OSCR

Compositionality of social gaze in the prefrontal-amygdala circuits.

Code ↔ Paper

18 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 18 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Neural data analysis › Canonical correlation analysis (CCA) ↔ analysisCode/Cross_area_CCA.m, the whole file · a weak match · score 0.98 · canonical correlation, L2 regularization, fold cross validation, Pearson correlation, independently shuffled, population activity
  2. [2] § Methods › Neural data analysis › Encoding model of representational dissimilarity matrix (RDM) ↔ analysisCode/rsa_core_analysis.py, lines 1–88 · score 0.93 · Euclidean distances, dissimilarity matrices, representational geometry, neural RDM, Neural activity, brain region
  3. [3] § Methods › Neural data analysis › Encoding model of representational dissimilarity matrix (RDM) ↔ analysisCode/rsa_core_analysis.py, lines 1–88 · score 0.85 · multiple linear regression, dependent variable, regression model, neural RDM, diagonal, vectorized
  4. [4] § Methods › Neural data analysis › Population decoding analyses › Content decoding ↔ analysisCode/Cross_primitive_decoding.py, lines 1–64 · score 0.82 · fold cross validation, neural population activity, classifier trained, shuffling controls, decoding, neurons
  5. [5] § Methods › Neural data analysis › Population decoding analyses › Content decoding ↔ analysisCode/Cross_area_CCA.m, the whole file · a weak match · score 0.78 · fold cross validation, shuffling controls, population activity, repetitions, gaze events, trained
  6. [6] § Methods › Neural data analysis › Population decoding analyses › Cross-conditions decoding ↔ analysisCode/Cross_primitive_decoding.py, lines 1–64 · score 0.76 · cross primitives decoding, fold cross validation, neural representation, social gaze primitives, abstract, classifiers
  7. [7] § Results › Linear mixed selectivity neurons facilitate the generalization of social gaze primitives ↔ codes_for_figures1-5.py, lines 530–587 · score 0.75 · nonlinear mixed selectivity, pure content selective, pure state selective, error bars, neuron, BLA
  8. [8] § Methods › Behavioral data analysis › General linear mixed-effects modeling of M2’s social gaze ↔ analysisCode/GLMM.R, lines 44–116 · score 0.75 · lme4, M1 duration, M1 content, M1 state, binomial, logit
  9. [9] § Results › Linear mixed selectivity neurons facilitate the generalization of social gaze primitives ↔ codes_for_figures1-5.py, lines 530–587 · score 0.73 · nonlinear mixed selectivity, pure content selectivity, pure state selectivity, neuron, ACCg, BLA
  10. [10] § Methods › Behavioral data analysis › Control analyses for dominance hierarchy › Dominance as an additional predictor in the GLMM ↔ analysisCode/GLMM.R, lines 44–116 · score 0.61 · lme4, glmer, binomial, logit, GLMM, day
  11. [11] § Results › Combinations of social gaze primitives determine interactive social gaze behavior ↔ codes_for_supplementary_figuresS2-S16.py, lines 402–439 · score 0.58 · log odds, error bars, confidence intervals, Vertical, GLMM, positions
  12. [12] § Results › Combinations of social gaze primitives determine interactive social gaze behavior ↔ codes_for_figures1-5.py, lines 94–146 · score 0.57 · log odds, Predicted probabilities, M1 content, GLMM, square, bars
  13. [13] § Results › Combinations of social gaze primitives determine interactive social gaze behavior ↔ codes_for_figures1-5.py, lines 188–237 · score 0.57 · Predicted probabilities, M1 duration, M1 state, social gaze, Interaction, M2
  14. [14] § Methods › Neural data analysis › Population decoding analyses › Cross-primitives decoding ↔ analysisCode/Cross_primitive_decoding.py, lines 112–137 · score 0.56 · cross primitives decoding, fold, accuracy, classifier, trained, shuffled
  15. [15] § Results › Linear mixed selectivity neurons facilitate the generalization of social gaze primitives ↔ codes_for_supplementary_figuresS2-S16.py, lines 544–583 · score 0.55 · variance inflation, S9, VIF, collinearity, threshold, predictors
  16. [16] § Results › Combinations of social gaze primitives determine interactive social gaze behavior ↔ codes_for_supplementary_figuresS2-S16.py, lines 119–159 · score 0.54 · transformed correlation, S2, Fisher, pupil, median, bouts
  17. [17] § Methods › Neural data analysis › Population PSTHs recoded by state or content preference ↔ codes_for_supplementary_figuresS2-S16.py, lines 742–776 · score 0.52 · Population PSTHs, gaze onset, Firing rates, 200 ms
  18. [18] § Methods › Behavioral data analysis › Control analyses for dominance hierarchy › Dominance as an additional predictor in the GLMM ↔ codes_for_supplementary_figuresS2-S16.py, lines 402–439 · score 0.51 · log odds, confidence intervals, Dominance, GLMM

