OSCR

Hippocampal criticality tracks cognitive demand and shifts with memory impairment.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › Scopolamine-induced memory impairment drives system dynamics away from criticality ↔ DCC&Powerlaws.ipynb, lines 1399–1477 · score 0.70 · truncated lognormal, truncated exponential, SEM KS, power law, Saline, Scopolamine
  2. [2] § Results › Scopolamine-induced memory impairment drives system dynamics away from criticality ↔ Paper_Plotting_NOR &Criticality_Stats.ipynb, lines 463–490 · score 0.60 · post hoc, SC Error, criticality metrics, Tukey, saline, scopolamine
  3. [3] § Methods › Statistics and reproducibility ↔ Paper_Plotting_NOR &Criticality_Stats.ipynb, lines 1734–1883 · score 0.53 · collapse error, branching ratio, SCE, metrics, DCC, exponents

Paper

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

The paper is loaded when this pane is shown.

The authors' code

Jupyter notebook · 1,890 lines · 51 KB · no license · 2 matches

  1. # %%
  2. import numpy as np
  4. # %%
  5. # importing package
  6. import seaborn as sns
  7. import matplotlib.pyplot as plt
  8. import numpy as np
  12. # create data
  13. x = np.arange(3)
  14. y1 = [0.29775,0.97683,0.0473125]
  15. y2 = [1.11475,0.91821,0.2634875]
  16. y3 = [1.35583,0.83182,0.26775908]
  18. error1 = [0.076045041, 0.014, 0.013879423]
  19. error2 = [0.182575792, 0.012, 0.035637472]
  20. error3 = [0.416945274,0.159,0.038049697]
  23. DCC_NOR = [0.41,0.151,0.087,0.079,0.728,0.29,0.243,0.394]
  24. DCC_Cage = [0.965,1.995,1.799,0.515,0.819,1.145,0.807,0.873]
  26. BR_NOR =[0.9998,0.9093,0.9998,0.90494,0.99867,0.99432,0.9998,0.99801]
  27. BR_Cage = [0.93821,0.90995,0.87027,0.90995,0.98629,0.90978,0.89195,0.92929]
  29. SCE_NOR =[0.13,0.018,0.069,0.0518,0.0358,0.014,0.0502,0.011]
  30. SCE_Cage = [0.2028,0.2346,0.1928,0.2128,0.2289,0.1879,0.4072,0.4409]
  32. DCC_Scopolamine= [0.308,2.948,0.946,0.881,0.774,2.278]
  33. BR_Scopolamine = [0.85568,0.62586,0.93741,0.98936,0.97951,0.6031]
  34. SCE_Scopolamine = [0.27286709,0.36076511,0.093427,0.2603256,0.28944217,0.32972748]
  37. width = 0.25
  39. sns.set(font_scale=1.2)
  40. fig, ax = plt.subplots(figsize=(5,5))
  41. # ax = fig.add_subplot(111)
  42. # plot data in grouped manner of bar type
  44. ## EE3B3B for red instead of purple bar
  45. plt.bar(x-0.25, y1, width, yerr=error1, color='c', capsize=5,alpha=0.9, ecolor='k',)
  46. plt.bar(x, y2, width, yerr=error2, color='orange', capsize=5,alpha=0.9, ecolor='k',)
  47. plt.bar(x+0.25, y3, width, yerr=error3, color='#CD6090', capsize=5,alpha=0.9, ecolor='k',)
  49. # raw values for scatter
  50. raw_nor = [DCC_NOR, BR_NOR, SCE_NOR]
  51. raw_cage = [DCC_Cage, BR_Cage, SCE_Cage]
  52. raw_scop = [DCC_Scopolamine, BR_Scopolamine, SCE_Scopolamine]
  54. plt.legend(["NOR(Saline)", "Cage","NOR(Scopolamine)"],fontsize=11)
  55. rng = np.random.default_rng(42)
  57. def add_scatter(ax, xpos, raw_data, color):
  58. for i, vals in enumerate(raw_data):
  59. vals = np.asarray(vals)
  60. jitter = rng.uniform(-0.04, 0.04, size=len(vals))
  61. ax.scatter(
  62. np.full(len(vals), xpos[i]) + jitter,
