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A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition.

Code ↔ Paper

17 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 17 matches
  1. [1] § 4. Methods › 4.5 Hemodynamic model ↔ JR ReDLat + Sleep/BOLDModel.py, lines 25–43 · score 0.93 · oxygen extraction, signal decay, stiffness constant, blood volume, deoxyhemoglobin content, resistance
  2. [2] § 2. Results ↔ JR Model Tutorial/JansenRitModelMulti.py, lines 1–84 · score 0.90 · Jansen Rit model, neural activity, modified version, cortical columns, pyramidal neurons, synaptic plasticity
  3. [3] § 2. Results ↔ JR model and running example/JansenRitModelMulti.py, lines 1–84 · score 0.90 · Jansen Rit model, neural activity, modified version, cortical columns, pyramidal neurons, synaptic plasticity
  4. [4] § 4. Methods › 4.2 Functional connectivity estimation ↔ JR Model Tutorial/Whole-Brain Part 1.ipynb, lines 206–267 · score 0.81 · 0.5–4 Hz, 13–30 Hz, 8–13 Hz, EEG frequency bands, 4–8 Hz, Bessel
  5. [5] § 2. Results › 2.3 Fitting to EEG source functional connectivity data ↔ JR Model Tutorial/Whole-Brain Part 1.ipynb, lines 206–267 · score 0.77 · 30–40 Hz, 0.5–4 Hz, 13–30 Hz, 8–13 Hz, frequency bands, 4–8 Hz
  6. [6] § 4. Methods › 4.4 Whole-brain model ↔ JR Model Tutorial/JansenRitModelMulti.py, lines 1–84 · score 0.76 · neural mass, modified version, pyramidal neurons, Jansen Rit, brain region, excitatory
  7. [7] § 2. Results › 2.1 Analysis of Jansen-Rit single-node dynamics ↔ JR ReDLat + Sleep/Single_Node.py, lines 232–272 · score 0.75 · single node, Peak frequency, Noise free, feedback inhibition, steady state, target firing rate
  8. [8] § 4. Methods › 4.2 Functional connectivity estimation ↔ JR Model Tutorial/runJR.py, lines 74–82 · score 0.72 · 0.5–4 Hz, 13–30 Hz, 8–13 Hz, 4–8 Hz, Bessel, filtered
  9. [9] § 4. Methods › 4.4 Whole-brain model ↔ JR ReDLat + Sleep/JansenRitModelMulti.py, lines 1–79 · score 0.72 · Jansen Rit neural, neural masses, modified version, mass model, pyramidal neurons, excitatory
  10. [10] § 2. Results › 2.3 Fitting to EEG source functional connectivity data ↔ JR Model Tutorial/runJR.py, lines 74–82 · score 0.71 · 30–40 Hz, 0.5–4 Hz, 13–30 Hz, 8–13 Hz, 4–8 Hz, filtered
  11. [11] § 4. Methods › 4.4 Whole-brain model ↔ JR model and running example/JansenRitModelMulti.py, lines 1–84 · score 0.70 · sigmoid function, pyramidal neurons, target firing rate, bounding, min, inverse
  12. [12] § 2. Results › 2.1 Analysis of Jansen-Rit single-node dynamics ↔ JR ReDLat + Sleep/Single_Node.py, lines 1–43 · score 0.66 · single node simulations, feedback inhibition, power spectrum, target firing rates, external, Jansen
  13. [13] § 4. Methods › 4.6 Simulations ↔ JR Model Tutorial/Whole-Brain Part 1.ipynb, lines 43–142 · score 0.66 · reduce memory consumption, spectral density, Power Spectrum, Welch, downsampled, plasticity
  14. [14] § 4. Methods › 4.5 Hemodynamic model ↔ JR ReDLat + Sleep/BOLDModel.py, lines 54–83 · score 0.65 · blood inflow, deoxyhemoglobin content, vasodilatory, firing rate, signals, simulated
  15. [15] § 4. Methods › 4.5 Hemodynamic model ↔ JR ReDLat + Sleep/BOLDModel.py, lines 25–43 · score 0.56 · venous blood, deoxygenated, fraction, V0, model
  16. [16] § 4. Methods › 4.2 Functional connectivity estimation ↔ JR ReDLat + Sleep/Sleep_EEG_fMRI.py, lines 80–150 · score 0.53 · 0.01–0.08 Hz, FC matrix, Pearson, correlation, 0.01 Hz, filtered
  17. [17] § 2. Results › 2.1 Analysis of Jansen-Rit single-node dynamics ↔ JR ReDLat + Sleep/Single_Node.py, lines 1–43 · score 0.51 · fast limit cycles, Jansen Rit, external, node, dynamics, model

