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Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL).

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Paper

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The authors' code

MATLAB · 262 lines · 8 KB · MIT

  2. %% Code by Kenji J. Nishimura adapted from:
  3. % Code by David J. Barker for Morales Laboratory
  4. % "Barker, D. J. et al., (2017). Lateral preoptic
  5. % control of the lateral habenula through convergent glutamate and GABA
  6. % transmission. Cell reports, 21(7), 1757-1769."
  8. close all; clear all; clc;
  10. PROJECTPATH = uigetdir(pwd, ...
  11. 'Select the main project folder');
  14. SDKPATH = fullfile(PROJECTPATH, 'TDTMatlabSDK');
  15. addpath(genpath(SDKPATH));
  18. MAINEXAMPLEPATH = uigetdir(PROJECTPATH, ...
  19. 'Select the folder containing entire subject TDT blocks');
  20. DATAPATH = fullfile(MAINEXAMPLEPATH);
  23. STREAM_STORE1 = 'x405C';
  24. STREAM_STORE2 = 'x465C';
  25. TRANGE = [-5 35];
  26. BASELINE_PER = [-5 0];
  27. ARTIFACT = Inf;
  28. N = 50;
  30. NoStress_group = [];
  31. Stress_group = [];
  32. auc_NoStress = [];
  33. auc_Stress = [];
  34. avg_NoStress = [];
  35. avg_Stress = [];
  36. ts2 = [];
  38. subject_folders = dir(DATAPATH);
  39. subject_folders = subject_folders([subject_folders.isdir]);
  42. for i = 1:length(subject_folders)
  43. folder_name = subject_folders(i).name;
  46. if ismember(folder_name, {'.', '..'})
  47. continue;
  48. end
  51. BLOCKPATH = fullfile(DATAPATH, folder_name);
  54. if ~isfolder(BLOCKPATH)
  55. warning('Folder does not exist: %s', BLOCKPATH);
  56. continue;
  57. end
  60. data = TDTbin2mat(BLOCKPATH, 'TYPE', {'epocs', 'scalars', 'streams'});
  63. data.epocs.Manual.onset = [data.epocs.PC0_.onset(1) + 180, data.epocs.PC0_.onset(1) + 240, data.epocs.PC0_.onset(1) + 300];
  64. data.epocs.Manual.offset = data.epocs.Manual.onset + 30;
  65. data.epocs.Manual.data = ones(numel(data.epocs.Manual.onset), 1);
  66. data.epocs.Manual.name = 'Manual';
  69. data = TDTfilter(data, 'Manual', 'TIME', TRANGE);
  72. art1 = ~cellfun('isempty', cellfun(@(x) x(x>ARTIFACT), data.streams.(STREAM_STORE1).filtered, 'UniformOutput',false));
  73. art2 = ~cellfun('isempty', cellfun(@(x) x(x<-ARTIFACT), data.streams.(STREAM_STORE1).filtered, 'UniformOutput',false));
  74. good = ~art1 & ~art2;
  75. data.streams.(STREAM_STORE1).filtered = data.streams.(STREAM_STORE1).filtered(good);
  77. art1 = ~cellfun('isempty', cellfun(@(x) x(x>ARTIFACT), data.streams.(STREAM_STORE2).filtered, 'UniformOutput',false));
  78. art2 = ~cellfun('isempty', cellfun(@(x) x(x<-ARTIFACT), data.streams.(STREAM_STORE2).filtered, 'UniformOutput',false));
  79. good2 = ~art1 & ~art2;
  80. data.streams.(STREAM_STORE2).filtered = data.streams.(STREAM_STORE2).filtered(good2);
  83. minLength1 = min(cellfun('prodofsize', data.streams.(STREAM_STORE1).filtered));
  84. minLength2 = min(cellfun('prodofsize', data.streams.(STREAM_STORE2).filtered));
  85. data.streams.(STREAM_STORE1).filtered = cellfun(@(x) x(1:minLength1), data.streams.(STREAM_STORE1).filtered, 'UniformOutput',false);
  86. data.streams.(STREAM_STORE2).filtered = cellfun(@(x) x(1:minLength2), data.streams.(STREAM_STORE2).filtered, 'UniformOutput',false);
  89. allSignals405 = cell2mat(data.streams.(STREAM_STORE1).filtered');
  90. allSignals465 = cell2mat(data.streams.(STREAM_STORE2).filtered');
  91. F405 = zeros(size(allSignals405(:,1:N:end-N+1)));
  92. F465 = zeros(size(allSignals465(:,1:N:end-N+1)));
  93. for ii = 1:size(allSignals405,1)
  94. F405(ii,:) = arrayfun(@(i) mean(allSignals405(ii,i:i+N-1)),1:N:length(allSignals405)-N+1);
  95. F465(ii,:) = arrayfun(@(i) mean(allSignals465(ii,i:i+N-1)),1:N:length(allSignals465)-N+1);
  96. end
  99. ts2 = linspace(TRANGE(1), TRANGE(1) + TRANGE(2), size(F465, 2));
  102. bls = polyfit(F405(:), F465(:), 1);
  103. Y_fit_all = bls(1) .* F405 + bls(2);
  104. Y_dF_all = F465 - Y_fit_all;
  107. zall = zeros(size(Y_dF_all));
  108. ind = ts2 >= BASELINE_PER(1) & ts2 <= BASELINE_PER(2);
  109. for j = 1:size(Y_dF_all, 1)
  110. zb = mean(Y_dF_all(j, ind));
  111. zsd = std(Y_dF_all(j, ind));
  112. zall(j, :) = (Y_dF_all(j, :) - zb) / zsd;
  113. end
  116. mean_zall_subject = mean(zall);
  119. post_zero_indices = find(ts2 > 0);
