OSCR

Sex-specific increased reactivity of the PVT and prolonged PVT→CeA circuit engagement following psilocin administration.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Air-puff stimulus ↔ Cohort7_structureBuild.m, lines 180–189 · score 0.52 · Peri event, baseline period, Air puffs, fiber, GCaMP
  2. [2] § Methods › Air-puff stimulus ↔ Cohort9_structurebuild.m, lines 180–189 · score 0.52 · Peri event, baseline period, Air puffs, fiber, GCaMP

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 256 lines · 12 KB · MIT · 1 match

  1. clc
  2. clear all
  4. %SDK always stays the same
  5. SDKPATH = '/Volumes/Devin/TDTSDK';
  6. addpath(genpath(SDKPATH));
  8. cd '/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA3/Passive/'
  9. Condition = dir;
  10. ConditionNames = {Condition.name};
  11. ConditionNames = ConditionNames(3:end)'; %Creates the different conditions for catagorizing in structure
  13. %This order depends on how the files are arranged in the folder; Make them
  14. %in an order that makes sense
  15. Conditions(1,1)=ConditionNames(1,1);
  16. Conditions(2,1)=ConditionNames(2,1);
  17. % Conditions(3,1)=ConditionNames(5,1);
  18. % Conditions(4,1)=ConditionNames(3,1);
  19. % Conditions(5,1)=ConditionNames(2,1);
  20. % Conditions(6,1)=ConditionNames(4,1);
  21. % Conditions(7,1)=ConditionNames(2,1);
  22. % Conditions(8,1)=ConditionNames(2,1);
  23. % Conditions(8,1)=ConditionNames(10,1);
  24. % Conditions(9,1)=ConditionNames(12,1);
  26. %change these depending on location and name of current data set
  27. Files(1,:) = {'/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA3/Passive/BASELINE/'};
  28. Files(2,:) = {'/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA3/Passive/TREATMENT/'};
  29. % Files(3,:) = {'/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA2/TwoDay/'};
  30. % Files(4,:) = {'/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA2/SevenDay/'};
  31. % Files(5,:) = {'/Volumes/Devin/Psilocin PVN Fiber Photometry/Cohort7/RAW DATA2/PostRestraint/'};
  32. % Files(6,:) = {'/Volumes/Devin 2T/Psilocin CeA Fiber Photometry/COHORT4/RAW DATA/INITIALAUDITORY/'};
  33. % Files(7,:) = {'/Volumes/Devin 2T/Psilocin CeA Fiber Photometry/COHORT4/RAW DATA/FINALAUDITORY/'};
  34. % Files(8,:) = {'/Volumes/Devin T7 HD/Psilocin CeA Fiber Photometry/COHORT2/RAW DATA/Extinction/'};
  37. %%
  38. for ii = 1:size(Conditions,1)
  40. D = dir(Files{ii});
  41. files = D; files = files(~ismember({files(:).name},{'.','..','desktop.ini','.DS_Store','._.DS_Store'}));
  43. filenames = {files.name}';
  44. % subdirs = filenames([files.isdir]);
  45. %filenames = filenames(4:end)';
  48. for k=1:size(filenames,1)
  50. if startsWith(filenames(k,1),'.')
  51. filenames{k,1}=[];
  52. end
  54. end
  56. FileLogic=~cellfun('isempty',filenames);
  57. filenames=filenames(FileLogic);
  59. for j=1:size(filenames,1)
  60. subj=filenames{j,1};
  61. subjects.(char(Conditions(ii))){j,1} = subj;
  62. subjects.(char(Conditions(ii))){j,1} = strrep(subjects.(char(Conditions(ii))){j,1},'-','');
  64. end
  66. for i=1:size(filenames,1)
  69. BLOCKPATH = [Files{ii} filenames{i}];
  72. data = TDTbin2mat(BLOCKPATH);
  74. GroupData.((char(Conditions(ii)))).((char(filenames{i}))) = data;
  77. end
  80. for jj=1:size(filenames,1)
  82. % Declare data stream and epoc names we will use downstream
  83. % These are the field names for the relevant streams of the data struct
  84. GCAMP = 'x465A';
  85. ISOS = 'x405A';
  86. if isfield(data.epocs, 'Puff')
  87. Airpuff = 'Puff';
  88. else
  89. Airpuff = 'Note';
  90. end
  92. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).offset = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).onset + .1;
  95. % Make some pretty colors for later plotting
  96. % http://math.loyola.edu/~loberbro/matlab/html/colorsInMatlab.html
  97. red = [0.8500, 0.3250, 0.0980];
  98. green = [0.4660, 0.6740, 0.1880];
  99. cyan = [0.3010, 0.7450, 0.9330];
  100. gray1 = [.7 .7 .7];
  101. gray2 = [.8 .8 .8];
  103. %% Basic plotting and artifact removal
  104. % Make a time array based on number of samples and sample freq of
  105. % demodulated streams
  106. time = (1:length(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data))/GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).fs;
  109. %% Artifact removal
  110. % There is often a large artifact on the onset of LEDs turning on
  111. % Remove data below a set time t
  112. t = 2; % time threshold below which we will discard
  113. ind = find(time>t,1); % find first index of when time crosses threshold
  114. time = time(ind:end); % reformat vector to only include allowed time
  115. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data(ind:end);
  116. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data(ind:end);
  119. %% Downsample data doing local averaging
  120. % Average around every Nth point and downsample Nx
  122. N = 10; % multiplicative for downsampling
  123. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data = arrayfun(@(i)...
  124. mean(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data(i:i+N-1)),...
  125. 1:N:length(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data)-N+1);
  126. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data = arrayfun(@(i)...
  127. mean(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data(i:i+N-1)),...
  128. 1:N:length(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data)-N+1);
  130. %%
  131. % Decimate time array and match length to demodulated stream
  132. time = time(1:N:end);
  133. time = time(1:length(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data));
