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Predictive acoustical processing in human cortical layers.

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 · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Modeling and statistical analysis ↔ Code_laminar_BOLD_model/LBR_param_priors.m, the whole file · a weak match · score 0.84 · laminar BOLD signal, ascending veins, cortical depth, distance, coupling, microvasculature
  2. [2] § Methods › Modeling and statistical analysis ↔ Code_laminar_BOLD_model/LBR_parameters.m, the whole file · a weak match · score 0.83 · laminar BOLD signal, ascending veins, cortical depth, distance, coupling, microvasculature
  3. [3] § Results › Submillimeter cortical auditory responses ↔ Code_laminar_BOLD_model/laminar_bold_model_new.m, lines 1–95 · score 0.65 · laminar BOLD model, draining veins, model parameters, depths
  4. [4] § Results › Submillimeter cortical auditory responses ↔ Code_laminar_BOLD_model/LBR_param_priors.m, the whole file · a weak match · score 0.63 · hemodynamic models, laminar BOLD response, cortical depth, NVC, coupling, baseline
  5. [5] § Methods › Modeling and statistical analysis ↔ Code_layering/spm_glm.m, the whole file · a weak match · score 0.60 · Free Energy, SPM, Bayesian, noise, predictable, model
  6. [6] § Results › Submillimeter cortical auditory responses ↔ Code_laminar_BOLD_model/LBR_parameters.m, the whole file · a weak match · score 0.58 · hemodynamic models, laminar BOLD response, cortical depth, coupling, baseline
  7. [7] § Methods › Modeling and statistical analysis ↔ Code_laminar_BOLD_model/laminar_bold_model_new.m, lines 1–95 · score 0.51 · blood volume, CBF, CBV, depth, Modeling

