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Singing to the newborn brain uncovers early traces of specialized neural networks.

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  1. [1] § Methods › Stimuli › Modulation spectra ↔ AMIspectrum.m, the whole file · a weak match · score 0.50 · AMi spectrum, amplitude modulation, envelope

Paper

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The authors' code

MATLAB · 88 lines · 2.7 KB · no license · 1 match

  1. function [AMIspec, fc, mf, step] = AMIspectrum(insig, fs, varargin)
  2. %AMIspectrum Amplitude modulation excitation pattern
  3. % [AMIspec, fc, mf, step] = AMIspectrum(insig, fs, varargin)
  4. % returns the AMi spectrum of signal insig, in excitation units (W).
  5. % fs: sampling frequency
  6. % AMIspec is a N-by-M function where N is the number of modulation
  7. % frequencies (mf) and M is the number of audio frequencies (fc).
  8. %
  9. % see Varnet et al. 2017 for more details
  10. %
  11. % Leo Varnet - 07/2023
  13. if nargin<2
  14. error('%s: Too few input arguments.',upper(mfilename));
  15. end;
  17. if ~isnumeric(insig)
  18. error('%s: insig must be numeric.',upper(mfilename));
  19. end;
  21. if ~isnumeric(fs) || ~isscalar(fs) || fs<=0
  22. error('%s: fs must be a positive scalar.',upper(mfilename));
  23. end;
  25. definput.import={'varnet2017'};
  26. definput.importdefaults={};
  28. do_silent = 1;
  30. [flags,kv] = ltfatarghelper({'flow','fhigh'},definput,varargin);
  32. % defines the modulation axis
  33. mflow = kv.mflow;
  34. mfhigh = kv.mfhigh;
  35. N_fsamples = kv.modbank_Nmod;
  36. % f_spectra_intervals = logspace(log10(mflow), log10(mfhigh), N_fsamples+1);
  37. % f_spectra = logspace(log10(sqrt(f_spectra_intervals(1)*f_spectra_intervals(2))), log10(sqrt(f_spectra_intervals(end)*f_spectra_intervals(end-1))), N_fsamples);
  39. % Number of steps in fractional octaves to go from mod_flow to mod_fhigh:
  40. % N_octave_steps = ceil(NthOct * log10(mod_fhigh/mod_flow)/log10(2));
  41. % mfc = mod_flow * 2.^((0:N_octave_steps)/NthOct); % includes one extra bin (because of the ceiling)
  42. % cutoff_oct = mfc*2^(-.5/NthOct); % half step down
  43. %%%
  45. t=(1:length(insig))/fs;
  47. %%% gammatone filtering
  48. [gamma_responses,fc] = auditoryfilterbank(insig,fs,kv.flow,kv.fhigh);
  49. f_bw = audfiltbw(fc);
  51. %%% AM extraction
  52. if do_silent == 0
  53. fprintf('E extraction\n');
  54. end
  55. E = abs(hilbert(squeeze(gamma_responses)));
  57. Nchan = length(fc);
  58. %%% AMi spectra
  59. if do_silent == 0
  60. fprintf('calculating envelope spectra\n');
  61. end
  63. % using the king2019_modfilterbank function
  64. [AMfilt, mf] = king2019_modfilterbank_updated(E,fs,'argimport',flags,kv);
  66. % % using the modfilterbank function
  67. % [AMfilt_temp, mf] = modfilterbank(E,fs,fc,'argimport',flags,kv);
  68. % AMfilt=nan(length(t),length(fc),length(mf));
  69. % for i=1:length(AMfilt_temp)
  70. % AMfilt(:,i,1:size(AMfilt_temp{i},2)) = abs(AMfilt_temp{i});
  71. % end
  73. AMrms = squeeze(sqrt(mean(AMfilt.^2,1)))*sqrt(2);%squeeze(rms(AMfilt,'dim',1));
  74. DC = squeeze(mean(E,1));%squeeze(rms(E,'dim',1));
  75. AMIspec = AMrms./(DC'*ones(1,length(mf)));%(AMrms.^2*sqrt(2))./(DC'.^2*ones(1,length(mf))); % check this line
  76. AMIspec = AMIspec';
  78. if nargout>3
  79. step.t = t;
  80. step.f_bw = f_bw;
  81. step.gamma_responses = gamma_responses;
  82. step.E = E;
  83. step.mf = mf;
  84. step.AMrms = AMrms';
  85. step.DC = DC;
  86. end
  88. end