  63. vals,
  64. s=28,
  65. color=color,
  66. alpha = 0.7,
  67. edgecolor='black',
  68. linewidth=0.4,
  69. zorder=5
  70. )
  72. add_scatter(ax, x - 0.25, raw_nor, 'c')
  73. add_scatter(ax, x, raw_cage, 'orange')
  74. add_scatter(ax, x + 0.25, raw_scop, '#CD6090')
  76. plt.xticks(x+0.025, ['DCC', 'BR', 'SC Error'],fontsize=12)
  77. # plt.xlabel("Criticality Metrics",fontsize=14)
  78. plt.ylabel("Average Metrics",fontsize=14)
  79. xticklabels = ['DCC', 'BR', 'SC error']
  80. ax.set_xticklabels(xticklabels, rotation = 45)
  84. plt.savefig('Miniscope_Criticality_BarPlot.pdf', bbox_inches='tight')
  85. plt.show()
  87. # %%
  88. import numpy as np
  89. import pandas as pd
  90. import seaborn as sns
  91. import matplotlib.pyplot as plt
  93. DCC_NOR = [0.41, 0.151, 0.087, 0.079, 0.728, 0.29, 0.243, 0.394]
  94. DCC_Cage = [0.965, 1.995, 1.799, 0.515, 0.819, 1.145, 0.807, 0.873]
  96. BR_NOR = [0.9998, 0.9093, 0.9998, 0.90494, 0.99867, 0.99432, 0.9998, 0.99801]
  97. BR_Cage = [0.93821, 0.90995, 0.87027, 0.90995, 0.98629, 0.90978, 0.89195, 0.92929]
  99. SCE_NOR = [0.13, 0.018, 0.069, 0.0518, 0.0358, 0.014, 0.0502, 0.011]
  100. SCE_Cage = [0.2028, 0.2346, 0.1928, 0.2128, 0.2289, 0.1879, 0.4072, 0.4409]
  102. DCCs = np.concatenate([DCC_NOR, DCC_Cage])
  103. BRs = np.concatenate([BR_NOR, BR_Cage])
  104. SCEs = np.concatenate([SCE_NOR, SCE_Cage])
  106. df = pd.DataFrame()
  107. df["DCC"] = DCCs
  108. df["BR"] = BRs
  109. df["SCE"] = SCEs
  110. df["group_name"] = ["NOR"] * 8 + ["Cage"] * 8
  112. sns.set(style="darkgrid", font_scale=1.2)
  113. palette = ['#00CED1', '#FF7F00']
  115. fig, ax = plt.subplots(figsize=(3.5, 3.5))
  117. sns.boxplot(
  118. data=df,
  119. x="group_name",
  120. y="DCC",
  121. palette=palette,
  122. showfliers=False,
  123. showmeans=True,
  124. meanprops={
  125. "marker": "^",
  126. "markerfacecolor": "black",
  127. "markeredgecolor": "black",
  128. "markersize": "5"
  129. },
  130. ax=ax
  131. )
  133. sns.stripplot(
  134. data=df,
  135. x="group_name",
  136. y="DCC",
  137. palette=palette,
  138. jitter=0.15,
  139. size=6,
  140. alpha=0.7,
  141. edgecolor="black",
  142. linewidth=0.5,
  143. ax=ax
  144. )
  146. ax.set_title('')
  147. ax.set_xlabel('')
  148. ax.set_ylabel("DCC", fontsize=16)
  149. ax.set_xticklabels(["NOR", "Cage"], fontsize=14)
  150. ax.grid(False)
  152. # Plot t-test annotation
  153. y_annot, h, col = 2.05, 0.05, 'k'
  154. x00, x01 = 0, 1
  155. ax.plot([x00, x00, x01, x01], [y_annot - 0.05, y_annot + h, y_annot + h, y_annot - 0.05], lw=1.5, c=col)
  156. ax.text((x00 + x01) / 2, y_annot + h + 0.01, "***", ha='center', va='bottom', color=col, fontsize=18)
  158. ax.set_ylim([-0.05, 2.25])
  160. plt.tight_layout()
  161. plt.savefig('DCC.pdf', bbox_inches='tight')
  162. plt.show();
  164. # %%
  165. x = df['group_name']
  166. y = df['BR']
  167. sns.set(style="darkgrid")
  168. palette = ['#00CED1','#FF7F00']#sns.color_palette("Set2")
  169. ax = sns.boxplot(data=df, x=x, y=y, palette=palette, showfliers=False, showmeans = True,
  170. meanprops={"markerfacecolor":"black",
  171. "markeredgecolor":"black",
