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Dopamine D2 receptors bypass canonical signaling to directly tune NMDA receptor function and aversive learning.

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  1. [1] § MATERIALS AND METHODS › Shuttle box active avoidance ↔ Arturo_SAA Matlab Analysis Codes/Group_session_behav_analysis_table.m, lines 335–396 · score 0.51 · opposite chamber, onset, boxes, movement, crossed

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The authors' code

MATLAB · 613 lines · 33 KB · MIT · 1 match

  1. function Group_session_behav_analysis_table(PATH2RESULTS,show_plot,save_plot,reanalysis,overwrite)
  2. % Created by Arturo Torres Herraez in 2025
  4. % The program sumarize the results in a table in which each row is a trial
  5. % and the different columns contain the information regarding identity of
  6. % the animal, the genotype, the sex, the session, the trials number, the latencies,
  7. % and the outcome of the trial. It also generate diverse plots summarizing
  8. % the results
  10. if contains(PATH2RESULTS,'\')
  11. connector = '\';
  12. else
  13. connector = '/';
  14. end
  16. PATH2RESULTS = [PATH2RESULTS,connector];
  17. mice2analyze = dir(PATH2RESULTS);
  18. for m = length(mice2analyze):-1:1
  19. if mice2analyze(m).isdir == 0 || strcmp(mice2analyze(m).name(1),'.') == 1
  20. mice2analyze(m) = [];
  21. end
  22. end
  24. genotypePerMouse = cell(length(mice2analyze),1);
  25. sexPerMouse = cell(length(mice2analyze),1);
  26. for i = length(mice2analyze):-1:1
  27. if strcmp(mice2analyze(i).name,'.') == 1 || strcmp(mice2analyze(i).name,'..') == 1 ...
  28. || mice2analyze(i).isdir == 0
  29. mice2analyze(i) = [];
  30. genotypePerMouse(i) = [];
  31. sexPerMouse(i) = [];
  32. else
  33. tmp = split(mice2analyze(i).name);
  34. genotypePerMouse{i} = tmp{2};
  35. sexPerMouse{i} = tmp{3};
  36. end
  37. end
  38. Nmice = length(mice2analyze);
  39. genotypes = unique(genotypePerMouse);
  40. sex = unique(sexPerMouse);
  41. sex{length(sex)+1,1} = 'all';
  42. for g = 1:length(genotypes)
  43. countpergenotype.(genotypes{g}).all = 0;
  44. for s = 1:length(sex)-1
  45. countpergenotype.(genotypes{g}).(sex{s}) = 0;
  46. end
  47. end
  49. for m = 1:Nmice
  50. countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m}) = ...
  51. countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})+1;
  52. countpergenotype.(genotypePerMouse{m}).all = countpergenotype.(genotypePerMouse{m}).all + 1;
  54. % Create a folder to save the data for this mouse and define the path
  55. Path2savingfolder = strcat(PATH2RESULTS,connector,mice2analyze(m).name,connector);
  57. if exist(strcat(Path2savingfolder,'All_sessions_behavior.mat'),'file') == 0
  58. done = 0;
  59. else
  60. done = 1;
  61. end
  63. if done == 0 || reanalysis == 1
  64. % Find sessions to analyze
  65. path2sessions = [PATH2RESULTS,connector,mice2analyze(m).name];
  66. sessions2analyze = dir(path2sessions);
  67. for i = length(sessions2analyze):-1:1
  68. if strcmp(sessions2analyze(i).name,'.') == 1 ...
  69. || strcmp(sessions2analyze(i).name,'..') == 1 ...
  70. || sessions2analyze(i).isdir == 0
  71. sessions2analyze(i) = [];
  72. end
  73. end
  74. sessionIdx = nan(length(sessions2analyze),1);
  75. for i = 1:length(sessions2analyze)
  76. idx = split(sessions2analyze(i).name,'_');
  77. sessionIdx(i) = str2double(idx{1});
  78. end
  80. count = 0;
  81. for i = 1:length(sessions2analyze)
  82. folder_name = [path2sessions,connector,sessions2analyze(i).name,connector];
  83. % Load behavior data
  84. if exist([folder_name,'Behavioral_results.mat'],'file') == 2
  85. load([folder_name,'Behavioral_results.mat'])
  87. count = count + 1;
  88. if count == 1
  89. All_sessions_summary_tab = SessionTab;
  90. All_sessions_Stim_Data = Stim_data;
  91. if m == 1
  92. LatencyPerSession = cell(length(sessions2analyze),1);
  93. meanStimDataPerSession = cell(length(sessions2analyze),1);
  94. for g = 1:length(genotypes)
  95. for s = 1:length(sex)
  96. if strcmp(sex{s},'All')
  97. PercPerSession.(genotypes{g}).(sex{s}).Avoidance = ...
  98. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  99. PercPerSession.(genotypes{g}).(sex{s}).Escape = ...
  100. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  101. PercPerSession.(genotypes{g}).(sex{s}).Stay = ...