Paper

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The authors' code

Python · 1,220 lines · 42 KB · no license · 5 matches

  1. #%% import libs
  2. import copy
  3. import matplotlib.pyplot as plt
  4. import numpy as np
  5. import pandas as pd
  6. import seaborn as sns
  7. import scipy.stats as stats
  8. import pickle as pkl
  9. import matplotlib.colors as mcolors
  10. import matplotlib.collections as mcoll
  11. import matplotlib.cm as cm
  12. from scipy.stats import chi2
  13. #%% --------- figure S2 ---------
  14. #%% import figure S2 data
  15. dataS2 = pkl.load(open('figure_S2_data.pkl', 'rb'))
  16. sociFracMartMiniBlocResh = dataS2['sociFracMartMiniBlocResh']
  17. pupiMartMiniBlocResh = dataS2['pupiMartMiniBlocResh']
  18. pearColl_trans = dataS2['pearColl_trans']
  19. sociFracMartMiniBloc = dataS2['sociFracMartMiniBloc']
  20. pupiMartMiniBloc = dataS2['pupiMartMiniBloc']
  21. #%% plot figure S2A
  22. inpuData = copy.deepcopy(sociFracMartMiniBlocResh)
  23. with plt.style.context('style_paper.mplstyle'):
  24. # sns.set(style = "ticks")
  25. f, ax1 = plt.subplots(ncols=1, nrows=1, sharey=True,figsize=[3.54/1.5,3.54/2])
  26. g1 = sns.heatmap(
  27. inpuData,
  28. yticklabels = [i for i in range(1,inpuData.shape[0]+1)],
  29. cmap = 'viridis',
  30. # square = True,
  31. # linewidths=0.2,
  32. ax = ax1)
  33. ax1.set_yticks([1,11,21,31,41],[1,11,21,31,41])
  34. ax1.set_xticks([1.5,4.5,7.5,10.5,13.5,16.5,19.5,22.5],[1,2,3,4,5,6,7,8])
  35. plt.xlabel('Run')
  36. plt.ylabel('Session')
  38. plt.tight_layout()
  39. fileName = 'figS2A_social_bouts_fraction.pdf'
  40. # plt.savefig(fileName,dpi = 600)
  41. plt.show()
  43. #%% plot figure S2B
  44. inpuData = copy.deepcopy(pupiMartMiniBlocResh)
  45. with plt.style.context('style_paper.mplstyle'):
  46. # sns.set(style = "ticks")
  47. f, ax1 = plt.subplots(ncols=1, nrows=1, sharey=True,figsize=[3.54/1.5,3.54/2])
  48. g1 = sns.heatmap(
  49. inpuData,
  50. # xticklabels = [i for i in range(1,9)],
  51. yticklabels = [i for i in range(1,inpuData.shape[0]+1)],
  52. # yticklabels = [i for i in range(1,inpuData.shape[0]+1)],
  53. # cbar=False,
  54. # cmap = 'viridis',
  55. # square = True,
  56. # linewidths=0.2,
  57. ax = ax1)
  58. ax1.set_yticks([1,11,21,31,41],[1,11,21,31,41])
  59. ax1.set_xticks([1.5,4.5,7.5,10.5,13.5,16.5,19.5,22.5],[1,2,3,4,5,6,7,8])
  60. # ax1.set_yticks([,11,21,31,41],[1,11,21,31,41])
  61. # ax1.set_yticks([1,6,11],[1,6,11])
  62. plt.xlabel('Block')
  63. plt.ylabel('Session')
  64. # ax1.set_xticks([])
  65. # ax1.set_yticklabels(['1','0.5','0','-0.5'],rotation = 0)
  66. # plt.title('')
  67. plt.tight_layout()
  68. # fileName = 'blocLabelMatr_all_group.pdf'
  69. fileName = 'figS2B_blocLabelMatr_all_pupil.pdf'
  70. # plt.savefig(fileName,dpi = 600)
  71. plt.show()
  73. print(stats.ttest_1samp(pearColl_trans, 0))
  78. #%% plot figure S2C
  79. all_stateIndex = []
  80. all_pupilIndex = []
  83. with plt.style.context('style_paper.mplstyle'):
  84. plt.figure(figsize=(3.54/1.5, 3.54/2))
  86. for i in range(42):
  87. stateIndex = sociFracMartMiniBloc[i, :, :].flatten()
  88. pupilIndex = pupiMartMiniBloc[i, :, :].flatten()
  89. # Convert to DataFrame for seaborn plotting
  90. df_i = pd.DataFrame({'StateIndex': stateIndex, 'PupilIndex': pupilIndex})
  92. # Plot the linear regression line for each day (index i)
  93. sns.regplot(x='StateIndex', y='PupilIndex', data=df_i, scatter=False, ci=False,
  94. scatter_kws={'s': 5,
  95. 'alpha':.5,
  96. "edgecolor": "none",
  97. "color": "black"},
  98. line_kws={'color': '#1f77b4',
  99. 'lw':1,
  100. 'alpha': 0.75}, ax=plt.gca())
  103. # plt.title('Linear Regression Lines for Each Day (42 Lines)')
  104. plt.xlabel('Social bouts fraction')
  105. plt.ylabel('Pupil size\n(normalized)')
  106. plt.tight_layout()
  107. fileName = 'figS2C_linear_regression_social_state_pupil.pdf'
  108. # plt.savefig(fileName,dpi = 600)
  111. plt.show()
  112. #%% plot figure S2D
  113. with plt.style.context('style_paper.mplstyle'):
  114. plt.figure(figsize=(3.54/1.5, 3.54/2))
  115. # Histogram of Fisher Z transformed correlations
  116. # plt.figure(figsize=(3.54/2, 3.54/3))
  117. plt.hist(pearColl_trans, bins=10, edgecolor='black', alpha=0.7, color = '#1f77b4')
  119. # Calculate the mean of the Fisher Z-transformed correlations
  120. mean_z = np.mean(pearColl_trans)
  122. # Plot the red line for the mean
  123. plt.axvline(mean_z, color='red', linestyle='dashed', linewidth=1)
  125. # Add annotation for the mean
  126. plt.annotate(f'Mean = {mean_z:.4f}', xy=(mean_z, 5), xytext=(mean_z + 0.01, 9),
  128. fontsize=8, color='red')
  130. # plt.title('Histogram of Fisher Z-transformed Pearson Correlations')
  131. # plt.xlabel('Fisher Z-transformed Correlation')
  132. # plt.ylabel('Frequency')
  133. # plt.show()
  134. # plt.title('Histogram of Fisher Z-transformed Pearson Correlations')
  135. plt.xlabel('Correlation\n(Fisher Z-transformed)')
  136. plt.ylabel('Frequency')
  139. # plt.xlabel('Social bouts fraction')
  140. # plt.ylabel('Pupil size\n(normalized)')
  141. plt.tight_layout()
  142. fileName = 'figS2D_Fisher Z-transformed_corr_social_state_pupil.pdf'
  143. # plt.savefig(fileName,dpi = 600)
  144. plt.show()
  145. #%% --------- figure S3 ---------
  146. #%% import figure S3 data
  147. dataS3 = pkl.load(open('figure_S3_data.pkl', 'rb'))
  148. duraDisColl = dataS3['duraDisColl']
  149. faceDisColl = dataS3['faceDisColl']
  150. #%% plot duration distribution
  151. # Set up figure with shared axes
  152. with plt.style.context('style_paper.mplstyle'):
  154. fig, axes = plt.subplots(7, 6, figsize=(8, 6),)
  155. axes = axes.flatten()
  157. for i in range(42):
  158. session_data = np.array(duraDisColl[i])
  159. median = np.median(session_data)
  161. # Bin edges and centers
  162. bins = np.histogram_bin_edges(session_data, bins=25)
  163. bin_centers = 0.5 * (bins[1:] + bins[:-1])
  164. bin_width = bins[1] - bins[0]
  165. half_width = bin_width * 0.5
  167. # Count values by group
  168. short_counts = np.zeros(len(bins) - 1)
  169. long_counts = np.zeros(len(bins) - 1)
  170. bin_indices = np.digitize(session_data, bins) - 1
  172. for val, idx in zip(session_data, bin_indices):
  173. if 0 <= idx < len(short_counts):
  174. if val < median:
  175. short_counts[idx] += 1
  176. else:
  177. long_counts[idx] += 1
  179. # Plot side-by-side bars
  180. axes[i].bar(bin_centers - half_width * 0.5, short_counts,
  181. width=half_width, color='white', edgecolor='gray', label='Short (< median)')
  182. axes[i].bar(bin_centers + half_width * 0.5, long_counts,
  183. width=half_width, color='black', edgecolor='black', label='Long (≥ median)')
  185. axes[i].set_title(f'#{i+1}', fontsize=8)
  187. # Add legend and axis labels only to first subplot
  188. if i == 0:
  189. axes[i].legend(fontsize=6, loc='upper right')
  191. # Set common labels
  192. fig.text(0.5, 0, 'Gaze duration (s)', ha='center', fontsize=10)
  193. fig.text(0.0, 0.5, 'Gaze bouts number', va='center', rotation='vertical', fontsize=10)
  195. plt.tight_layout(rect=[0.06, 0.04, 1, 1])
  198. plt.tight_layout()
  199. fileName = 'figS3A_gazeDuraDistribu.pdf'