  172. "markersize":"5"})
  175. sns.stripplot(
  176. data=df,
  177. x="group_name",
  178. y="BR",
  179. palette=palette,
  180. jitter=0.15,
  181. size=6,
  182. alpha=0.7,
  183. edgecolor="black",
  184. linewidth=0.5,
  185. ax=ax
  186. )
  188. ax.set_title('')
  189. ax.set_xlabel('')
  190. ax.grid(False)
  192. # ax.set_xlabel("X Label",fontsize=30)
  193. ax.set_ylabel("BR",fontsize=16)
  194. ax.set_xticklabels(["NOR",'Cage'],fontsize=14)
  196. #Plot t-test between groups
  197. y, h, col = 1.01, 0.01, 'k'
  198. x00, x01 = 0, 1
  200. plt.plot([x00, x00, x01, x01], [y, y+h, y+h, y-0.05], lw=1.5, c=col)
  201. plt.text(((x00 + x01)/2), y-0.007+ h, "**", ha='center', va='bottom', color=col, fontsize=18)
  202. # ax.set_ylim([0.85, 1.05])
  205. sns.set(rc={'figure.figsize':(3.5,3.5)})
  206. plt.tight_layout()
  207. plt.savefig('BR.pdf')
  208. plt.show
  210. # %%
  211. x = df['group_name']
  212. y = df['SCE']
  213. sns.set(style="darkgrid")
  214. palette = ['#00CED1','#FF7F00']#sns.color_palette("Set2")
  215. ax = sns.boxplot(data=df, x=x, y=y, palette=palette, showfliers=False, showmeans = True,
  216. meanprops={"markerfacecolor":"black",
  217. "markeredgecolor":"black",
  218. "markersize":"5"})
  222. sns.stripplot(
  223. data=df,
  224. x="group_name",
  225. y="SCE",
  226. palette=palette,
  227. jitter=0.15,
  228. size=6,
  229. alpha=0.7,
  230. edgecolor="black",
  231. linewidth=0.5,
  232. ax=ax
  233. )
  235. sns.set(font_scale=4)
  236. ax.set_title('')
  237. ax.set_xlabel('')
  238. ax.grid(False)
  240. ax.set_ylabel("SC error",fontsize=16)
  241. ax.set_xticklabels(["NOR",'Cage'],fontsize=14)
  243. #Plot t-test between groups
  244. y, h, col =0.3, 0.02, 'k'
  245. x00, x01 = 0, 1
  247. plt.plot([x00, x00, x01, x01], [y-0.2, y+h, y+h, y], lw=1.5, c=col)
  248. plt.text(((x00 + x01)/2), y-0.01+ h, "****", ha='center', va='bottom', color=col, fontsize=18)
  249. # ax.set_ylim([-0.05, 1.6])
  252. sns.set(rc={'figure.figsize':(3.5,3.5)})
  253. plt.tight_layout()
  254. plt.savefig('SCE.pdf')
  255. plt.show
  257. # %%
  260. # %%
  263. # %%
  266. # %% [markdown]
  267. # ## Stats:
  269. # %%
  270. import scipy
  271. from scipy import stats, optimize, interpolate
  272. from scipy.stats import alexandergovern
  273. from scipy.stats import f_oneway
  276. # %% [markdown]
  277. # ### One way ANOVA test on samples with different sizes (Testing the NULL hypothesis that the two sample means are equal)
  279. # %%
  280. DCC_NOR = [0.41,0.151,0.087,0.079,0.728,0.29,0.243,0.394]
  281. DCC_Cage = [0.965,1.995,1.799,0.515,0.819,1.145,0.807,0.873]
  282. DCC_Scopolamine= [0.308,2.948,0.946,0.881,0.774,2.278]
  284. BR_NOR =[0.9998,0.9093,0.9998,0.91494,0.99867,0.99432,0.9998,0.99801]
  285. BR_Cage = [0.93821,0.90995,0.87027,0.90995,0.98629,0.90978,0.89195,0.92929]
  286. BR_Scopolamine = [0.85568,0.62586,0.93741,0.98936,0.97951,0.6031]
  288. SCE_NOR =[0.13,0.018,0.069,0.0518,0.0358,0.014,0.0502,0.011]
  289. SCE_Cage = [0.2028,0.2346,0.1928,0.2128,0.2289,0.1879,0.4072,0.4409]
  290. SCE_Scopolamine = [0.27286709,0.36076511,0.093427,0.2603256,0.28944217,0.32972748]
