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Place and behavioral modulation of hippocampal neurons during immobility.

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  1. [1] § Methods › Data analysis › LFP analysis ↔ helper_functions/common_vars.m, the whole file · a weak match · score 0.86 · 70–90 Hz, 0.5–4 Hz, gamma range, delta range, theta range, 6–10 Hz
  2. [2] § Results › Hippocampal place coding during awake immobility ↔ figures/fig2/fig2.m, lines 87–155 · score 0.78 · triggered spectrogram, ripple rate, brain state, theta delta, whisker pad motion, active immobility
  3. [3] § Methods › Data analysis › Analysis of place cell firing during awake immobility ↔ figures/fig2/fig2.m, lines 253–286 · score 0.75 · Population tuning curves, signed distance, quiet immobility, place field, belt, peak
  4. [4] § Results › Hippocampal place coding during awake immobility ↔ figures/fig2/fig2.m, lines 25–85 · score 0.66 · Sharp wave ripples, state transitions, whisker pad motion, behavioral states, active immobility, gamma
  5. [5] § Results › Theta oscillations during active immobility ↔ figures/fig4/fig4.m, lines 17–75 · score 0.65 · Histogram distributions, delta ratio, active immobility, 5–10 Hz, scatter, epochs
  6. [6] § Results › CA1 place cell recordings and behavioral analysis during running and immobility ↔ figures/fig1/fig1.m, lines 113–153 · score 0.64 · power spectra, pupil area, Bonferroni corrected, whisker pad motion, spectrogram, LFP
  7. [7] § Methods › Data analysis › Analysis of place cell firing during awake immobility ↔ figures/fig5/fig5.m, lines 151–209 · score 0.63 · Population tuning curves, signed distance, belt, quiet, modulated, active
  8. [8] § Methods › Data analysis › LFP analysis ↔ figures/fig2/fig2.m, lines 8–23 · score 0.63 · gamma range, delta range, theta range, band
  9. [9] § Methods › Data analysis › Theta oscillations segmentation and analysis ↔ figures/fig_S4/fig_S4.m, lines 131–193 · score 0.59 · oscillatory bands, power changes, Whisker pad, quantiles, duration, theta
  10. [10] § Results › Theta oscillations during active immobility ↔ figures/fig4/fig4.m, lines 199–253 · score 0.58 · theta oscillations, delta ratio, whisker pad motion, quantiles, duration, power
  11. [11] § Methods › Data analysis › Bayesian decoding analysis ↔ figures/fig_S3/fig_S3.m, lines 20–30 · score 0.58 · confusion matrix, Bayesian decoding, reactivations, position, location
  12. [12] § Results › Heterogeneous behavioral modulation of place cells during immobility ↔ figures/fig5/fig5.m, lines 90–138 · score 0.57 · behaviorally modulated cells, immobility location, log10, transformed, fit, Figure 5
  13. [13] § Methods › Data analysis › Statistical analysis ↔ figures/fig2/fig2.m, lines 315–359 · score 0.57 · Wilcoxon signed rank, Bonferroni correction, alpha, tailed, error
  14. [14] § Methods › Data analysis › Statistical analysis ↔ figures/fig_S2/fig_S2.m, lines 18–56 · score 0.57 · Wilcoxon signed rank, Bonferroni correction, alpha, tailed, error
  15. [15] § Results › Hippocampal place coding during awake immobility ↔ figures/fig_S2/fig_S2.m, lines 18–56 · score 0.56 · Wilcoxon signed rank, Bonferroni corrected, behavioral state, tailed, trained, errors
  16. [16] § Results › Hippocampal place coding during awake immobility ↔ figures/fig2/fig2.m, lines 315–359 · score 0.56 · Wilcoxon signed rank, Bonferroni corrected, behavioral state, tailed, trained, errors
  17. [17] § Methods › Juxtacellular recordings and stimulation ↔ figures/fig2/fig2.m, lines 87–155 · score 0.56 · 1.5–2 %, whisker pad motion, active immobility, transition, onset
  18. [18] § Results › Heterogeneous behavioral modulation of place cells during immobility ↔ figures/fig_S2/fig_S2.m, lines 58–85 · score 0.55 · neuron dropping procedure, Bayesian decoding, quiet immobility, error, active
  19. [19] § Results › Heterogeneous behavioral modulation of place cells during immobility ↔ figures/fig_S6/fig_S6.m, lines 125–157 · score 0.54 · deep superficial, electrophysiological properties, spike, field, immobility, cells
  20. [20] § Methods › Data analysis › Theta oscillations segmentation and analysis ↔ figures/fig4/fig4.m, lines 345–370 · score 0.53 · theta epochs, Whisker pad motion, quantiles, linear, duration, peak
  21. [21] § Methods › Data analysis › Bayesian decoding analysis ↔ figures/fig_S2/fig_S2.m, lines 58–85 · score 0.52 · neuron dropping procedure, quiet immobility, decoding, error, Bayesian, active
  22. [22] § Methods › Data analysis › Sharp-wave ripple detection and analysis ↔ figures/fig2/fig2.m, lines 25–85 · score 0.52 · Sharp wave ripples, active immobility, SPW, traces
  23. [23] § Methods › Data analysis › Theta oscillations segmentation and analysis ↔ figures/fig4/fig4.m, lines 199–253 · score 0.51 · theta oscillations, delta ratio, epochs, duration, power