  102. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  103. meanAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  104. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  105. meanEscapeLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  106. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  107. meanFirstCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  108. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  109. meanLastCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  110. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  111. medianAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  112. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  113. medianEscapeLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  114. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  115. medianFirstCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  116. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  117. medianLastCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  118. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  119. SessionBaselineActivityPerSession.(genotypes{g}).(sex{s}) = ...
  120. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  121. medianBaselineCrossingLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  122. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})));
  123. latencyNames = fieldnames(latency);
  124. for l = 1:length(latencyNames)
  125. if contains(latencyNames{l},'tone')
  126. LatencyPerSession{i}.(genotypes{g}).(sex{s}).(latencyNames{l}) = ...
  127. nan(size(latency.(latencyNames{l}),1),sum(strcmp(genotypePerMouse,genotypes{g})));
  128. end
  129. end
  130. baselining = fieldnames(Stim_data);
  131. for b = 1:length(baselining)
  132. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).all = ...
  133. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})));
  134. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).avoid = ...
  135. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})));
  136. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).escape = ...
  137. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})));
  138. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).stay = ...
  139. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})));
  140. end
  141. else
  142. PercPerSession.(genotypes{g}).(sex{s}).Avoidance = ...
  143. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  144. & strcmp(sexPerMouse,sex{s})));
  145. PercPerSession.(genotypes{g}).(sex{s}).Escape = ...
  146. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  147. & strcmp(sexPerMouse,sex{s})));
  148. PercPerSession.(genotypes{g}).(sex{s}).Stay = ...
  149. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  150. & strcmp(sexPerMouse,sex{s})));
  151. meanAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  152. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  153. & strcmp(sexPerMouse,sex{s})));
  154. meanEscapeLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  155. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  156. & strcmp(sexPerMouse,sex{s})));
  157. meanFirstCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  158. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  159. & strcmp(sexPerMouse,sex{s})));
  160. meanLastCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  161. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  162. & strcmp(sexPerMouse,sex{s})));
  163. medianAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  164. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  165. & strcmp(sexPerMouse,sex{s})));
  166. medianEscapeLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  167. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  168. & strcmp(sexPerMouse,sex{s})));
  169. medianFirstCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  170. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  171. & strcmp(sexPerMouse,sex{s})));
  172. medianLastCrossingDirectedMoveLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  173. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  174. & strcmp(sexPerMouse,sex{s})));
  175. SessionBaselineActivityPerSession.(genotypes{g}).(sex{s}) = ...
  176. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  177. & strcmp(sexPerMouse,sex{s})));
  178. medianBaselineCrossingLatencyPerSession.(genotypes{g}).(sex{s}) = ...