  120. auc_subject = trapz(ts2(post_zero_indices), mean_zall_subject(post_zero_indices));
  123. average_post_zero = mean(mean_zall_subject(post_zero_indices));
  126. if startsWith(folder_name, 'N')
  127. NoStress_group = [NoStress_group; mean_zall_subject];
  128. auc_NoStress = [auc_NoStress; auc_subject];
  129. avg_NoStress = [avg_NoStress; average_post_zero];
  130. elseif startsWith(folder_name, 'S')
  131. Stress_group = [Stress_group; mean_zall_subject];
  132. auc_Stress = [auc_Stress; auc_subject];
  133. avg_Stress = [avg_Stress; average_post_zero];
  134. end
  137. intervals = [0 10; 10 20; 20 30 ];
  140. auc_intervals_NoStress = zeros(size(NoStress_group, 1), size(intervals, 1));
  141. auc_intervals_Stress = zeros(size(Stress_group, 1), size(intervals, 1));
  144. for i = 1:size(intervals, 1)
  145. start_time = intervals(i, 1);
  146. end_time = intervals(i, 2);
  147. indices = ts2 >= start_time & ts2 <= end_time;
  150. for j = 1:size(NoStress_group, 1)
  151. auc_intervals_NoStress(j, i) = trapz(ts2(indices), NoStress_group(j, indices));
  152. end
  153. end
  156. for i = 1:size(intervals, 1)
  157. start_time = intervals(i, 1);
  158. end_time = intervals(i, 2);
  159. indices = ts2 >= start_time & ts2 <= end_time;
  162. for j = 1:size(Stress_group, 1)
  163. auc_intervals_Stress(j, i) = trapz(ts2(indices), Stress_group(j, indices));
  164. end
  165. end
  169. end
  171. %% Plot values
  174. mean_NoStress = mean(NoStress_group, 1);
  175. std_NoStress = std(NoStress_group, 0, 1) / sqrt(size(NoStress_group, 1));
  177. mean_Stress = mean(Stress_group, 1);
  178. std_Stress = std(Stress_group, 0, 1) / sqrt(size(Stress_group, 1));
  181. mean_avg_NoStress = mean(avg_NoStress);
  182. std_avg_NoStress = std(avg_NoStress) / sqrt(length(avg_NoStress));
  184. mean_avg_Stress = mean(avg_Stress);
  185. std_avg_Stress = std(avg_Stress) / sqrt(length(avg_Stress));
  188. figure;
  191. color_NoStress = [0.5, 0.5, 0.5];
  192. color_Stress = [0.8500, 0.3250, 0.0980];
  195. XX = [ts2, fliplr(ts2)];
  196. YY = [mean_NoStress - std_NoStress, fliplr(mean_NoStress + std_NoStress)];
  197. plot(ts2, mean_NoStress, 'Color', color_NoStress, 'LineWidth', 2); hold on;
  198. fill(XX, YY, color_NoStress, 'FaceAlpha', 0.2, 'EdgeColor', 'none', 'HandleVisibility', 'off');
  201. YY = [mean_Stress - std_Stress, fliplr(mean_Stress + std_Stress)];
  202. plot(ts2, mean_Stress, 'Color', color_Stress, 'LineWidth', 2);
  203. fill(XX, YY, color_Stress, 'FaceAlpha', 0.2, 'EdgeColor', 'none', 'HandleVisibility', 'off');
  206. xlabel('Time, s');
  207. ylabel('Z-score');
  208. title('ToneTest2: Comparison of NoStress vs Stress Groups');
  211. legend({'NoStress', 'Stress'}, 'TextColor', 'black', 'FontSize', 10, 'Location', 'northeast');
  214. mean_auc_NoStress = mean(auc_NoStress);
  215. std_auc_NoStress = std(auc_NoStress) / sqrt(length(auc_NoStress));
  217. mean_auc_Stress = mean(auc_Stress);
  218. std_auc_Stress = std(auc_Stress) / sqrt(length(auc_Stress));
  221. figure;
  222. bar_data = [mean_auc_NoStress, mean_auc_Stress];
  223. bar_errors = [std_auc_NoStress, std_auc_Stress];
  224. bar_handle = bar(bar_data);
  227. bar_handle.FaceColor = 'flat';
  228. bar_handle.CData(1,:) = [0, 0.4470, 0.7410]; % blue for NoStress
  229. bar_handle.CData(2,:) = [0.8500, 0.3250, 0.0980]; % red for Stress
  232. hold on;
  233. errorbar(1:2, bar_data, bar_errors, 'k', 'LineStyle', 'none', 'LineWidth', 1.5);
  234. hold off;
  237. set(gca, 'xticklabel', {'NoStress', 'Stress'});
  238. ylabel('Area Under Curve (AUC)');
  239. title('Comparison of AUC between NoStress and Stress Groups');
  243. figure;
  244. bar_data_avg = [mean_avg_NoStress, mean_avg_Stress];
  245. bar_errors_avg = [std_avg_NoStress, std_avg_Stress];
  246. bar_handle_avg = bar(bar_data_avg);
  249. bar_handle_avg.FaceColor = 'flat';
  250. bar_handle_avg.CData(1,:) = [0, 0.4470, 0.7410]; % blue for NoStress
  251. bar_handle_avg.CData(2,:) = [0.8500, 0.3250, 0.0980]; % red for Stress
  254. hold on;
  255. errorbar(1:2, bar_data_avg, bar_errors_avg, 'k', 'LineStyle', 'none', 'LineWidth', 1.5);
  256. hold off;
  259. set(gca, 'xticklabel', {'NoStress', 'Stress'});
  260. ylabel('Average Value After 0');
  261. title('Comparison of Average Values After 0 Seconds between NoStress and Stress Groups');