AMIspectrum.m at commit f42e0f3, no license · at the source

Overview

Authors: Caterina Marino1,2, Jessica Gemignani1,2, Léo Varnet3, Lorenzo Zanetto4,5, Eugenio Baraldi4,5, Judit Gervain1,2,6
ORCID iDs: Caterina Marino, Jessica Gemignani, Léo Varnet, Lorenzo Zanetto
  1. Department of Developmental Psychology and Socialisation, University of Padua, Padua, Italy
  2. Padova Neuroscience Center, Padua, Italy
  3. Laboratoire des Systèmes Perceptifs, Département d’Études Cognitives, École normale supérieure, PSL Research University, CNRS, 29 rue d’Ulm, 75005 Paris, France
  4. Department of Women’s and Children’s Health, University of Padua, Padua, Italy
  5. Institute of Pediatric Research Città della Speranza, Padua, Italy
  6. Integrative Neuroscience and Cognition Center, Université Paris Cité & CNRS, Paris, France
Institutions: University of Padua (Italy); Centre National de la Recherche Scientifique (France); Université Paris Sciences et Lettres (France); École Normale Supérieure - PSL (France); Laboratoire des Systèmes Perceptifs (France); Université Paris Cité (France); Centre Neurosciences intégratives et Cognition (France)
Journal: Communications psychology, volume 4, issue 1, article 97
Dates: received 25 August 2025; accepted 20 March 2026; published online 15 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s44271-026-00451-6 · PMID 41986646 · PMCID PMC13278952 · OpenAlex W7154472146
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Preprocessing
Keywords: Development studies, Human behaviour, Language and linguistics
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (773202)
Citations: cited by 1 paper (Europe PMC); 75 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 1 match between paragraphs and lines of code.

LeoVarnet/TMST

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: f42e0f33f225bb23dd1820906212fb2798f7ed1e, 1 October 2025
Languages: MATLAB (22)
Size: 33 files, 22 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (6 files), Wavelet Toolbox (3 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
23 files
At the source: github.com/LeoVarnet/TMST

Code availability statement

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Read it in the paper: doi.org/10.1038/s44271-026-00451-6.

Tracing map

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  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Availability statements

The paper has a data availability statement and a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing them here; in short, from what the harvester recognized in them:

  • they point to a dataset: OSF qge6b
  • they point to the authors' code: LeoVarnet/TMST
  • they say that the data are available on request

Read them in the paper: doi.org/10.1038/s44271-026-00451-6.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 1 funder, 62 references.

Cite

This paper

Marino, C., Gemignani, J., Varnet, L., Zanetto, L., Baraldi, E., & Gervain, J. (2026). Singing to the newborn brain uncovers early traces of specialized neural networks. Communications psychology, 4(1), 97. https://doi.org/10.1038/s44271-026-00451-6

BibTeX

@article{marino2026singing,
author = {Marino, Caterina and Gemignani, Jessica and Varnet, Léo and Zanetto, Lorenzo and Baraldi, Eugenio and Gervain, Judit},
title = {{Singing to the newborn brain uncovers early traces of specialized neural networks}},
journal = {Communications psychology},
year = {2026},
month = apr,
volume = {4},
number = {1},
pages = {97},
publisher = {Nature Publishing Group},
issn = {2731-9121},
doi = {10.1038/s44271-026-00451-6},
url = {https://doi.org/10.1038/s44271-026-00451-6},
pmid = {41986646},
pmcid = {PMC13278952}
}

RIS

TY - JOUR
AU - Marino, Caterina
AU - Gemignani, Jessica
AU - Varnet, Léo
AU - Zanetto, Lorenzo
AU - Baraldi, Eugenio
AU - Gervain, Judit
TI - Singing to the newborn brain uncovers early traces of specialized neural networks
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/04/15
VL - 4
IS - 1
SP - 97
SN - 2731-9121
PB - Nature Publishing Group
DO - 10.1038/s44271-026-00451-6
UR - https://doi.org/10.1038/s44271-026-00451-6
LA - en
ER -

CSL-JSON

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"id": "10.1038/s44271-026-00451-6",
"type": "article-journal",
"title": "Singing to the newborn brain uncovers early traces of specialized neural networks",
"container-title": "Communications psychology",
"author": [
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"family": "Marino",
"given": "Caterina"
},
{
"family": "Gemignani",
"given": "Jessica"
},
{
"family": "Varnet",
"given": "Léo"
},
{
"family": "Zanetto",
"given": "Lorenzo"
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}
],
"container-title-short": "Commun Psychol",
"volume": "4",
"issue": "1",
"page": "97",
"DOI": "10.1038/s44271-026-00451-6",
"PMID": "41986646",
"PMCID": "PMC13278952",
"ISSN": "2731-9121",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s44271-026-00451-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
15
]
]
}
}

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