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Lifespan trajectories of the brain's functional complexity characterized by multiscale sample entropy.

Code ↔ Paper

2 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 2 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 › Sample entropy (SE) ↔ complexity_v2/FuzEn.m, the whole file · a weak match · score 0.54 · embedding dimensions, distance, matching, signals, threshold, entropy
  2. [2] § Materials and methods › Statistical analysis ↔ complexity_v2/old/hurst_Detrended.m, lines 1–119 · score 0.51 · Confidence intervals, slope, exponential

Paper

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

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The authors' code

MATLAB · 55 lines · 1.6 KB · no license · 1 match

  1. function [Out_FuzEn,P] = FuzEn(x,m,r,n,tau)
  2. %
  3. % This function calculates fuzzy entropy (FuzEn) of a univariate signal x
  4. %
  5. % Inputs:
  6. %
  7. % x: univariate signal - a vector of size 1 x N (the number of sample points)
  8. % m: embedding dimension
  9. % r: threshold (it is usually equal to 0.15 of the standard deviation of a signal - because we normalize signals to have a standard deviation of 1, here, r is usually equal to 0.15)
  10. % n: fuzzy power (it is usually equal to 2)
  11. % tau: time lag (it is usually equal to 1)
  12. %
  13. % Outputs:
  14. %
  15. % Out_FuzEn: scalar quantity - the FuzEn of x
  16. % P: a vector of length 2 - [the global quantity in dimension m, the global quantity in dimension m+1]
  17. %
  18. %
  19. % Ref:
  20. % [1] H. Azami and J. Escudero, "Refined Multiscale Fuzzy Entropy based on Standard Deviation for Biomedical Signal Analysis", Medical & Biological Engineering &
  21. % Computing, 2016.
  22. % [2] W. Chen, Z. Wang, H. Xie, and W. Yu,"Characterization of surface EMG signal based on fuzzy entropy", IEEE Transactions on neural systems and rehabilitation engineering, vol. 15, no. 2, pp.266-272, 2007.
  23. %
  24. %
  25. %
  26. %%
  29. if nargin == 4, tau = 1; end
  30. if nargin == 3, n = 2; tau=1; end
  31. if tau > 1, x = downsample(x, tau); end
  33. N = length(x);
  34. P = zeros(1,2);
  35. xMat = zeros(m+1,N-m);
  36. for i = 1:m+1
  37. xMat(i,:) = x(i:N-m+i-1);
  38. end
  40. for k = m:m+1
  41. count = zeros(1,N-m);
  42. tempMat = xMat(1:k,:);
  44. for i = 1:N-k
  45. % calculate Chebyshev distance without counting self-matches
  46. dist = max(abs(tempMat(:,i+1:N-m) - repmat(tempMat(:,i),1,N-m-i)));
  47. DF=exp((-dist.^n)/r);
  48. count(i) = sum(DF)/(N-m);
  49. end
  51. P(k-m+1) = sum(count)/(N-m);
  52. end
  54. Out_FuzEn = log(P(1)/P(2));
  55. end

FuzEn.m at commit 4bce8dd, no license · at the source

Overview

Authors: Dilmini Wijesinghe1, Kirsten Lynch1, Leon Aksman1, Dean C. Delis2, Danny JJ Wang1, Kay Jann1
ORCID iDs: Dilmini Wijesinghe
  1. USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine at USC, Los Angeles, CA, USA
  2. Department of Psychiatry, University of California San Diego, San Diego, CA, USA
Institutions: University of Southern California (United States); University of California San Diego (United States)
Journal: NeuroImage, volume 338, article 122085
Dates: published online 1 July 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.neuroimage.2026.122085 · PMID 42386108 · PMCID PMC13489196 · OpenAlex W4408279497
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.03.03.641327
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Machine learning, Complexity, Connectivity, Statistics, fMRI & imaging
Keywords: Resting-state fMRI, Complexity, Multiscale sample entropy, Lifespan trajectories, Cognition
MeSH: Aging*, Brain*, Executive Function*, Adolescent, Adult, Aged, Aged, 80 and over, Brain Mapping, Child, Entropy, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult (* major topic)
Topic: Neural Networks and Applications (Artificial Intelligence, Computer Science), according to OpenAlex
Funding: National Institutes of Health (R01-AG066711, S10OD032285); NIA NIH HHS (R01 AG066711); NIH HHS (S10 OD032285)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

