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Prefrontal-raphe-habenular circuit drives fear memory recall and updating.

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] § Methods › Data processing for fiber photometry of the mPFC→MRR pathway in head-fixed mice ↔ Normalizer.m, lines 11–58 · score 0.63 · window width, Activity dependent, isosbestic, IQR, signals, fiber

Paper

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The authors' code

MATLAB · 58 lines · 3.4 KB · CC-BY-4.0 · 1 match

  1. classdef Normalizer < uint8
  2. enumeration
  3. LINEAR_FIT (1) % linear fit 405 to 465, norm = (465-fit405)/fit405
  4. NO_NOISE_SCALING (2) % same as LINEAR_FIT, but only 5s movmedian of 405 gets scakaled, noise not
  5. ZSCORE_DEBLEACHED (3) % separately zscoring debleached 405 and 465 signals
  6. DIVIDE_BY_STD (4) % same as ZSCORE_DEBLEACHED WITHOUT subtracting mean
  7. DIVIDE_BY_IQR (5) % same as DIVIDE_BY_STD, but with interquartile range instread of STD
  8. end
  10. methods(Static)
  11. function [norm1, norm2, fitParams, normParams] = customNormalize(args)
  12. % return a normalized fiber photometry vector of isosbestic and activity dependent signals
  14. arguments
  15. args.timestamps (:, 1) {mustBeNumeric}
  16. args.vector405 (:, 1) {mustBeNumeric}
  17. args.vector405Trend (:, 1) {mustBeNumeric} = 0
  18. args.vector465 (:, 1) {mustBeNumeric}
  19. args.vector465Trend (:, 1) {mustBeNumeric} = 0
  20. args.method (1, 1) helper.Normalizer
  21. args.datarate (1, 1) {mustBePositive, mustBeScalarOrEmpty} = 1
  22. args.movmedianWindowS {mustBePositive, mustBeScalarOrEmpty} = 1
  23. end
  24. switch args.method
  25. case helper.Normalizer.LINEAR_FIT
  26. fitParams = polyfit(args.vector405, args.vector465, 1);
  27. scaled405Like465 = fitParams(1) .* args.vector405 + fitParams(2);
  28. diff465_Fit405 = args.vector465 - scaled405Like465;
  29. norm1 = (diff465_Fit405 ./ scaled405Like465) * 100;
  30. case helper.Normalizer.NO_NOISE_SCALING
  31. fitParams = polyfit(args.vector405, args.vector465, 1);
  32. noiseEqualizationWindowWidth = round(args.movmedianWindowS * args.datarate);
  33. isosFiltered = movmedian(args.vector405, noiseEqualizationWindowWidth);
  34. isosNoiseOnly = args.vector405 - isosFiltered;
  35. scaled405Like465NoiseNotScaled = fitParams(1) .* isosFiltered + fitParams(2) + isosNoiseOnly;
  36. diff465_Fit405 = args.vector465 - scaled405Like465NoiseNotScaled;
  37. norm1 = (diff465_Fit405 ./ scaled405Like465NoiseNotScaled) * 100;
  38. case helper.Normalizer.ZSCORE_DEBLEACHED
  39. norm1 = (args.vector405 - mean(args.vector405)) / std(args.vector405);
  40. norm2 = (args.vector465 - mean(args.vector465)) / std(args.vector465);
  41. case helper.Normalizer.DIVIDE_BY_STD
  42. norm1 = args.vector405 / std(args.vector405);
  43. norm2 = args.vector465 / std(args.vector465);
  44. normParams(1) = std(args.vector405);
  45. normParams(2) = std(args.vector465);
  46. fitParams = [];
  47. case helper.Normalizer.DIVIDE_BY_IQR
  48. norm1 = args.vector405 / iqr(args.vector405);
  49. norm2 = args.vector465 / iqr(args.vector465);
  50. normParams(1) = iqr(args.vector405);
  51. normParams(2) = iqr(args.vector465);
  52. fitParams = [];
  53. otherwise
  54. error('Invalid method')
  55. end
  56. end
  57. end
  58. end

