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Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain.

Code ↔ Paper

6 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 6 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 › Neural-Oscillatory Measures › Target power. ↔ observed/observed.m, the whole file · a weak match · score 0.83 · 14–24 Hz, 30–59 Hz, 60–80 Hz, 8–12 Hz, 4–6 Hz, 14 Hz
  2. [2] § METHODS › Neural-Oscillatory Measures › Target power. ↔ surrogate/surrogate.m, the whole file · a weak match · score 0.83 · 14–24 Hz, 30–59 Hz, 60–80 Hz, 8–12 Hz, 4–6 Hz, 14 Hz
  3. [3] § METHODS › Processing › Structural connectivity. ↔ surrogate/surrogate.m, the whole file · a weak match · score 0.69 · inter hemispheric connections, MICA MICs, binarized, Destrieux, OMEGA, SC
  4. [4] § METHODS › Processing › Structural connectivity. ↔ observed/observed.m, the whole file · a weak match · score 0.69 · inter hemispheric connections, MICA MICs, binarized, Destrieux, OMEGA, SC
  5. [5] § METHODS › Functional Gradient Analysis ↔ gradientAnalysis.m, the whole file · a weak match · score 0.52 · diffusion embedding, gradients, flipped, FC, HCP
  6. [6] § RESULTS › Dependence of Communication on Phase Coherence ↔ visualise.m, lines 2–60 · score 0.50 · beta coherence, alpha coherence, gamma lo, gamma hi, theta, power

Paper

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The authors' code

MATLAB · 61 lines · 4.1 KB · no license · 2 matches

  1. % Main script that calls EWC and neural osc functions - generates the communication principles for 1 subject (NOT SURROGATE CORRECTED) - repeat over subs to generate observed/original dataset
  3. addpath(genpath('../funcs/'));
  4. sub % sub index
  5. N = 100; % Number of regions % 100 for schaefer (HCP), 148 for Destrieux (OMEGA)
  6. Fs = 2035; % Sampling frequency (Hz) % 2400 for OMEGA, 2035 for HCP
  7. thresh=3;
  8. recording=load(sprintf("flex-comm-principles/data/HCP/%d_resting.mat",sub));
  9. main_data=recording.main_data;
  10. delay=recording.delay; % Delay between regions, in seconds, converted to timesteps
  11. load("flex-comm-principles/data/HCP/SC_ds_hcp_tract_pro_ctx_sch7n100p_sctx_na_n_100_m_1000_group_cnss_slcthr_5_nf_pa.mat") % For HCP (main text results) loads the group normative SC
  12. % SC=load("flex-comm-principles/data/OMEGA/MICA_MICS_destrieux_group_SC.txt") % For HCP (main text results) loads the group normative SC
  13. % -------COMMENT OUT THIS BLOCK IF USING MICA-MICS SC------
  14. SC=threshold_proportional(SC,0.15); % Keep top 15% connections
  15. SC(SC>0)=1; % Binarize
  16. SC(1:N/2,N/2+1:end)=0; % Remove inter hemispheric connections
  17. SC(N/2+1:end,1:N/2)=0; % ^^^
  18. %----------------------------------------------------------
  19. main_bandpass(:,:,1)=bandpass(main_data,[4,6],Fs);
  20. main_bandpass(:,:,2)=bandpass(main_data,[8,12],Fs);
  21. main_bandpass(:,:,3)=bandpass(main_data,[14,24],Fs);
  22. main_bandpass(:,:,4)=bandpass(main_data,[30,59],Fs);
  23. main_bandpass(:,:,5)=bandpass(main_data,[60,80],Fs);
  24. epoch = 10*Fs; % 10 second epochs
  25. win = 1*Fs; % 1 second window
  26. numepochs=floor(size(main_data,1)/epoch);
  27. P = zeros(N,N,numepochs);
  28. P_std = zeros(N,N,numepochs);
  29. Tpow = zeros(N,N,numepochs);
  30. Apow = zeros(N,N,numepochs);
  31. Bpow = zeros(N,N,numepochs);
  32. Glopow = zeros(N,N,numepochs);
  33. Ghipow = zeros(N,N,numepochs);
  34. PLV_theta = zeros(N,N,numepochs);
  35. PLV_alpha = zeros(N,N,numepochs);
  36. PLV_beta = zeros(N,N,numepochs);
  37. PLV_gammalo = zeros(N,N,numepochs);
  38. PLV_gammahi = zeros(N,N,numepochs);
  39. for s=1:numepochs
  40. data = main_data(((s-1)*epoch)+1:(s*epoch),:);
  41. bandpassSig=hilbert(main_bandpass(((s-1)*epoch)+1:(s*epoch),:,:));
  42. sigevents = eventiden(data,delay,win,thresh);
  43. [P(:,:,s),~]=PearsonEWC(data,SC,win,N,delay,sigevents); % Mean PC over all significant events in target region
  44. [Tpow(:,:,s),Apow(:,:,s),Bpow(:,:,s),Glopow(:,:,s),Ghipow(:,:,s),PLV_theta(:,:,s),PLV_alpha(:,:,s),PLV_beta(:,:,s),PLV_gammalo(:,:,s),PLV_gammahi(:,:,s)] = neural_osc(data,bandpassSig,SC,win,N,delay,sigevents,Fs);
  45. end
  46. P = abs(P);
  47. comm_principle_mean = zeros(N,10);
  48. for i=1:N
  49. comm_principle_mean(i,1)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(Tpow(i,find(SC(i,:)),:)),[],1));
  50. comm_principle_mean(i,2)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(Apow(i,find(SC(i,:)),:)),[],1));
  51. comm_principle_mean(i,3)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(Bpow(i,find(SC(i,:)),:)),[],1));
  52. comm_principle_mean(i,4)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(Glopow(i,find(SC(i,:)),:)),[],1));
  53. comm_principle_mean(i,5)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(Ghipow(i,find(SC(i,:)),:)),[],1));
  54. comm_principle_mean(i,6)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(PLV_theta(i,find(SC(i,:)),:)),[],1));
  55. comm_principle_mean(i,7)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(PLV_alpha(i,find(SC(i,:)),:)),[],1));
  56. comm_principle_mean(i,8)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(PLV_beta(i,find(SC(i,:)),:)),[],1));
  57. comm_principle_mean(i,9)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(PLV_gammalo(i,find(SC(i,:)),:)),[],1));
  58. comm_principle_mean(i,10)=corr(reshape(squeeze(P(i,find(SC(i,:)),:)),[],1),reshape(squeeze(PLV_gammahi(i,find(SC(i,:)),:)),[],1));
  59. end
  60. comm_principle_mean(isnan(comm_principle_mean))=0;
  61. save(sprintf("%d_results",sub),"comm_principle_mean","Tpow","Apow","Bpow","Glopow","Ghipow","PLV_theta","PLV_alpha","PLV_beta","PLV_gammalo","PLV_gammahi","P")