  179. nan(length(sessions2analyze),sum(strcmp(genotypePerMouse,genotypes{g})...
  180. & strcmp(sexPerMouse,sex{s})));
  181. latencyNames = fieldnames(latency);
  182. for l = 1:length(latencyNames)
  183. if contains(latencyNames{l},'tone')
  184. LatencyPerSession{i}.(genotypes{g}).(sex{s}).(latencyNames{l}) = ...
  185. nan(size(latency.(latencyNames{l}),1),sum(strcmp(genotypePerMouse,genotypes{g})...
  186. & strcmp(sexPerMouse,sex{s})));
  187. end
  188. end
  189. baselining = fieldnames(Stim_data);
  190. for b = 1:length(baselining)
  191. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).all = ...
  192. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})...
  193. & strcmp(sexPerMouse,sex{s})));
  194. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).avoid = ...
  195. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})...
  196. & strcmp(sexPerMouse,sex{s})));
  197. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).escape = ...
  198. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})...
  199. & strcmp(sexPerMouse,sex{s})));
  200. meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).stay = ...
  201. nan(length(t_trials),sum(strcmp(genotypePerMouse,genotypes{g})...
  202. & strcmp(sexPerMouse,sex{s})));
  203. end
  204. end
  205. end
  206. end
  207. end
  208. else
  209. [All_sessions_summary_tab] = vertcat(All_sessions_summary_tab,SessionTab);
  210. baselining = fieldnames(Stim_data);
  211. for b = 1:length(baselining)
  212. All_sessions_Stim_Data.(baselining{b}) = [All_sessions_Stim_Data.(baselining{b});...
  213. Stim_data.(baselining{b})];
  214. end
  215. end
  217. for specificSex = 1
  218. PercPerSession.(genotypePerMouse{m}).(sexPerMouse{m}).Avoidance(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  219. PercAvoidance;
  220. PercPerSession.(genotypePerMouse{m}).(sexPerMouse{m}).Escape(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  221. PercEscape;
  222. PercPerSession.(genotypePerMouse{m}).(sexPerMouse{m}).Stay(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  223. PercStay;
  224. meanAvoidanceLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  225. mean_AvoidanceLatency;
  226. meanEscapeLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  227. mean_EscapeLatency;
  228. meanFirstCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  229. mean_FirstCrossingDirectedMoveLatency;
  230. meanLastCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  231. mean_LastCrossingDirectedMoveLatency;
  232. medianAvoidanceLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  233. median_AvoidanceLatency;
  234. medianEscapeLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  235. median_EscapeLatency;
  236. medianFirstCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  237. median_FirstCrossingDirectedMoveLatency;
  238. medianLastCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  239. median_LastCrossingDirectedMoveLatency;
  240. SessionBaselineActivityPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  241. SessionbaselineActivity;
  242. medianBaselineCrossingLatencyPerSession.(genotypePerMouse{m}).(sexPerMouse{m})(i,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  243. median_Baseline_crossingLatency;
  244. latencyNames = fieldnames(latency);
  245. for l = 1:length(latencyNames)
  246. if contains(latencyNames{l},'tone')
  247. LatencyPerSession{i}.(genotypePerMouse{m}).(sexPerMouse{m}).(latencyNames{l})(:,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  248. latency.(latencyNames{l});
  249. end
  250. end
  251. baselining = fieldnames(Stim_data);
  252. for b = 1:length(baselining)
  253. meanStimDataPerSession{i}.(genotypePerMouse{m}).(sexPerMouse{m}).(baselining{b}).all(:,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  254. mean(Stim_data.(baselining{b}),1,'omitnan')';
  255. meanStimDataPerSession{i}.(genotypePerMouse{m}).(sexPerMouse{m}).(baselining{b}).avoid(:,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  256. mean(Stim_data.(baselining{b})(logical(SessionTab.isAvoid),:),1,'omitnan')';
  257. meanStimDataPerSession{i}.(genotypePerMouse{m}).(sexPerMouse{m}).(baselining{b}).escape(:,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  258. mean(Stim_data.(baselining{b})(logical(SessionTab.isEscape),:),1,'omitnan')';