  135. %% Correcting Corrupted Stream Size
  137. if size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data,2)~=size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data,2)
  139. if size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data,2)>size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data,2)
  141. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data=GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data(1,1:size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data));
  142. end
  144. if size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data,2)<size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data,2)
  146. GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data=GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data(1,1:size(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data,2));
  148. end
  149. end
  152. %% Detrending and dFF
  153. bls = polyfit(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data,GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data,1);
  154. Y_fit_all = bls(1) .* GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data + bls(2);
  155. Y_dF_all = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).data - Y_fit_all; %dF (units mV) is not dFF
  157. %%
  158. % Full dFF according to Lerner et al. 2015
  159. % http://dx.doi.org/10.1016/j.cell.2015.07.014
  160. % dFF using 405 fit as baseline
  161. dFF = 100*(Y_dF_all)./Y_fit_all;
  162. std_dFF = std(double(dFF));
  163. ISOS = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(ISOS).data;
  164. std_ISOS = std(double(ISOS));
  165. BL_F = mean(Y_fit_all);
  167. %% Turn AIRPUFF Events into AIRPUFF Bouts
  168. % Make a continuous time series of air puff events (epocs) and plot
  170. AIR_on = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).onset;
  171. AIR_off = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).offset;
  172. AIR_x = reshape(kron([AIR_on, AIR_off], [1, 1])', [], 1);
  173. sz = length(AIR_on);
  174. d = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).data';
  175. d = ones(length(d),1)';
  176. y_scale = 5; %adjust according to data needs
  177. y_shift = -10; %scale and shift are just for asthetics
  178. AIR_y = reshape([zeros(1, sz); d; d; zeros(1, sz)], 1, []);
  180. %% Time Filter Around AIR PUFF Bout Epocs
  181. % Note that we are using dFF of the full time-series, not peri-event dFF
  182. % where f0 is taken from a pre-event baseline period. That is done in
  183. % another fiber photometry data analysis example.
  185. PRE_TIME = 5; % Five seconds before event onset
  186. POST_TIME = 10; % ten seconds after
  187. fs = GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).streams.(GCAMP).fs/N; % recall we downsampled by N = 100 earlier
  188. % time span for peri-event filtering, PRE and POST
  189. TRANGE = [-1*PRE_TIME*floor(fs),POST_TIME*floor(fs)];
  191. %%
  192. % Pre-allocate memory
  193. trials = numel(GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).onset);
  194. dFF_snips = cell(trials,1);
  195. ISOS_snips = cell(trials,1);
  196. array_ind = zeros(trials,1);
  197. pre_stim = zeros(trials,1);
  198. post_stim = zeros(trials,1);
  200. %%
  201. % Make stream snips based on trigger onset
  202. for i = 1:trials
  203. % If the bout cannot include pre-time seconds before event, make zero
  204. if GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).onset(i) < PRE_TIME
  205. dFF_snips{i} = single(zeros(1,(TRANGE(2)-TRANGE(1))));
  206. ISOS_snips{i} = single(zeros(1,(TRANGE(2)-TRANGE(1))));
  207. continue
  208. else
  209. % Find first time index after bout onset
  210. array_ind(i) = find(time > GroupData.((char(Conditions(ii)))).((char(filenames{jj}))).epocs.(Airpuff).onset(i),1);
  212. % Find index corresponding to pre and post stim durations
  213. pre_stim(i) = array_ind(i) + TRANGE(1);
  214. post_stim(i) = array_ind(i) + TRANGE(2);
  215. dFF_snips{i} = dFF(pre_stim(i):post_stim(i));
  216. ISOS_snips{i} = ISOS(pre_stim(i):post_stim(i));
  217. end
  218. end
  220. %%
  221. % Make all snippet cells the same size based on minimum snippet length
  222. minLength = min(cellfun('prodofsize', dFF_snips));
  223. dFF_snips = cellfun(@(x) x(1:minLength), dFF_snips, 'UniformOutput',false);
  225. % Convert to a matrix and get mean
  226. allSignals = cell2mat(dFF_snips);
  227. GROUP_allSignals = allSignals;
  228. mean_allSignals = mean(allSignals);
  229. std_allSignals = std(mean_allSignals);
  231. allSignals_ISOS = cell2mat(ISOS_snips);
  232. GROUP_allSignals_ISOS = allSignals_ISOS;
  233. mean_allSignals_ISOS = mean(allSignals_ISOS);
  234. std_allSignals_ISOS = std(mean_allSignals);
  237. % Make a time vector snippet for peri-events
  238. peri_time = (1:length(mean_allSignals))/fs - PRE_TIME;
  242. GROUP_STREAM.((char(Conditions(ii)))).((char(filenames{jj}))).signal=(GROUP_allSignals);
  243. GROUP_STREAM.((char(Conditions(ii)))).((char(filenames{jj}))).ISOS_signal=(GROUP_allSignals_ISOS);
  244. GROUP_STREAM.((char(Conditions(ii)))).((char(filenames{jj}))).peri_time=(peri_time);
  245. GROUP_STREAM.((char(Conditions(ii)))).((char(filenames{jj}))).BL_F=(BL_F);
  248. end
  249. end
  251. clear AIR* Air* all* array* bls cyan gray* green red BLOCK* Condition ConditionNames d D data dFF* file* File* fs GC* i ii ind ISOS* j* mean* min* N* peri* post* POST* pre* PRE* std* subj sz t time TRANGE trials Y* y*
  253. %%
  255. % cd '/Users/devineffinger/Desktop'
  256. save(strcat('Cohort7_Passive_Final_Revision',string(datetime('now','Format','MMddyy'))),'-v7.3');