  200. # plt.savefig(fileName,dpi = 600)
  201. plt.show()
  202. # plt.show()
  203. #%% plot distribution of gaze content
  204. faceDisColl_face = [faceDisColl[i][0] for i in range(42)]
  205. faceDisColl_object = [faceDisColl[i][1] for i in range(42)]
  206. with plt.style.context('style_paper.mplstyle'):
  207. fig, axes = plt.subplots(1, 2, figsize=(4, 3),)
  208. axes = axes.flatten()
  209. plt.subplot(121)
  210. plt.hist(faceDisColl_face,color = '#E97F26')
  211. plt.ylabel('Sessions')
  212. plt.xlabel('Face gaze numbers')
  213. plt.subplot(122)
  214. plt.hist(faceDisColl_object,color = '#6BBA44')
  215. plt.xlabel('Object gaze numbers')
  216. # plt.
  217. fileName = 'figS3B_gazeContentDistribu.pdf'
  218. # plt.savefig(fileName,dpi = 600)
  219. plt.show()
  222. #%% --------- figure S4 ---------
  223. #%% import figure S4 data
  224. dataS4 = pkl.load(open('figure_S4_data.pkl', 'rb'))
  225. lrt_vs_full = dataS4['lrt_vs_full']
  226. delta_aic_vs_full = dataS4['delta_aic_vs_full']
  227. #%% plot figure S4A
  228. # LRT results vs full model
  229. # lrt_vs_full = pd.DataFrame({
  230. # "Removed_Primitive": ["State", "Content", "Duration"],
  231. # "Delta_2LL": [16.080, 58.362, 16.283],
  232. # "df": [4, 4, 4],
  233. # "p_value": [2.913e-03, 6.406e-12, 2.662e-03]
  234. # })
  236. # # ΔAIC vs full model
  237. # delta_aic_vs_full = pd.DataFrame({
  238. # "Removed_Primitive": ["State", "Content", "Duration"],
  239. # "DeltaAIC": [8.080167, 50.362017, 8.282597]
  240. # })
  241. with plt.style.context('style_paper.mplstyle'):
  242. fig, ax = plt.subplots(figsize=(4, 5))
  244. bars = ax.bar(
  245. lrt_vs_full["Removed_Primitive"],
  246. lrt_vs_full["Delta_2LL"],
  247. color="lightgray"
  248. )
  249. # Text annotations
  250. for i, row in lrt_vs_full.iterrows():
  251. ax.text(
  252. i,
  253. row["Delta_2LL"] + 1,
  254. f"df={row['df']}\nP={row['p_value']:.3g}",
  255. ha="center",
  256. va="bottom",
  257. fontsize=10
  258. )
  259. # Chi-square critical line (df = 4, alpha = 0.05)
  260. crit_df4 = chi2.ppf(0.95, df=4)
  261. ax.axhline(
  262. crit_df4,
  263. linestyle="--",
  264. alpha=0.6,
  265. color='gray'
  266. )
  268. ax.set_ylabel(r"$\Delta$ -2 log L (relative to full model)", fontsize=20)
  269. ax.set_xlabel("Primitive removed", fontsize=20)
  270. ax.tick_params(labelsize=18)
  272. plt.tight_layout()
  273. # plt.savefig("figS4A_Delta_2LL_primitives_remove.pdf", dpi=600)
  274. plt.show()
  275. # plt.close()
  276. #%% plot ΔAIC vs full model
  277. with plt.style.context('style_paper.mplstyle'):
  278. fig, ax = plt.subplots(figsize=(4, 5))
  279. # ax.axhline(2, linestyle="--", alpha=0.6, color='k')
  280. # ax.axhline(6, linestyle=":", alpha=0.6, color='k')
  282. ax.bar(
  283. delta_aic_vs_full["Removed_Primitive"],
  284. delta_aic_vs_full["DeltaAIC"],
  285. color="steelblue",alpha=0.8
  286. )
  288. # AIC rule-of-thumb thresholds
  291. ax.set_ylabel(r"$\Delta$ AIC (relative to full model)", fontsize=20)
  292. ax.set_xlabel("Primitive removed", fontsize=20)
  293. ax.tick_params(labelsize=18)
  295. plt.tight_layout()
  297. # plt.savefig("figS4B_Delta_AIC_primitives_remove.pdf", dpi=600)
  298. plt.show()
  299. # plt.close()
  300. #%% --------- figure S5 ---------
  301. #%% import figure S5 data
  302. dataS5 = pkl.load(open('figure_S5_data.pkl', 'rb'))
  303. coefs_pub = dataS5['coefs_pub']
  304. plot_state_df = dataS5['plot_state_df']
  305. #%% plot figure S5A
  306. # Sort predictors for better visualization
  307. coefs_pub_sorted = coefs_pub.sort_values("Estimate (log-odds)")
  309. with plt.style.context('style_paper.mplstyle'):
  311. fig, ax = plt.subplots(figsize=(5, 2))
  312. ax.errorbar(
  313. coefs_pub_sorted["Estimate (log-odds)"],
  314. coefs_pub_sorted["Predictor"],
  315. xerr=[
  316. coefs_pub_sorted["Estimate (log-odds)"] - coefs_pub_sorted["95% CI (Lower)"],
  317. coefs_pub_sorted["95% CI (Upper)"] - coefs_pub_sorted["Estimate (log-odds)"]
  318. ],
  319. fmt='.',
  320. color='#1f77b4', # consistent scientific blue
  321. ecolor='gray',
  322. capsize=0
  323. )
  324. ax.axvline(0, color='black', linestyle='--', linewidth=0.8)
  325. ax.set_xlabel("Estimate (log odds)", fontsize=9)
  326. ax.set_ylabel("Predictor",)
  327. ax.tick_params(axis='both')
  328. plt.tight_layout()
  329. fileName = 'figS5A_GLMM_Fixed_Effects_Social_State.pdf'
  330. # plt.savefig(fileName,dpi = 600)
  331. plt.show()
  333. #%% figS5B Predicted Probability for M1_state (Low vs. High)
  334. # Create bar plot
  335. with plt.style.context('style_paper.mplstyle'):
  337. fig, ax = plt.subplots(figsize=(1.75, 2))
  338. ax.bar(plot_state_df['M1_state'], plot_state_df['prob'],
  339. yerr=[plot_state_df['prob'] - plot_state_df['asymp.LCL'],
  340. plot_state_df['asymp.UCL'] - plot_state_df['prob']],
  341. capsize=0, color=['lightgray', 'orchid'], edgecolor='black')
  343. ax.set_title("Main effect: M1 state", fontsize=10)
  344. ax.set_ylabel("Predicted probability\n of M2 high social state")
  345. ax.set_xlabel("M1 state")
  346. ax.set_ylim(0, 0.8)
  347. # plt.grid(axis='y', linestyle='--', alpha=0.7)
  348. plt.tight_layout()
  350. fileName = 'figS5B_Predicted_Probability_of_M2_Social_State_by_M1_State.pdf'
  351. # plt.savefig(fileName,dpi = 600)
  352. plt.show()
  355. #%% --------- figure S6 ---------
  356. #%% import figure S6 data
  357. figure_S6_data = pkl.load(open('figure_S6_data.pkl', 'rb'))
  358. coefs_fixed = figure_S6_data['coefs_fixed']
  359. coefs_random = figure_S6_data['coefs_random']
  364. #%%
  366. # coefs_fixed = pd.DataFrame({
  367. # "term": [
  368. # "Intercept",
  369. # "M1_state1",
  370. # "M1_content1",
  371. # "M1_duration1",
  372. # "dominant1",
  373. # "M1_state1 × M1_content1",
  374. # "M1_state1 × M1_duration1",
  375. # "M1_content1 × M1_duration1",
  376. # "M1_state1 × dominant1",
  377. # "M1_content1 × dominant1",
  378. # "M1_duration1 × dominant1",
  379. # "M1_state1 × M1_content1 × M1_duration1",
  380. # "M1_state1 × M1_content1 × dominant1",
  381. # "M1_state1 × M1_duration1 × dominant1",
  382. # "M1_content1 × M1_duration1 × dominant1",
  383. # "M1_state1 × M1_content1 × M1_duration1 × dominant1"
  384. # ],
  385. # "estimate": [
  386. # -1.96, 0.388, 0.659, -0.0376, -0.158,
  387. # -0.369, 0.196, -0.108, -0.0488, -0.226,
  388. # 0.234, -0.170, 0.180, -0.388, -0.162, 0.175
  389. # ],
  390. # "conf.low": [
  391. # -2.56, -0.00504, 0.408, -0.380, -0.985,
  392. # -0.791, -0.388, -0.489, -0.535, -0.565,
  393. # -0.187, -0.793, -0.361, -1.11, -0.659, -0.621
  394. # ],
  395. # "conf.high": [
  396. # -1.35, 0.782, 0.910, 0.305, 0.670,
  397. # 0.0521, 0.779, 0.273, 0.438, 0.113,
  398. # 0.656, 0.454, 0.720, 0.331, 0.336, 0.972
  399. # ]
  400. # })
  402. #%%
  403. coefs_fixed = coefs_fixed.sort_values("estimate")
  404. y_pos = range(len(coefs_fixed))
  405. #%%
  406. with plt.style.context('style_paper.mplstyle'):
  408. plt.figure(figsize=(6, 4))
  410. # error bars
  411. plt.hlines(
  412. y=y_pos,
  413. xmin=coefs_fixed["conf.low"],
  414. xmax=coefs_fixed["conf.high"],
  415. color="gray",
  416. linewidth=1
