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The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening.

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 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and Methods › High-frequency activity (HFa) ↔ saving_and_preproc.ipynb, the whole file · a weak match · score 0.52 · 80–120 Hz, Hilbert, bands, sub, 80 Hz, amplitude
  2. [2] § Materials and Methods › High-frequency activity (HFa) ↔ utils.py, lines 115–172 · score 0.51 · 80–120 Hz, Hilbert, bands, 80 Hz, amplitude, HFa
  3. [3] § Materials and Methods › Activity profile ↔ Information_theoretics.ipynb, lines 709–762 · score 0.51 · standard deviation, activity profile, bins, threshold, avalanches, correlation
  4. [4] § Materials and Methods › Activity profile ↔ figura_1.ipynb, lines 315–368 · score 0.51 · standard deviation, activity profile, bins, threshold, avalanches, correlation

Paper

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The authors' code

Jupyter notebook · 75 lines · 2.1 KB · no license · 1 match

  1. # %% [markdown]
  2. # # Saving and preprocessing
  3. #
  4. # ## Here we show the code that we used to compute the HFa.
  6. # %%
  7. import h5py
  8. import mne
  9. import numpy as np
  10. import pandas as pd
  11. from os.path import join as pjoin
  12. from itertools import product
  14. import matplotlib.pyplot as plt
  15. from scipy import stats
  16. from matplotlib import colors
  17. from utils import extract_amplitude
  18. import os
  19. import pickle
  21. import warnings
  22. warnings.simplefilter('ignore')
  24. path='C:/Users/matte/OneDrive/Documenti/matteo/'
  26. n, bwidth = 4, 10 # 10 bands, width=10
  27. freqs = [[80 + (bwidth * ii), 90 + (bwidth * ii)]
  28. for ii in range(n)]
  30. arr_mu = os.listdir(path+'seeg_fif_data/music')
  31. arr_rest = os.listdir(path+'seeg_fif_data/speech')
  32. arr_speech = os.listdir(path+'seeg_fif_data/rest')
  34. subject_set_mu=set()
  35. subject_set_speech=set()
  36. subject_set_rest=set()
  38. for st in arr_mu:
  39. print(st)
  40. subject_set_mu.add(st.partition('_')[0])
  41. print(st.partition('_')[0])
  43. for st in arr_speech:
  44. subject_set_speech.add(st.partition('_')[0])
  46. for st in arr_rest:
  47. subject_set_rest.add(st.partition('_')[0])
  49. subject_list=list(subject_set_mu.intersection(subject_set_speech,subject_set_rest))
  50. #Here I create a set of the H channels
  51. for subject in subject_list:
  52. with h5py.File(pjoin(path+'seeg_data_h5py/h5_electrodes/', subject + '_electrodes.hdf5'), 'r') as f:
  53. print(f.keys())
  54. print('chnames', f['chnames'].shape)
  56. chnames = f['chnames'][...].astype('U')
  58. for isub, subject in enumerate(['sub-c1b7ce54ce05']):
  59. print('We are elaborating the data concerning subject', subject,'step', isub )
  60. for i, sound in enumerate(['rest']):
  61. print(sound)
  62. path1= pjoin(path+'seeg_fif_data/', sound, )
  63. raw = mne.io.Raw(pjoin(path1, subject + '_' + sound + '_split_up_raw.fif'), preload=True)
  64. raw_filt=extract_amplitude(
  65. raw, # fif file from mne
  66. freqs, # these were the bands between 80-120
  67. normalize=False,
  68. n_hilbert=None,
  69. picks=None,
  70. n_jobs=-1)
  71. #raw_filt = raw.filter(fmin, fmax, n_jobs=-1)
  72. raw_down= raw_filt.resample(100, npad='auto')
  73. data = raw_filt.get_data()