Cohort7_structureBuild.m, under MIT · at the source

Overview

Authors: D P Effinger1,2,3, J L Hoffman2, S G Quadir2, C S Rollison2, D Toedt2, M Echeveste Sanchez2, M W High2, C W Hodge2, M A Herman1,2
ORCID iDs: S G Quadir, D Toedt, M Echeveste Sanchez, M W High, M A Herman
  1. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
  2. Bowles Center for Alcohol Studies, University of North Carolina at Chapel Hill, Chapel Hill, NC USA
  3. Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO USA
Institutions: University of North Carolina at Chapel Hill (United States); University of Colorado Anschutz (United States)
Journal: Nature communications, volume 17, issue 1, article 5094
Dates: received 16 June 2025; accepted 20 March 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71481-1 · PMID 41963345 · PMCID PMC13246750 · OpenAlex W7153282894
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), systems (subfield)
Methods: Statistics, Single-unit activity, calcium imaging
Keywords: Neural circuits, Stress and resilience
MeSH: Central Amygdaloid Nucleus*, Hallucinogens*, Paraventricular Hypothalamic Nucleus*, Psilocybin*, Animals, Female, Male, Neurons, Proto-Oncogene Proteins c-fos, Rats, Rats, Sprague-Dawley (* major topic)
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: U.S. Department of Health &amp; Human Services | NIH | National Institute of General Medical Sciences (GM135095); U.S. Department of Health &amp; Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (AA030493, AA007573); U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (GM135095); U.S. Department of Health & Human Services | NIH | National Institute on Alcohol Abuse and Alcoholism (NIAAA) (AA030493, AA007573)
Citations: cited by 1 paper (Europe PMC); 81 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