FiberPhotometry.m at commit b658ea0, under MIT · at the source

Overview

Authors: Kenji J Nishimura1, Denisse Paredes1, Nathaniel A Nocera1, Dhruv Aggarwal1, Michael R Drew1
ORCID iDs: Kenji J Nishimura, Michael R Drew
  1. Center for Learning and Memory, Department of Neuroscience, University of Texas at Austin Austin United States
Institutions: The University of Texas at Austin (United States)
Journal: eLife, volume 14, article RP107670
Dates: published online 22 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107670 · PMID 42329677 · PMCID PMC13286568 · OpenAlex W4412699534
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Single-unit activity, calcium imaging
Keywords: stress, sensitization, fear learning, stress-enhanced fear learning, paraventricular thalamus, Mouse
MeSH: Fear*, Learning*, Midline Thalamic Nuclei*, Stress, Psychological*, Animals, Male, Mice, Mice, Inbred C57BL, Proto-Oncogene Proteins c-fos (* major topic)
Journal subjects: Neuroscience
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R01MH117426, R21MH128610, T32MH106454)
Citations: cited by 1 paper (Europe PMC); 53 references in the paper
Research resources: anti-c-Fos (Rabbit polyclonal) RRID:AB_2231974, RRID:AB_2661852, AAV8-hSyn-mCherry RRID:Addgene_114472, AAV1-syn-jGCaMP8s-WPRE RRID:Addgene_162374, AAV8-hSyn-hM3D(Gq)-mCherry RRID:Addgene_50474, AAV8-hSyn-hM4D(Gi)-mCherry RRID:Addgene_50475, MATLAB RRID:SCR_001622, Prism 6 RRID:SCR_002798, ImageJ RRID:SCR_003070, Synapse RRID:SCR_006495, ZEN Microscopy Software RRID:SCR_013672, ANY-maze RRID:SCR_014289, Video Freeze Software RRID:SCR_014574