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 157 lines · 4.5 KB · no license · 3 matches

  1. #!/usr/bin/env python3
  2. # -*- coding: utf-8 -*-
  3. """
  4. Created on Thu Aug 8 15:18:02 2019
  6. @author: Carlos Coronel
  8. Generalized Hemodynamic Model to reproduce fMRI BOLD-like signals.
  10. [1] Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J.
  11. (2007). Comparing hemodynamic models with DCM. Neuroimage, 38(3), 387-401.
  13. [2] Deco, Gustavo, et al. "Whole-brain multimodal neuroimaging model using serotonin
  14. receptor maps explains non-linear functional effects of LSD." Current Biology
  15. 28.19 (2018): 3065-3074.
  16. """
  18. import numpy as np
  19. from numba import jit,float64
  20. from numba.core.errors import NumbaPerformanceWarning
  21. import warnings
  22. warnings.simplefilter('ignore', category=NumbaPerformanceWarning)
  25. #PARAMETERS
  26. taus = 0.65 #time constant for signal decay
  27. tauf = 0.41 #time constant for feedback regulation
  28. tauo = 0.98 #time constant for volume and deoxyhemoglobin content change
  29. #itauX = inverse of tauX
  30. itaus = 1 / taus
  31. itauf = 1 / tauf
  32. itauo = 1 / tauo
  33. nu = 40.3 #frequency offset at the outer surface of the magnetized
  34. #vessel for fully deoxygenated blood at 1.5 Tesla (s^-1)
  35. r0 = 25 #slope of the relation between the intravascular relaxation
  36. #rate and oxygen saturation (s^-1)
  37. alpha = 0.32 #resistance of the veins; stiffness constant
  38. ialpha = 1 / alpha
  39. epsilon = 0.5 #ratio of intra and extravascular signal
  41. E0 = 0.4 #resting oxygen extraction fraction
  42. TE = 0.04 #echo time (!!determined by the experiment)
  43. V0 = 0.04 #resting venous blood volume fraction
  45. #Kinetics constants
  46. k1 = 4.3 * nu * E0 * TE
  47. k2 = epsilon * r0 * E0 * TE
  48. k3 = 1 - epsilon
  50. def update():
  51. BOLD_response.recompile()
  52. BOLD_signal.recompile()
  54. @jit(float64[:,:](float64[:,:],float64[:],float64), nopython = True)
  55. def BOLD_response(y, rE, t):
  56. """
  57. This function generates a BOLD response using the firing rates rE.
  59. Parameters
  60. ----------
  61. y : numpy array.
  62. Contains the following variables:
  63. s: vasodilatory signal. If it increases, the blood vessel experiments
  64. vasodilatation.
  65. f: blood inflow. Increases with the vasodilatation.
  66. v: blood volumen. Increases with blood inflow.