  259. meanStimDataPerSession{i}.(genotypePerMouse{m}).(sexPerMouse{m}).(baselining{b}).stay(:,countpergenotype.(genotypePerMouse{m}).(sexPerMouse{m})) = ...
  260. mean(Stim_data.(baselining{b})(logical(SessionTab.isStay),:),1,'omitnan')';
  261. end
  262. end
  263. for all = 1
  264. PercPerSession.(genotypePerMouse{m}).all.Avoidance(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  265. PercAvoidance;
  266. PercPerSession.(genotypePerMouse{m}).all.Escape(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  267. PercEscape;
  268. PercPerSession.(genotypePerMouse{m}).all.Stay(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  269. PercStay;
  270. meanAvoidanceLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  271. mean_AvoidanceLatency;
  272. meanEscapeLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  273. mean_EscapeLatency;
  274. meanFirstCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  275. mean_FirstCrossingDirectedMoveLatency;
  276. meanLastCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  277. mean_LastCrossingDirectedMoveLatency;
  278. medianAvoidanceLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  279. median_AvoidanceLatency;
  280. medianEscapeLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  281. median_EscapeLatency;
  282. medianFirstCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  283. median_FirstCrossingDirectedMoveLatency;
  284. medianLastCrossingDirectedMoveLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  285. median_LastCrossingDirectedMoveLatency;
  286. SessionBaselineActivityPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  287. SessionbaselineActivity;
  288. medianBaselineCrossingLatencyPerSession.(genotypePerMouse{m}).all(i,countpergenotype.(genotypePerMouse{m}).all) = ...
  289. median_Baseline_crossingLatency;
  290. latencyNames = fieldnames(latency);
  291. for l = 1:length(latencyNames)
  292. if contains(latencyNames{l},'tone')
  293. LatencyPerSession{i}.(genotypePerMouse{m}).all.(latencyNames{l})(:,countpergenotype.(genotypePerMouse{m}).all) = ...
  294. latency.(latencyNames{l});
  295. end
  296. end
  297. baselining = fieldnames(Stim_data);
  298. for b = 1:length(baselining)
  299. meanStimDataPerSession{i}.(genotypePerMouse{m}).all.(baselining{b}).all(:,countpergenotype.(genotypePerMouse{m}).all) = ...
  300. mean(Stim_data.(baselining{b}),1,'omitnan')';
  301. meanStimDataPerSession{i}.(genotypePerMouse{m}).all.(baselining{b}).avoid(:,countpergenotype.(genotypePerMouse{m}).all) = ...
  302. mean(Stim_data.(baselining{b})(logical(SessionTab.isAvoid),:),1,'omitnan')';
  303. meanStimDataPerSession{i}.(genotypePerMouse{m}).all.(baselining{b}).escape(:,countpergenotype.(genotypePerMouse{m}).all) = ...
  304. mean(Stim_data.(baselining{b})(logical(SessionTab.isEscape),:),1,'omitnan')';
  305. meanStimDataPerSession{i}.(genotypePerMouse{m}).all.(baselining{b}).stay(:,countpergenotype.(genotypePerMouse{m}).all) = ...
  306. mean(Stim_data.(baselining{b})(logical(SessionTab.isStay),:),1,'omitnan')';
  307. end
  308. end
  309. end
  310. end
  312. if exist('All_sessions_summary_tab','var') == 1
  313. save([Path2savingfolder,'All_sessions_summary_tab.mat'],'All_sessions_summary_tab')
  314. end
  316. if exist('All_sessions_Stim_data','var') == 1
  317. FPmice = FPmice + 1;
  318. save([Path2savingfolder,'All_sessions_Stim_data.mat'],'All_sessions_Stim_Data','t_trials')
  319. end
  320. if m == 1
  321. AllMiceTab = All_sessions_summary_tab;
  322. AllMiceStimData = All_sessions_Stim_Data;
  323. else
  324. [AllMiceTab] = vertcat(AllMiceTab,All_sessions_summary_tab);
  325. baselining = fieldnames(Stim_data);
  326. for b = 1:length(baselining)
  327. AllMiceStimData.(baselining{b}) = [AllMiceStimData.(baselining{b});...
  328. All_sessions_Stim_Data.(baselining{b})];
  329. end
  330. end
  331. end
  332. end
  334. %% Plot figures
  335. %%% Plot latencies across trials
  336. for i = 1:size(LatencyPerSession,1)
  337. % Plot latencies to crossing to opposite chamber after CS onset per trial
  338. figure
  339. if show_plot == 0
  340. set(gcf,'visible','off')
  341. end
  342. subplot(2,1,1)
  343. hold on
  344. for g = 1:length(genotypes)
  345. for s = 1:2
  346. X = mean(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2crossing,2,'omitnan');
  347. Y = std(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2crossing,1,2,'omitnan')./...