  417. )
  419. # points
  420. plt.scatter(
  421. coefs_fixed["estimate"],
  422. y_pos,
  423. color="blue",
  424. s=120
  425. )
  427. # vertical zero line
  428. plt.axvline(0, linestyle="--", color="black", linewidth=0.8)
  430. # axes
  431. plt.yticks(y_pos, coefs_fixed["term"])
  432. plt.xlabel("Estimate (Log Odds)")
  433. plt.title("GLMM Fixed Effects with 95% Confidence Intervals")
  435. plt.grid(axis="x", alpha=0.3)
  436. plt.grid(axis="y", visible=False)
  437. plt.tight_layout()
  438. plt.savefig("figS6A_GLMM_fixed_effects_with_fixed_dominant.pdf", format="pdf", dpi=600)
  439. plt.show()
  440. # plt.close()
  442. #%% plot figure S6B
  443. # coefs_random = pd.DataFrame({
  444. # "term": [
  445. # "Intercept",
  446. # "M1_state1",
  447. # "M1_content1",
  448. # "M1_duration1",
  449. # "M1_state1 × M1_content1",
  450. # "M1_state1 × M1_duration1",
  451. # "M1_content1 × M1_duration1",
  452. # "M1_state1 × M1_content1 × M1_duration1"
  453. # ],
  454. # "estimate": [
  455. # -2.02, 0.361, 0.543, 0.122,
  456. # -0.289, -0.0655, -0.239, 0.00232
  457. # ],
  458. # "conf.low": [
  459. # -2.48, 0.132, 0.378, -0.0765,
  460. # -0.544, -0.401, -0.475, -0.372
  461. # ],
  462. # "conf.high": [
  463. # -1.57, 0.590, 0.707, 0.320,
  464. # -0.0330, 0.270, -0.00237, 0.376
  465. # ]
  466. # })
  467. coefs_random = coefs_random.sort_values("estimate")
  468. y_pos = range(len(coefs_random))
  469. with plt.style.context('style_paper.mplstyle'):
  470. plt.figure(figsize=(6, 4))
  472. plt.hlines(
  473. y=y_pos,
  474. xmin=coefs_random["conf.low"],
  475. xmax=coefs_random["conf.high"],
  476. color="gray",
  477. linewidth=1
  478. )
  480. plt.scatter(
  481. coefs_random["estimate"],
  482. y_pos,
  483. color="blue",
  484. s=120
  485. )
  487. plt.axvline(0, linestyle="--", color="black", linewidth=0.8)
  489. plt.yticks(y_pos, coefs_random["term"])
  490. plt.xlabel("Estimate (Log Odds)")
  491. plt.title("GLMM Fixed Effects with 95% Confidence Intervals")
  493. plt.grid(axis="x", alpha=0.3)
  494. plt.grid(axis="y", visible=False)
  496. plt.tight_layout()
  497. # plt.savefig("figS6B_GLMM_fixed_effects_with_random_dominant.pdf", format="pdf", dpi=600)
  498. plt.show()
  499. # plt.close()
  501. #%% --------- figure S7 ---------
  502. #%% import figure S7 data
  503. dataS7 = pkl.load(open('figure_S7_data.pkl', 'rb'))
  504. permTestMeanPartiaRegre = dataS7['permTestMeanPartiaRegre']
  505. permTestStdPartiaRegre = dataS7['permTestStdPartiaRegre']
  506. coffMatrPartiaRegre = dataS7['coffMatrPartiaRegre']
  507. #%% plot figure S7
  508. hypoLabes = ['Content', 'State', 'Duration']
  509. areas = ['BLA','ACCg','dmPFC','OFC']
  510. with plt.style.context('style_paper.mplstyle'):
  511. fig, ax = plt.subplots(figsize=(3.45*2.35,3.54/1.5))
  512. widt = 0.2
  513. colors = sns.color_palette()
  514. i = 0
  515. for j in [1,0,2,3]:# range(0,4):
  516. # i = 0
  517. plt.bar([1+i*widt,2+i*widt,3+i*widt],coffMatrPartiaRegre[j,[1,0,2],0],width = widt*0.75,label = areas[i],color = colors[j])
  518. if i==3:
  519. plt.errorbar([1+i*widt,2+i*widt,3+i*widt],permTestMeanPartiaRegre[j,[1,0,2]], yerr=permTestStdPartiaRegre[j,[1,0,2]]*1,
  520. lw = 0.5,
  521. color = 'black',
  522. linestyle='none',
  523. label='Shuffled data ± 1 std',capsize=1,)
  524. else:
  525. plt.errorbar([1+i*widt,2+i*widt,3+i*widt],permTestMeanPartiaRegre[j,[1,0,2]],
  526. yerr=permTestStdPartiaRegre[j,[1,0,2]]*1,
  527. lw = 0.5,
  528. color = 'black',
  529. linestyle='none',
  530. capsize=1)
  532. i+=1
  533. plt.xticks([1+1.5*widt,2+1.5*widt,3+1.5*widt],hypoLabes )
  534. plt.xlabel('Model RDM')
  535. plt.ylabel('Partial regression coefficient')
  536. # plt.legend(loc='upper right', bbox_to_anchor=(1, 1.1))
  537. plt.legend(bbox_to_anchor=(1, 0.8))
  538. # plt.tight_layout()
  539. fileName = 'figS7_partRegrRDMShufPost_mini_block_updated.pdf'
  541. # plt.savefig(fileName,dpi = 600)
  542. plt.show()
  544. #%% --------- figure S9 ---------
  545. #%% import figure S9 data
  546. dataS9 = pkl.load(open('figure_S9_data.pkl', 'rb'))
  547. df_vif = dataS9['df_vif']
  548. #%% --------- plot ----------
  549. with plt.style.context('style_paper.mplstyle'):
  550. fig, axes = plt.subplots(
  551. 1, 1,
  552. figsize=(6./2/2, 2.6/1.5),
  553. # gridspec_kw={"width_ratios": [1, 1.2]}
  554. )
  555. sns.boxplot(
  556. data=df_vif,
  557. x="Predictor",
  558. y="VIF",
  559. ax=axes,
  560. palette=["#4C72B0", "#DD8452"],
  561. width=0.5,
  562. flierprops=dict(
  563. marker='o',
  564. markersize=2,
  565. markerfacecolor='gray',
  566. markeredgecolor='gray',
  567. alpha=0.6
  568. )
  569. )
  571. # VIF threshold
  572. axes.axhline(5, ls="--", lw=0.8, color="gray", alpha=0.6)
  574. axes.set_xlabel("")
  575. axes.set_ylabel("Variance inflation\nfactor (VIF)")
  576. axes.set_title("Collinearity")
  577. axes.set_ylim([0.5, 5.2])
  579. axes.set_xticks([0,1],['Content','State'])
  580. plt.tight_layout()
  581. fileName = 'figS9_pairCorrVIF_pool_neurons.pdf'
  582. # plt.savefig(fileName,dpi = 600)
  583. plt.show()
  585. #%% --------- figure S10 ---------
  586. #%% import figure S10 data
  587. dataS10 = pkl.load(open('figure_S10_data.pkl', 'rb'))
  588. PSTHColl = dataS10['PSTHColl']
  590. examNeurDataColl = dataS10['examNeurDataColl']
  593. #%% helper function for plotting mean with shaded error bars
  594. def plot_mean_with_shade(data, label, color, linestyle='-', alpha=0.3,
  595. edge_alpha=1.0, edge_lw=0.8, edge_ls='-',timepoints = np.arange(200)):
  596. mean = np.mean(data, axis=0)
  597. sem = np.std(data, axis=0) / np.sqrt(data.shape[0])
  598. lower = mean - sem
  599. upper = mean + sem
  600. # Shaded fill
  601. ax.fill_between(timepoints, lower, upper, color=color, alpha=alpha)
  602. # Edge lines as dashed or solid lines
  603. ax.plot(timepoints, upper, color=color, linestyle=edge_ls, linewidth=edge_lw, alpha=edge_alpha)
  604. ax.plot(timepoints, lower, color=color, linestyle=edge_ls, linewidth=edge_lw, alpha=edge_alpha)
  605. # Mean line
  606. ax.plot(timepoints, mean, color=color, linestyle=linestyle, label=label)
  607. #%% plot example neuron PSTHs (face vs object, high vs low state)
  608. for i in PSTHColl.keys():
  609. socialHighActiv = PSTHColl[i]['socialHighActiv']
  610. socialLowActiv = PSTHColl[i]['socialLowActiv']
  611. nonSocialHighActiv = PSTHColl[i]['nonSocialHighActiv']
  612. nonSocialLowActiv = PSTHColl[i]['nonSocialLowActiv']
  614. with plt.style.context('style_paper.mplstyle'):
  616. fig, ax = plt.subplots(figsize=[3.54/2, 3.54/2.5])
  617. timepoints = np.arange(100)
  619. # Plot all four conditions with edge-highlighted shaded error
  620. plot_mean_with_shade(socialHighActiv, 'Face High', color='#e97f26', linestyle='-',edge_ls='', alpha=0.8)
  621. plot_mean_with_shade(socialLowActiv, 'Face Low', color='#E97F26', linestyle='--', alpha=0.2, edge_ls='', edge_alpha=0.15)
  623. plot_mean_with_shade(nonSocialHighActiv, 'Object High', color='#6bba44', linestyle='-', edge_ls='',alpha=0.8)
  624. plot_mean_with_shade(nonSocialLowActiv, 'Object Low', color='#6bba44', linestyle='--', alpha=0.2, edge_ls='', edge_alpha=0.15)