  67. q: deoxyhemoglobin content.
  68. rE: numpy array.
  69. Firing rates of neural populations/neurons.
  70. t : float.
  71. Current simulation time point.
  72. Returns
  73. -------
  74. Numpy array with s, f, v and q derivatives at time t.
  75. """
  76. s, f, v, q = y
  78. s_dot = 1 * rE + 0 - itaus * s - itauf * (f - 1)
  79. f_dot = s
  80. v_dot = (f - v ** ialpha) * itauo
  81. q_dot = (f * (1 - (1 - E0) ** (1 / f)) / E0 - q * v ** ialpha / v) * itauo
  83. return(np.vstack((s_dot, f_dot, v_dot, q_dot)))
  85. @jit(float64[:,:](float64[:,:],float64[:,:]), nopython = True)
  86. def BOLD_signal(q, v):
  87. """
  88. This function returns the BOLD signal using deoxyhemoglobin content and
  89. blood volumen as inputs.
  91. Parameters
  92. ----------
  93. q: numpy array.
  94. deoxyhemoglobin content over time.
  95. v: numpy array.
  96. blood volumen over time.
  97. Returns
  98. -------
  99. Numpy array with BOLD-like signals.
  100. """
  101. return(V0 * (k1 * (1 - q) + k2 * (1 - q / v) + k3 * (1 - v)))
  104. def Sim(rE, nnodes, dt):
  105. """
  106. Simulate the BOLD-like signals (raw non-filtered) with the current parameter values.
  108. Note that the time unit in this model is seconds.
  110. Parameters
  111. ----------
  112. rE : numpy array
  113. time x nodes matrix which values contains the firing rates of each node.
  114. nnodes : integer
  115. number of nodes.
  116. dt : float.
  117. actual integration step (the inverse of the sampling rate)
  119. Raises
  120. ------
  121. ValueError
  122. An error raises if the number of nodes of rE did not match with the given
  123. number by nnodes
  125. Returns
  126. -------
  127. y : numpy array
  128. Raw BOLD-like signals for each node
  129. """
  131. Ntotal = rE.shape[0]
  133. ic_BOLD = np.ones((1, nnodes)) * np.array([0.1, 1, 1, 1])[:, None] #initial conditions
  134. BOLD_vars = np.zeros((Ntotal,4,nnodes)) #matrix for storing the values
  135. BOLD_vars[0,:,:] = ic_BOLD
  138. #Solve the ODEs with Euler
  140. for i in range(1,Ntotal):
  141. BOLD_vars[i,:,:] = BOLD_vars[i - 1,:,:] + dt * BOLD_response(BOLD_vars[i - 1,:,:], rE[i - 1,:], i - 1)
  143. y = BOLD_signal(BOLD_vars[:,3,:], BOLD_vars[:,2,:])
  145. return(y)
  147. def ParamsBOLD():
  148. pardict={}
  149. for var in ('taus','tauf','tauo','nu','r0','alpha','epsilon','E0',
  150. 'V0','TE','k1','k2','k3'):
  151. pardict[var]=eval(var)
  153. return pardict