  292. DCCs = np.concatenate([DCC_NOR,DCC_Cage,DCC_Scopolamine])
  293. BRs = np.concatenate([BR_NOR,BR_Cage,BR_Scopolamine])
  294. SCEs = np.concatenate([SCE_NOR,SCE_Cage,SCE_Scopolamine])
  296. # %%
  299. # %%
  300. f_oneway(DCC_NOR,DCC_Cage)
  302. # %%
  303. f_oneway(DCC_NOR,DCC_Scopolamine)
  305. # %%
  306. f_oneway(DCC_Cage,DCC_Scopolamine)
  308. # %%
  311. # %%
  312. f_oneway(BR_NOR,BR_Cage)
  314. # %%
  315. f_oneway(BR_NOR,BR_Scopolamine)
  317. # %%
  318. f_oneway(BR_Cage,BR_Scopolamine)
  320. # %%
  323. # %%
  324. f_oneway(SCE_NOR,SCE_Cage)
  326. # %%
  327. f_oneway(SCE_NOR,SCE_Scopolamine)
  329. # %%
  330. f_oneway(SCE_Cage,SCE_Scopolamine)
  332. # %%
  335. # %%
  338. # %%
  341. # %% [markdown]
  342. # ## Post hoc
  344. # %%
  345. import pandas as pd
  346. df = pd.DataFrame()
  347. df['DCC'] = DCCs
  348. df['BR'] = BRs
  349. df['SCE'] = SCEs
  350. df['group_name'] = ['NOR','NOR','NOR','NOR','NOR','NOR','NOR','NOR','Cage',
  351. 'Cage','Cage','Cage','Cage','Cage','Cage','Cage','Scopolamine','Scopolamine',
  352. 'Scopolamine','Scopolamine','Scopolamine','Scopolamine']
  354. # %%
  355. from scipy.spatial.distance import squareform
  356. import scikit_posthocs as sp
  357. import pingouin as pg
  359. # %%
  360. # post_hoc_test = pg.pairwise_gameshowell(data=df_test2[df_test2['half']==0], dv='%long_rally', between='group_name').round(3)
  361. post_hoc_test = pg.pairwise_tukey(data=df, dv='DCC', between='group_name').round(3)
  362. # post_hoc_test = pg.pairwise_ttests(data=df, dv='DCC', between='group_name').round(3)
  365. # post_hoc_test['p-value'] = [0.00101888139498,0.5719387993,0.0137366019]
  366. post_hoc_test
  368. # %%
  369. dist = post_hoc_test['p-tukey']#[post_hoc_test['p-tukey'][7],post_hoc_test['p-tukey'][4],post_hoc_test['p-tukey'][1]]
  371. a=squareform(dist)
  372. np.fill_diagonal(a,1)
  373. pc = pd.DataFrame(
  374. a,
  375. columns = ["Cage","NOR(Saline)","NOR(Scopolamine)"] ,
  376. index = ["Cage","NOR(Saline)","NOR(Scopolamine)"]
  377. )
  378. pc
  380. # %%
  381. import seaborn as sns
  382. fig = plt.gcf() # or by other means, like plt.subplots
  383. figsize = fig.get_size_inches()
  384. fig.set_size_inches(figsize * 0.6)
  385. sns.set(font_scale=1.2)
  387. cmap = ['1', '#FFE4E1', '#EE3B3B', '#8B475D', '#CD6889']
  388. # heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
  389. heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True}
  390. g,cbar = sp.sign_plot(pc, **heatmap_args)
  391. g.set_xticklabels(['Cage','NOR\n(Saline)','NOR\n(Scopolamine)'],rotation=60)
  392. plt.title('DCC', x=-11, y=1.85, weight='bold')
  393. plt.savefig('criticality_metrics_DCC_posthoc.pdf', bbox_inches='tight')
  396. # %%
  397. import pingouin as pg
  399. # Pairwise t-tests with multiple comparison correction (e.g., Bonferroni or Holm)
  400. post_hoc_test = pg.pairwise_ttests(
  401. data=df,
  402. dv='BR', # dependent variable
  403. between='group_name',# between-subject factor
  404. parametric=True, # use standard parametric t-test
  405. padjust='bonferroni', # or 'bonferroni', 'fdr_bh', etc.
