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Control of associative learning by the mediodorsal thalamus-orbitofrontal cortex circuit.

Code ↔ Paper

5 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 5 matches
  1. [1] § Results › MD-input-defined PV neurons regulate cue value selectivity and learning-related adaptations in OFC broad-spiking cells ↔ CodeAndDataForPopulation.zip/main_LeZW.m, lines 37–68 · score 0.78 · narrow spiking cells, cue SI intercepts, broad spiking cell, cue SI slopes, outcome SI slopes, learning mice
  2. [2] § Results › MD projections to OFC support cue value selectivity and learning-related adaptations in the OFC ↔ CodeAndDataForPopulation.zip/main_LeZW.m, lines 37–68 · score 0.66 · narrow spiking cells, cue SI intercepts, broad spiking cells, cue SI slopes, outcome SI slopes, optotrode
  3. [3] § Methods › Behavioral task ↔ CodeAndDataForExample.zip/Script_GLM_MDneu41.m, lines 157–245 · score 0.63 · Go cues, response window, triggered, duration, stimulus, delay
  4. [4] § Results › Experience-dependent changes in MD and OFC neuronal responses ↔ CodeAndDataForPopulation.zip/GLM.m, lines 1–34 · score 0.60 · onset cue, lick rate, binary, encoding, event, stimulus
  5. [5] § Methods › Data analysis ↔ CodeAndDataForPopulation.zip/GLM.m, lines 1–34 · score 0.56 · onset cue, lick rate, event, GLM, CR, bins

Paper

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The authors' code

MATLAB · 98 lines · 4 KB · CC-BY-4.0 · 2 matches

  1. %% main_LeZW.m
  2. % Main script for generating population result figures in the manuscript.
  3. % This script loads pre-processed data from Neuropixel recordings,
  4. % calcium imaging, and optogenetic experiments, and calls various
  5. % plotting functions to reproduce population results in Figures 3, 4, 5, and 7.
  6. %
  7. % Data files required:
  8. % - NP.mat : Neuropixel electrophysiology data (MD & OFC regions)
  9. % - Calcium.mat : MD neuron calcium imaging data
  10. % - Optotrode.mat : Optogenetic + OFC electrophysiology data
  11. %
  12. % Figure overview:
  13. % Figure 3 : population results of Neuropixel (NP) - CPD, CueSI slope, OutcomeSI slope/intercept
  14. % Figure 4 : population results of Calcium imaging - CueSI and OutcomeSI
  15. % Figure 5 : population results for the effect of MD axons inacivation on OFC neurons - Well-trained & Learning phase
  16. % Figure 7 : population results for the effect of PV neuoron inacivation on OFC broad-spiking cells - Well-trained & Learning phase
  18. %% Add script directory and all subdirectories to MATLAB path
  19. clear all;
  20. close all;
  22. mfPath = mfilename('fullpath');
  23. addpath(genpath(fileparts(mfPath)));
  25. %% Figure 3
  26. load('NP.mat');
  27. fig3c = plotNP_CPD(NP_MD,NP_OFC); % CPD analysis
  29. figure3l = plotNP_SlopeOfCueSI(NP_MD,NP_OFC); % cue SI analysis
  30. [fig3m,fig3n] = plotNP_SlopeIneterceptOfOutcomeSI_3periods(NP_MD,NP_OFC); % outcome SI analysis
  32. %% Figure 4
  33. load('Calcium.mat');
  34. fig4ef = plotCalcium_Cue(Calcium); % cue SI analysis
  35. fig4h = plotCalcium_OutcomeSI(Calcium); % outcome SI analysis
  37. %% Figure 5
  38. load('Optotrode.mat');
  40. % well-trained mice
  41. DI_nonsig_all = Fiber.WellTrain.Nonsig_off_on_all;
  42. DI_sig_all = Fiber.WellTrain.Sig_off_on_all;
  43. fig5de = plotOptotrode_FiberWellTrain(DI_nonsig_all,DI_sig_all,'all'); % laser-off vs laser-on DI (all cells)
  45. DI_nonsig_broad = Fiber.WellTrain.Nonsig_off_on_broad;
  46. DI_sig_broad = Fiber.WellTrain.Sig_off_on_broad;
  47. fig5g = plotOptotrode_FiberWellTrain(DI_nonsig_broad,DI_sig_broad,'broad'); % laser-off vs laser-on DI (broad-spiking cells)
  49. DI_nonsig_narrow = Fiber.WellTrain.Nonsig_off_on_narrow;
  50. DI_sig_narrow = Fiber.WellTrain.Sig_off_on_narrow;
  51. fig5i = plotOptotrode_FiberWellTrain(DI_nonsig_narrow,DI_sig_narrow,'narrow'); % laser-off vs laser-on DI (narrow-spiking cells)
  53. % learning mice
  54. CueSI_slope_all = Fiber.Learning.CueSI_slope_off_on_all;
  55. CueSI_slope_broad = Fiber.Learning.CueSI_slope_off_on_broad;
  56. CueSI_intercept_all = Fiber.Learning.CueSI_intercept_off_on_all;
  57. CueSI_intercept_broad = Fiber.Learning.CueSI_intercept_off_on_broad;
  58. % laser-off vs laser-on cue SI slope/intercept (all cells)
  59. fig5k = plotOptotrode_FiberLearningCue(CueSI_slope_all,CueSI_intercept_all,'all');
  60. % laser-off vs laser-on cue SI slope/intercept (broad-spiking cells)
  61. fig5l = plotOptotrode_FiberLearningCue(CueSI_slope_broad,CueSI_intercept_broad,'broad');
  63. OutcomeSI_slope_all = Fiber.Learning.OutcomeSI_slope_off_on_all;
  64. OutcomeSI_slope_broad = Fiber.Learning.OutcomeSI_slope_off_on_broad;
  65. % laser-off vs laser-on outcome SI slope/intercept (all cells)
  66. fig5n = plotOptotrode_FiberLearningOutcome(OutcomeSI_slope_all,'all');
  67. % laser-off vs laser-on outcome SI slope/intercept (broad-spiking cells)
  68. fig5o = plotOptotrode_FiberLearningOutcome(OutcomeSI_slope_broad,'broad');
  70. %% Figure 7
  71. % well-trained mice
  72. DI_nonsig_broad = PV.WellTrain.Nonsig_off_on_broad;
  73. DI_sig_broad = PV.WellTrain.Sig_off_on_broad;
  74. % laser-off vs laser-on DI (broad-spiking cells)
  75. fig7d = plotOptotrode_PVWellTrain(DI_nonsig_broad,DI_sig_broad,'broad');
  77. % learning mice
  78. CueSI_slope_off_on_broad = PV.Learning.CueSI_slope_off_on_broad;
  79. Intercept_slope_off_on_broad = PV.Learning.CueSI_intercept_off_on_broad;
  80. % laser-off vs laser-on cue SI slope/intercept (broad-spiking cells)
  81. fig7g = plotOptotrode_PVLearningCue(CueSI_slope_off_on_broad,Intercept_slope_off_on_broad);
  83. OutcomeSI_slope_broad = PV.Learning.OutcomeSI_slope_off_on_broad;
  84. % laser-off vs laser-on outcome SI slope/intercept (broad-spiking cells)
  85. fig7j = plotOptotrode_PVLearningOutcome(OutcomeSI_slope_broad);