saving_and_preproc.ipynb at commit 636ecdd, no license · at the source

Overview

Authors: Matteo Neri1,2, Claudio Runfola1, Pierre Guilleminot1, Noemie te Rietmolen1,3, Pierpaolo Sorrentino1, Daniele Schon1, Benjamin Morillon1, Giovanni Rabuffo1
ORCID iDs: Matteo Neri
  1. Aix Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France
  2. Aix Marseille Université, CNRS, INT, Institut de Neurosciences de la Timone, Marseille, France
  3. Language and Computation in Neural Systems Group, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Aix-Marseille Université (France); Institut de Neurosciences des Systèmes (France); Institut de Neurosciences de la Timone (France); Max Planck Institute for Psycholinguistics (Netherlands)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1348
Dates: received 1 October 2024; accepted 29 June 2026; published online 27 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1348 · PMID 42666749 · PMCID PMC13522956 · OpenAlex W7202158565
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning
Keywords: intracranial EEG, naturalistic stimuli, speech, music, high-gamma bursts, neuronal avalanches, temporal response function
MeSH: Auditory Perception*, Brain*, Gamma Rhythm*, Music*, Speech Perception*, Acoustic Stimulation, Adult, Electrocorticography, Female, Humans, Male, Young Adult (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HORIZON EUROPE European Research Council (ERC-CoG-101043344); Fondation Pour l’Audition (FPA RD-2022-09); France 2030 Investment Plan (ANR-16-CONV000X, ANR-17-EURE-0029); Excellence Initiative of Aix-Marseille University - A*MIDEX (AMX-19-IET-004)
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

A central challenge in systems neuroscience is to understand how distributed brain networks organize activity during naturalistic cognition. Here, we investigate whether transient, high-amplitude bursts of high-gamma activity, referred to as neuronal avalanches, provide a compact and functionally informative description of large-scale neural dynamics during auditory processing. Using intracranial stereotactic intracranial electroencephalography (sEEG) recordings from epileptic patients, we analyzed brain activity during speech listening, music listening, and rest. We show that high-amplitude bursts, defined as the top 1% of high-gamma activations, are strongly stimulus-driven: they synchronize across participants exposed to the same auditory input, exhibit condition-specific spatial topographies, and display distinct propagation patterns across cortical networks. Notably, although these events represent only a small fraction of the signal, they preserve substantial stimulus-related information. Temporal response function analyses reveal that avalanche activity reliably encodes both speech and music stimuli, even under extreme data sparsification. Together, these findings demonstrate that neuronal avalanches capture stimulus-relevant, large-scale neural dynamics and provide a computationally efficient framework for studying cognition in naturalistic settings.

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 4 matches between paragraphs and lines of code.

Mattehub/sorciere

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 636ecdd33719f1d66140719673eba750ac7fe4a3, 24 July 2025
Languages: Jupyter (9), Python (9), Shell (2)
Size: 75 files, 20 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, 9 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (17 files), Matplotlib (16 files), pandas (15 files), SciPy (15 files), h5py (14 files), MNE-Python (14 files), seaborn (11 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
21 files
At the source: github.com/Mattehub/sorciere

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;
  • 20 scripts, each with its path and the digest of its content;
  • 4 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

The sEEG data used for the analysis in this study are available from the authors upon reasonable request. The code is available in the public repository, https://github.com/Mattehub/sorciere.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 7 keywords, 12 MeSH terms, 4 funders, 94 references.

Cite

This paper

Neri, M., Runfola, C., Guilleminot, P., te Rietmolen, N., Sorrentino, P., Schon, D., Morillon, B., & Rabuffo, G. (2026). The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1348. https://doi.org/10.1162/imag.a.1348

BibTeX

@article{neri2026role,
author = {Neri, Matteo and Runfola, Claudio and Guilleminot, Pierre and te Rietmolen, Noemie and Sorrentino, Pierpaolo and Schon, Daniele and Morillon, Benjamin and Rabuffo, Giovanni},
title = {{The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1348},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1348},
url = {https://doi.org/10.1162/imag.a.1348},
pmid = {42666749},
pmcid = {PMC13522956}
}

RIS

TY - JOUR
AU - Neri, Matteo
AU - Runfola, Claudio
AU - Guilleminot, Pierre
AU - te Rietmolen, Noemie
AU - Sorrentino, Pierpaolo
AU - Schon, Daniele
AU - Morillon, Benjamin
AU - Rabuffo, Giovanni
TI - The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/27
VL - 4
SP - IMAG.a.1348
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1348
UR - https://doi.org/10.1162/imag.a.1348
LA - en
ER -

CSL-JSON

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