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Threat intensity shapes cortical engram architecture supporting remote memory retrieval.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Quantification and statistical analysis ↔ Mitric-Beerens_etal_2026_NatCommun_Fig6_DataAnalysis.R, lines 1–40 · score 0.62 · random intercept, spine densities, multilevel, slice, model, variables
  2. [2] § Results › PL engram cells have more long thin spines than non-engram cells after mild CTC ↔ Mitric-Beerens_etal_2026_NatCommun_Fig6_DataAnalysis.R, lines 42–105 · score 0.61 · Predictor model, Spine volume, Spine length, Morphology, dendrites, Figure 6
  3. [3] § Results › Threat intensity gates engagement of the PL engram in remote memory expression ↔ Mitric-Beerens_etal_2026_NatCommun_Fig6_DataAnalysis.R, lines 1–40 · score 0.58 · remote memory retrieval, Threat intensity, engram, cells

Paper

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The authors' code

R · 107 lines · 4.3 KB · no license · 3 matches

  1. ### Example Analysis Code - Mitric, Beerens et al., 2026.
  2. ### Threat intensity shapes cortical engram architecture supporting remote memory retrieval. Nature Communications
  3. ## FIG 6 - structural analysis
  5. #### set up R
  6. # clear existing workspace objects
  7. rm(list = ls())
  8. graphics.off()
  10. # set working directory to where the data file is located & results should be saved (change to your own directory)
  11. setwd("~/Documents/Spine analysis data")
  14. ### example analysis multilevel analysis
  16. ## on dendrite level
  17. # e.g. spine density, spine type density
  18. # exchange dendrite_var1 for variable of interest
  20. df1 <- read.table("SpineDensity.csv", sep=";", dec=",", header=TRUE)
  21. #OR
  22. df1 <- read.table("SpineTypeDensity.csv", sep=";", dec=",", header=TRUE)
  24. # fixed intercept model
  25. var1_model0 <- glm(dendrite_var1 ~ 1, data = df1)
  26. summary(var1_model0)
  28. # random intercept model
  29. var1_model1 <- glmer(dendrite_var1 ~ 1 + (1 |animal), data = df1, family = Gamma(link="log"))
  30. summary(var1_model1)
  31. var1_model2 <- glmer(dendrite_var1 ~ 1 + (1 |animal_slice), data = df1, family = Gamma(link="log"))
  32. summary(var1_model2)
  33. var1_model3 <- glmer(dendrite_var1 ~ 1 + (1 |animal_slice_cell), data = df1, family = Gamma(link="log"))
  34. summary(var1_model3)
  35. var1_model4 <- glmer(dendrite_var1 ~ 1 + (1 |animal/animal_slice), data = df1, family = Gamma(link="log"))
  36. summary(var1_model4)
  37. var1_model5 <- glmer(dendrite_var1 ~ 1 + (1 |animal_slice/animal_slice_cell), data = df1, family = Gamma(link="log"))
  38. summary(var1_model5)
  39. var1_model6 <- glmer(dendrite_var1 ~ 1 + (1 |animal/animal_slice/animal_slice_cell), data = df1, family = Gamma(link="log"))
  40. summary(var1_model6)
  42. # Chi square test random intercept model with smallest AIC vs fixed intercept model
  43. anova(var1_modelx, var1_model0, test="Chisq")
  45. # if sig, continue with predictor model
  47. # predictor model
  48. # adjust random_intercept
  49. var1_model7 <- glmer(dendrite_var1 ~ 1 + engram_status + (1 |random_intercept), data = df1,family = Gamma(link="log"))
  50. summary(var1_model7)
  52. # Chi square test predictor model vs random intercept model
  53. anova(var1_model7, var1_modelx, test="Chisq")
  55. emmeans(var1_model7, pairwise ~ engram_status, adjust = "fdr")
  59. ## on spine level
  60. # e.g. spine volume, spine length
  61. # exchange spine_var2 for variable of interest
  63. df2 <- read.table("Morphology.csv", sep=";", dec=",", header=TRUE)
  65. # fixed intercept model
  66. var2_model0 <- glm(spine_var2 ~ 1, data = df2, family = Gamma(link="log"))
  67. summary(var2_model0)
  69. # random intercept model
  70. var2_model1 <- glmer(spine_var2 ~ 1 + (1 |animal), data = df2, family = Gamma(link="log"))
  71. summary(var2_model01)
  72. var2_model2 <- glmer(spine_var2 ~ 1 + (1 |animal_slice), data = df2, family = Gamma(link="log"))
  73. summary(var2_model02)
  74. var2_model3 <- glmer(spine_var2 ~ 1 + (1 |animal_slice_cell), data = df2, family = Gamma(link="log"))
  75. summary(var2_model03)
  76. var2_model4 <- glmer(spine_var2 ~ 1 + (1 |animal_slice_cell_dendrite), data = df2, family = Gamma(link="log"))
  77. summary(var2_model04)
  78. var2_model5 <- glmer(spine_var2 ~ 1 + (1 |animal/animal_slice), data = df2, family = Gamma(link="log"))
  79. summary(var2_model05)
  80. var2_model6 <- glmer(spine_var2 ~ 1 + (1 |animal_slice/animal_slice_cell), data = df2, family = Gamma(link="log"))
  81. summary(var2_model06)
  82. var2_model7 <- glmer(spine_var2 ~ 1 + (1 |animal_slice_cell/animal_slice_cell_dendrite), data = df2, family = Gamma(link="log"))
  83. summary(var2_model07)
  84. var2_model8 <- glmer(spine_var2 ~ 1 + (1 |animal/animal_slice/animal_slice_cell), data = df2, family = Gamma(link="log"))
  85. summary(var2_model08)
  86. var2_model9 <- glmer(spine_var2 ~ 1 + (1 |animal_slice/animal_slice_cell/animal_slice_cell_dendrite), data = df2, family = Gamma(link="log"))
  87. summary(var2_model09)
  88. var2_model10 <- glmer(spine_var2 ~ 1 + (1 |animal/animal_slice/animal_slice_cell/animal_slice_cell_dendrite), data = df2, family = Gamma(link="log"))
  89. summary(var2_model10)
  91. # Chi square test random intercept model with smallest AIC vs fixed intercept model
  92. anova(var2_model07, var2_model0, test="Chisq")
  95. # if sig, continue with predictor model
  97. # predictor model
  98. # adjust random_intercept
  99. var2_model11 <- glmer(spine_var2 ~ 1 + engram_status + (1 |random_intercept), data = df2,family = Gamma(link="log"))
  100. summary(var2_model11)
  102. # Chi square test predictor model vs random intercept model
  103. anova(var2_model11, var2_model07, test="Chisq")
  105. emmeans(var2_model11, pairwise ~ engram_status, adjust = "fdr")

