OSCR

A standardized non-linear approach to studying menstrual cycle effects on brain and behavior.

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
  1. [1] § Results › Scaling cyclepoint ↔ 01_Cyclepoint calculation/cyclepoint_calculation.r, lines 11–23 · score 0.62 · follicular phase, luteal phase, Cycle days, cycle length, cyclepoint

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 · 49 lines · 1.4 KB · no license · 1 match

  2. ################## CYCLEPOINT CALCULATION ##################
  4. #### Original / unadjusted:
  6. df$cyclepoint <- df$cycleDay / df$cycleLength
  9. #### Adjusted for lesser variance in luteal phase length:
  11. adjustedCyclePoint <- function(cycleDay, cycleLength) {
  12. if (is.na(cycleDay) || is.na(cycleLength)){
  13. return ()
  14. }
  15. follicular_length <- cycleLength - 14
  16. if (cycleDay <= follicular_length){ # if in follicular phase
  17. return (cycleDay/follicular_length*0.5)
  18. } else { # if in luteal phase
  19. luteal_day <- cycleDay - follicular_length
  20. luteal_point <- luteal_day / 14
  21. return (0.5 + luteal_point * 0.5)
  22. }
  23. }
  25. df$adjustedCyclePoint <- mapply(adjustedCyclePoint, df$cycleday, df$cyclelength)
  28. #### Adjusted for ovulation (biomarker-based):
  30. adjustedCyclePoint <- function(cycleDay, cycleLength, ovulationFlag) {
  31. if (is.na(cycleDay) || is.na(cycleLength)){
  32. return ()
  33. }
  34. follicular_length <- cycleLength - ovulationFlag
  35. if (cycleDay <= follicular_length){
  36. return (cycleDay/follicular_length*0.5)
  37. } else {
  38. luteal_day <- cycleDay - follicular_length
  39. luteal_point <- luteal_day / ovulationFlag
  40. return (0.5 + luteal_point * 0.5)
  41. }
  42. }
  44. df$adjustedCyclePoint <- mapply(adjustedCyclePoint, df$cycleday, df$cyclelength, df$ovulationflag)
  46. ### NOTE: Ovulation flag should reflect the day after ovulation ###
  47. ## (confirmed or estimated depending on type of biomarker used) ##
  49. ## For all calculations: df = your dataframe

cyclepoint_calculation.r at commit 7932e56, no license · at the source

Overview

Authors: Mateja Perović1,2, Michael L Mack1
  1. Department of Psychology, University of Toronto, Toronto, ON, Canada
  2. Centre for Addiction and Mental Health, Toronto, ON, Canada
Institutions: Centre for Addiction and Mental Health (Canada); University of Toronto (Canada)
Journal: Frontiers in cognition, volume 5, article 1839153
Dates: received 25 March 2026; accepted 18 May 2026; published online 23 June 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fcogn.2026.1839153 · PMID 42416545 · PMCID PMC13337944 · OpenAlex W7165624026
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), methods / tools (subfield)
Methods: Statistics, fMRI & imaging
Keywords: continuous menstrual cycle, generalized additive model—GAM, menstrual cycle and cognition, menstrual cycle GAMM, standardized menstrual cycle
Topic: Menstrual Health and Disorders (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Funding: Fondation Brain Canada; Natural Sciences and Engineering Research Council of Canada (RGPIN 2017 ?, RGPIN-2024-05884)
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Menstrual cycles are major biological events with extensive effects on the brain and cognition, experienced by half of the human population. To develop a comprehensive account of human cognition, it is necessary to successfully integrate and characterize menstrual cycle effects in cognitive science research. However, current approaches to menstrual cycle analysis suffer from low data resolution and are not well-equipped to capture the highly variable, non-linear changes in outcomes of interest across the cycle. We present a validated standardized method remedying these issues, demonstrate its utility using hormonal, behavioral, and neuroimaging data, and provide an open-source toolkit to facilitate its use.

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 1 match between paragraphs and lines of code.

macklab/cyclepoint

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7932e569ff551aa29b48f949c5395024b6421170, 27 June 2026
Languages: R (1), Jupyter (1)
Size: 26 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Materials and equipment”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NiBabel (1 file), Nilearn (1 file), NumPy (1 file), pandas (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/macklab/cyclepoint

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;
  • 2 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

Datasets cited

Data availability statement

The original contributions presented in the study are included in the article and are available in an open source repository (https://github.com/macklab/cyclepoint). Further inquiries can be directed to the corresponding author.