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

Overview

Authors: Ziwei Le1,2, Can Huang1,2, Wen Zhang1, Dechen Liu1, Jingyu Ren1,2, Haishan Yao1
ORCID iDs: Dechen Liu, Haishan Yao
  1. Institute of Neuroscience, State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
  2. University of Chinese Academy of Sciences, Beijing, China
Institutions: Chinese Academy of Sciences (China); Center for Excellence in Brain Science and Intelligence Technology (China); University of Chinese Academy of Sciences (China)
Journal: Nature communications, volume 17, issue 1, article 9175
Dates: received 16 June 2025; accepted 17 July 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-76118-x · PMID 42660953 · PMCID PMC13522478 · OpenAlex W7171509333
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Cognitive neuroscience, Neural circuits
MeSH: Association Learning*, Mediodorsal Thalamic Nucleus*, Prefrontal Cortex*, Animals, Cues, Interneurons, Male, Mice, Mice, Inbred C57BL, Neural Pathways, Neurons, Odorants, Parvalbumins (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 92 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

Zenodo 21332049

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 3 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (15 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
22 files
At the source:

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:

  • it points to the authors' code: Zenodo 21332049

Read it in the paper: doi.org/10.1038/s41467-026-76118-x.

Tracing map

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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;
  • 22 scripts, each with its path and the digest of its content;
  • 5 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

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Data availability statement

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-76118-x.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 13 MeSH terms, 90 references.

Cite

This paper

Le, Z., Huang, C., Zhang, W., Liu, D., Ren, J., & Yao, H. (2026). Control of associative learning by the mediodorsal thalamus-orbitofrontal cortex circuit. Nature communications, 17(1), 9175. https://doi.org/10.1038/s41467-026-76118-x

BibTeX

@article{le2026control,
author = {Le, Ziwei and Huang, Can and Zhang, Wen and Liu, Dechen and Ren, Jingyu and Yao, Haishan},
title = {{Control of associative learning by the mediodorsal thalamus-orbitofrontal cortex circuit}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {9175},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-76118-x},
url = {https://doi.org/10.1038/s41467-026-76118-x},
pmid = {42660953},
pmcid = {PMC13522478}
}

RIS

TY - JOUR
AU - Le, Ziwei
AU - Huang, Can
AU - Zhang, Wen
AU - Liu, Dechen
AU - Ren, Jingyu
AU - Yao, Haishan
TI - Control of associative learning by the mediodorsal thalamus-orbitofrontal cortex circuit
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/28
VL - 17
IS - 1
SP - 9175
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-76118-x
UR - https://doi.org/10.1038/s41467-026-76118-x
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-76118-x",
"type": "article-journal",
"title": "Control of associative learning by the mediodorsal thalamus-orbitofrontal cortex circuit",
"container-title": "Nature communications",
"author": [
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"family": "Le",
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"volume": "17",
"issue": "1",
"page": "9175",
"DOI": "10.1038/s41467-026-76118-x",
"PMID": "42660953",
"PMCID": "PMC13522478",
"ISSN": "2041-1723",
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