Mitric-Beerens_etal_2026_NatCommun_Fig6_DataAnalysis.R at commit 139c75d, no license · at the source

Overview

Authors: Miodrag M. Mitrić1, Sanne Beerens1, Panthea Nemat1, Esther Visser1, Luca Lorraine van Leeuwen1, Rolinka J. van der Loo1, August B. Smit1, Priyanka Rao-Ruiz1, Michel C. van den Oever1
ORCID iDs: Miodrag M. Mitrić, Panthea Nemat, Esther Visser, August B. Smit, Priyanka Rao-Ruiz, Michel C. van den Oever
  1. Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam,Amsterdam, The Netherlands
Institutions: Vrije Universiteit Amsterdam (Netherlands)
Journal: Nature communications, volume 17, issue 1, article 7447
Dates: received 18 June 2025; accepted 29 May 2026; published online 11 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74231-5 · PMID 42277042 · PMCID PMC13408494 · OpenAlex W7164379807
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), mouse (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Fear conditioning, Spine regulation and structure
MeSH: Fear*, Memory, Long-Term*, Prefrontal Cortex*, Pyramidal Cells*, Animals, Chemogenetics, Conditioning, Classical, Dendrites, Dendritic Spines, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (016.168.313, 024.004.012)
Citations: not cited yet (Europe PMC); 55 references in the paper

Abstract

The strength of persistent threat memories depends on the intensity of an aversive experience. We combined engram tagging with chemogenetics, electrophysiology and spine analyses in male mice to identify how threat intensity, following mild or strong contextual threat conditioning (CTC), shapes physiological and structural properties of prelimbic cortex (PL) pyramidal neurons. PL engram cells selectively mediate retrieval of a mild remote threat memory, and develop neuroadaptations that are time-dependent, dendritic segment-specific, and modulated by threat intensity. Specifically, increased presynaptic release probability, together with reduced postsynaptic strength and spine size, developed regardless of threat intensity. However, addition of long thin spines occurred exclusively on oblique dendrites of engram neurons after mild CTC, aligning with the PL engram contribution to mild, but not strong, threat memory. Our findings reveal how threat intensity shapes cortical engram architecture, which is pivotal for understanding the neural representation of threat memory strength and persistence.

