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Frontal cortex organization supporting audiovisual processing during naturalistic viewing.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Encoding modeling procedure ↔ mTRF_tutorial.m, lines 1 to 83 · score 0.85 · fold cross validation, neural signals, mTRF, encoding model, ridge, CV · Report a mismatch
  2. [2] § Results › Audiovisual encoding exhibits modality-specific representations ↔ mTRF_tutorial.m, lines 1 to 83 · score 0.69 · fold cross validation, neural signals, mTRF, Encoding modeling, audio, electrodes · Report a mismatch
  3. [3] § Methods › Audiovisual feature extraction ↔ .virtual_documents/AV_featureExt_tutorial.ipynb, lines 78 to 100 · score 0.50 · transformer blocks, batch, layers, models · Report a mismatch
  4. [4] § Methods › Audiovisual feature extraction ↔ AV_featureExt_tutorial.ipynb, lines 73 to 101 · score 0.50 · transformer blocks, batch, layers, models · Report a mismatch

Paper

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The authors' code

MATLAB · 104 lines · 3.6 KB · MIT · 2 matches

  1. % This tutorial script demonstrates how to implement mTRF encoding model on
  2. % neural data. In this tutorial, I will go through the application of the
  3. % mTRF model using an example patient data, with the audio spectrogram as
  4. % the stimulus feature (aka. the STRF model).
  5. %
  6. % The current script applies the mTRF toolbox, which can be downloaded from
  7. % here: https://github.com/mickcrosse/mTRF-Toolbox
  8. %
  9. % - Faxin Zhou, Feb. 12, 2026
  10. % =========================================================================
  12. % SET-UP WORKING DIRECTORY
  13. % -------------------------------------------------------------------------
  14. % clear all
  15. cd /Users/faxin/Documents/Github/FLM_Paper
  18. % LOAD NEURAL DATA
  19. % -------------------------------------------------------------------------
  20. y = load('Data/HG_data.mat').y; % high-gamma data (ts * elecs)
  21. ch = load('Data/HG_data.mat').ch; % MNI coordinates
  22. srate = 512; % neural signal sampling rate
  25. % LOAD AUDIO SPECTROGRAM
  26. % -------------------------------------------------------------------------
  27. % Frequencies were averaged into 10 bins for faster implementation.
  28. X = load('Data/audio_spectrogram.mat').aud_spec; % (ts * features)
  30. % We can also visualize the spectrogram
  31. imagesc(X')
  32. title('Audio Spectrogram')
  33. xlabel('time (samples)')
  34. ylabel('frequency (bins)')
  37. % mTRF (HYPER)PARAMETERS
  38. % -------------------------------------------------------------------------
  39. tmin = 0; tmax = 400; % mTRF window is 0 to 400 ms
  40. nfold = 4; % n fold cross validation
  41. R_mod = 'ridge'; % methods for regularization. 'Tikhonov' or 'ridge'
  42. R = 10000; % regularization parameter;
  45. % mTRF START!
  46. % -------------------------------------------------------------------------
  47. [r_mean, r, M] = CV_mTRF(X, y, srate, nfold, tmin, tmax, R, R_mod);
  50. % VISUALIZE TWO CLUSTERS IN THE BRAIN WITH MITHRA TOOLBOX
  51. % -------------------------------------------------------------------------
  52. % brain figures have been saved to "./Results/mTRF_results.png"
  53. % OBTAIN BRAIN TEMPLATES
  54. P.vis_mode = 'MNI';
  55. [VT_lh, VT_rh] = brain_plot_prep(P, '/Users/faxin/Documents/Data_Analysis/Interesting/iEEG_visualization/visualization-tools-v2/matlab');
  58. % VISUALIZATION
  59. ElecColor = [0.0, 0.0, 0.0];
  60. BrainColor = [1, 1, 1];
  61. alpha = 0.8;
  62. radius = 2.8;
  63. clim = [0, 0.4];
  64. aud_cmap = cmap_gen([0.9, 0.9, 0.9], ...
  65. sscanf('a70000', '%2x%2x%2x', [1 3]) / 255);
  66. figure,
  67. VT_lh.PlotElecOnBrain(ch, ...
  68. 'ElecColor', r_mean', ...
  69. 'BrainColor', BrainColor, ...
  70. 'flag_AddFigure', false, ...
  71. 'FaceAlpha', alpha, ...
  72. 'radius', radius, ...
  73. 'cmap', aud_cmap,...
  74. 'clim', clim);
  80. % =========================================================================
  82. % mTRF TRAINGING FUNCTION
  83. % -------------------------------------------------------------------------
  84. function [r_mean, r, G_M] = CV_mTRF(X, Y, srate, nfold, tmin, tmax, R, R_mod)
  85. N = length(Y);
  86. I = 1 : N;
  87. r = zeros(nfold, size(Y, 2));
  88. for n = 0 : nfold - 1
  89. % data split
  90. test_I = N * n / nfold + 1 : N * (n + 1) / nfold;
  91. train_I = I; train_I(test_I) = [];
  92. Y_train = Y(train_I, :); Y_test = Y(test_I, :);
  93. X_train = X(train_I, :); X_test = X(test_I, :);
  94. % model training
  95. M = mTRFtrain(X_train, Y_train, srate, 1, tmin, tmax, R, ...
  96. 'method', R_mod, 'split', 5, 'zeropad', 0);
  97. % model prediction
  98. [~, S] = mTRFpredict(X_test, Y_test, M);
  99. r(n + 1, :) = S.r;
  100. M.nfold = n + 1;
  101. G_M(n + 1) = M;
  102. end
  103. r_mean = mean(r);
  104. end