  626. # Axes and ticks
  627. ax.set_xlabel('Time from gaze onset (s)')
  628. ax.set_ylabel('Firing rate\n(normalized)')
  630. plt.xticks([0,50,100,150,200],
  631. [ -1, -0.5,0,0.5,1]) # Set x-ticks to match
  634. ax.set_xlim(50, 200)
  635. plt.tight_layout()
  636. figureName = f'figS10_PSTH_number_{i}.pdf'
  637. # plt.savefig(figureName,dpi = 600)
  638. plt.show()
  639. plt.close()
  640. #%% plot example neuron firing rate bar plot (face vs object, high vs low state)
  642. for i in examNeurDataColl.keys():
  643. # means = examNeurDataColl[i]['means']
  644. # sems = examNeurDataColl[i]['sems']
  645. counts = examNeurDataColl[i]['counts']
  646. df = examNeurDataColl[i]['df']
  647. normFiringRates = df['FiringRate'].values
  648. x_labels = ['Face', 'Object']
  649. x = np.arange(len(x_labels))
  650. bar_width = 0.35
  651. with plt.style.context('style_paper.mplstyle'):
  652. fig, ax = plt.subplots(
  653. ncols=1,
  654. nrows=1,
  655. figsize=[3.54 / 2, 3.54 / 2.5]
  656. )
  657. face_color = '#e97f26'
  658. object_color = '#6bba44'
  659. for ii, content in enumerate(x_labels):
  660. color = face_color if content == 'Face' else object_color
  661. x_high = x[ii] - bar_width / 1.8
  662. x_low = x[ii] + bar_width / 1.8
  664. high_key = (content, 'High state')
  665. low_key = (content, 'Low state')
  667. high_vals = df[
  668. (df['Content'] == content)
  669. & (df['State'] == 'High state')
  670. ]['FiringRate'].values
  672. low_vals = df[
  673. (df['Content'] == content)
  674. & (df['State'] == 'Low state')
  675. ]['FiringRate'].values
  676. # -------------------------
  677. # Boxplot as distribution summary
  678. # -------------------------
  679. bp = ax.boxplot(
  680. [high_vals, low_vals],
  681. positions=[x_high, x_low],
  682. widths=0.22,
  683. patch_artist=True,
  684. showfliers=False,
  685. medianprops={
  686. 'color': 'black',
  687. 'linewidth': 1.0
  688. },
  689. boxprops={
  690. 'linewidth': 1.0
  691. },
  692. whiskerprops={
  693. 'linewidth': 0.8
  694. },
  695. capprops={
  696. 'linewidth': 0.8
  697. },
  698. zorder=2
  699. )
  700. bp['boxes'][0].set_facecolor(color)
  701. bp['boxes'][0].set_alpha(0.30)
  702. bp['boxes'][0].set_edgecolor(color)
  703. bp['boxes'][1].set_facecolor('white')
  704. bp['boxes'][1].set_alpha(1.0)
  705. bp['boxes'][1].set_edgecolor(color)
  706. bp['boxes'][1].set_linestyle('--')
  707. for whisker in bp['whiskers']:
  708. whisker.set_color(color)
  709. for cap in bp['caps']:
  710. cap.set_color(color)
  711. ax.set_xticks(x)
  712. ax.set_xticklabels(x_labels)
  713. ax.set_xlabel('Content')
  714. ax.set_ylabel('Firing rate\n(normalized)')
  715. # ax.set_ylim(0, 1.12)
  716. ax.spines['top'].set_visible(False)
  717. ax.spines['right'].set_visible(False)
  718. fileName = (
  719. 'figS10_example_neur'
  720. + '_inde_'
  721. + str(i)
  722. + '_'
  723. + '_boxplot_update.pdf'
  724. )
  725. plt.tight_layout()
  726. # Uncomment to save
  727. # plt.savefig(figPath + fileName, dpi=300, bbox_inches='tight')
  728. plt.show()
  729. #%% --------- figure S11 ---------
  730. #%% import figure S11 data
  731. dataS11 = pkl.load(open('figure_S11_data.pkl', 'rb'))
  732. psthPreNonPreColl = dataS11['psthPreNonPreColl']
  733. #%% plot population PSTHs for preferred vs non-preferred conditions, with significant clusters highlighted
  734. areas =['ACCg','BLA','dmPFC','OFC']
  735. # =========================================================
  736. # loop: content & state
  737. # =========================================================
  738. bin_size_ms = 10
  739. time_ms = (np.arange(200) - 100) * bin_size_ms
  740. time_bins = np.arange(200)
  742. for primitive in ['content', 'state']:
  743. for ss in range(4):
  744. mean_p = psthPreNonPreColl[primitive][areas[ss]]['mean_p']
  745. sem_p = psthPreNonPreColl[primitive][areas[ss]]['sem_p']
  746. mean_n = psthPreNonPreColl[primitive][areas[ss]]['mean_n']
  747. sem_n = psthPreNonPreColl[primitive][areas[ss]]['sem_n']
  748. sig_clusters = psthPreNonPreColl[primitive][areas[ss]]['sig_clusters']
  749. # ---------- plot ----------
  750. with plt.style.context('style_paper.mplstyle'):
  751. fig, ax = plt.subplots(figsize=(3.2, 2.4))
  752. ax.plot(time_bins, mean_p, label='Preferred')
  753. ax.fill_between(time_bins, mean_p-sem_p, mean_p+sem_p, alpha=0.3)
  755. ax.plot(time_bins, mean_n, label='Non-preferred')
  756. ax.fill_between(time_bins, mean_n-sem_n, mean_n+sem_n, alpha=0.3)
  757. for cl in sig_clusters:
  758. ax.plot([cl[0],cl[-1]],[0.20,0.20],color='k')
  759. ax.axvline(100, ls='--', lw=0.8, color='gray')
  760. ax.set_xticks([0, 50, 100, 150, 200])
  761. ax.set_xticklabels([-1000, -500, 0, 500, 1000])
  763. ax.set_xlabel('Time from gaze onset (ms)')
  764. ax.set_ylabel('Firing rate (normalized)')
  765. ax.set_title(f'{areas[ss]} population PSTH ({primitive})')
  766. ax.legend(frameon=False)
  767. plt.tight_layout()
  768. fileName = f'figure_S11_{areas[ss]} population PSTH ({primitive})'+'.pdf'
  769. # plt.savefig(fileName,dpi = 600)
  770. plt.show()
  771. #%% --------- figure S12 ---------
  772. #%% import figure S12 data
  773. dataS12 = pkl.load(open('figure_S12_data.pkl', 'rb'))
  774. coffMatr= dataS12['coffMatr']
  775. permTestMean= dataS12['permTestMean']
  776. permTestStd= dataS12['permTestStd']
  778. # %% plot
  780. hypoLabes = [ 'Content','State', 'Duration']
  781. areas = ['BLA','ACCg','dmPFC','OFC']
  782. with plt.style.context('style_paper.mplstyle'):
  783. fig, ax = plt.subplots(figsize=(3.45*2.35,3.54/1.5))
  784. widt = 0.2
  785. colors = sns.color_palette()
  786. i = 0
  787. # for j in range([0,4)]:
  788. for j in [1,0,2,3]:
  789. # i = 0
  790. plt.bar([1+i*widt,2+i*widt,3+i*widt],coffMatr[j,[1,0,2],0],width = widt*0.75,label = areas[i],color = colors[j])
  791. if i==3:
  792. plt.errorbar([1+i*widt,2+i*widt,3+i*widt],permTestMean[j,[1,0,2]], yerr=permTestStd[j,[1,0,2]]*1,
  793. lw = 0.5,
  794. color = 'black',
  795. linestyle='none',
  796. label='Shuffled data ± 1 std',capsize=1)
  797. else:
  798. plt.errorbar([1+i*widt,2+i*widt,3+i*widt],permTestMean[j,[1,0,2]],
  799. yerr=permTestStd[j,[1,0,2]]*1,
  800. lw = 0.5,
  801. color = 'black',
  802. linestyle='none',
  803. capsize=1)
  805. i+=1
  806. plt.xticks([1+1.5*widt,2+1.5*widt,3+1.5*widt],hypoLabes )
  807. plt.xlabel('Model RDM')
  808. plt.ylabel('β estimate')
  809. plt.legend(bbox_to_anchor=(1, 0.8))
  811. # plt.tight_layout()
  812. fileName = 'figS12_encoding_model_remove_non_selective.pdf'
  813. # plt.savefig(fileName,dpi = 600)
  814. plt.show()
  815. #%% --------- figure S13 ---------
  816. #%% import figure S13 data
  817. dataS13 = pkl.load(open('figure_S13_data.pkl', 'rb'))
  818. crossDecodingMatrixACCg = dataS13['crossDecodingMatrixACCg']
  819. crossDecodingMatrixBLA = dataS13['crossDecodingMatrixBLA']
  820. crossDecodingMatrixdmPFC = dataS13['crossDecodingMatrixdmPFC']
  821. crossDecodingMatrixOFC = dataS13['crossDecodingMatrixOFC']
  822. #%% plot the data
  824. with plt.style.context("style_paper.mplstyle"):
  825. fig, ax = plt.subplots(figsize=(3.54/1.25, 3.45/2)) # Adjust aspect ratio for horizontal layout
