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Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors.

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The authors' code

MATLAB · 355 lines · 11 KB · no license

  1. function analyze_blink_1810
  3. % this function analyze blink in Iku/Will's rig
  4. % First, run 'detect_video_LED_1808_Iku' (this takes time)
  5. % Next, run 'videosync_every10_1808_Iku'
  6. % Also run 'detect_blink_1810' (this takes time)
  8. % c{1} = 'E:\2024-Hokudai\behavior\headfix\eachAnimal\IK149_biSNL\CCrp1';
  9. c{1} = 'E:\2024-Hokudai\behavior\headfix\eachAnimal\IK216_CrhAi9\CCrp2';
  10. % c{1} = 'E:\2024-Hokudai\behavior\headfix\eachAnimal\IK220_DA2m+tdT\CCrp5';
  11. % c{1} = 'E:\2024-Hokudai\behavior\headfix\eachAnimal\IK218_CrhAi9\CCrp1';
  12. % c{1} = 'D:\2024-Hokudai\behavior\headfix\eachAnimal\IK134_DA2m+tdT_VSDLSTS\CCrptest';
  13. % c{1} = 'E:\2023-Keio\behavior\headfix\eachAnimal\IK96_CRF+tdT_VS-TS-VTA-SNL\videos\CCrp3';
  14. % c{1} = 'E:\2023-Keio\behavior\headfix\eachAnimal\IK97_CRF+tdT_VS-TS-VTA-SNL\videos\CCrp5';
  16. cd(c{1});
  17. % foldername = dir('*-09-14_*');
  18. % cd(foldername.name);
  20. % read fiber photometry
  21. file = strcat('IK216_CCrp_250515');
  22. file_ID = fopen(file, 'r');
  23. CC_cart_analog = fread(file_ID, inf, 'double', 0, 'b');
  25. A = reshape(CC_cart_analog, 2, [ ]);
  26. % B = reshape (A, 2, 8, [ ]);
  27. B = reshape (A, 2, 10, [ ]);
  29. trial_type_sig = B (:,4,:);
  30. trial_type_sig = reshape (trial_type_sig,[],1);
  32. odor3 = B (:,8,:);
  33. odor3 = reshape (odor3,[],1);
  34. odor3_on = crossing(odor3,[],2); %threshold(mV)
  35. odor3_on_ts = (odor3_on(1:2:end)).';
  36. odor3_off_ts = (odor3_on(2:2:end)).';
  38. puff = B (:,10,:); %free water or puff
  39. puff = reshape (puff,[],1);
  40. puff_on = crossing(puff,[],2); %threshold(mV)
  41. puff_on_ts = (puff_on(1:2:end)).';
  42. puff_off_ts = (puff_on(2:2:end)).';
  44. odor2 = B (:,7,:);
  45. odor2 = reshape (odor2,[],1);
  46. odor2_on = crossing(odor2,[],2); %threshold(mV)
  47. odor2_on_ts = (odor2_on(1:2:end)).';
  48. odor2_off_ts = (odor2_on(2:2:end)).';
  50. odor4 = B (:,9,:);%bigstim
  51. odor4 = reshape (odor4,[],1);
  52. odor4_on = crossing(odor4,[],2); %threshold(mV)
  53. odor4_on_ts = (odor4_on(1:2:end)).';
  54. odor4_off_ts = (odor4_on(2:2:end)).';
  56. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  57. % Find the start of each trial and type %%%%%%%%%%%%%%%%%%%%%%%%%%%%
  59. trial_type_sig_on = crossing(trial_type_sig,1:length(trial_type_sig),2.5);
  60. % trial_type_sig_off = (trial_type_sig_on(2:2:end)).';
  61. trial_type_sig_on = (trial_type_sig_on(1:2:end)).';
  64. trial_type_sig_on(:,2) = trial_type_sig_on(:,1);
  65. trial_type_sig_on(:,2) = 0;
  67. for i = 1:size(trial_type_sig_on,1)
  69. temp_B = 1;
  71. temp_A = trial_type_sig_on(i,1);
  73. for f = i+1:size(trial_type_sig_on,1)
  74. if trial_type_sig_on(f,1) < temp_A + 1000 %identify multiple signals within 1s
  76. temp_B = temp_B + 1;
  78. end
  79. end
  81. trial_type_sig_on(i,2) = temp_B; %number of signals within 1s after trial_type_sig_on(i,1)