doi:10.5061/dryad.3ffbg79qr

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Statistics and reproducibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5061/dryad.3ffbg79qr

doi:10.5061/dryad.fbg79cp7b

License: none: the authors keep all their rights
State: the link is dead, verified on 29 September 2026
Evidence: found in the paper
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 29 September 2026: the link is dead (HTTP 404)
  • 29 September 2026: the link is dead (HTTP 404)
At the source: doi.org/10.5061/dryad.fbg79cp7b

Zenodo 10082749

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (3)
Size: 3 files, 3 scripts
Software Heritage: not checked
Found in: the Zenodo software companion of the Dryad dataset
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
3 files
At the source: zenodo.org/records/10082749

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:

Read it in the paper: doi.org/10.1038/s41467-026-71481-1.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 2 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 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/s41467-026-71481-1.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 11 MeSH terms, 4 funders, 75 references.

Cite

This paper

Effinger, D. P., Hoffman, J. L., Quadir, S. G., Rollison, C. S., Toedt, D., Echeveste Sanchez, M., High, M. W., Hodge, C. W., & Herman, M. A. (2026). Sex-specific increased reactivity of the PVT and prolonged PVT→CeA circuit engagement following psilocin administration. Nature communications, 17(1), 5094. https://doi.org/10.1038/s41467-026-71481-1

BibTeX

@article{effinger2026sex,
author = {Effinger, D P and Hoffman, J L and Quadir, S G and Rollison, C S and Toedt, D and Echeveste Sanchez, M and High, M W and Hodge, C W and Herman, M A},
title = {{Sex-specific increased reactivity of the PVT and prolonged PVT→CeA circuit engagement following psilocin administration}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5094},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71481-1},
url = {https://doi.org/10.1038/s41467-026-71481-1},
pmid = {41963345},
pmcid = {PMC13246750}
}

RIS

TY - JOUR
AU - Effinger, D P
AU - Hoffman, J L
AU - Quadir, S G
AU - Rollison, C S
AU - Toedt, D
AU - Echeveste Sanchez, M
AU - High, M W
AU - Hodge, C W
AU - Herman, M A
TI - Sex-specific increased reactivity of the PVT and prolonged PVT→CeA circuit engagement following psilocin administration
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/10
VL - 17
IS - 1
SP - 5094
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71481-1
UR - https://doi.org/10.1038/s41467-026-71481-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71481-1",
"type": "article-journal",
"title": "Sex-specific increased reactivity of the PVT and prolonged PVT→CeA circuit engagement following psilocin administration",
"container-title": "Nature communications",
"author": [
{
"family": "Effinger",
"given": "D P"
},
{
"family": "Hoffman",
"given": "J L"
},
{
"family": "Quadir",
"given": "S G"
},
{
"family": "Rollison",
"given": "C S"
},
{
"family": "Toedt",
"given": "D"
},
{
"family": "Echeveste Sanchez",
"given": "M"
},
{
"family": "High",
"given": "M W"
},
{
"family": "Hodge",
"given": "C W"
},
{
"family": "Herman",
"given": "M A"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5094",
"DOI": "10.1038/s41467-026-71481-1",
"PMID": "41963345",
"PMCID": "PMC13246750",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71481-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
10
]
]
}
}

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[8] doi:10.1002/hbm.70596 [code]
Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.
Journal: Human brain mapping
In common: systems, 4 references
[9] doi:10.1002/hbm.70522 [code]
Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study.
Journal: Human brain mapping
In common: 4 references
[10] doi:10.7554/elife.102189
Thalamo-accumbal circuit adaptations following extended oxycodone abstinence.
Journal: eLife
In common: rat, 3 references

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