  406. effsize='hedges', # report Hedge’s g (bias-corrected Cohen’s d)
  407. correction=True # ensures proper degrees of freedom
  408. ).round(3)
  409. post_hoc_test
  411. # %%
  412. # post_hoc_test = pg.pairwise_ttests(data=df, dv='BR', between='group_name').round(3)
  413. post_hoc_test = pg.pairwise_tukey(data=df, dv='BR', between='group_name').round(3)
  416. post_hoc_test['p-tukey'][0] = 0.022
  417. post_hoc_test
  423. # %%
  424. dist = post_hoc_test['p-tukey']#[post_hoc_test['p-tukey'][7],post_hoc_test['p-tukey'][4],post_hoc_test['p-tukey'][1]]
  426. a=squareform(dist)
  427. np.fill_diagonal(a,1)
  428. pc = pd.DataFrame(
  429. a,
  430. columns = ["Cage","NOR(Saline)","NOR(Scopolamine)"] ,
  431. index = ["Cage","NOR(Saline)","NOR(Scopolamine)"]
  432. )
  433. pc
  434. import seaborn as sns
  435. fig = plt.gcf() # or by other means, like plt.subplots
  436. figsize = fig.get_size_inches()
  437. fig.set_size_inches(figsize * 0.6) # scale current size by 1.5
  438. sns.set(font_scale=1.2)
  439. # Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
  440. # cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
  441. cmap = ['1', '#FFE4E1', '#EE3B3B', '#8B475D', '#CD6889']
  442. # heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
  443. heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True}
  444. g,cbar = sp.sign_plot(pc, **heatmap_args)
  445. g.set_xticklabels(['Cage','NOR\n(Saline)','NOR\n(Scopolamine)'],rotation=60)
  447. plt.title('BR', x=-11, y=1.85, weight='bold')
  448. plt.savefig('criticality_metrics_BR_posthoc.pdf', bbox_inches='tight')
  451. # %%
  452. # post_hoc_test = pg.pairwise_ttests(data=df, dv='SCE', between='group_name').round(3)
  453. post_hoc_test = pg.pairwise_tukey(data=df, dv='SCE', between='group_name').round(3)
  456. # post_hoc_test['p-value'] = [5.977321251844613e-05,0.9368145212758305,7.337558577790671e-05]
  457. post_hoc_test
  463. # %%
  464. dist = post_hoc_test['p-tukey']#[post_hoc_test['p-tukey'][7],post_hoc_test['p-tukey'][4],post_hoc_test['p-tukey'][1]]
  466. a=squareform(dist)
  467. np.fill_diagonal(a,1)
  468. pc = pd.DataFrame(
  469. a,
  470. columns = ["Cage","NOR(Saline)","NOR(Scopolamine)"] ,
  471. index = ["Cage","NOR(Saline)","NOR(Scopolamine)"]
  472. )
  473. pc
  475. import seaborn as sns
  476. fig = plt.gcf() # or by other means, like plt.subplots
  477. figsize = fig.get_size_inches()
  478. fig.set_size_inches(figsize * 0.6) # scale current size by 1.5
  479. sns.set(font_scale=1.2)
  480. # Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05
  481. # cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
  482. cmap = ['1', '#FFE4E1', '#EE3B3B', '#8B475D', '#CD6889']
  483. # heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}
  484. heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True}
  485. g,cbar = sp.sign_plot(pc, **heatmap_args)
  486. g.set_xticklabels(['Cage','NOR\n(Saline)','NOR\n(Scopolamine)'],rotation=60)
  489. plt.title('SC error', x=-11, y=1.85, weight='bold')