observed.m at commit 4c02558, no license · at the source

Overview

Authors: Varun Madan Mohan1, Thomas F Varley2, Anthony M Harris3, Robin F H Cash1, Caio Seguin4,5, Andrew Zalesky1,4
ORCID iDs: Varun Madan Mohan, Thomas F Varley, Anthony M Harris, Robin F H Cash, Caio Seguin, Andrew Zalesky
  1. Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia
  2. The Vermont Complex Systems Center, University of Vermont, Burlington, USA
  3. School of Psychology, The University of Queensland, St. Lucia, Australia
  4. Department of Psychiatry, Melbourne Medical School, University of Melbourne, Melbourne, Australia
  5. Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
Institutions: The University of Melbourne (Australia); University of Vermont (United States); The University of Queensland (Australia); Indiana University Bloomington (United States); Indiana University (United States)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 2, pages 508-530
Dates: received 17 October 2025; accepted 26 January 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.550 · PMID 42039093 · PMCID PMC13108498 · OpenAlex W7128158001
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Connectivity, Machine learning, fMRI & imaging, Physiology & signal measures
Keywords: Communication, Neural oscillations, MEG, Connectome
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Rebecca L. Cooper Medical Research Foundation; University of Melbourne; National Institutes of Health (1U54‐MH‐091657); McDonnell Center for Systems Neuroscience (1U54MH091657); Medical Research Council (1U54MH091657); Australian Research Council (DE220101019, FT220100091, DP170101815); National Health and Medical Research Council (2017527, 10.13039/501100000925)
Citations: not cited yet (Europe PMC); 86 references in the paper

Abstract

How the brain orchestrates the flow of information between its multiple functional units flexibly, quickly, and accurately remains a fundamental question in neuroscience. Multiple theories identify neural oscillations as a likely basis for this process. However, a lack of empirical validation of proposed theories, particularly at the whole-brain scale, has hampered consensus on oscillatory principles governing neural communication, limiting our understanding of a process central to perception and cognition and its integration into experiments and clinical applications. Here, we empirically validate previously proposed neural-oscillatory communication mechanisms in the human brain—specifically involving power and inter-areal phase coherence—at the whole-brain scale. We do this by estimating the dependence of inferred communication on oscillatory measures that have been theorized to facilitate communication, in source-localized resting-state magnetoencephalography recordings. We find that power and phase coherence in the alpha, beta, and high-gamma bands track communication better than others. Crucially, the relation between communication and oscillatory measures varied across regions, indicating spatial heterogeneity in routing mechanisms. Notably, power and coherence-based principles tracked communication patterns of unimodal regions better than those of transmodal regions. In sum, these findings suggest that the human brain implements regionally specific communication mechanisms with complex neural-oscillatory dependence.

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 6 matches between paragraphs and lines of code.

vmadanmohan/flex-comm-principles

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 4c0255825f918e8ec20f8f7c6568bdb9ecfbe291, 18 May 2026
Languages: MATLAB (11), Shell (1)
Size: 93 files, 12 scripts
Software Heritage: not archived
Found in: “DATA AND CODE AVAILABILITY”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (4 files), Brain Connectivity Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
13 files
At the source: github.com/vmadanmohan/flex-comm-principles

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Data and code availability

All the code used to analyze data is available at https://github.com/vmadanmohan/flex-comm-principles.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 7 funders, 82 references.

Cite

This paper

Madan Mohan, V., Varley, T. F., Harris, A. M., Cash, R. F. H., Seguin, C., & Zalesky, A. (2026). Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain. Network neuroscience (Cambridge, Mass.), 10(2), 508-530. https://doi.org/10.1162/netn.a.550

BibTeX

@article{madanmohan2026evaluating,
author = {Madan Mohan, Varun and Varley, Thomas F and Harris, Anthony M and Cash, Robin F H and Seguin, Caio and Zalesky, Andrew},
title = {{Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {10},
number = {2},
pages = {508--530},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.550},
url = {https://doi.org/10.1162/netn.a.550},
pmid = {42039093},
pmcid = {PMC13108498}
}

RIS

TY - JOUR
AU - Madan Mohan, Varun
AU - Varley, Thomas F
AU - Harris, Anthony M
AU - Cash, Robin F H
AU - Seguin, Caio
AU - Zalesky, Andrew
TI - Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/04/22
VL - 10
IS - 2
SP - 508
EP - 530
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.550
UR - https://doi.org/10.1162/netn.a.550
LA - en
ER -

CSL-JSON

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