Paper

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The authors' code

MATLAB · 112 lines · 4.1 KB · no license · 2 matches

  1. function [M] = LBR_param_priors(N,K,A,B,C),
  2. % LBR_parameters defines structure of parameters for laminar BOLD response
  3. % (LBR) model (see LBR_model.m). It applies parameter values
  4. % as describe in Table 1 of Havlicek, M. & Uludag, K. (2019) BioRxiv
  5. %
  6. % INPUT: K - Number of cortical depths
  7. %
  8. % OUTPUT: P - structure with all default parameters for LBR model
  9. %
  10. % AUTHOR: Martin Havlicek, 5 August, 2019
  11. %
  12. % REFERENCE: Havlicek, M. & Uludag, K. (2019) A dynamical model of the
  13. % laminar BOLD response, BioRxiv, doi: https://doi.org/10.1101/609099
  14. %
  15. % EXAMPLE:
  16. % K = 6;
  17. % P = LBR_parameters(K);
  18. % disp(P);
  19. %--------------------------------------------------------------------------
  20. M.N = N; % Number of depths
  21. M.K = K;
  22. % Neuronal parameter:
  23. %--------------------------------------------------------------------------
  24. P0.sigma = 3;
  25. P0.mu = 1.5;
  26. P0.lam = 0.1;
  27. P0.A = A;
  28. P0.B = B;
  29. P0.C = C;
  30. P0.Bmu = 0;
  31. P0.Blam = 0;
  32. % NVC parameters:
  33. % --------------------------------------------------------------------------
  34. P0.c1 = 0.6;
  35. P0.c2 = 1.5;
  36. P0.c3 = 0.6;
  37. depths = linspace(0,100,2*K+1); % Normalized distance to the center of individual depths (in %)
  38. M.depths = depths(2:2:end)';
  39. P0.nsig = 0.005;
  40. % LAMINAR HEMODYNAMIC MODEL:
  41. %--------------------------------------------------------------------------
  42. % Baseline physiological parameters:
  43. P0.V0t = 3.5; % Total (regional) amount of CBV0 in the gray matter (in mL) [1-6]
  44. P0.w_v = 0.5; % CBV0 fraction of microvasculature (i.e. venules here )with respect to the total amount
  45. P0.x_v = []; % CBV0 fraction across depths in venules
  46. P0.x_d = []; % CBV0 fraction across depths in ascending veins
  47. P0.s_v = 0; % Slope of CBV increase (decrease) in venules [0-0.3]
  48. P0.s_d = 0.4; % Slope of CBV increase in ascending vein [0-1.5]
  49. P0.s_d0 = 0; % Slope of CBV increase in ascending vein [0-1.5]
  50. P0.s_d2 = 0; % Slope of CBV increase in ascending vein [0-1.5]
  51. P0.t0v = 1; % Transit time through microvasculature(in second)
  52. P0.E0v = 0.35; % Baseline oxygen extraction fraction in venules
  53. P0.E0d = 0.35; % Baseline oxygen extraction fraction in venules
  54. P0.E0p = 0.35; % Baseline oxygen extraction fraction in venules
  55. % Parameters describing relative relationship between physiological variable:
  56. % CBF-CBV coupling (steady-state)
  57. P0.al_v = 0.3; % For venules
  58. P0.al_d = 0.2; % For ascending vein
  59. % CBF-CMRO2 coupling (steady-state)
  60. P0.nr = 4; % n-ratio (Ref. Buxton et al. (2004) NeuroImage)
  61. % CBF-CBV dynamic uncoupling
  62. P0.tau_v_in = 2; % For venules - inflation
  63. P0.tau_v_de = 2; % - deflation
  64. P0.tau_d_in = 2; % For AV - inflation
  65. P0.tau_d_de = 2; % - deflation
  66. % LAMINAR BOLD SIGNAL MODEL:
  67. %--------------------------------------------------------------------------
  68. M.TE = 0.028; % echo-time (in sec)
  69. % Hematocrit fraction
  70. P0.Hct_v = 0.35; % For venules, Ref. Lu et al. (2002) NeuroImage
  71. P0.Hct_d = 0.38; % For ascending vein
  72. P0.Hct_p = 0.42; % For pial vein
  73. M.B0 = 7; % Magnetic field strenght (in Tesla)
  74. P0.gyro = 2*pi*42.6*10^6; % Gyromagnetic constant for Hydrogen
  75. P0.suscep = 0.264*10^-6; % Susceptibility difference between fully oxygenated and deoxygenated blood
  76. % Water proton density:
  77. P0.rho_t = 0.89; % For gray matter tissue
  78. P0.rho_v = 0.95 - P0.Hct_v*0.22; % For blood (venules) Ref. Lu et al. (2002) NeuroImage
  79. P0.rho_d = 0.95 - P0.Hct_d*0.22; % For blood (ascending vein)
  80. P0.rho_p = 0.95 - P0.Hct_p*0.22; % For blood (pial vein)
  81. P0.rho_tp = 0.95; % For gray matter tissue % CSF
  82. % Relaxation rates for 7 T (in sec-1)
  83. P0.R2s_t = 34; % For gray matter tissue
  84. P0.R2s_v = 80; % For blood (venules)
  85. P0.R2s_d = 85; % For blood (ascending vein)
  86. P0.R2s_p = 90; % For blood (pial vein)
  87. % Slope of change in R2* of blood with change in extraction fration during activation
  88. P0.r0v = 228; % For venules
  89. P0.r0d = 232; % For ascending vein
  90. P0.M0 = 100;
  91. M.P0 = P0;
  92. M.x = zeros(N*4+K*4,1);
  93. M.xn = zeros(N,4);
  94. M.xk = zeros(K,4);