BOLDModel.py at commit 13fab02, no license · at the source

Overview

Authors: Carlos Coronel-Oliveros1,2,3, Fernando Lehue4, Rubén Herzog5, Iván Mindlin6, Marilyn Gatica7,8, Natalia Kowalczyk-Grębska9, Vicente Medel10, Josephine Cruzat1, Raul Gonzalez-Gomez1, Hernán Hernandez1, Enzo Tagliazucchi1, Pavel Prado11, Patricio Orio12,13, Agustín Ibáñez1,2,3,14,15
ORCID iDs: Raul Gonzalez-Gomez, Patricio Orio, Agustín Ibáñez
15 affiliations
  1. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile
  2. Trinity College Dublin, The University of Dublin, Dublin, Ireland
  3. Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland
  4. Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile
  5. Department of Psychology, University of the Balearic Islands, Palma de Mallorca, Spain
  6. Institut du Cerveau - Paris Brain Institute - ICM, Sorbonne Université, Inserm, CNRS, Paris, France
  7. Network Science Institute, School of Medicine, Northeastern University London, London, United Kingdom
  8. Precision Imaging, School of Medicine, University of Nottingham, Nottingham, United Kingdom
  9. Faculty of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland
  10. Faculty of Biological Sciences, Pontifical Catholic University of Chile, Santiago, Chile
  11. Escuela de Fonoaudiología, Facultad de Ciencias de la Rehabilitación y Calidad de Vida, Universidad San Sebastián, Santiago, Chile
  12. Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile
  13. Instituto de Neurociencia, Facultad de Ciencias, Universidad de Valparaíso, Playa Ancha, Valparaíso, Chile
  14. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
  15. Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
Institutions: University of California, San Francisco (United States); Trinity College Dublin (Ireland); Adolfo Ibáñez University (Chile); Global Brain Health Institute (United States); Consejo Superior de Investigaciones Científicas (Spain); Institute for Cross-Disciplinary Physics and Complex Systems (Spain); Universitat de les Illes Balears (Spain); Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Institut du Cerveau (France); University of Nottingham (United Kingdom); Northeastern University London (United Kingdom); Uniwersytet SWPS (Poland); San Sebastián University (Chile); Valparaiso University (United States); University of Valparaíso (Chile)
Journal: PLoS computational biology, volume 22, issue 5, article e1013463
Dates: received 22 August 2025; accepted 27 April 2026; published online 13 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1013463 · PMID 42127149 · PMCID PMC13183287 · OpenAlex W4413856431
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.26.672269
Status: code verified
Categories: EEG (modality), human (organism), computational (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Statistics, Preprocessing, Graphs, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
MeSH: Brain*, Feedback, Physiological*, Models, Neurological*, Computational Biology, Computer Simulation, Electroencephalography, Homeostasis, Humans, Magnetic Resonance Imaging, Neuronal Plasticity, Neurons, Sleep (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Narodowym Centrum Nauki (2013/11/N/HS6/01335); Foundation for the National Institutes of Health (R01 AG057234, R01 AG075775, R01 AG21051, R01 AG083799, CARDS-NIH); NIA NIH HHS (R01 AG083799, R01 AG075775); Agencia Nacional de Investigación y Desarrollo (1250091 and 1210176 and 1220995, ANID/PIA/ANILLOS ACT210096, FONDEF ID20I10152, and ANID/FONDAP 15150012); Wellcome Trust (335293/Z/25/Z); Rainwater Charitable Foundation; Ministerio de Ciencia Innovación y Universidades (RYC2022-035106-I); HORIZON EUROPE Framework Programme (101236426); Alzheimer's Association (SG-20-725707); MarÃa de Maeztu Program for Units of Excellence in R&D (CEX2021-001164-M/10.13039/501100011033)
Citations: cited by 1 paper (Europe PMC); 84 references in the paper

Abstract

Whole-brain models are valuable tools for understanding brain dynamics in health and disease by enabling the testing of causal mechanisms and identification of therapeutic targets through dynamic simulations. Among these models, biophysically inspired neural mass models have been widely used to simulate electrophysiological recordings, such as MEG and EEG. However, traditional models face limitations, including susceptibility to over-saturation of the sigmoid function by model hyperexcitability, which constrains their ability to capture the full richness of neural dynamics. Here, we thoroughly characterize a previously introduced multi-frequency Jansen-Rit neural mass model with inhibitory synaptic plasticity (ISP) aimed at overcoming these limitations. The ISP adjusts inhibitory feedback onto pyramidal neurons to clamp their firing rates around a target value. This mechanism allows for fine control of neuronal firing rates, preventing over-saturation in whole-brain simulations. In this model, we analyzed how different model parameters modulate oscillatory frequency and connectivity. As a demonstration, we considered simultaneously fitting EEG and fMRI recordings during NREM sleep. Bifurcation analysis showed that ISP widened the range of parameters in which the model exhibited sustained oscillations; the target firing rate can modulate oscillatory dynamics, producing different oscillatory regimes, from slower (δ, θ and α) to faster (β and γ) oscillations. High-frequency activity emerged from low global coupling, high firing rates, and a high proportion of γ versus α subpopulations. The ISP was necessary in the multi-frequency model to successfully fit EEG functional connectivity across frequency bands. Finally, ISP-controlled reductions in excitability reproduced both the slow-wave activity and the reduced connectivity in NREM sleep. Altogether, our model is compatible with biological evidence of the effects of excitability on modulating brain rhythms and connectivity, as observed in sleep, neurodegeneration, and chemical neuromodulation. This biophysical model with ISP provides a springboard for realistic brain simulations in health and disease.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