Resting state functional magnetic resonance imaging (rs-fMRI) is a widely used imaging modality that can capture spontaneous neural activity of the brain. The human brain is a complex system, and emerging evidence suggests that the complexity of neural activity may serve as an index of its information processing capacity. In this study, we used multiscale sample entropy (MSE) to analyze the complexity of rs-fMRI time series from 504 healthy subjects aged 6 to 85 years. We constructed global and regional trajectories of the brain’s functional complexity across the lifespan and examined its correlation with executive function. We observed a nonlinear trajectory of fMRI-complexity, with a peak occurring at 23 years of age (95% CI: 21.27,26.38 years). Males reached the peak complexity at 26 years (95% CI: 19.95, 33.14 years), whereas females peaked at 23 years (95% CI 20.16, 29.18 years). Significant correlations were found between complexity and Number-Letter Switching of the Trail Making Test in the parietal and medial temporal lobes, while Inhibition and Inhibition/Switching of the Color Word Interference Test showed significant negative correlations with rs-fMRI complexity in the lateral and medial frontal cortex. These results provide insight into the behavior of fMRI-complexity across lifespan stages and highlight its association with executive function. As a non-invasive biomarker, fMRI-complexity may offer a novel approach to understanding the brain’s information processing capacity and its deficits in illness.

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 2 matches between paragraphs and lines of code.

kayjann/complexity

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4bce8dd3ef735f6e5370b4c145e2738d9f141d7b, 1 August 2021
Languages: MATLAB (38)
Size: 68 files, 38 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (9 files), Image Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
39 files
At the source: github.com/kayjann/complexity

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;
  • 38 scripts, each with its path and the digest of its content;
  • 2 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 availability

The data used in this project are managed by the Nathan Kline Institute and are available upon request. The in-house complexity toolbox can be accessed via Github, github.com/kayjann/complexity.

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 2, 28 September 2026

  • Publisher: n/a → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 16 MeSH terms, 3 funders, 46 references.

Cite

This paper

Wijesinghe, D., Lynch, K., Aksman, L., Delis, D. C., Wang, D. J., & Jann, K. (2026). Lifespan trajectories of the brain's functional complexity characterized by multiscale sample entropy. NeuroImage, 338, 122085. https://doi.org/10.1016/j.neuroimage.2026.122085

BibTeX

@article{wijesinghe2026lifespan,
author = {Wijesinghe, Dilmini and Lynch, Kirsten and Aksman, Leon and Delis, Dean C. and Wang, Danny JJ and Jann, Kay},
title = {{Lifespan trajectories of the brain's functional complexity characterized by multiscale sample entropy}},
journal = {NeuroImage},
year = {2026},
month = jul,
volume = {338},
pages = {122085},
publisher = {Elsevier BV},
issn = {1053-8119},
doi = {10.1016/j.neuroimage.2026.122085},
url = {https://doi.org/10.1016/j.neuroimage.2026.122085},
pmid = {42386108},
pmcid = {PMC13489196}
}

RIS

TY - JOUR
AU - Wijesinghe, Dilmini
AU - Lynch, Kirsten
AU - Aksman, Leon
AU - Delis, Dean C.
AU - Wang, Danny JJ
AU - Jann, Kay
TI - Lifespan trajectories of the brain's functional complexity characterized by multiscale sample entropy
T2 - NeuroImage
J2 - Neuroimage
PY - 2026
DA - 2026/07/01
VL - 338
SP - 122085
SN - 1053-8119
PB - Elsevier BV
DO - 10.1016/j.neuroimage.2026.122085
UR - https://doi.org/10.1016/j.neuroimage.2026.122085
LA - en
ER -

CSL-JSON

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

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