idx = data_alpha1.trialinfo(:,8); % the column that defines pre vs retro
  64. cfg = [];
  65. cfg.trials = find(idx==1); % pre
  66. alpha1_pre = ft_selectdata(cfg, data_alpha1);
  68. cfg = [];
  69. cfg.trials = find(idx==2); % retro
  70. alpha1_retro = ft_selectdata(cfg, data_alpha1);
  71. clear data_alpha1
  73. % tfr pre
  74. data_planar = h_ax2plan(alpha1_pre); clear alpha1_pre
  75. freq_planar = ft_freqanalysis(cfg_tfr, data_planar); clear data_planar
  76. tfr_alpha1_pre{ci} = ft_combineplanar(struct('method','sum'), freq_planar); clear freq_planar
  78. % tfr retro
  79. data_planar = h_ax2plan(alpha1_retro); clear alpha1_retro
  80. freq_planar = ft_freqanalysis(cfg_tfr, data_planar); clear data_planar
  81. tfr_alpha1_retro{ci} = ft_combineplanar(struct('method','sum'), freq_planar); clear freq_planar
  83. ci
  84. end % for ci
  86. dir3_data=['/project/3035002.01/gamze/revisiondata/comp_tfr/' subjectName '/'];
  87. mkdir(dir3_data) %create sub dir
  88. save(fullfile(dir3_data, 'firstcuelock.alpha1.pre.mat'), 'tfr_alpha1_pre', '-v7.3');
  89. save(fullfile(dir3_data, 'firstcuelock.alpha1.retro.mat'), 'tfr_alpha1_retro', '-v7.3');
  90. clear tfr_alpha1_pre tfr_alpha1_retro
  91. end % if isempty
  94. if ~isempty(alpha2_components)
  96. tfr_alpha2_pre = cell(1,numel(alpha2_components));
  97. tfr_alpha2_retro = cell(1,numel(alpha2_components));
  99. for ci=1:numel(alpha2_components)
  100. cfg = [];
  101. cfg.component = setdiff(1:nIC, alpha2_components(ci)); % remove everything except alpha1 comps
  102. data_alpha2 = ft_rejectcomponent(cfg, comp, data);
  104. idx = data_alpha2.trialinfo(:,8); % the column that defines pre vs retro
  106. cfg = [];
  107. cfg.trials = find(idx==1); % pre
  108. alpha2_pre = ft_selectdata(cfg, data_alpha2);
  110. cfg = [];
  111. cfg.trials = find(idx==2); % retro
  112. alpha2_retro = ft_selectdata(cfg, data_alpha2);
  113. clear data_alpha2
  115. % tfr pre
  116. data_planar = h_ax2plan(alpha2_pre); clear alpha2_pre
  117. freq_planar = ft_freqanalysis(cfg_tfr, data_planar); clear data_planar
  118. tfr_alpha2_pre{ci} = ft_combineplanar(struct('method','sum'), freq_planar); clear freq_planar
  120. % tfr retro
  121. data_planar = h_ax2plan(alpha2_retro); clear alpha2_retro
  122. freq_planar = ft_freqanalysis(cfg_tfr, data_planar); clear data_planar
  123. tfr_alpha2_retro{ci} = ft_combineplanar(struct('method','sum'), freq_planar); clear freq_planar
  125. ci
  126. end % for ci
  128. dir3_data=['/project/3035002.01/gamze/revisiondata/comp_tfr/' subjectName '/'];
  129. mkdir(dir3_data) %create sub dir
  130. save(fullfile(dir3_data, 'firstcuelock.alpha2.pre.mat'), 'tfr_alpha2_pre', '-v7.3');
  131. save(fullfile(dir3_data, 'firstcuelock.alpha2.retro.mat'), 'tfr_alpha2_retro', '-v7.3');
  133. end % if isempty
  135. end % for nsuj