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 3 matches between paragraphs and lines of code.

AllenInstitute/MIES

License: BSD-2-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5e6410391e0a92988c37df39c3ea435bd96af058, 17 September 2026
Languages: Shell (28), C/C++ (14), Python (2), C++ (2), C (1)
Size: 1,065 files, 47 scripts
Software Heritage: archived
Found in: the text, “Electrophysiological recordings and analysis”
Holds: README, license file, environment (tools/documentation/Dockerfile, tools/documentation/requirements.in, tools/documentation/requirements.txt, tools/ftp-upload/Dockerfile, tools/nwb-read-tests/Dockerfile, tools/nwb-read-tests/requirements.in, tools/nwb-read-tests/requirements.txt, tools/pre-commit/Dockerfile, tools/pre-commit/requirements.in, tools/pre-commit/requirements.txt, tools/report-generator/Dockerfile), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: h5py (1 file), Neurodata Without Borders (PyNWB, MatNWB) (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
49 files
At the source: github.com/AllenInstitute/MIES/

PantheaNemat/Mitric-Beerens_etal_2026_Nat.Commun

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead
  • 27 September 2026: the link is dead
At the source: github.com/PantheaNemat/Mitric-Beerens_etal_2026_Nat.Commun

Zenodo 19483826

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source:

pantheanemat/mitric-beerens_etal_2026_nat.commun.

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 139c75d04b4a7e5ea12cf8c830932f553720eaa4, 25 May 2026
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/PantheaNemat/Mitric-Beerens_etal_2026_Nat.Commun./tree/MultilevelAnalysis

Code availability

Code used for spine analysis is available on Github(https://github.com/PantheaNemat/Mitric-Beerens_etal_2026_Nat.Commun.) and Zenodo (10.5281/zenodo.19483826).

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

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 49 scripts, each with its path and the digest of its content;
  • 3 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

All behavioral, cellular, electrophysiological and structural data generated in this study are provided in the Source Data file. Source data are provided with this paper.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 12 MeSH terms, 1 funder, 55 references.

Cite

This paper

Mitrić, M. M., Beerens, S., Nemat, P., Visser, E., van Leeuwen, L. L., van der Loo, R. J., Smit, A. B., Rao-Ruiz, P., & van den Oever, M. C. (2026). Threat intensity shapes cortical engram architecture supporting remote memory retrieval. Nature communications, 17(1), 7447. https://doi.org/10.1038/s41467-026-74231-5

BibTeX

@article{mitric2026threat,
author = {Mitrić, Miodrag M. and Beerens, Sanne and Nemat, Panthea and Visser, Esther and van Leeuwen, Luca Lorraine and van der Loo, Rolinka J. and Smit, August B. and Rao-Ruiz, Priyanka and van den Oever, Michel C.},
title = {{Threat intensity shapes cortical engram architecture supporting remote memory retrieval}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7447},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74231-5},
url = {https://doi.org/10.1038/s41467-026-74231-5},
pmid = {42277042},
pmcid = {PMC13408494}
}

RIS

TY - JOUR
AU - Mitrić, Miodrag M.
AU - Beerens, Sanne
AU - Nemat, Panthea
AU - Visser, Esther
AU - van Leeuwen, Luca Lorraine
AU - van der Loo, Rolinka J.
AU - Smit, August B.
AU - Rao-Ruiz, Priyanka
AU - van den Oever, Michel C.
TI - Threat intensity shapes cortical engram architecture supporting remote memory retrieval
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/11
VL - 17
IS - 1
SP - 7447
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74231-5
UR - https://doi.org/10.1038/s41467-026-74231-5
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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