mTRF_tutorial.m at commit 11b1954, under MIT · at the source

Overview

Authors: Faxin Zhou1, Amirhossein Khalilian-Gourtani2, Patricia Dugan2, Andrew Michalak2, Orrin Devinsky2, Peter Rozman3, Werner Doyle3, Daniel Friedman2, Adeen Flinker1,2
ORCID iDs: Faxin Zhou, Orrin Devinsky, Adeen Flinker
  1. Department of Biomedical Engineering, Tandon School of Engineering, New York University, New York, NY USA
  2. Department of Neurology, School of Medicine, New York University, New York, NY USA
  3. Department of Neurosurgery, School of Medicine, New York University, New York, NY USA
Institutions: New York University (United States)
Journal: Nature communications, volume 17, issue 1, article 5355
Dates: received 13 June 2025; accepted 22 May 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73947-8 · PMID 42331796 · PMCID PMC13287464 · OpenAlex W4411746071
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.26.661755
Status: code verified (how the code is found and verified)
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, fMRI & imaging
Keywords: Human behaviour, Neural encoding
MeSH: Auditory Perception*, Frontal Lobe*, Visual Perception*, Acoustic Stimulation, Adult, Brain Mapping, Electrocorticography, Female, Humans, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Multisensory perception and integration (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) (R01DC018805, R01NS115929); National Science Foundation (NSF) (IIS-2309057)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

flinkerlab/audiovisual_frontal_organization

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 11b19544dd4ad6493bf33a016f8d95899aab52f3, 14 February 2026
Languages: Jupyter (2), MATLAB (2)
Size: 12 files, 4 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), OpenCV (2 files), PyTorch (2 files), SciPy (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/flinkerlab/audiovisual_frontal_organization

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-73947-8.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 12 MeSH terms, 2 funders, 79 references.

Cite

This paper

Zhou, F., Khalilian-Gourtani, A., Dugan, P., Michalak, A., Devinsky, O., Rozman, P., Doyle, W., Friedman, D., & Flinker, A. (2026). Frontal cortex organization supporting audiovisual processing during naturalistic viewing. Nature communications, 17(1), 5355. https://doi.org/10.1038/s41467-026-73947-8

BibTeX

@article{zhou2026frontal,
author = {Zhou, Faxin and Khalilian-Gourtani, Amirhossein and Dugan, Patricia and Michalak, Andrew and Devinsky, Orrin and Rozman, Peter and Doyle, Werner and Friedman, Daniel and Flinker, Adeen},
title = {{Frontal cortex organization supporting audiovisual processing during naturalistic viewing}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {5355},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73947-8},
url = {https://doi.org/10.1038/s41467-026-73947-8},
pmid = {42331796},
pmcid = {PMC13287464}
}

RIS

TY - JOUR
AU - Zhou, Faxin
AU - Khalilian-Gourtani, Amirhossein
AU - Dugan, Patricia
AU - Michalak, Andrew
AU - Devinsky, Orrin
AU - Rozman, Peter
AU - Doyle, Werner
AU - Friedman, Daniel
AU - Flinker, Adeen
TI - Frontal cortex organization supporting audiovisual processing during naturalistic viewing
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/22
VL - 17
IS - 1
SP - 5355
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73947-8
UR - https://doi.org/10.1038/s41467-026-73947-8
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Zhou",
"given": "Faxin"
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"PMCID": "PMC13287464",
"ISSN": "2041-1723",
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"language": "en",
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"date-parts": [
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