Normalizer.m, under CC-BY-4.0 · at the source

Overview

Authors: Boldizsár Zsolt Balog1,2, Albert M Barth1,3, Krisztián Zichó1,4, Abel Major1,5, Zsuzsanna Bardóczi1, János Brunner6, Charlotte Seng7, Márta Jelitai3, Katalin E Sos1, Réka Z Sebestény1, András Szőnyi8, Nikolas Karalis8,9, Áron Orosz1,4, Eszter Sipos6, Erik Misák1, György Cserey10, Andreas Lüthi8, Viktor Varga3, Csaba Földy7, János Szabadics6, Gábor Nyiri1
ORCID iDs: Boldizsár Zsolt Balog, Krisztián Zichó, Abel Major, János Brunner, Charlotte Seng, Katalin E Sos, Réka Z Sebestény, Erik Misák, György Cserey, Andreas Lüthi, Viktor Varga, Csaba Földy, Gábor Nyiri
  1. Systems & Integrative Neuroscience Research Group, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
  2. Tamás Roska Doctoral School of Sciences and Technology, Pázmány Péter Catholic University, Budapest, Hungary
  3. Laboratory of Subcortical Modulation, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
  4. Semmelweis University Doctoral School, János Szentágothai Neurosciences Divisions, Budapest, Hungary
  5. Semmelweis University Doctoral School, Dental Research Division, Budapest, Hungary
  6. Laboratory of Cellular Neuropharmacology, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
  7. Laboratory of Neural Connectivity, Brain Research Institute, Faculties of Medicine and Science, University of Zürich, Zürich, Switzerland
  8. Laboratory for Cellular Mechanisms of Learning and Memory, Friedrich Miescher Institute, Basel, Switzerland
  9. Paris Brain Institute, Inserm, Sorbonne Université, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
  10. Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
Institutions: Pázmány Péter Catholic University (Hungary); HUN-REN Institute of Experimental Medicine (Hungary); Hungarian Research Network (Hungary); Semmelweis University (Hungary); University of Zurich (Switzerland); Friedrich Miescher Institute (Switzerland); Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France)
Journal: Nature communications, volume 17, issue 1, article 9686
Dates: received 27 August 2025; accepted 30 July 2026; published online 12 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-76574-5 · PMID 42716922 · PMCID PMC13558683 · OpenAlex W7202292283
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), systems (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Fear conditioning, Neural circuits
MeSH: Fear*, Habenula*, Mental Recall*, Prefrontal Cortex*, Amygdala, Animals, Hippocampus, Male, Memory, Mice, Mice, Inbred C57BL, Neural Pathways, Neurons, Vesicular Glutamate Transport Protein 2 (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Swiss National Science Foundation (219390, 208976, 310030); European Research Council (772452)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Fear memory recall is a vital defensive response, yet its underlying neuronal mechanism remains incompletely understood. Here, we identify an effective fear recall pathway. We found that hippocampal and amygdalar memory centers directly target a group of medial prefrontal cortex (mPFC) neurons, which are necessary for fear recall and directly target vesicular glutamate transporter 2 (vGluT2)-expressing neurons in the pontine median raphe region (MRRvGluT2 neurons) in mice. We found that MRRvGluT2 neurons are necessary for recalling contextual and cued fear memories, they exhibit scalable fear-dependent activity, bidirectionally regulate depression-like behaviors, and show prolonged activity after a fearful experience. During fear memory recall, MRRvGluT2 neurons activate the lateral habenula (LHb), a region implicated in major depression. MRRvGluT2 neurons are electrophysiologically and genetically diverse, and present in rodent and primate species. The identified mPFC–MRR–LHb pathway critically mediates fear memory recall, providing potential therapeutic targets for fear-related disorders.

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 1 match between paragraphs and lines of code.

nikolaskaralis

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Fiber photometry of MRR vGluT2 neurons in freely”
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)
At the source: github.com/nikolaskaralis/

Zenodo 21026721

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

Code availability

Custom scripts used during the work were developed for routine data processing, analysis workflows, and figure generation, using established methods and standard approaches. All experimental procedures, methods, and software used are described in the Methods section in sufficient detail to enable reproduction of the study. Core fiber photometry analysis code was deposited to Zenodo (https://doi.org/10.5281/zenodo.21026721).

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:

  • 2 repositories 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 RNA sequencing data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) database92 under accession code GSE300145 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE300145). All other relevant data generated in this study are provided in the manuscript and its Supplementary Information/Source Data file. Source data are provided with this paper.

Custom scripts used during the work were developed for routine data processing, analysis workflows, and figure generation, using established methods and standard approaches. All experimental procedures, methods, and software used are described in the Methods section in sufficient detail to enable reproduction of the study. Core fiber photometry analysis code was deposited to Zenodo (https://doi.org/10.5281/zenodo.21026721).

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, 21 authors, 2 keywords, 14 MeSH terms, 2 funders, 90 references.

Cite

This paper

Balog, B. Z., Barth, A. M., Zichó, K., Major, A., Bardóczi, Z., Brunner, J., Seng, C., Jelitai, M., Sos, K. E., Sebestény, R. Z., Szőnyi, A., Karalis, N., Orosz, Á., Sipos, E., Misák, E., Cserey, G., Lüthi, A., Varga, V., Földy, C., . . . Nyiri, G. (2026). Prefrontal-raphe-habenular circuit drives fear memory recall and updating. Nature communications, 17(1), 9686. https://doi.org/10.1038/s41467-026-76574-5

BibTeX

@article{balog2026prefrontal,
author = {Balog, Boldizsár Zsolt and Barth, Albert M and Zichó, Krisztián and Major, Abel and Bardóczi, Zsuzsanna and Brunner, János and Seng, Charlotte and Jelitai, Márta and Sos, Katalin E and Sebestény, Réka Z and Szőnyi, András and Karalis, Nikolas and Orosz, Áron and Sipos, Eszter and Misák, Erik and Cserey, György and Lüthi, Andreas and Varga, Viktor and Földy, Csaba and Szabadics, János and Nyiri, Gábor},
title = {{Prefrontal-raphe-habenular circuit drives fear memory recall and updating}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9686},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76574-5},
url = {https://doi.org/10.1038/s41467-026-76574-5},
pmid = {42716922},
pmcid = {PMC13558683}
}

RIS

TY - JOUR
AU - Balog, Boldizsár Zsolt
AU - Barth, Albert M
AU - Zichó, Krisztián
AU - Major, Abel
AU - Bardóczi, Zsuzsanna
AU - Brunner, János
AU - Seng, Charlotte
AU - Jelitai, Márta
AU - Sos, Katalin E
AU - Sebestény, Réka Z
AU - Szőnyi, András
AU - Karalis, Nikolas
AU - Orosz, Áron
AU - Sipos, Eszter
AU - Misák, Erik
AU - Cserey, György
AU - Lüthi, Andreas
AU - Varga, Viktor
AU - Földy, Csaba
AU - Szabadics, János
AU - Nyiri, Gábor
TI - Prefrontal-raphe-habenular circuit drives fear memory recall and updating
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/12
VL - 17
IS - 1
SP - 9686
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76574-5
UR - https://doi.org/10.1038/s41467-026-76574-5
LA - en
ER -

CSL-JSON

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