  348. sqrt(sum(~isnan(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2crossing),2));
  349. if strcmp(genotypes{g},'KO')
  350. if strcmp(sex{s},'F')
  351. error_area(1:N_trials,X,Y,'r',0.25)
  352. else
  353. error_area(1:N_trials,X,Y,'m',0.25)
  354. end
  355. else
  356. if strcmp(sex{s},'F')
  357. error_area(1:N_trials,X,Y,'b',0.25)
  358. else
  359. error_area(1:N_trials,X,Y,'c',0.25)
  360. end
  361. end
  362. end
  363. end
  364. box off
  365. xlabel('Trial Number')
  366. ylabel('Latency to cross (s)')
  367. title(['Mean Latency to crossing per trial Session ',num2str(i)])
  369. % Plot latencies to the moveent leading to crossing to opposite chamber after CS onset per trial
  370. subplot(2,1,2)
  371. hold on
  372. for g= 1:length(genotypes)
  373. for s = 1:2
  374. X = mean(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2LastCrossingDirectedMove,2,'omitnan');
  375. Y = std(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2LastCrossingDirectedMove,1,2,'omitnan')./...
  376. sqrt(sum(~isnan(LatencyPerSession{i}.(genotypes{g}).(sex{s}).tone2LastCrossingDirectedMove),2));
  377. if strcmp(genotypes{g},'KO')
  378. if strcmp(sex{s},'F')
  379. error_area(1:N_trials,X,Y,'r',0.25)
  380. else
  381. error_area(1:N_trials,X,Y,'m',0.25)
  382. end
  383. else
  384. if strcmp(sex{s},'F')
  385. error_area(1:N_trials,X,Y,'b',0.25)
  386. else
  387. error_area(1:N_trials,X,Y,'c',0.25)
  388. end
  389. end
  390. end
  391. end
  392. box off
  393. xlabel('Trial Number')
  394. ylabel('Latency to crossing movement onset (s)')
  395. title(['Mean Latency to crossing movement onset per trial Session ',num2str(i)])
  396. end
  397. %%% Plot movement aligned to CS onset
  398. trial_types = fieldnames(meanStimDataPerSession{1}.(genotypes{1}).(sex{1}).(baselining{1}));
  399. for i = 1:size(meanStimDataPerSession,1)
  400. for b = 1:length(baselining)
  401. figure
  402. if show_plot == 0
  403. set(gcf,'visible','off')
  404. end
  405. for ttype = 1:length(trial_types)
  406. subplot(2,2,ttype)
  407. hold on
  408. for g= 1:length(genotypes)
  409. for s = 1:2
  410. X = mean(meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).(trial_types{ttype}),2,'omitnan');
  411. Y = std(meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).(trial_types{ttype}),1,2,'omitnan')./...
  412. sqrt(sum(~isnan(meanStimDataPerSession{i}.(genotypes{g}).(sex{s}).(baselining{b}).(trial_types{ttype})),2));
  413. if strcmp(genotypes{g},'KO')
  414. if strcmp(sex{s},'F')
  415. error_area(t_trials,X,Y,'r',0.25)
  416. else
  417. error_area(t_trials,X,Y,'m',0.25)
  418. end
  419. else
  420. if strcmp(sex{s},'F')
  421. error_area(t_trials,X,Y,'b',0.25)
  422. else
  423. error_area(t_trials,X,Y,'c',0.25)
  424. end
  425. end
  426. end
  427. end
  428. box off
  429. xlabel('Time (s)')
  430. xlim([-5 12])
  431. ylim([-5 20])
  432. ylabel('Average BeamBreakPerSec')
  433. xline(0,'k')
  434. xline(7,'m')
  435. title([trial_types{ttype},' trials'])
  436. end
  437. sgt = sgtitle(['Average BeamBreakPerSec ',baselining{b},' Session ',num2str(i)]);
  438. sgt.FontSize = 12;
  439. if save_plot
  440. saveas(gcf,[PATH2RESULTS,'Average BeamBreakPerSec ',baselining{b},' Session ',num2str(i),'.png'])
  441. saveas(gcf,[PATH2RESULTS,'Average BeamBreakPerSec ',baselining{b},' Session ',num2str(i),'.fig'])
  442. end
  443. end
  444. end
  446. %%% Plot percentage of avoidance or escape across sessions
  447. % Per genotype and sex
  448. figure
  449. if show_plot == 0
  450. set(gcf,'visible','off')
  451. end
  452. hold on
  453. subplot(2,1,1)
  454. for g = 1:length(genotypes)
  455. for s = 1:2
  456. X = mean(PercPerSession.(genotypes{g}).(sex{s}).Avoidance,2,'omitnan');
  457. Y = std(PercPerSession.(genotypes{g}).(sex{s}).Avoidance,1,2,'omitnan')./...
  458. sqrt(sum(~isnan(PercPerSession.(genotypes{g}).(sex{s}).Avoidance),2));
  459. if strcmp(genotypes{g},'KO')
  460. if strcmp(sex{s},'F')
  461. error_area(1:size(X,1),X,Y,'r',0.25)
  462. else
  463. error_area(1:size(X,1),X,Y,'m',0.25)
  464. end
  465. else
  466. if strcmp(sex{s},'F')
  467. error_area(1:size(X,1),X,Y,'b',0.25)
  468. else
  469. error_area(1:size(X,1),X,Y,'c',0.25)
  470. end
  471. end
  472. end
  473. end
  474. box off
  475. ylim([0 100])
  476. xlabel('Session Number')
  477. ylabel('Percentage of avoidance')