  826. height = 0.1 # Bar height instead of width
  827. sns.heatmap(crossDecodingMatrixBLA,# adjust the matrix you want to plot
  828. square = True,vmin=0.5, vmax=1,annot=True,fmt=".2f",
  829. annot_kws={"size": 8},
  830. cbar_kws={'label': 'Decoding accuracy'})
  831. plt.xticks([0.5,1.5],['State','Content'])
  832. plt.yticks([0.5,1.5],['State','Content'])
  833. plt.xlabel('Testing')
  834. plt.ylabel('Training')
  835. # plt.title('BLA')
  836. plt.tight_layout()
  837. fileName = 'figS13_BLA_remo_non_sele.pdf'
  838. # plt.savefig(fileName,dpi = 600)
  839. plt.show()
  840. #%% --------- figure S14 ---------
  841. #%% import figure S14 data
  842. dataS14 = pkl.load(open('figure_S14_data.pkl', 'rb'))
  843. areas1 = dataS14['areas1']
  844. areas2 = dataS14['areas2']
  845. areas_paris = dataS14['areas_paris']
  846. inpuDataContentColl = dataS14['inpuDataContentColl']
  847. inpuDataAnnoContentColl = dataS14['inpuDataAnnoContentColl']
  849. #%% plot CCA results for content
  850. # Define significance annotation
  851. def significance_marker(val):
  852. if val < 0.001:
  853. return "***"
  854. elif val < 0.01:
  855. return "**"
  856. elif val < 0.05:
  857. return "*"
  858. else:
  859. return ""
  862. areas = ['ACCg','BLA','dmPFC','OFC']
  864. areas1 = ['ACCg','ACCg','ACCg','BLA', 'BLA', 'dmPFC']
  865. areas2 = ['BLA','dmPFC','OFC','dmPFC', 'OFC', 'OFC']
  867. areas_paris = ['ACCg_BLA','ACCg_dmPFC', 'ACCg_OFC','BLA_dmPFC','BLA_OFC', 'dmPFC_OFC']
  871. for i in range(6):
  872. inpuDataContent = copy.deepcopy(inpuDataContentColl[areas_paris[i]])
  873. inpuDataAnnoContent = copy.deepcopy(inpuDataAnnoContentColl[areas_paris[i]])
  874. with plt.style.context('style_paper.mplstyle'):
  875. # sns.set(style = "ticks")
  876. f, ax1 = plt.subplots(ncols=1, nrows=1, sharey=True,figsize=[3.54/1.5,3.54/2])
  877. g1 = sns.heatmap(
  878. inpuDataContent[1:4,1:4],
  880. annot=inpuDataAnnoContent[1:4,1:4], fmt="",
  881. # cbar=False,
  882. cmap = 'viridis',
  883. square = True,
  884. linewidths=0.2,
  885. vmin=0, vmax=0.5,
  886. ax = ax1)
  887. ax1.set_yticklabels(['-0.2','0','0.2'],rotation = 0)
  888. ax1.set_xticklabels(['-0.2','0','0.2'],rotation = 0)
  890. # ax1.set_yticklabels(['-0.4','-0.2','0','0.2','0.4'],rotation = 0)
  891. # ax1.set_xticklabels(['-0.4','-0.2','0','0.2','0.4'],rotation = 0)
  893. ax1.set_xlabel(areas2[i]+' time (s)')
  894. ax1.set_ylabel(areas1[i] +' time (s)')
  895. # ax1.set_yticklabels(['1','0.5','0','-0.5'],rotation = 0)
  896. # plt.title('')
  897. # plt.title([are,'State'])
  898. plt.tight_layout()
  899. fileName = 'figS14_CCA'+'content_'+areas1[i]+'_'+areas2[i]+'.pdf'
  900. # plt.savefig(fileName,dpi = 600)
  901. plt.show()
  903. #%% --------- figure S15 ---------
  904. #%% import figure S15 data
  905. dataS15 = pkl.load(open('figure_S15_data.pkl', 'rb'))
  906. areas1 = dataS15['areas1']
  907. areas2 = dataS15['areas2']
  908. areas_paris = dataS15['areas_paris']
  909. inpuDataStateColl = dataS15['inpuDataStateColl']
  910. inpuDataAnnoStateColl = dataS15['inpuDataAnnoStateColl']
  911. #%% plot CCA results for state, with significance annotation
  912. # Define significance annotation
  913. def significance_marker(val):
  914. if val < 0.001:
  915. return "***"
  916. elif val < 0.01:
  917. return "**"
  918. elif val < 0.05:
  919. return "*"
  920. else:
  921. return ""
  923. areas = ['ACCg','BLA','dmPFC','OFC']
  925. areas1 = ['ACCg','ACCg','ACCg','BLA', 'BLA', 'dmPFC']
  926. areas2 = ['BLA','dmPFC','OFC','dmPFC', 'OFC', 'OFC']
  927. areas_paris = ['ACCg_BLA','ACCg_dmPFC', 'ACCg_OFC','BLA_dmPFC','BLA_OFC', 'dmPFC_OFC']
  929. for i in range(6):
  930. inpuDataState = copy.deepcopy(inpuDataStateColl[areas_paris[i]])
  931. inpuDataAnnoState = copy.deepcopy(inpuDataAnnoStateColl[areas_paris[i]])
  932. with plt.style.context('style_paper.mplstyle'):
  933. # sns.set(style = "ticks")
  934. f, ax1 = plt.subplots(ncols=1, nrows=1, sharey=True,figsize=[3.54/1.5,3.54/2])
  935. g1 = sns.heatmap(
  936. inpuDataState[1:4,1:4],
  938. annot=inpuDataAnnoState[1:4,1:4], fmt="",
  939. # cbar=False,
  940. cmap = 'viridis',
  941. square = True,
  942. linewidths=0.2,
  943. vmin=0, vmax=0.5,
  944. ax = ax1)
  945. ax1.set_yticklabels(['-0.2','0','0.2'],rotation = 0)
  946. ax1.set_xticklabels(['-0.2','0','0.2'],rotation = 0)
  948. # ax1.set_yticklabels(['-0.4','-0.2','0','0.2','0.4'],rotation = 0)
  949. # ax1.set_xticklabels(['-0.4','-0.2','0','0.2','0.4'],rotation = 0)
  951. ax1.set_xlabel(areas2[i]+' time (s)')
  952. ax1.set_ylabel(areas1[i] +' time (s)')
  953. # ax1.set_yticklabels(['1','0.5','0','-0.5'],rotation = 0)
  954. # plt.title('')
  955. # plt.title([are,'State'])
  956. plt.tight_layout()
  957. fileName = 'figS15_CCA'+'state_'+areas1[i]+'_'+areas2[i]+'.pdf'
  958. # plt.savefig(fileName,dpi = 600)
  959. plt.show()
  961. #%% --------- figure S16 ---------
  962. #%% import figure S16 data
  963. figure_S16A_data = pkl.load(open('figure_S16A_data.pkl','rb'))
  964. edges_content_pre = figure_S16A_data['edges_content_pre']
  965. edges_content_fixation = figure_S16A_data['edges_content_fixation']
  966. edges_content_post = figure_S16A_data['edges_content_post']
  967. edges_state_pre = figure_S16A_data['edges_state_pre']
  968. edges_state_fixation = figure_S16A_data['edges_state_fixation']
  969. edges_state_post = figure_S16A_data['edges_state_post']
  971. figure_S16B_data = pkl.load(open('figure_S16B_data.pkl','rb'))
  972. content_directionality_index_mean = figure_S16B_data['content_directionality_index_mean']
  973. content_directionality_index_SEMs = figure_S16B_data['content_directionality_index_SEMs']
  974. state_directionality_index_mean = figure_S16B_data['state_directionality_index_mean']
  975. state_directionality_index_SEMs = figure_S16B_data['state_directionality_index_SEMs']
  977. # Define 4 nodes (fixed positions)
  978. node_labels = ["OFC", "dmPFC", "ACCg", "BLA"]
  979. positions = {
  980. "OFC": (0, 0),
  981. "dmPFC": (1, 1),
  982. "ACCg": (2, 0),
  983. "BLA": (1, -1)
  984. }
  986. # Assign unique colors to each node
  987. node_colors = {
  988. "OFC": "#FF7F00", # Red
  989. "dmPFC": "#099D84", # Blue
  990. "ACCg": "#CA2521", # Green
  991. "BLA": "#456ACF" # Purple
  992. }
  994. # Define edges with significance values
  995. edges = [
  996. ("OFC", "dmPFC", 0.8), # Higher significance
  997. ("dmPFC", "ACCg", 0.3), # Lower significance
  998. ("ACCg", "BLA", 0.5),
  999. ("BLA", "OFC", 0.9), # Most significant
  1000. ("OFC", "ACCg", 0.6),
  1001. ("dmPFC", "BLA", 0.1)
  1002. ]
  1004. # Convert edges to coordinate pairs
  1005. edge_coords = [([positions[start], positions[end]], significance) for start, end, significance in edges_content_post]# Using edges_content_post as example
  1007. # Normalize significance values for color and width
  1008. vmin, vmax = 0, 0.5 # Set fixed colorbar range
  1009. norm = mcolors.Normalize(vmin=vmin, vmax=vmax)
  1010. cmap = plt.cm.Greys # Grayscale color map
  1012. # Prepare edge segments and styles
  1013. segments = []
  1014. colors = []
  1015. widths = []
