OSCR

Temporal heterogeneity shapes diffusion dynamics in complex networks.

Code ↔ Paper

1 match 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 1 match · it ties a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results ↔ code/process/getLout.R, the whole file · a weak match · score 0.63 · tract tracing, row sums, retrograde, weighted, synuclein, connectivity

Paper

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

The paper is loaded when this pane is shown.

The authors' code

R · 46 lines · 1.6 KB · MIT · 1 match

  1. rm(list=setdiff(ls(),'params'))
  2. basedir <- params$basedir
  3. setwd(basedir)
  4. savedir <- paste(params$opdir,'processed/',sep='')
  5. dir.create(savedir,recursive=T)
  7. load(paste(params$opdir,'processed/pathdata.RData',sep='')) # load path data and ROI names
  9. #####################################
  10. ### Tract tracing connectome only ###
  11. #####################################
  13. W <- readMat(paste(params$opdir,'processed/W.mat',sep=''))$W
  14. n.regions <- nrow(W)
  15. W <- W * !diag(n.regions) # get rid of diagonal
  16. W <- W / (max(Re(eigen(W)$values))) # scale to max eigenvalue
  18. # Where i is row element and j is column element
  19. # Wij is a connection from region i to region j
  20. # convention is the opposite, so without transposing W
  21. # I am capturing "retrograde" connectivity
  23. in.deg <- colSums(W)
  24. out.deg <- rowSums(W)
  25. L.out <- diag(x = out.deg) - W # outdegree laplacian
  26. save(L.out,file = paste(params$opdir,'processed/Lout.RData',sep=''))
  28. ##################################
  29. ### Synuclein weighted matrix ####
  30. ##################################
  32. Synuclein <- as.matrix(read.csv('Data83018/Snca.csv'))
  33. W <- readMat(paste(params$opdir,'processed/W.mat',sep=''))$W
  34. W <- W * !diag(n.regions) # get rid of diagonal
  35. W <- diag(as.numeric(Synuclein)) %*% W
  36. W <- W / (max(Re(eigen(W)$values))) # scale to max eigenvalue
  38. # Where i is row element and j is column element
  39. # Wij is a connection from region i to region j
  40. # convention is the opposite, so without transposing W
  41. # I am capturing "retrograde" connectivity
  43. in.deg <- colSums(W)
  44. out.deg <- rowSums(W)
  45. L.out <- diag(x = out.deg) - W # outdegree laplacian
  46. save(L.out,file = paste(params$opdir,'processed/Lout_syn.RData',sep=''))

getLout.R at commit b967d08, under MIT · at the source

Overview

Authors: Cheng Luo1,2, Renaud Lambiotte3, Peng Ji1,2,4
ORCID iDs: Cheng Luo, Renaud Lambiotte, Peng Ji
  1. Interdisciplinary Research Centre for Complex Systems, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University,Shanghai, China
  2. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education,Shanghai, China
  3. Mathematical Institute, University of Oxford,Oxford, UK
  4. MOE Frontiers Center for Brain Science, Fudan University,Shanghai, China
Institutions: Fudan University (China); Ministry of Education of the People's Republic of China (China); University of Oxford (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 5601
Dates: received 24 November 2025; accepted 8 April 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-72161-w · PMID 42026059 · PMCID PMC13315865 · OpenAlex W7155375724
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), Parkinson's (population), systems (subfield)
Keywords: Complex networks, Nonlinear phenomena
MeSH: Brain*, Models, Neurological*, Nerve Net*, alpha-Synuclein, Animals, Diffusion, Markov Chains, Mice (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (62576108)
Citations: not cited yet (Europe PMC); 26 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 1 match between paragraphs and lines of code.

ejcorn/connectome_diffusion

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b967d0851f66073a0bfe25d39374466639b9685f, 28 February 2020
Languages: R (20), MATLAB (4)
Size: 44 files, 24 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brain Connectivity Toolbox (1 file), cowplot (1 file), ggplot2 (1 file), Statistics and Machine Learning Toolbox (1 file), mgcv (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
26 files
At the source: github.com/ejcorn/connectome_diffusion

naivefrog0817-chengluo/temporal_diffusion

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 27f23fd033e7655e9464094f822820b0ee2ba2e5, 1 February 2026
Languages: Python (26)
Size: 50 files, 26 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (23 files), Matplotlib (18 files), SciPy (10 files), NetworkX (9 files), pandas (4 files), seaborn (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
27 files
At the source: github.com/naivefrog0817-chengluo/temporal_diffusion

Code availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-72161-w.