Paper

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The authors' code

MATLAB · 359 lines · 14 KB · no license · 8 matches

  1. %% Fig 2 - analysis script to reproduce key findings
  2. % Place and behavioral modulation of hippocampal neurons during immobility
  3. % Nicola Sartorato, Ioannis S. Zouridis, Ulzii-Utas Narantsatsralt, Eduardo Blanco-Hernández, and Andrea Burgalossi
  5. % note: the script is meant to be run per sections in the order here provided.
  6. % MATLAB version:2022a
  8. %% loading data for figure 2
  10. clear;
  12. % load parameter variables
  13. common_vars;
  15. % load data
  16. f2 = load("fig2.mat");
  17. load("bayesian_decoding.mat");
  18. load("cells.mat");
  20. % definition of delta, theta and gamma bands
  21. f_delta = delta_range(1) <= f2.f_spectr & f2.f_spectr <= delta_range(2);
  22. f_theta = theta_range(1) <= f2.f_spectr & f2.f_spectr <= theta_range(2);
  23. f_gamma = gamma_range(1) <= f2.f_spectr & f2.f_spectr <= gamma_range(2);
  25. %% Preprocessing: Behavioral state transitions to active immobility
  27. norm_window = [-2 0]; % in s, normalization window
  29. S_to_active_log = 10*log10(f2.S_to_active); % get spectrograms in log
  30. S_avg_to_active = mean(S_to_active_log,3); % get avg session spectrograms
  32. n_sessions = size(S_to_active_log,3); % get n of sessions
  34. t_norm = norm_window(1) <= f2.x_spectr & f2.x_spectr <= norm_window(2); % definition of norm. window for spectrogram
  35. t_norm_whisk = norm_window(1) <= f2.x_whisk & f2.x_whisk <= norm_window(2); % definition of norm. window for whisk trace
  37. % Theta delta normalization
  38. m = mean(f2.thede_to_active(t_norm,:),1); % session avg
  39. s = std(f2.thede_to_active(t_norm,:),[],1); % session std
  40. thede_to_active_norm = (f2.thede_to_active - m) ./ s; % normalization
  41. m_thede_norm = mean(thede_to_active_norm,2); % avg trace
  42. s_thede_norm = std(thede_to_active_norm,[],2) ./ sqrt(n_sessions); % sem trace
  44. % Theta normalization
  45. avg_theta = mean(S_to_active_log(:,f_theta,:),2);
  46. theta_tcourse = reshape(avg_theta,size(avg_theta,1),n_sessions); % extract theta time course
  47. m = mean(theta_tcourse(t_norm,:),1); % session avg
  48. s = std(theta_tcourse(t_norm,:),0,1); % session std
  49. theta_tc_norm = (theta_tcourse - m) ./ s; % normalization
  50. m_theta = mean(theta_tc_norm,2); % avg trace
  51. s_theta = std(theta_tc_norm,[],2) ./ sqrt(n_sessions); % sem trace
  53. % Gamma normalization
  54. avg_gamma = mean(S_to_active_log(:,f_gamma,:),2);
  55. gamma_tcourse = reshape(avg_gamma,size(S_to_active_log,1),n_sessions); % extract gamma time course
  56. m = mean(gamma_tcourse(t_norm,:),1); % session avg
  57. s = std(gamma_tcourse(t_norm,:),0,1); % session std
  58. gamma_tc_norm = (gamma_tcourse - m) ./ s; % normalization
  59. m_gamma = mean(gamma_tc_norm,2); % avg trace
  60. s_gamma = std(gamma_tc_norm,[],2) ./ sqrt(n_sessions); % sem trace
  62. % Delta normalization
  63. avg_delta = mean(S_to_active_log(:,f_delta,:),2);
  64. delta_tcourse = reshape(avg_delta,size(avg_delta,1),n_sessions); % extract delta time course
  65. m = mean(delta_tcourse(t_norm,:),1); % session avg