  1016. min_width, max_width = 1, 10 # Min and max thickness
  1018. for (segment, sig) in edge_coords:
  1019. segments.append(segment)
  1020. colors.append(cmap(norm(sig))) # Map significance to color
  1021. widths.append(min_width + (max_width - min_width) * norm(sig)) # Scale width
  1023. # Plot with custom style
  1024. with plt.style.context('style_paper.mplstyle'):
  1025. fig, ax = plt.subplots(figsize=(3.54/1.5,3.54/1.2))
  1026. ax.set_aspect('equal')
  1028. # Draw edges with gradient and significance scaling
  1029. lc = mcoll.LineCollection(segments, colors=colors, linewidths=widths)
  1030. ax.add_collection(lc)
  1032. # Draw nodes with individual colors and black edges
  1033. node_size = 4000 # Adjusted for better proportion
  1034. for label, (x, y) in positions.items():
  1035. ax.scatter(x, y, color=node_colors[label], s=node_size, edgecolors='white', linewidth=1, zorder=3)
  1036. ax.text(x, y, label, fontsize=8, ha='center', va='center', color='black', fontweight='bold')
  1038. # Add colorbar for edge significance with fixed range
  1039. sm = cm.ScalarMappable(cmap=cmap, norm=norm)
  1040. sm.set_array([]) # Required for colorbar
  1041. cbar = plt.colorbar(sm, ax=ax,orientation="horizontal")
  1042. cbar.set_label("Cross Validated CC rhos")
  1044. # Set fixed colorbar range
  1045. sm.norm.vmin = vmin
  1046. sm.norm.vmax = vmax
  1047. ax.set_xlim(-0.5, 2.5)
  1048. ax.set_ylim(-1.5, 1.5)
  1049. # Remove all axes
  1050. ax.set_xticks([])
  1051. ax.set_yticks([])
  1052. ax.axis("off") # Completely removes axis lines, labels, and ticks
  1053. plt.tight_layout()
  1055. fileName = 'figS16A_'+'CCA'+'edges_content_post'+'_func_conn'+'_without_linear_mixed_cells.pdf'
  1056. # plt.savefig(fileName,bbox_inches="tight",dpi = 600)
  1058. plt.show()
  1059. #%% load figure S16B data for state and content directionality index distributions (for box plot)
  1060. dataS16B = pkl.load(open('figure_S16B.pkl', 'rb'))
  1061. directionality_index_Accg_Bla_state = dataS16B['Accg_Bla_state']
  1062. directionality_index_Bla_Dmpfc_state = dataS16B['Bla_Dmpfc_state']
  1063. directionality_index_Bla_Ofc_content = dataS16B['Bla_Ofc_content']
  1064. directionality_index_Accg_Bla_content = dataS16B['Accg_Bla_content']
  1066. #%% plot box plots for directionality index distributions, with alternating background and dual y-axis labels
  1067. with plt.style.context('style_paper.mplstyle'):
  1068. fig, ax = plt.subplots(figsize=(3.54/2, 3.54/2))
  1070. # Y positions
  1071. y_positions = np.array([1, 2]) * 1.5
  1073. # Full distributions
  1074. distributions = [
  1075. np.asarray(directionality_index_Accg_Bla_state).flatten(),
  1076. np.asarray(directionality_index_Bla_Dmpfc_state).flatten(),
  1077. ]
  1078. distributions = [d[~np.isnan(d)] for d in distributions]
  1080. means = [np.nanmean(d) for d in distributions]
  1081. errors = [np.nanstd(d) / np.sqrt(len(d)) for d in distributions]
  1083. # Alternating background
  1084. for i in range(len(y_positions)):
  1085. ax.axhspan(
  1086. y_positions[i] - 0.5,
  1087. y_positions[i] + 0.5,
  1088. color='lightgray' if i % 2 == 0 else 'white',
  1089. alpha=0.3,
  1090. zorder=1,
  1091. )
  1093. # ---- Box plot ----
  1094. bp = ax.boxplot(
  1095. distributions,
  1096. positions=y_positions,
  1097. vert=False,
  1098. widths=0.55,
  1099. patch_artist=True,
  1100. showfliers=False,
  1101. medianprops={'color': 'black', 'linewidth': 1.5},
  1102. boxprops={'linewidth': 0.6, 'edgecolor': 'black'},
  1103. whiskerprops={'linewidth': 0.6, 'color': 'black'},
  1104. capprops={'linewidth': 0.6, 'color': 'black'},
  1105. zorder=3,
  1106. )
  1108. for box in bp['boxes']:
  1109. box.set_facecolor('black')
  1110. box.set_alpha(0.4)
  1112. # ---- Reference line ----
  1113. ax.axvline(
  1114. 0,
  1115. color='black',
  1116. linestyle=':',
  1117. linewidth=0.7,
  1118. alpha=0.6,
  1119. zorder=1.5,
  1120. )
  1122. # ---- Axes ----
  1123. ax.set_xlabel('Directionality Index')
  1124. ax.set_yticks(y_positions)
  1126. ytick_labels_left = ['ACCg → BLA', 'BLA → dmPFC']
  1127. ytick_labels_right = ['BLA → ACCg', 'dmPFC → BLA']
  1129. ax.set_yticklabels(ytick_labels_left)
  1131. ax_right = ax.twinx()
  1132. ax_right.set_yticks(y_positions)
  1133. ax_right.set_yticklabels(ytick_labels_right)
  1134. ax_right.set_ylim(ax.get_ylim())
  1136. ax.xaxis.grid(True, linestyle='--', alpha=0.6)
  1138. ax_right.spines['right'].set_visible(True)
  1139. ax_right.spines['right'].set_color('black')
  1140. ax_right.spines['right'].set_linewidth(1)
  1142. plt.tight_layout()
  1144. fileName = 'figS16B_CCA_Directionality_index_func_conn_state_BLAonly_without_linear_mixed_cells.pdf'
  1145. # plt.savefig(dataPath + fileName, bbox_inches='tight', dpi=600)
  1147. plt.show()
  1149. with plt.style.context('style_paper.mplstyle'):
  1151. fig, ax = plt.subplots(figsize=(3.54/2, 3.54/2))
  1153. # Y positions
  1154. y_positions = np.array([1, 2]) * 1.5
  1156. # Full distributions (1000 iterations each)
  1157. distributions = [
  1158. np.asarray(directionality_index_Bla_Ofc_content).flatten(),
  1159. np.asarray(directionality_index_Accg_Bla_content).flatten(),
  1160. ]
  1161. distributions = [d[~np.isnan(d)] for d in distributions]
  1163. means = [np.nanmean(d) for d in distributions]
  1164. errors = [np.nanstd(d) / np.sqrt(len(d)) for d in distributions]
  1166. # Alternating background
  1167. for i in range(len(y_positions)):
  1168. ax.axhspan(
  1169. y_positions[i] - 0.5, y_positions[i] + 0.5,
  1170. color='lightgray' if i % 2 == 0 else 'white',
  1171. alpha=0.3,
  1172. zorder=1,
  1173. )
  1175. # ---- Box plot ----
  1176. bp = ax.boxplot(
  1177. distributions,
  1178. positions=y_positions,
  1179. vert=False,
  1180. widths=0.55,
  1181. patch_artist=True,
  1182. showfliers=False,
  1183. medianprops={'color': 'black', 'linewidth': 1.5},
  1184. boxprops={'linewidth': 0.6, 'edgecolor': 'black'},
  1185. whiskerprops={'linewidth': 0.6, 'color': 'black'},
  1186. capprops={'linewidth': 0.6, 'color': 'black'},
  1187. zorder=3,
  1188. )
  1189. for box in bp['boxes']:
  1190. box.set_facecolor('black')
  1191. box.set_alpha(0.4)
  1193. # ---- Reference line ----
  1194. ax.axvline(0, color='black', linestyle=':', linewidth=0.7, alpha=0.6, zorder=1.5)
  1196. # ---- Axes ----
  1197. ax.set_xlabel('Directionality Index')
  1198. ax.set_yticks(y_positions)
  1200. ytick_labels_left = ['BLA → OFC', 'ACCg → BLA']
  1201. ytick_labels_right = ['OFC → BLA', 'BLA → ACCg']
  1202. ax.set_yticklabels(ytick_labels_left)
  1204. ax_right = ax.twinx()
  1205. ax_right.set_yticks(y_positions)
  1206. ax_right.set_yticklabels(ytick_labels_right)
  1207. ax_right.set_ylim(ax.get_ylim())
  1209. ax.xaxis.grid(True, linestyle='--', alpha=0.6)
  1210. ax_right.spines['right'].set_visible(True)
  1211. ax_right.spines['right'].set_color('black')
  1212. ax_right.spines['right'].set_linewidth(1)
  1214. plt.tight_layout()
  1215. fileName = 'figS15B_CCA_Directionality_index_func_conn_content_BLAonly_without_linear_mixed_cells.pdf'
  1216. # plt.savefig(dataPath + fileName, bbox_inches='tight', dpi=600)
  1218. plt.show()
  1220. # %%