carlosmig/EEG-Dementias

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 13fab025b3f04b6faf47c5704c6f9b893ee60cb0, 2 September 2026
Languages: Python (19), Jupyter (3)
Size: 59 files, 22 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (22 files), Matplotlib (15 files), SciPy (13 files), Numba (5 files), Brain Connectivity Toolbox (2 files), scikit-image (2 files), scikit-learn (2 files), NetworkX (1 file), NiBabel (1 file), Nilearn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
23 files
At the source: github.com/carlosmig/EEG-Dementias

Zenodo 18841259

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 8 files
Software Heritage: not checked
Found in: “Data Availability”
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)
At the source: doi.org/10.5281/zenodo.18841259

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 22 scripts, each with its path and the digest of its content;
  • 17 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

Datasets cited

Data Availability

All the codes and basic data used to perform the simulations are freely available in GitHub at https://github.com/carlosmig/EEG-Dementias (“JR ReDLat + Sleep” folder), and mirrored in Zenodo (DOI: https://doi.org/10.5281/zenodo.18841259). The structural connectivity matrices (ReDLat and non-video game players) and the source-localized EEG time series are also available in the same repository. Simultaneous EEG-fMRI data is openly available in an already published work [https://doi.org/10.1016/j.neuron.2014.03.020], although the parcellated time-series can also be found at https://zenodo.org/records/16755776 [https://doi.org/10.1371/journal.pcbi.1012852]. We used the BrainNet Viewer toolbox [https://doi.org/10.1371/journal.pone.0068910] and FSL-FMRIB [https://doi.org/10.1016/j.neuroimage.2011.09.015] for visualization.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 12 MeSH terms, 10 funders, 81 references.

Cite

This paper

Coronel-Oliveros, C., Lehue, F., Herzog, R., Mindlin, I., Gatica, M., Kowalczyk-Grębska, N., Medel, V., Cruzat, J., Gonzalez-Gomez, R., Hernandez, H., Tagliazucchi, E., Prado, P., Orio, P., & Ibáñez, A. (2026). A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition. PLoS computational biology, 22(5), e1013463. https://doi.org/10.1371/journal.pcbi.1013463

BibTeX

@article{coroneloliveros2026multi,
author = {Coronel-Oliveros, Carlos and Lehue, Fernando and Herzog, Rubén and Mindlin, Iván and Gatica, Marilyn and Kowalczyk-Grębska, Natalia and Medel, Vicente and Cruzat, Josephine and Gonzalez-Gomez, Raul and Hernandez, Hernán and Tagliazucchi, Enzo and Prado, Pavel and Orio, Patricio and Ibáñez, Agustín},
title = {{A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition}},
journal = {PLoS computational biology},
year = {2026},
month = may,
volume = {22},
number = {5},
pages = {e1013463},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013463},
url = {https://doi.org/10.1371/journal.pcbi.1013463},
pmid = {42127149},
pmcid = {PMC13183287}
}

RIS

TY - JOUR
AU - Coronel-Oliveros, Carlos
AU - Lehue, Fernando
AU - Herzog, Rubén
AU - Mindlin, Iván
AU - Gatica, Marilyn
AU - Kowalczyk-Grębska, Natalia
AU - Medel, Vicente
AU - Cruzat, Josephine
AU - Gonzalez-Gomez, Raul
AU - Hernandez, Hernán
AU - Tagliazucchi, Enzo
AU - Prado, Pavel
AU - Orio, Patricio
AU - Ibáñez, Agustín
TI - A multi-frequency whole-brain neural mass model with homeostatic feedback inhibition
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/05/13
VL - 22
IS - 5
SP - e1013463
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013463
UR - https://doi.org/10.1371/journal.pcbi.1013463
LA - en
ER -

CSL-JSON

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