  66. s = std(delta_tcourse(t_norm,:),0,1); % session std
  67. delta_tc_norm = (delta_tcourse - m) ./ s; % normalization
  68. m_delta = mean(delta_tc_norm,2); % avg trace
  69. s_delta = std(delta_tc_norm,[],2) ./ sqrt(n_sessions); % sem trace
  71. % Sharp-wave ripple normalization
  72. pre_whisk_rr = -1.5 < f2.rr_x & f2.rr_x < 0;
  73. mean_rr_pre = mean(f2.rr_sessions(:,pre_whisk_rr),2); % avg SPW-R pre whisk
  74. std_rr_pre = std(f2.rr_sessions(:,pre_whisk_rr),0,2); % std SPW-R pre whisk
  75. post_whisk_rr = 0 < f2.rr_x & f2.rr_x < 1.5; % avg SPW-R post whisk
  76. mean_rr_post = mean(f2.rr_sessions(:,post_whisk_rr),2); % avg SPW-R post whisk
  77. rr_norm = (f2.rr_sessions - mean_rr_pre) ./ std_rr_pre; % normalization trace to baseline
  78. rr_incr = (mean_rr_post - mean_rr_pre) ./ std_rr_pre; % normalization for increase
  80. % Whisker-pad motion normalization
  81. m = mean(f2.whisk_to_active(:,t_norm_whisk),2); % session avg
  82. s = std(f2.whisk_to_active(:,t_norm_whisk),[],2); % session std
  83. whisk_to_active_norm = (f2.whisk_to_active - m) ./ s; % normalization
  84. m_whisk = mean(whisk_to_active_norm,1); % avg trace
  85. s_whisk = std(whisk_to_active_norm,[],1) ./ sqrt(n_sessions); % sem trace
  87. %% Fig. 2b. Brain state upon upon transitions to active immobility
  89. figure;
  90. tiledlayout(6,1)
  92. % Triggered spectrogram on whisker-pad motion
  93. a2 = nexttile(1,[2 1]);
  94. imagesc(f2.x_spectr,f2.f_spectr,S_avg_to_active')
  95. set(gca, 'YDir','normal')
  96. xline(0,'Color','white')
  97. clim([-14.5 1])
  98. ylim([1 30])
  99. colormap("turbo")
  100. ylabel("Frequency (Hz)");
  102. % Theta-delta
  103. a3 = nexttile(3);
  104. yline(0,'Color','black')
  105. hold on;
  106. plot(f2.x_spectr,m_thede_norm)
  107. plot(f2.x_spectr,m_thede_norm + s_thede_norm,'Color','black','LineStyle','--')
  108. plot(f2.x_spectr,m_thede_norm - s_thede_norm,'Color','black','LineStyle','--')
  109. ylabel("Theta/delta (zscore)");
  111. % Delta theta gamma
  112. a4 = nexttile(4);
  113. yline(0,'Color','black')
  114. hold on;
  115. plot(f2.x_spectr,m_theta,'LineWidth',2)
  116. plot(f2.x_spectr,m_gamma,'LineWidth',2)
  117. plot(f2.x_spectr,m_theta + s_theta,'Color','black','LineStyle','--')
  118. plot(f2.x_spectr,m_theta - s_theta,'Color','black','LineStyle','--')
  119. plot(f2.x_spectr,m_gamma + s_gamma,'Color','black','LineStyle','--')
  120. plot(f2.x_spectr,m_gamma - s_gamma,'Color','black','LineStyle','--')
  121. ylabel("Theta, gamma (zscore)")
  122. ylim([-2.8 2.8])
  124. yyaxis right;
  125. hold on;
  126. plot(f2.x_spectr,m_delta,'LineWidth',2)
  127. plot(f2.x_spectr,m_delta + s_delta,'Color','black','LineStyle','--')
  128. plot(f2.x_spectr,m_delta - s_delta,'Color','black','LineStyle','--')
  129. ylabel("Delta (zscore)")
  131. ylim([-8 8])
  133. % SPW-R rate
  134. a5 = nexttile(5);
  135. plot(f2.rr_x,mean(rr_norm),'LineWidth',2)
  136. hold on
  137. yline(0,'Color','black')
  138. plot(f2.rr_x,mean(rr_norm) + std(rr_norm) / sqrt(n_sessions),"Color",'black','LineStyle','--')
  139. plot(f2.rr_x,mean(rr_norm) - std(rr_norm) / sqrt(n_sessions),"Color",'black','LineStyle','--')