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

  • Funding: added Fondation Brain Canada; Natural Sciences and Engineering Research Council of Canada: RGPIN 2017 ?, RGPIN-2024-05884

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 35 references.

Cite

This paper

Perović, M., & Mack, M. L. (2026). A standardized non-linear approach to studying menstrual cycle effects on brain and behavior. Frontiers in cognition, 5, 1839153. https://doi.org/10.3389/fcogn.2026.1839153

BibTeX

@article{perovic2026standardized,
author = {Perović, Mateja and Mack, Michael L},
title = {{A standardized non-linear approach to studying menstrual cycle effects on brain and behavior}},
journal = {Frontiers in cognition},
year = {2026},
month = jun,
volume = {5},
pages = {1839153},
publisher = {Frontiers Media SA},
issn = {2813-4532},
doi = {10.3389/fcogn.2026.1839153},
url = {https://doi.org/10.3389/fcogn.2026.1839153},
pmid = {42416545},
pmcid = {PMC13337944}
}

RIS

TY - JOUR
AU - Perović, Mateja
AU - Mack, Michael L
TI - A standardized non-linear approach to studying menstrual cycle effects on brain and behavior
T2 - Frontiers in cognition
J2 - Front Cognit
PY - 2026
DA - 2026/06/23
VL - 5
SP - 1839153
SN - 2813-4532
PB - Frontiers Media SA
DO - 10.3389/fcogn.2026.1839153
UR - https://doi.org/10.3389/fcogn.2026.1839153
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fcogn.2026.1839153",
"type": "article-journal",
"title": "A standardized non-linear approach to studying menstrual cycle effects on brain and behavior",
"container-title": "Frontiers in cognition",
"author": [
{
"family": "Perović",
"given": "Mateja"
},
{
"family": "Mack",
"given": "Michael L"
}
],
"container-title-short": "Front Cognit",
"volume": "5",
"page": "1839153",
"DOI": "10.3389/fcogn.2026.1839153",
"PMID": "42416545",
"PMCID": "PMC13337944",
"ISSN": "2813-4532",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fcogn.2026.1839153",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
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:10.1017/s0033291726104103 [code]
Linking brain structure to stress reactivity: cingulate surface area predicts acute cortisol responses.
Journal: Psychological medicine
In common: Nilearn, statsmodels, NiBabel, 4 other tools, 2 references
[2] doi:10.1162/imag.a.1362 [code]
Human fMRI at 11.7T: Assessing feasibility, stability, and reliability on the Iseult scanner.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Nilearn, statsmodels, NiBabel, 4 other tools, methods / tools
[3] doi:10.1002/epi4.70336 [code]
Quantifying extra-lesional interhemispheric cortical asymmetry in focal cortical dysplasia type II.
Journal: Epilepsia open
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[4] doi:10.1162/netn.a.544 [code]
Brain network reconfiguration during reward prediction error processing.
Journal: Network neuroscience (Cambridge, Mass.)
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[5] doi:10.64898/2026.08.18.26360725 [code]
Temporal pole blurring in hippocampal sclerosis reflects seizure-disrupted myelination
Journal: medRxiv (preprint)
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[6] doi:10.1038/s41467-026-76452-0 [code]
Music evokes shared neural representations of imagined narratives across sensory modalities.
Journal: Nature communications
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[7] doi:10.64898/2026.08.13.26360304 [code]
Lifespan brain structural variation reveals shared organization across mental health conditions
Journal: medRxiv (preprint)
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[8] doi:10.1016/j.isci.2026.117180 [code]
Developmental changes in similarity between neural representations of mental arithmetic and artificial neural networks.
Journal: iScience
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[9] doi:10.1038/s41514-026-00456-9 [code]
Exploring the link between body physiology and cognition: the role of the brain and aging.
Journal: npj aging
In common: Nilearn, statsmodels, NiBabel, 4 other tools
[10] doi:10.1038/s41467-026-75959-w [code]
Charting higher-order models of brain function beyond pairwise interactions.
Journal: Nature communications
In common: Nilearn, statsmodels, NiBabel, 4 other tools

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.