codes_for_supplementary_figuresS2-S16.py at commit fe69fce, no license · at the source

Overview

Authors: Guangyao Qi1, Olga Dal Monte1,2, Siqi Fan1,3, Steve W. C. Chang1,4,5,6
ORCID iDs: Guangyao Qi, Siqi Fan, Steve W. C. Chang
  1. Department of Psychology, Yale University,New Haven, CT USA
  2. Department of Psychology, University of Turin,Torino, Italy
  3. The Laboratory of Neural Systems, The Rockefeller University,New York, NY USA
  4. Department of Neuroscience, Yale University School of Medicine,New Haven, CT USA
  5. Kavli Institute for Neuroscience, Yale University School of Medicine,New Haven, CT USA
  6. Wu Tsai Institute, Yale University,New Haven, CT USA
Institutions: Yale University (United States); University of Turin (Italy); Rockefeller University (United States)
Journal: Nature communications, volume 17, issue 1, article 7478
Dates: received 29 September 2025; accepted 2 June 2026; published online 12 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74347-8 · PMID 42285960 · PMCID PMC13407908 · OpenAlex W4412895001
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.28.667161
Status: code verified
Categories: non-human primate (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Social behaviour, Social neuroscience
MeSH: Amygdala*, Fixation, Ocular*, Prefrontal Cortex*, Social Behavior*, Animals, Female, Gyrus Cinguli, Macaca mulatta, Male, Neurons (* major topic)
Topic: Primate Behavior and Ecology (Social Psychology, Psychology), according to OpenAlex
Funding: National Institute of Mental Health (R01MH128190, R01MH120081, R01MH110750)
Citations: cited by 2 papers (Europe PMC); 80 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

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changlabneuro/social_gaze_compositionality

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fe69fceed216c477d8c8dbd8dab8bee14e1401d2, 6 May 2026
Languages: Python (6), MATLAB (1), R (1)
Size: 46 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), scikit-learn (4 files), Matplotlib (3 files), pandas (3 files), SciPy (3 files), seaborn (2 files), broom (1 file), car (1 file), emmeans (1 file), lme4 (1 file), Pingouin (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/changlabneuro/social_gaze_compositionality

Zenodo 20061507

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), scikit-learn (4 files), Matplotlib (3 files), pandas (3 files), SciPy (3 files), seaborn (2 files), broom (1 file), car (1 file), emmeans (1 file), lme4 (1 file), Pingouin (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source:

qiguangyao/social_gaze_compositionality

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fe69fceed216c477d8c8dbd8dab8bee14e1401d2, 6 May 2026
Languages: Python (6), MATLAB (1), R (1)
Size: 46 files, 8 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), scikit-learn (4 files), Matplotlib (3 files), pandas (3 files), SciPy (3 files), seaborn (2 files), broom (1 file), car (1 file), emmeans (1 file), lme4 (1 file), Pingouin (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/QiGuangyao/social_gaze_compositionality/tree/v1.0.0

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-74347-8.

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Code and data availability statement

The paper has a code and 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/s41467-026-74347-8.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 10 MeSH terms, 1 funder, 73 references.

Cite

This paper

Qi, G., Dal Monte, O., Fan, S., & Chang, S. W. C. (2026). Compositionality of social gaze in the prefrontal-amygdala circuits. Nature communications, 17(1), 7478. https://doi.org/10.1038/s41467-026-74347-8

BibTeX

@article{qi2026compositionality,
author = {Qi, Guangyao and Dal Monte, Olga and Fan, Siqi and Chang, Steve W. C.},
title = {{Compositionality of social gaze in the prefrontal-amygdala circuits}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7478},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74347-8},
url = {https://doi.org/10.1038/s41467-026-74347-8},
pmid = {42285960},
pmcid = {PMC13407908}
}

RIS

TY - JOUR
AU - Qi, Guangyao
AU - Dal Monte, Olga
AU - Fan, Siqi
AU - Chang, Steve W. C.
TI - Compositionality of social gaze in the prefrontal-amygdala circuits
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/12
VL - 17
IS - 1
SP - 7478
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74347-8
UR - https://doi.org/10.1038/s41467-026-74347-8
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-74347-8",
"type": "article-journal",
"title": "Compositionality of social gaze in the prefrontal-amygdala circuits",
"container-title": "Nature communications",
"author": [
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"family": "Qi",
"given": "Guangyao"
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{
"family": "Dal Monte",
"given": "Olga"
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{
"family": "Fan",
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},
{
"family": "Chang",
"given": "Steve W. C."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7478",
"DOI": "10.1038/s41467-026-74347-8",
"PMID": "42285960",
"PMCID": "PMC13407908",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74347-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
12
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]
}
}

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