  140. xlabel("time from whisking onset (s)")
  141. ylabel("ripple rate (norm)")
  143. % Whisker-pad motion
  144. a6 = nexttile(6);
  145. yline(0,'Color','black')
  146. hold on;
  147. plot(f2.x_whisk,m_whisk)
  148. plot(f2.x_whisk,m_whisk + s_whisk,'Color','black','LineStyle','--')
  149. plot(f2.x_whisk,m_whisk - s_whisk,'Color','black','LineStyle','--')
  150. ylabel("Whisker-pad motion (zscore)");
  152. xlabel("Time from whisker-pad motion event (s)")
  154. linkaxes([a2 a3 a4 a5 a6],'x')
  155. xlim([-1.5 2])
  157. %% Fig. 2c. Quantification of brain state upon transitions to active immobility
  159. pre_whisk = f2.x_spectr < 0;
  160. post_whisk = f2.x_spectr >= 0;
  162. the_de_incr = [];
  163. delta_incr = [];
  164. theta_incr = [];
  165. gamma_incr = [];
  167. % calculating increases in each band for each session
  168. for iS=1: n_sessions
  169. % theta delta
  170. m_pre = mean(f2.thede_to_active(pre_whisk,iS),1);
  171. s_pre = std(f2.thede_to_active(pre_whisk,iS),0,1);
  172. m_post = mean(f2.thede_to_active(post_whisk,iS),1);
  173. the_de_incr = [the_de_incr; (m_post - m_pre)/s_pre];
  175. % delta
  176. ts = mean(S_to_active_log(:,f_delta,iS),2);
  177. m_pre = mean(ts(pre_whisk),1);
  178. s_pre = std(ts(pre_whisk),0,1);
  179. m_post = mean(ts(post_whisk),1);
  180. delta_incr = [delta_incr; (m_post - m_pre)/s_pre];
  182. % theta
  183. ts = mean(S_to_active_log(:,f_theta,iS),2);
  184. m_pre = mean(ts(pre_whisk),1);
  185. s_pre = std(ts(pre_whisk),0,1);
  186. m_post = mean(ts(post_whisk),1);
  187. theta_incr = [theta_incr; (m_post - m_pre)/s_pre];
  189. % gamma
  190. ts = mean(S_to_active_log(:,f_gamma,iS),2);
  191. m_pre = mean(ts(pre_whisk),1);
  192. s_pre = std(ts(pre_whisk),0,1);
  193. m_post = mean(ts(post_whisk),1);
  194. gamma_incr = [gamma_incr; (m_post - m_pre)/s_pre];
  195. end
  197. % boxplot zscore
  198. figure;
  199. y = [the_de_incr; delta_incr; theta_incr; gamma_incr; rr_incr];
  200. x = [repmat("theta-delta",n_sessions,1); repmat("delta",n_sessions,1); repmat("theta",n_sessions,1); repmat("gamma",n_sessions,1); repmat("SPW-R",size(rr_incr,1),1)];
  201. violinplot(y,x,'GroupOrder',{'theta-delta','delta','theta','gamma','SPW-R'})
  202. yline(0,'Color','black')
  203. ylabel("increase (std)");
  205. % Two-tailed Wilcoxon signed rank tests
  207. % delta
  208. p_theta_delta = signrank(the_de_incr,0)
  210. % delta
  211. p_delta = signrank(delta_incr,0)
  213. % theta
  214. p_theta = signrank(theta_incr,0)
  216. % gamma
  217. p_gamma = signrank(gamma_incr,0)
  219. % SPW-R
  220. p_SPWR = signrank(rr_incr,0)
  222. %% Fig. 2d. Firing of place cell inside and outside place field
  224. % define variables
  225. pc_infield=f2.fr_after_whisker_onset.pc_in_field;
  226. pc_outfield=f2.fr_after_whisker_onset.pc_out_field;
  227. time=f2.fr_after_whisker_onset.time_after_whisker;
  229. figure
  230. subplot(1,2,1);
  231. plot(time,mean(pc_infield),'r','LineWidth',2)
  232. hold on
  233. plot(time,mean(pc_infield)+std(pc_infield)./sqrt(size(pc_infield,1)),':k')
  234. plot(time,mean(pc_infield)-std(pc_infield)./sqrt(size(pc_infield,1)),':k')