  83. end
  85. % delete duplicates of the same trial
  87. for i = 1:size(trial_type_sig_on,1)
  89. temp_B = 1;
  91. temp_A = trial_type_sig_on(i,1);
  93. for f = i+1:size(trial_type_sig_on,1)
  94. if trial_type_sig_on(f,1) < temp_A + 1000
  96. trial_type_sig_on(f,:) = 0;
  98. end
  99. end
  101. end
  103. odor2_water_ts = [];odor2_omission_ts=[];odor3_ts = [];odor4_puff_ts = [];odor4_omission_ts = [];
  105. trial_type1 = find(trial_type_sig_on(:,2)==1);
  106. trial_type1_ts = trial_type_sig_on(trial_type1);
  107. for i = 1:length(trial_type1)
  108. odor2_water = find(odor2_on_ts > trial_type1_ts(i) & odor2_on_ts < trial_type1_ts(i)+1000);
  109. odor2_water_ts = [odor2_water_ts; odor2_on_ts(odor2_water)];
  110. end
  112. trial_type2 = find(trial_type_sig_on(:,2)==2);
  113. trial_type2_ts = trial_type_sig_on(trial_type2);
  114. for i = 1:length(trial_type2)
  115. odor2_omission = find(odor2_on_ts > trial_type2_ts(i) & odor2_on_ts < trial_type2_ts(i)+1000);
  116. odor2_omission_ts = [odor2_omission_ts; odor2_on_ts(odor2_omission)];
  117. end
  119. trial_type4 = find(trial_type_sig_on(:,2)==4);
  120. trial_type4_ts = trial_type_sig_on(trial_type4);
  121. for i = 1:length(trial_type4)
  122. odor4_puff = find(odor4_on_ts > trial_type4_ts(i) & odor4_on_ts < trial_type4_ts(i)+1000);
  123. odor4_puff_ts = [odor4_puff_ts; odor4_on_ts(odor4_puff)];
  124. end
  126. trial_type11 = find(trial_type_sig_on(:,2)==11);
  127. trial_type11_ts = trial_type_sig_on(trial_type11);
  128. for i = 1:length(trial_type11)
  129. odor4_omission = find(odor4_on_ts > trial_type11_ts(i) & odor4_on_ts < trial_type11_ts(i)+1000);
  130. odor4_omission_ts = [odor4_omission_ts; odor4_on_ts(odor4_omission)];
  131. end
  133. %% analyze eye area
  135. % calculate trigger frame
  136. % Trial type 1: odor2 water on
  137. % Trial type 2: odor2 water off
  138. % Trial type 3: odor3 water off
  139. % Trial type 4: odor4 puff on
  140. % Trial type 11: odor4 puff off
  142. load ('video_time') % 'video_t'
  143. frame_interval = median(diff(video_t)) %should be ~33.3 ms
  144. figure
  145. hist(diff(video_t))
  146. title('frame interval')
  147. xlabel('ms')
  149. puff_frame = [];
  150. for i = 1:length(puff_on_ts)
  151. if puff_on_ts(i)<video_t(end)
  152. puff_frame1 = find(video_t < puff_on_ts(i),1,'last');
  153. puff_frame = [puff_frame; puff_frame1];
  154. end
  155. end
  157. odor_frame = [];
  158. odor_ts = [odor2_on_ts;odor3_on_ts;odor4_on_ts];
  159. odor_ts = sort(odor_ts);
  160. for i = 1:length(odor_ts)
  161. if odor_ts(i)<video_t(end)
  162. odor_frame1 = find(video_t < odor_ts(i),1,'last');
  163. odor_frame = [odor_frame; odor_frame1];
  164. end
  165. end
  167. odor2_frame = [];
  168. for i = 1:length(odor2_on_ts)
  169. %for i = 1:length(odor2_water_ts)
  170. if odor2_on_ts(i)<video_t(end)
  171. %if odor2_water_ts(i)<video_t(end)
  172. odor2_frame1 = find(video_t < odor2_on_ts(i),1,'last');
  173. %odor2_frame1 = find(video_t < odor2_water_ts(i),1,'last');
  174. odor2_frame = [odor2_frame; odor2_frame1];
  175. end