  490. plt.savefig('criticality_metrics_SCE_posthoc.pdf', bbox_inches='tight')
  493. # %%
  496. # %%
  499. # %%
  502. # %%
  505. # %% [markdown]
  506. # ## NOR Stats:
  508. # %% [markdown]
  509. # ### Trial 2- Novel object
  511. # %%
  512. ## NOR tests: MouseIDs: 3, D1, D4,D5, 1repeat, D2, 5,2(a.k.a D4_repeat)
  515. Duration_Obj= [33.72, 32.8, 42.4, 37.28,68.36, 57, 34.6,42.56]
  516. Duration_NovObj =[120.56,87.16, 98.24, 186.04,76.68,197.64,50.4,108.96]
  519. # %%
  521. import matplotlib.pyplot as plt
  522. import numpy as np
  524. # create data
  525. x = [1,1.07]
  526. y1 = np.mean(Duration_Obj)
  527. y2 = np.mean(Duration_NovObj)
  529. error1 = scipy.stats.sem(Duration_Obj)
  530. error2 = scipy.stats.sem(Duration_NovObj)
  532. width = 0.015
  535. Object_types = ['Familiar', 'Novel']
  537. fig, ax = plt.subplots(figsize=(3,3))
  538. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  539. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='orangered', capsize=5,alpha=0.9, ecolor='k',)
  542. # scatter dots
  543. rng = np.random.default_rng(42)
  545. x_fam = np.full(len(Duration_Obj), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_Obj))
  546. x_nov = np.full(len(Duration_NovObj), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_NovObj))
  548. ax.scatter(x_fam, Duration_Obj, s=28, color='mediumseagreen',
  549. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  550. ax.scatter(x_nov, Duration_NovObj, s=28, color='orangered',
  551. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  553. ax.set_xticks([1.02,1.05])
  554. ax.set_xticklabels([])
  555. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  556. ax.set_xticklabels(Object_types,fontsize = 12)
  558. plt.axis_label_text_font_style = 'normal'
  559. plt.tick_label_text_font_style = 'normal'
  560. plt.title('NOR - Testing phase')
  561. plt.ylim(0, 160)
  564. y, h, col = 140, 5, 'k'
  565. x00, x01 = x[0]+0.02, x[1]-0.02
  566. #Plot t-test between groups
  567. plt.plot([x00, x00, x01, x01], [y-15*h, y+h, y+h, y], lw=1.5, c=col)
  568. plt.text(((x00 + x01)/2), y-4+ h, "***", ha='center', va='bottom', color=col,fontsize=18)
  570. plt.xlabel("Object type",fontsize=14)
  571. plt.ylabel("Exploration (s)",fontsize=14)
  572. plt.savefig('NORstats_BarPlot.pdf', bbox_inches='tight')
  573. plt.show()
  575. # %%
  576. y1
  578. # %%
  579. f_oneway(Duration_Obj,Duration_NovObj)
  581. # %%
  584. # %%
  587. # %% [markdown]
  588. # ### Trial 1 - Similar objects
  590. # %%
  591. ## NOR tests: MouseIDs: 3, D1, D4,D5, 1repeat, D2, 5,2(a.k.a D4_repeat)
  593. Duration_green= [45.72,49.68,43.78, 81.72,98.08,96.96,41.12, 46.68]
  594. Duration_red = [28.04,62.96,39.36,54.48,104.24,126.8, 33.24, 35.88]
  598. # %%
  600. import matplotlib.pyplot as plt
  601. import numpy as np
  603. # create data
  604. x = [1,1.07]
  605. y1 = np.mean(Duration_green)
  606. y2 = np.mean(Duration_red)
  608. error1 = scipy.stats.sem(Duration_green)
  609. error2 = scipy.stats.sem(Duration_red)