Abstract

Exposure to stress can cause long-lasting enhancement of fear and other defensive responses that extend beyond the cues or contexts associated with the original traumatic event. These nonassociative consequences of stress, referred to as fear sensitization, are thought to underlie some symptoms of trauma-related disorders. Fear sensitization has been predominantly studied using the stress-enhanced fear learning (SEFL) paradigm, which models the stress-induced amplification of fear learning. Less is known about the mechanisms through which unlearned fear responses are sensitized by stress. Here, we investigated the neural mechanisms for sensitization of unlearned fear responses using a paradigm we termed stress-enhanced fear responding (SEFR). In this model, mice exposed to a single session of footshock stress exhibit enhanced freezing to a novel tone stimulus. To investigate brain regions that might mediate SEFR, we first used c-Fos mapping to identify neural activity changes associated with stress-induced enhancement of unlearned fear. Our c-Fos screen identified the posterior paraventricular thalamus (pPVT) as a region that was persistently hyperactive after footshock stress and whose activity correlated with behavioral expression of SEFR. Using fiber photometry, we observed that SEFR, but not SEFL, was associated with increased activity in the pPVT. Next, we found that chemogenetic inhibition of the pPVT blocked both the induction of SEFR during stress and its later expression, while artificial stimulation of pPVT in stress-naive mice was sufficient to recapitulate SEFR. Interestingly, pPVT inhibition or stimulation did not affect acquisition or expression of SEFL. In conclusion, our results indicate that sensitization of fear learning (SEFL) and sensitization of unlearned fear (SEFR) have distinct neural mechanisms. Our results identify pPVT hyperactivity as a mechanism for stress-induced sensitization of unlearned fear and highlight pPVT as a potential target for treating arousal and reactivity symptoms of trauma- and stressor-related disorders.

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

Repository

Its files are read in the Code ↔ Paper reader above.

kjnish/fiberphotometry

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b658ea0798dc1db76c81f4efcdc4638af92b9938, 19 May 2026
Languages: MATLAB (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the text, “Fiber photometry”
Holds: license file
Not found: README, 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/kjnish/fiberphotometry

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;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Behavior, in vivo calcium imaging, and c-Fos quantification datasets produced for this study are available on the Texas Data Repository at https://doi.org/10.18738/T8/AZJENK (Nishimura, 2026b). Relevant analysis code is available at https://github.com/kjnish/FiberPhotometry (copy archived at Nishimura, 2026a).

The following dataset was generated:

NishimuraKJ 2026Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL)Texas Data Repository10.18738/T8/AZJENKPMC1328656842329677

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, pages, dates, 5 authors, 6 keywords, 9 MeSH terms, 1 funder, 52 references, 13 RRIDs.

Cite

This paper

Nishimura, K. J., Paredes, D., Nocera, N. A., Aggarwal, D., & Drew, M. R. (2026). Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL). eLife, 14, RP107670. https://doi.org/10.7554/elife.107670

BibTeX

@article{nishimura2026paraventricular,
author = {Nishimura, Kenji J and Paredes, Denisse and Nocera, Nathaniel A and Aggarwal, Dhruv and Drew, Michael R},
title = {{Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL)}},
journal = {eLife},
year = {2026},
month = jun,
volume = {14},
pages = {RP107670},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107670},
url = {https://doi.org/10.7554/elife.107670},
pmid = {42329677},
pmcid = {PMC13286568}
}

RIS

TY - JOUR
AU - Nishimura, Kenji J
AU - Paredes, Denisse
AU - Nocera, Nathaniel A
AU - Aggarwal, Dhruv
AU - Drew, Michael R
TI - Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL)
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/06/22
VL - 14
SP - RP107670
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107670
UR - https://doi.org/10.7554/elife.107670
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.107670",
"type": "article-journal",
"title": "Paraventricular thalamus hyperactivity mediates stress-induced sensitization of unlearned fear but not stress-enhanced fear learning (SEFL)",
"container-title": "eLife",
"author": [
{
"family": "Nishimura",
"given": "Kenji J"
},
{
"family": "Paredes",
"given": "Denisse"
},
{
"family": "Nocera",
"given": "Nathaniel A"
},
{
"family": "Aggarwal",
"given": "Dhruv"
},
{
"family": "Drew",
"given": "Michael R"
}
],
"container-title-short": "Elife",
"volume": "14",
"page": "RP107670",
"DOI": "10.7554/elife.107670",
"PMID": "42329677",
"PMCID": "PMC13286568",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.107670",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
22
]
]
}
}

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