  176. end
  178. odor3_frame = [];
  179. for i = 1:length(odor3_on_ts)
  180. if odor3_on_ts(i)<video_t(end)
  181. odor3_frame1 = find(video_t < odor3_on_ts(i),1,'last');
  182. odor3_frame = [odor3_frame; odor3_frame1];
  183. end
  184. end
  186. odor4_frame = [];
  187. for i = 1:length(odor4_on_ts)
  188. if odor4_on_ts(i)<video_t(end)
  189. odor4_frame1 = find(video_t < odor4_on_ts(i),1,'last');
  190. odor4_frame = [odor4_frame; odor4_frame1];
  191. end
  192. end
  194. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  195. %% get average eye area using 2-1s before trials
  197. load ('eye') %'eye_Area','eye_Long','eye_Short'
  199. eye_Area = smooth(eye_Area,10);
  200. ts = odor_frame;
  201. %ind = find( ts-168>0,1,'first');
  202. ind = find( ts-60>0,1,'first');
  203. %ind2 = find( ts-84< length(eye_Area),1,'last');
  204. ind2 = find( ts-30< length(eye_Area),1,'last');
  205. ts = ts(ind:ind2);
  206. % plotind = bsxfun(@plus, repmat([-168:-84],length(ts),1),ts);
  207. plotind = bsxfun(@plus, repmat([-60:-30],length(ts),1),ts);
  208. baseline = eye_Area(plotind);
  209. F_each = mean(baseline,2);
  210. F_mean = mean(F_each);
  212. max_eyeArea = max(F_each); % can use this instead of average
  213. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  214. %% average plot
  216. % trigger = {puff_frame};
  217. % trigger = {odor2_frame; odor3_frame; odor4_frame; puff_frame};%test
  218. trigger = {odor2_frame; odor3_frame; odor4_frame};%training
  219. % triggerP = {odor2_frame+168; odor3_frame+168; odor4_puff_frame};
  221. trigger_n = [length(odor2_frame),length(odor3_frame),length(odor4_frame)]
  223. scrsz = get(groot,'ScreenSize');
  224. figure('Position',[1 scrsz(4)/1.5 scrsz(3)/1.5 scrsz(4)/1.5])
  225. % plotColors = {'b','r','g', 'k'};%test
  226. plotColors = {'b','r','g'};%training
  227. plotdata = eye_Area;
  228. %plotWin = [-168:336]; %84 frame/second
  229. plotWin = [-60:180]; %30 frame/second
  230. %responseWin = [314:334]; %choose response time, 168 is trigger
  231. responseWin = [120:150]; %choose response time, 60 is trigger, 1to3sec, CS
  232. % responseWin = [150:210]; %choose response time, 60 is trigger, 3to5sec, US
  233. % legend_name = {'80%water','80%puff','nothing', 'freeP'};%test
  234. legend_name = {'100%water','100%puff','nothing'};%training
  235. % legend_name = {'bigStim','bigWater'};
  238. DeltaF = [];
  239. Tmark = [];
  240. Trial_number = [];
  241. response_all = [];
  242. Response = [];
  243. ste_Response = [];
  244. for i = 1:length(trigger)
  245. % ts = round(trigger{i});
  246. ts = trigger{i};
  247. if ~exist('triggerB');
  248. triggerB = trigger;
  249. end
  250. % tsB = round(triggerB{i});
  251. tsB = triggerB{i};
  253. ind = find( tsB+ plotWin(1)>0,1,'first');
  254. ind2 = find( ts+ plotWin(end)< length(plotdata),1,'last');
  255. ts = ts(ind:ind2);
  256. plotind = bsxfun(@plus, repmat(plotWin,length(ts),1),ts);
  257. rawTrace = plotdata(plotind);
  259. tsB = tsB(ind:ind2);
  260. plotind = bsxfun(@plus, repmat(plotWin,length(ts),1),tsB);
  261. rawTraceB = plotdata(plotind);