  478. subplot(2,1,2)
  479. for g = 1:length(genotypes)
  480. for s = 1:2
  481. X = mean(PercPerSession.(genotypes{g}).(sex{s}).Escape,2,'omitnan');
  482. Y = std(PercPerSession.(genotypes{g}).(sex{s}).Escape,1,2,'omitnan')./...
  483. sqrt(sum(~isnan(PercPerSession.(genotypes{g}).(sex{s}).Escape),2));
  484. if strcmp(genotypes{g},'KO')
  485. if strcmp(sex{s},'F')
  486. error_area(1:size(X,1),X,Y,'r',0.25)
  487. else
  488. error_area(1:size(X,1),X,Y,'m',0.25)
  489. end
  490. else
  491. if strcmp(sex{s},'F')
  492. error_area(1:size(X,1),X,Y,'b',0.25)
  493. else
  494. error_area(1:size(X,1),X,Y,'c',0.25)
  495. end
  496. end
  497. end
  498. end
  499. box off
  500. ylim([0 100])
  501. xlabel('Session Number')
  502. ylabel('Percentage of escape')
  504. % Per genotype
  505. figure
  506. if show_plot == 0
  507. set(gcf,'visible','off')
  508. end
  509. hold on
  510. subplot(2,1,1)
  511. for g = 1:length(genotypes)
  512. for s = 1:2
  513. X = mean(PercPerSession.(genotypes{g}).all.Avoidance,2,'omitnan');
  514. Y = std(PercPerSession.(genotypes{g}).all.Avoidance,1,2,'omitnan')./...
  515. sqrt(sum(~isnan(PercPerSession.(genotypes{g}).all.Avoidance),2));
  516. if strcmp(genotypes{g},'KO')
  517. error_area(1:size(X,1),X,Y,'r',0.25)
  518. else
  519. error_area(1:size(X,1),X,Y,'b',0.25)
  520. end
  521. end
  522. end
  523. box off
  524. ylim([0 100])
  525. xlabel('Session Number')
  526. ylabel('Percentage of avoidance')
  527. subplot(2,1,2)
  528. for g = 1:length(genotypes)
  529. for s = 1:2
  530. X = mean(PercPerSession.(genotypes{g}).all.Escape,2,'omitnan');
  531. Y = std(PercPerSession.(genotypes{g}).all.Escape,1,2,'omitnan')./...
  532. sqrt(sum(~isnan(PercPerSession.(genotypes{g}).all.Escape),2));
  533. if strcmp(genotypes{g},'KO')
  534. error_area(1:size(X,1),X,Y,'r',0.25)
  535. else
  536. error_area(1:size(X,1),X,Y,'b',0.25)
  537. end
  538. end
  539. end
  540. box off
  541. ylim([0 100])
  542. xlabel('Session Number')
  543. ylabel('Percentage of escape')
  545. %%% Plot mean latency to avoidance or escape across sessions
  546. % Per genotype and sex
  547. figure
  548. if show_plot == 0
  549. set(gcf,'visible','off')
  550. end
  551. hold on
  552. subplot(2,1,1)
  553. for g = 1:length(genotypes)
  554. for s = 1:2
  555. X = mean(meanAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}),2,'omitnan');
  556. Y = std(meanAvoidanceLatencyPerSession.(genotypes{g}).(sex{s}),1,2,'omitnan')./...
  557. sqrt(sum(~isnan(meanAvoidanceLatencyPerSession.(genotypes{g}).(sex{s})),2));
  558. if strcmp(genotypes{g},'KO')
  559. if strcmp(sex{s},'F')
  560. error_area(1:size(X,1),X,Y,'r',0.25)
  561. else
  562. error_area(1:size(X,1),X,Y,'m',0.25)
  563. end
  564. else
  565. if strcmp(sex{s},'F')
  566. error_area(1:size(X,1),X,Y,'b',0.25)
  567. else
  568. error_area(1:size(X,1),X,Y,'c',0.25)
  569. end
  570. end
  571. end
  572. end
  573. box off
  574. xlabel('Session Number')
  575. ylabel('Mean latency to avoid')
  576. subplot(2,1,2)
  577. for g = 1:length(genotypes)
  578. for s = 1:2
  579. X = mean(meanEscapeLatencyPerSession.(genotypes{g}).(sex{s}),2,'omitnan');
  580. Y = std(meanEscapeLatencyPerSession.(genotypes{g}).(sex{s}),1,2,'omitnan')./...
  581. sqrt(sum(~isnan(meanEscapeLatencyPerSession.(genotypes{g}).(sex{s})),2));
  582. if strcmp(genotypes{g},'KO')
  583. if strcmp(sex{s},'F')
  584. error_area(1:size(X,1),X,Y,'r',0.25)
  585. else
  586. error_area(1:size(X,1),X,Y,'m',0.25)
  587. end
  588. else
  589. if strcmp(sex{s},'F')
  590. error_area(1:size(X,1),X,Y,'b',0.25)
  591. else
  592. error_area(1:size(X,1),X,Y,'c',0.25)
  593. end
  594. end
  595. end
  596. end
  597. box off
  598. xlabel('Session Number')
  599. ylabel('Mean latency to escape')
  601. %% Save data
  602. if contains(PATH2RESULTS,'\')
  603. save([PATH2RESULTS,'\All_mice_summary_tab.mat'],'AllMiceTab')
  604. if exist('AllMice_Stim_data','var') == 1
  605. save([PATH2RESULTS,'\AllMice_Stim_data.mat'],'AllMice_Stim_data','AllMice_zscore_Stim_data','t_trials','-v7.3')
  606. end
  607. else
  608. save([PATH2RESULTS,'/All_mice_summary_tab.mat'],'AllMiceTab')
  609. if exist('AllMice_Stim_data','var') == 1
  610. save([PATH2RESULTS,'/AllMice_Stim_data.mat'],'AllMice_Stim_data','AllMice_zscore_Stim_data','t_trials', '-v7.3')
  611. end
  612. end