  235. ylim([0,10])
  236. xlim([-4,4])
  237. xlabel 'time after whisker onset'
  238. ylabel 'firing rate'
  239. title 'place cells in field'
  241. subplot(1,2,2);
  242. plot(time,mean(pc_outfield),'r','LineWidth',2)
  243. hold on
  244. plot(time,mean(pc_outfield)+std(pc_outfield)./sqrt(size(pc_outfield,1)),':k')
  245. plot(time,mean(pc_outfield)-std(pc_outfield)./sqrt(size(pc_outfield,1)),':k')
  246. ylim([0,10])
  247. xlim([-4,4])
  248. xlabel 'time after whisker onset'
  249. ylabel 'firing rate'
  250. title 'place cells out field'
  253. %% Fig. 2e. Population tuning curves for active and quiet immobility
  255. % get tuning curves
  256. x = imm_per_tc.sign_dist .* length_belt;
  257. y_active = imm_per_tc.fr_active ./ imm_per_tc.fr_in_field;
  258. y_quiet = imm_per_tc.fr_quiet ./ imm_per_tc.fr_in_field;
  260. [x_tc_active,y_tc_active,y_sem_tc_active] = population_tuning_curve(x,y_active);
  261. [x_tc_quiet,y_tc_quiet,y_sem_tc_quiet] = population_tuning_curve(x,y_quiet);
  263. % get avg place field
  264. figure;
  265. tiledlayout(5,1);
  266. nexttile;
  267. i_peak_pf = size(f2.place_fields,2)/2;
  268. place_field_norm = f2.place_fields ./ f2.place_fields(:,i_peak_pf);
  269. plot(f2.x_pf,mean(place_field_norm),'Color','black','LineWidth',2);
  270. ylim([0.2 1.1])
  271. xlim([-90 90])
  273. % plot population tuning curves
  274. nexttile(2,[4 1]);
  275. plot(x_tc_active,y_tc_active,'Color','red','LineWidth',2);
  276. hold on;
  277. plot(x_tc_quiet,y_tc_quiet,'Color','blue','LineWidth',2);
  279. plot(x_tc_active,y_tc_active+y_sem_tc_active,'Color','red');
  280. plot(x_tc_active,y_tc_active-y_sem_tc_active,'Color','red');
  282. plot(x_tc_quiet,y_tc_quiet+y_sem_tc_quiet,'Color','red');
  283. plot(x_tc_quiet,y_tc_quiet-y_sem_tc_quiet,'Color','red');
  284. xline(0,'Color','red','LineWidth',2);
  286. legend(["active imm.","quiet imm."]);
  288. %% Fig. 2f. Firing rate in function of position and behavioral state
  290. % preparation
  291. active_imm_in_field = f2.fr_in_field_t.fr_active_imm ./ f2.fr_in_field_t.fr_in_field_run; % in field - active imm
  292. quiet_imm_in_field = f2.fr_in_field_t.fr_quiet_imm ./ f2.fr_in_field_t.fr_in_field_run; % in field - quiet imm
  294. active_imm_out_field = f2.fr_out_field_t.fr_active_imm ./ f2.fr_out_field_t.fr_in_field_run; % out of field - active imm
  295. quiet_imm_out_field = f2.fr_out_field_t.fr_quiet_imm ./ f2.fr_out_field_t.fr_in_field_run; % out of field - quiet imm
  297. categoryLabels = {'Active imm., in field','Quiet imm., in field','Active imm., out of field','Quiet imm., out of field'};
  298. y = [active_imm_in_field; quiet_imm_in_field; active_imm_out_field; quiet_imm_out_field];
  299. x = [repmat(convertCharsToStrings(categoryLabels{1}),size(active_imm_in_field));
  300. repmat(convertCharsToStrings(categoryLabels{2}),size(quiet_imm_in_field));
  301. repmat(convertCharsToStrings(categoryLabels{3}),size(active_imm_out_field));
  302. repmat(convertCharsToStrings(categoryLabels{4}),size(quiet_imm_out_field))];
  304. % violin plot
  305. figure;
  306. violinplot(y,x,'GroupOrder',categoryLabels,'ShowData',false);