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;
  • 50 scripts, each with its path and the digest of its content;
  • 1 match 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.

Code and data availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-72161-w.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 2 keywords, 8 MeSH terms, 1 funder, 21 references.

Cite

This paper

Luo, C., Lambiotte, R., & Ji, P. (2026). Temporal heterogeneity shapes diffusion dynamics in complex networks. Nature communications, 17(1), 5601. https://doi.org/10.1038/s41467-026-72161-w

BibTeX

@article{luo2026temporal,
author = {Luo, Cheng and Lambiotte, Renaud and Ji, Peng},
title = {{Temporal heterogeneity shapes diffusion dynamics in complex networks}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5601},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72161-w},
url = {https://doi.org/10.1038/s41467-026-72161-w},
pmid = {42026059},
pmcid = {PMC13315865}
}

RIS

TY - JOUR
AU - Luo, Cheng
AU - Lambiotte, Renaud
AU - Ji, Peng
TI - Temporal heterogeneity shapes diffusion dynamics in complex networks
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/23
VL - 17
IS - 1
SP - 5601
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72161-w
UR - https://doi.org/10.1038/s41467-026-72161-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-72161-w",
"type": "article-journal",
"title": "Temporal heterogeneity shapes diffusion dynamics in complex networks",
"container-title": "Nature communications",
"author": [
{
"family": "Luo",
"given": "Cheng"
},
{
"family": "Lambiotte",
"given": "Renaud"
},
{
"family": "Ji",
"given": "Peng"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5601",
"DOI": "10.1038/s41467-026-72161-w",
"PMID": "42026059",
"PMCID": "PMC13315865",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-72161-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: mgcv, NetworkX, cowplot, 7 other tools
[2] doi:10.7554/elife.103097 [code]
Canonical neurodevelopmental trajectories of structural and functional manifolds.
Journal: eLife
In common: mgcv, Brain Connectivity Toolbox, cowplot, 6 other tools
[3] doi:10.1162/imag.a.1278 [code]
Young and old adult brains experience opposite effects of acute sleep restriction on the functional connectivity network.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Brain Connectivity Toolbox, NetworkX, Statistics and Machine Learning Toolbox, 7 other tools
[4] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: Brain Connectivity Toolbox, NetworkX, Statistics and Machine Learning Toolbox, 7 other tools
[5] doi:10.1016/j.xcrm.2026.102800 [code]
Recombinant dimeric PICK1 peptide inhibitors for long-term relief of chronic pain by AAV therapeutics.
Journal: Cell reports. Medicine
In common: mgcv, NetworkX, cowplot, 6 other tools, mouse
[6] doi:10.1038/s42003-026-10276-y [code]
The cellular correlates and adolescent reorganisation of cortical myelination networks in the common marmoset.
Journal: Communications biology
In common: mgcv, Brain Connectivity Toolbox, Statistics and Machine Learning Toolbox, 6 other tools
[7] doi:10.1038/s41586-026-10490-y [code]
Lineage and organ signals sequentially build organ intrinsic nervous systems.
Journal: Nature
In common: NetworkX, cowplot, Statistics and Machine Learning Toolbox, 7 other tools, mouse
[8] doi:10.1038/s41467-026-76011-7 [code]
Human cortex organizes dynamic co-fluctuations along the sensorimotor-association axis.
Journal: Nature communications
In common: mgcv, Brain Connectivity Toolbox, Statistics and Machine Learning Toolbox, 5 other tools, systems
[9] doi:10.1002/ana.78203 [code]
AI-Driven Mapping of Seizure Spread Patterns.
Journal: Annals of neurology
In common: Brain Connectivity Toolbox, NetworkX, cowplot, 6 other tools
[10] doi:10.1162/imag.a.1310 [code]
Experimental quality control induces changes in Allen mouse brain connectomes.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Brain Connectivity Toolbox, ggplot2, tidyverse, 3 other tools, mouse, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.