  263. %F = mean(rawTraceB(:,85:150),2);
  264. F = mean(rawTraceB(:,31:50),2);
  265. % deltaF = bsxfun(@minus,rawTrace,F)/2.5;
  266. deltaF = bsxfun(@minus,rawTrace,F);
  267. deltaF = deltaF/F_mean;
  268. %deltaF_F = bsxfun(@rdivide,rawTrace,F);
  269. m_plot = mean(deltaF);
  270. % m_plot = mean(deltaF(end-20:end,:));
  271. % m_plot = deltaF(6,:);
  272. s_plot = std(deltaF)/sqrt(length(ts));
  273. errorbar_patch(plotWin,m_plot,s_plot,plotColors{i});
  275. response = deltaF(:,responseWin);
  276. response = mean(response');
  277. response_all = [response_all,response];
  278. Response = [Response mean(response)]
  279. ste_Response = [ste_Response std(response)/sqrt(length(response))];
  280. if ~exist('triggerP')
  281. triggerP = triggerB;
  282. end
  283. tsP = round(triggerP{i});
  284. tsP = tsP(ind:ind2);
  285. DeltaF = [DeltaF;deltaF];
  286. Tmark = [Tmark;tsP-ts];
  287. Trial_number = [Trial_number size(deltaF,1)];
  288. end
  291. legend(legend_name)
  292. xlabel('time - event (s)')
  293. ylabel('fraction change of eye area')
  294. box off
  295. set(gca,'tickdir','out')
  296. set(gca,'TickLength',2*(get(gca,'TickLength')))
  297. set(gca,'FontSize',20)
  298. set(gcf,'color','w')
  299. h=gca;
  300. %h.XTick = -168:84:336;
  301. % h.XTick = -60:30:120;
  302. % h.XTickLabel = {-2:1:4};
  303. h.XTick = -60:30:180;
  304. h.XTickLabel = {-2:1:6};
  307. figure
  308. bar(Response)
  309. hold on
  310. errorbar(Response, ste_Response,'b.')
  311. hold on
  312. title('eye area')
  313. ylabel('responses (0-3s)')
  314. h=gca;
  315. h.XTickLabel = legend_name;
  317. %% raster plot
  319. scrsz = get(groot,'ScreenSize');
  320. figure('Position',[1 scrsz(4)/1.5 scrsz(3)/1.5 scrsz(4)/1.5])
  321. %1) bin the data
  322. trialNum = size(DeltaF,1); binSize = 6;%7
  323. binedF = squeeze(mean(reshape(DeltaF(:,1:(plotWin(end)-plotWin(1))),trialNum, binSize,[]),2));
  324. %imagesc(binedF,[-1 1]);
  325. % imagesc(binedF,[-0.5 0.5]);
  326. imagesc(binedF,[-1 1]);
  327. colormap yellowblue
  328. xlabel('time - event (s)');
  329. h=gca;
  330. %h.XTick = 0.5:84/binSize:5*84/binSize;
  331. h.XTick = 0.5:30/binSize:5*30/binSize;
  332. h.XTickLabel = {-2:6};
  333. hold on;
  334. % 2) plot the triggers
  335. for k = 1:trialNum
  336. % x = [(Tmark(k)-plotWin(1))/binSize (Tmark(k)-plotWin(1))/binSize]; % odor timing
  337. %x = [(Tmark(k)-plotWin(1)+168)/binSize+0.5 (Tmark(k)-plotWin(1)+168)/binSize+0.5]; %water timing
  338. % x = [(Tmark(k)-plotWin(1)+60)/binSize+0.5 (Tmark(k)-plotWin(1)+60)/binSize+0.5]; %water timing
  339. % y = [k-0.5 k+0.5];
  340. % plot(x,y,'r')
  341. end
  342. % x2 = [(-plotWin(1)+1000)/binSize (-plotWin(1)+1000)/binSize];%water
  343. x2 = [-plotWin(1)/binSize+0.5 -plotWin(1)/binSize+0.5]; % odor
  344. plot(x2,[0 trialNum+0.5],'c')
  346. % 3) divide triggers
  347. for j = 1:length(Trial_number)-1
  348. plot([0 (plotWin(end)-plotWin(1))/binSize],[sum(Trial_number(1:j))+0.5 sum(Trial_number(1:j))+0.5],'m','Linewidth',1)
  350. save_name = strcat('blink_aligned');
  351. save (save_name,'DeltaF','Trial_number')
  353. end
  354. end