  307. title(strcat("Firing during imm. (stops in PF = ",num2str(length(active_imm_in_field)),"; stops out PF = ",num2str(length(active_imm_out_field)),")"));
  308. ylabel("FR imm. (norm.)");
  310. % Kruskal Wallis test
  311. [pval,tbl,stats] = kruskalwallis(y,x,'off');
  312. c = multcompare(stats,"Display","off");
  313. tblSign = array2table(c,"VariableNames", ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"])
  315. %% Fig. 2g. Bayesian decoding during immobility
  317. labels = {'run -> run','run -> active imm.','run -> quiet imm.','active imm. -> run','quiet imm. -> run'}; % Labels name, "training -> testing"
  318. labels_s = convertCharsToStrings(labels); % convert to string
  320. % Pooling all imm. periods, renaming
  321. err_train_run_test_run = cell2mat(bayesian_dec.errors_t.z_stops_train_run_test_run')'; % run -> run
  322. err_train_run_test_stop_active = cell2mat(bayesian_dec.errors_t.z_stops_train_run_test_stop_active')'; % run -> active imm
  323. err_train_run_test_stop_quiet = cell2mat(bayesian_dec.errors_t.z_stops_train_run_test_stop_quiet')'; % run -> quiet imm
  324. err_train_stop_active_test_run = cell2mat(bayesian_dec.errors_t.z_stops_train_stop_active_test_run')'; % active imm -> run
  325. err_train_stop_quiet_test_run = cell2mat(bayesian_dec.errors_t.z_stops_train_stop_quiet_test_run')'; % quiet imm -> run
  327. % Preprocessing for boxplots
  328. n_stops = length(err_train_run_test_run);
  329. y = [err_train_run_test_run; err_train_run_test_stop_active; err_train_run_test_stop_quiet; err_train_stop_active_test_run; err_train_stop_quiet_test_run];
  330. x = [];
  331. for iL=1: length(labels)
  332. x = [x; repmat(convertCharsToStrings(labels_s{iL}),n_stops,1)];
  333. end
  335. % Decoding boxplot errors - imm. periods
  336. figure;
  337. violinplot(y,x,'GroupOrder',labels,'ShowData',false)
  338. yline(0,'LineStyle','--')
  339. ylabel("error (zscore)")
  340. title("imm. periods, N=88")
  342. % Wilcoxon sign rank tests, one for each group, H1: median error < 0
  343. [p_run_run,~,s_run_run] = signrank(err_train_run_test_run,0,'tail','left');
  344. [p_run_active_imm,~,s_run_active_imm] = signrank(err_train_run_test_stop_active,0,'tail','left');
  345. [p_run_quiet_imm,~,s_run_quiet_imm] = signrank(err_train_run_test_stop_quiet,0,'tail','left');
  346. [p_active_imm_run,~,s_active_imm_run] = signrank(err_train_stop_active_test_run,0,'tail','left');
  347. [p_quiet_imm_run,~,s_quiet_imm_run] = signrank(err_train_stop_quiet_test_run,0,'tail','left');
  349. % visualize p values
  350. string(labels)
  351. pvals_stops = [p_run_run p_run_active_imm p_run_quiet_imm p_active_imm_run p_quiet_imm_run];
  352. string(pvals_stops)
  354. % Bonferroni correction for multiple comparisons (5 tests)
  355. string(labels)
  356. bonf_stops = pvals_stops < (alpha_sign / length(pvals_stops)) % all groups significant except err_train_run_test_stop_quiet
  358. % Wilcoxon sign rank pairwise test, behavioral state difference only when training on imm.
  359. [p_imm_beh_state,~,s_imm_beh_state] = signrank(err_train_stop_active_test_run,err_train_stop_quiet_test_run,'tail','left');