LBR_param_priors.m at commit 2b29ddc, no license · at the source

Overview

Authors: Lonike K. Faes1,2, Isma Zulfiqar1,3, Luca Vizioli2, Zidan Yu4,5,6, Yuan-hao Wu7, Jiyun N. Shin8, Martijn A. Cloos4,5,9,10, Ryszard Auksztulewicz11,12, Lucia Melloni8,13,14, Kamil Uludag15,16,17,18,19, Essa Yacoub2, Federico De Martino1,2
19 affiliations
  1. Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University,Maastricht, The Netherlands
  2. Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota,Minneapolis, USA
  3. Department of Experimental Psychology, Faculty of Brain Sciences, University College London,London, United Kingdom
  4. Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine,New York, USA
  5. Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine,New York, USA
  6. MRI Research Center, University of Hawaii,Honolulu, USA
  7. Neuroscience Institute, New York University Grossman School of Medicine,New York, USA
  8. Department of Neurology, New York University Grossman School of Medicine,New York, USA
  9. Australian Institute for Bioengineering and Nanotechnology, University of Queensland,St. Lucia, Australia
  10. Donders Center for Cognitive Neuroimaging, Radboud University,Nijmegen, The Netherlands
  11. Department of Education and Psychology, Free University Berlin,Berlin, Germany
  12. Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University,Maastricht, The Netherlands
  13. Max Planck For Empirical Aesthetics, Frankfurt am Main, Germany
  14. Predictive Brain Department, Research Center One Health Ruhr, University Alliance Ruhr, Ruhr-Universität Bochum,Bochum, Germany
  15. Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute,Toronto, Canada
  16. Physical Sciences Platform, Sunnybrook Research Institute,Toronto, Canada
  17. Department of Medical Biophysics, University of Toronto,Toronto, Canada
  18. Krembil Brain Institute, University Health Network,Toronto, Canada
  19. Center for Neuroscience Imaging Research, Institute for Basic Science,Suwon, Korea
Journal: Nature communications, volume 17, issue 1, article 6862
Dates: received 8 January 2025; accepted 7 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73540-z · PMID 42191715 · PMCID PMC13389289 · OpenAlex W4406284941
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Machine learning
Keywords: Cortex, Perception
MeSH: Auditory Cortex*, Auditory Perception*, Temporal Lobe*, Acoustic Stimulation, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Young Adult (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 2 papers (Europe PMC); 87 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

lonikefaes/predictive_tones

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2b29ddc8b402a125c543c3dc208d29453e65f3b8, 16 January 2025
Languages: MATLAB (4217), C (115), C/C++ (23), C++ (4), JavaScript (2), Java (1)
Size: 5,203 files, 4,362 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (818 files), SPM (197 files), Signal Processing Toolbox (18 files), Image Processing Toolbox (16 files), Statistics and Machine Learning Toolbox (16 files), GIfTI library for MATLAB (12 files), FreeSurfer (6 files), Optimization Toolbox (3 files), Brain Connectivity Toolbox (1 file), EEGLAB (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2,000 files

Zenodo 18460665

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
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Read it in the paper: doi.org/10.1038/s41467-026-73540-z.

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Read it in the paper: doi.org/10.1038/s41467-026-73540-z.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 12 MeSH terms, 1 funder, 86 references.

Cite

This paper

Faes, L. K., Zulfiqar, I., Vizioli, L., Yu, Z., Wu, Y.-h., Shin, J. N., Cloos, M. A., Auksztulewicz, R., Melloni, L., Uludag, K., Yacoub, E., & De Martino, F. (2026). Predictive acoustical processing in human cortical layers. Nature communications, 17(1), 6862. https://doi.org/10.1038/s41467-026-73540-z

BibTeX

@article{faes2026predictive,
author = {Faes, Lonike K. and Zulfiqar, Isma and Vizioli, Luca and Yu, Zidan and Wu, Yuan-hao and Shin, Jiyun N. and Cloos, Martijn A. and Auksztulewicz, Ryszard and Melloni, Lucia and Uludag, Kamil and Yacoub, Essa and De Martino, Federico},
title = {{Predictive acoustical processing in human cortical layers}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6862},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73540-z},
url = {https://doi.org/10.1038/s41467-026-73540-z},
pmid = {42191715},
pmcid = {PMC13389289}
}

RIS

TY - JOUR
AU - Faes, Lonike K.
AU - Zulfiqar, Isma
AU - Vizioli, Luca
AU - Yu, Zidan
AU - Wu, Yuan-hao
AU - Shin, Jiyun N.
AU - Cloos, Martijn A.
AU - Auksztulewicz, Ryszard
AU - Melloni, Lucia
AU - Uludag, Kamil
AU - Yacoub, Essa
AU - De Martino, Federico
TI - Predictive acoustical processing in human cortical layers
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/26
VL - 17
IS - 1
SP - 6862
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73540-z
UR - https://doi.org/10.1038/s41467-026-73540-z
LA - en
ER -

CSL-JSON

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