analyze_blink_CC_2401.m at commit 4fabf57, no license · at the source

Overview

Authors: Ryota Tsuruga1, Yu Tajika1, Masabumi Minami1, Iku Tsutsui-Kimura1
ORCID iDs: Ryota Tsuruga, Masabumi Minami, Iku Tsutsui-Kimura
  1. Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
Institutions: Hokkaido University (Japan)
Journal: Communications biology, volume 9, issue 1, article 980
Dates: received 16 September 2025; accepted 4 June 2026; published online 24 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10485-5 · PMID 42498719 · PMCID PMC13400609 · OpenAlex W7170770745
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism)
Methods: Statistics, Preprocessing, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Classical conditioning, Habituation
MeSH: Appetitive Behavior*, Avoidance Learning*, Behavior, Animal*, Corpus Striatum*, Dopamine*, Reward*, Animals, Conditioning, Classical, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: MEXT | Japan Society for the Promotion of Science (25K02415)
Citations: cited by 1 paper (Europe PMC); 73 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.

itk2219/TSdopamine_behavior_photometry_optogenetics

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4fabf57fa2a15b3e63e52b9636f59fd77bea9e44, 6 September 2025
Languages: MATLAB (3)
Size: 3 files, 3 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/itk2219/TSdopamine_behavior_photometry_optogenetics

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/s42003-026-10485-5.

Tracing map

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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:

Read it in the paper: doi.org/10.1038/s42003-026-10485-5.

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Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 11 MeSH terms, 1 funder, 70 references.

Cite

This paper

Tsuruga, R., Tajika, Y., Minami, M., & Tsutsui-Kimura, I. (2026). Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors. Communications biology, 9(1), 980. https://doi.org/10.1038/s42003-026-10485-5

BibTeX

@article{tsuruga2026dopamine,
author = {Tsuruga, Ryota and Tajika, Yu and Minami, Masabumi and Tsutsui-Kimura, Iku},
title = {{Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors}},
journal = {Communications biology},
year = {2026},
month = jul,
volume = {9},
number = {1},
pages = {980},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10485-5},
url = {https://doi.org/10.1038/s42003-026-10485-5},
pmid = {42498719},
pmcid = {PMC13400609}
}

RIS

TY - JOUR
AU - Tsuruga, Ryota
AU - Tajika, Yu
AU - Minami, Masabumi
AU - Tsutsui-Kimura, Iku
TI - Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/07/24
VL - 9
IS - 1
SP - 980
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10485-5
UR - https://doi.org/10.1038/s42003-026-10485-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-10485-5",
"type": "article-journal",
"title": "Dopamine dynamics as a regulatory mechanism for shifting between defensive and reward-seeking behaviors",
"container-title": "Communications biology",
"author": [
{
"family": "Tsuruga",
"given": "Ryota"
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{
"family": "Tajika",
"given": "Yu"
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{
"family": "Minami",
"given": "Masabumi"
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{
"family": "Tsutsui-Kimura",
"given": "Iku"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "980",
"DOI": "10.1038/s42003-026-10485-5",
"PMID": "42498719",
"PMCID": "PMC13400609",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10485-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
24
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]
}
}

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