fig2.m at commit 5745200, no license · at the source

Overview

Authors: Nicola Sartorato1,2,3, Ioannis S. Zouridis1,2,3,4, Ulzii-Utas Narantsatsralt1,2,3,5, Eduardo Blanco-Hernández1,2, Andrea Burgalossi1,2
ORCID iDs: Nicola Sartorato, Ioannis S. Zouridis, Eduardo Blanco-Hernández, Andrea Burgalossi
  1. Institute for Neurobiology, Eberhard Karls University of Tübingen,Tübingen, Germany
  2. Werner Reichardt Centre for Integrative Neuroscience,Tübingen, Germany
  3. Graduate Training Centre of Neuroscience, International Max Planck Research School (IMPRS),Tübingen, Germany
  4. Max Planck Institute for Biological Cybernetics,Tübingen, Germany
  5. Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn,Bonn, Germany
Institutions: University of Tübingen (Germany); Max Planck Society (Germany); Max Planck Institute for Biological Cybernetics (Germany); University Hospital Bonn (Germany)
Journal: Nature communications, volume 17, issue 1, article 7261
Dates: received 11 December 2024; accepted 1 July 2026; published online 24 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-75492-w · PMID 42498723 · PMCID PMC13400744 · OpenAlex W7170394472
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Hippocampus, Neural circuits
MeSH: Behavior, Animal*, CA1 Region, Hippocampal*, Hippocampus*, Neurons*, Place Cells*, Animals, Local Field Potential Measurement, Male, Memory, Episodic, Mice, Mice, Inbred C57BL, Theta Rhythm, Wakefulness (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (BU 3126/2-1, BU 3126/3-1); Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID); University of Tübingen (intramural funding)
Citations: not cited yet (Europe PMC); 143 references in the paper
Research resources: Eight wild-type C57BL/6J mice RRID:IMSR_JAX:000664

Abstract

Behavioral state fluctuations profoundly impact episodic memory processing. To explore the underlying mechanisms, we recorded CA1 place cells in head-fixed male mice and focused on awake immobility to capture spontaneous behavioral state fluctuations by facial motion, pupillometry, and local field potential (LFP) analysis. We found that during awake immobility, the duration of spontaneous whisker-pad motion events correlated with ongoing levels of arousal and modulated both the frequency and the power of theta oscillations. CA1 place cells continued to encode location during immobility, with the spatial code being primarily driven by a subset of behaviorally-modulated place cells which increased their firing upon behavioral state transitions. Single-cell stimulation during immobility was sufficient for the induction of place fields, indicating that plasticity mechanisms can be engaged even in the absence of locomotion. Altogether, these data indicate that behavioral state fluctuations might contribute to episodic memory processing by modulating theta oscillatory dynamics and hippocampal gain via the engagement of a discrete place-cell ensemble.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

BurgalossiPublic/HP-BehModulation

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5745200519905b3711245e92bc2b19edadd87f35, 3 July 2026
Languages: MATLAB (18)
Size: 35 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (10 files), Violinplot-Matlab (4 files), Signal Processing Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/BurgalossiPublic/HP-BehModulation

Code availability

Custom code associated with this study is available at: https://github.com/BurgalossiPublic/HP-BehModulation.

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

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:

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Data

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

Data availability

Preprocessed raw data are available at: https://github.com/BurgalossiPublic/HP-BehModulation. Source data are provided with this paper.

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, 5 authors, 2 keywords, 13 MeSH terms, 3 funders, 142 references, 1 RRID.

Cite

This paper

Sartorato, N., Zouridis, I. S., Narantsatsralt, U.-U., Blanco-Hernández, E., & Burgalossi, A. (2026). Place and behavioral modulation of hippocampal neurons during immobility. Nature communications, 17(1), 7261. https://doi.org/10.1038/s41467-026-75492-w

BibTeX

@article{sartorato2026place,
author = {Sartorato, Nicola and Zouridis, Ioannis S. and Narantsatsralt, Ulzii-Utas and Blanco-Hernández, Eduardo and Burgalossi, Andrea},
title = {{Place and behavioral modulation of hippocampal neurons during immobility}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {7261},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-75492-w},
url = {https://doi.org/10.1038/s41467-026-75492-w},
pmid = {42498723},
pmcid = {PMC13400744}
}

RIS

TY - JOUR
AU - Sartorato, Nicola
AU - Zouridis, Ioannis S.
AU - Narantsatsralt, Ulzii-Utas
AU - Blanco-Hernández, Eduardo
AU - Burgalossi, Andrea
TI - Place and behavioral modulation of hippocampal neurons during immobility
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/24
VL - 17
IS - 1
SP - 7261
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-75492-w
UR - https://doi.org/10.1038/s41467-026-75492-w
LA - en
ER -

CSL-JSON

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