Group_session_behav_analysis_table.m at commit 59e2555, under MIT · at the source

Overview

Authors: Sheng Gong1,2, Joy Adler1, Ying Zhu3,4, Charlize Szeto5, Lisa Lin3,4, Caroline Rauffenbart3,4, Arturo Torres-Herraez3,4, Wesley B. Asher3,4, Jonathan A. Javitch3,4,6, Christopher P. Ford1
ORCID iDs: Sheng Gong, Joy Adler, Ying Zhu, Charlize Szeto, Arturo Torres-Herraez, Wesley B. Asher, Jonathan A. Javitch, Christopher P. Ford
  1. Department of Pharmacology, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO 80045, USA
  2. Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, VA 20147, USA
  3. Department of Psychiatry, Columbia University Vagelos College of Physicians & Surgeons, New York, NY 10032, USA
  4. Division of Molecular Therapeutics, New York State Psychiatric Institute, New York, NY 10032, USA
  5. Barnard College of Columbia University, New York, NY 10032, USA
  6. Department of Molecular Pharmacology and Therapeutics, Columbia University Vagelos College of Physicians & Surgeons, New York, NY 10032, USA
Institutions: Howard Hughes Medical Institute (United States); Janelia Research Campus (United States); University of Colorado Anschutz (United States); New York State Psychiatric Institute; Columbia University (United States); Barnard College (United States)
Journal: Science advances, volume 12, issue 30, article eaee6579
Dates: received 10 December 2025; accepted 16 June 2026; published online 23 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aee6579 · PMID 42490424 · PMCID PMC13394467 · OpenAlex W7170170769
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials
MeSH: Learning*, Receptors, Dopamine D2*, Receptors, N-Methyl-D-Aspartate*, Signal Transduction*, Animals, Dopamine, Long-Term Potentiation, Medium Spiny Neurons, Mice, Nucleus Accumbens, Synapses, Synaptic Transmission (* major topic)
Journal subjects: Neuroscience, Neurophysiology
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R01-DA35821, R01 MH054137, R01-NS95809, R01-NS138043, F31-DA64363); Hope for Depression Research Foundation; St. Jude Children's Research Hospital (GPCR Collaborative)
Citations: not cited yet (Europe PMC); 135 references in the paper