er_computing_ica_timeseries_cue_complevel.m, no license · at the source

Overview

Authors: Gamze Bilgen1,2, Elie Rassi1,3, Alma ElShafei1, Julio Rodriguez-Larios4, Saskia Haegens5,6
ORCID iDs: Gamze Bilgen, Elie Rassi, Alma ElShafei, Julio Rodriguez-Larios, Saskia Haegens
  1. Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, The Netherlands
  2. Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
  3. Department of Psychology and Centre for Cognitive Neuroscience, University of Salzburg, Salzburg, Austria
  4. Brunel University London, London, United Kingdom
  5. Department of Psychiatry, Columbia University, New York, NY, United States
  6. Division of Systems Neuroscience, New York State Psychiatric Institute, New York, NY, United States
Institutions: Radboud University Nijmegen (Netherlands); University of Trento (Italy); Donders Institute for Brain, Cognition and Behaviour (Netherlands); University of Salzburg (Austria); Brunel University of London (United Kingdom); New York State Psychiatric Institute; Columbia University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1335
Dates: received 14 April 2025; accepted 14 July 2026; published online 17 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1335 · PMID 42614911 · PMCID PMC13483082 · OpenAlex W7170167433
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), cognitive (subfield)
Methods: Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Spectral & time-frequency, Source localization, Physiology & signal measures
Keywords: brain oscillations, alpha rhythm, ICA, working memory, attention, MEG
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (016.Vidi.185.137); NIMH NIH HHS (R01 MH123679)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

Alpha oscillations (~10 Hz) have been hypothesized to gate information flow in the brain by inhibiting task-irrelevant and disinhibiting task-relevant processing. Recent work using independent component analysis (ICA) showed that functionally distinct alpha components co-exist during a working-memory task. Here, we recorded magnetoencephalography (MEG) while participants performed a visual delayed match-to-sample task with pre- and retro-cueing to investigate the gating role of alpha and the impact of top-down attention on these dynamics. Consistent with prior findings, we observed that alpha power temporally tracked the presentation of the informative cue. Using ICA, we replicate previous results regarding the co-existence of two distinct alpha components (Alpha1 and Alpha2), and extend these findings by showing that these alpha components are differentially modulated during the decision delay with opposite relationship to behavioral outcomes. Furthermore, we report on preparatory mechanisms involving Alpha1 and Alpha2 and show that their subsequent impact on task performance varies with cue informativeness and the task relevance of upcoming input. When task relevance was unknown (i.e., in the retro-cue condition), increases in Alpha1 power before task-relevant input were associated with poorer performance. In contrast, when task relevance was known (i.e., in the pre-cue condition), pre-stimulus increases in Alpha2 power before task-irrelevant (i.e., to-be unattended) input was associated with better task performance. Combined, these findings are in line with our hypothesis that Alpha1 rhythms modulate visual processing while Alpha2 rhythms modulate the short-term storage of visual information. Crucially, these distinct (anticipatory) alpha dynamics only became evident using a decomposition approach, emphasizing the necessity of analysis methods that can disentangle overlapping rhythmic dynamics that stem from different sources.

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 1 match between paragraphs and lines of code.

OSF gkbsm

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (2)
Size: 4 files, 2 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: FieldTrip (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/gkbsm/

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

Tracing map

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  • 2 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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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

Data and code are available here: https://osf.io/gkbsm/

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 2 funders, 60 references.

Cite

This paper

Bilgen, G., Rassi, E., ElShafei, A., Rodriguez-Larios, J., & Haegens, S. (2026). Functionally distinct alpha components are differentially modulated by attention and affect behavior. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1335. https://doi.org/10.1162/imag.a.1335

BibTeX

@article{bilgen2026functionally,
author = {Bilgen, Gamze and Rassi, Elie and ElShafei, Alma and Rodriguez-Larios, Julio and Haegens, Saskia},
title = {{Functionally distinct alpha components are differentially modulated by attention and affect behavior}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1335},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1335},
url = {https://doi.org/10.1162/imag.a.1335},
pmid = {42614911},
pmcid = {PMC13483082}
}

RIS

TY - JOUR
AU - Bilgen, Gamze
AU - Rassi, Elie
AU - ElShafei, Alma
AU - Rodriguez-Larios, Julio
AU - Haegens, Saskia
TI - Functionally distinct alpha components are differentially modulated by attention and affect behavior
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/17
VL - 4
SP - IMAG.a.1335
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1335
UR - https://doi.org/10.1162/imag.a.1335
LA - en
ER -

CSL-JSON

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