Abstract

Dopamine D2 receptors (D2Rs) modulate reward learning and aversive behaviors, with dysfunction linked to addiction and psychiatric disorders. D2Rs regulate behavior through modulation of striatal glutamatergic transmission, yet how D2Rs control postsynaptic glutamate signaling remains poorly understood. Using molecular tools to selectively disrupt heteromeric interactions while preserving canonical signaling, we show that physical coupling between D2Rs and GluN2B-containing N-methyl-d-aspartate (NMDA) receptors in nucleus accumbens medium spiny neurons enables D2Rs to suppress NMDA receptor function independent of canonical G protein and arrestin signaling. This modulation was input specific, occurred at thalamic but not cortical synapses converging on the same neurons, constrained long-term potentiation, and, within the medial ventral nucleus accumbens, facilitated aversive learning. These findings reveal that D2Rs can bypass second messenger systems to tune glutamatergic transmission through receptor-receptor interactions, providing a mechanism by which dopamine selectively gates specific glutamatergic inputs to control striatal plasticity and behavioral adaptation.

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.

Zenodo 19076090

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
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)
10 files
At the source: dx.doi.org/10.5281/zenodo.19076090

fordlab/d2-glun2b-paper

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 59e25559529e22b9de9ed527c90a1ab7e806692a, 17 March 2026
Languages: MATLAB (9)
Size: 12 files, 9 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
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
10 files
At the source: github.com/FordLab/D2-GluN2B-paper/tree/v1.0

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 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

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study did not generate new materials. Code used for behavior analysis of Fig. 7 and fig. S5 can be found at DOI: 10.5281/zenodo.19076090 (http://dx.doi.org/10.5281/zenodo.19076090). The plasmids used to make the minigene, scramble, and dopamine receptor viral constructs can be provided by J.A.J. pending scientific review and a completed material transfer agreement through Columbia University. Requests should be submitted to .

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, 10 authors, 12 MeSH terms, 3 funders, 135 references.

Cite

This paper

Gong, S., Adler, J., Zhu, Y., Szeto, C., Lin, L., Rauffenbart, C., Torres-Herraez, A., Asher, W. B., Javitch, J. A., & Ford, C. P. (2026). Dopamine D2 receptors bypass canonical signaling to directly tune NMDA receptor function and aversive learning. Science advances, 12(30), eaee6579. https://doi.org/10.1126/sciadv.aee6579

BibTeX

@article{gong2026dopamine,
author = {Gong, Sheng and Adler, Joy and Zhu, Ying and Szeto, Charlize and Lin, Lisa and Rauffenbart, Caroline and Torres-Herraez, Arturo and Asher, Wesley B. and Javitch, Jonathan A. and Ford, Christopher P.},
title = {{Dopamine D2 receptors bypass canonical signaling to directly tune NMDA receptor function and aversive learning}},
journal = {Science advances},
year = {2026},
month = jul,
volume = {12},
number = {30},
pages = {eaee6579},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aee6579},
url = {https://doi.org/10.1126/sciadv.aee6579},
pmid = {42490424},
pmcid = {PMC13394467}
}

RIS

TY - JOUR
AU - Gong, Sheng
AU - Adler, Joy
AU - Zhu, Ying
AU - Szeto, Charlize
AU - Lin, Lisa
AU - Rauffenbart, Caroline
AU - Torres-Herraez, Arturo
AU - Asher, Wesley B.
AU - Javitch, Jonathan A.
AU - Ford, Christopher P.
TI - Dopamine D2 receptors bypass canonical signaling to directly tune NMDA receptor function and aversive learning
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/07/23
VL - 12
IS - 30
SP - eaee6579
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aee6579
UR - https://doi.org/10.1126/sciadv.aee6579
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aee6579",
"type": "article-journal",
"title": "Dopamine D2 receptors bypass canonical signaling to directly tune NMDA receptor function and aversive learning",
"container-title": "Science advances",
"author": [
{
"family": "Gong",
"given": "Sheng"
},
{
"family": "Adler",
"given": "Joy"
},
{
"family": "Zhu",
"given": "Ying"
},
{
"family": "Szeto",
"given": "Charlize"
},
{
"family": "Lin",
"given": "Lisa"
},
{
"family": "Rauffenbart",
"given": "Caroline"
},
{
"family": "Torres-Herraez",
"given": "Arturo"
},
{
"family": "Asher",
"given": "Wesley B."
},
{
"family": "Javitch",
"given": "Jonathan A."
},
{
"family": "Ford",
"given": "Christopher P."
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "30",
"page": "eaee6579",
"DOI": "10.1126/sciadv.aee6579",
"PMID": "42490424",
"PMCID": "PMC13394467",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aee6579",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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