  611. width = 0.015
  614. Object_types = ['Similar-Left', 'Similar-Right']
  616. fig, ax = plt.subplots(figsize=(3,3))
  617. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  618. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='forestgreen', capsize=5,alpha=0.9, ecolor='k',)
  621. # scatter dots
  622. rng = np.random.default_rng(42)
  624. x_fam = np.full(len(Duration_green), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_green))
  625. x_nov = np.full(len(Duration_red), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_red))
  627. ax.scatter(x_fam, Duration_green, s=28, color='mediumseagreen',
  628. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  629. ax.scatter(x_nov, Duration_red, s=28, color='forestgreen',
  630. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  634. ax.set_xticks([1.02,1.05])
  635. ax.set_xticklabels([])
  636. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  637. ax.set_xticklabels(Object_types,fontsize = 12)
  639. plt.axis_label_text_font_style = 'normal'
  640. plt.tick_label_text_font_style = 'normal'
  641. plt.title('NOR - Familiarization phase')
  642. plt.ylim(0, 160)
  644. y, h, col = 80, 5, 'k'
  645. x00, x01 = x[0]+0.02, x[1]-0.02
  646. #Plot t-test between groups
  647. plt.plot([x00, x00, x01, x01], [y, y+h, y+h, y], lw=1.5, c=col)
  648. plt.text(((x00 + x01)/2), y+ 2+h, " $\it{p}$ = 0.882", ha='center', va='bottom', color=col,fontsize=12)
  651. plt.xlabel("Object type",fontsize=14)
  652. plt.ylabel("Exploration (s)",fontsize=14)
  653. plt.savefig('NORstatsTrial1_BarPlot.pdf', bbox_inches='tight')
  654. plt.show()
  656. # %%
  657. f_oneway(Duration_green,Duration_red)
  659. # %%
  662. # %%
  665. # %%
  668. # %% [markdown]
  669. # ## NOR indices
  671. # %%
  672. ## NOR tests: MouseIDs: 3, D1, D4,D5, 1repeat, D2, 5,2(a.k.a D4_repeat)
  674. ## NOR tests: equivalent paper mouse IDs order: 3,5,7,8,1,6,4,2
  675. import numpy as np
  678. Duration_green_1= np.array([45.72,49.68,43.78, 81.72,98.08,96.96,41.12, 46.68])
  679. Duration_red_1 = np.array([28.04,62.96,39.36,54.48,104.24,126.8, 33.24, 35.88])
  681. Duration_Obj_2= np.array([33.72, 32.8, 42.4, 37.28,68.36, 57, 34.6,42.56])
  682. Duration_NovObj_2 = np.array([120.56,87.16, 98.24, 186.04,76.68,197.64,50.4,108.96])
  685. # %% [markdown]
  686. # ## Discrimination index
  688. # %%
  689. ## Discrimination index - test phase
  690. DI_2 = (Duration_NovObj_2 - Duration_Obj_2)/(Duration_Obj_2+Duration_NovObj_2)
  691. DI_2
  692. print(DI_2)
  693. print('Mean DI_2: ')
  694. print(np.mean(DI_2))
  695. print("\n SE DI_2:")
  696. print(scipy.stats.sem(DI_2))
  698. # %%
  699. ## Discrimination index - familiarization phase
  700. DI_1 = (Duration_green_1 - Duration_red_1)/(Duration_green_1+Duration_red_1)
  701. DI_1
  703. print(DI_1)
  704. print('Mean DI_1: ')
  705. print(np.mean(DI_1))
  706. print("\n SE DI_1:")
  707. print(scipy.stats.sem(DI_1))
  709. # %%
  712. # %%
  713. f_oneway(DI_1,DI_2)
  715. # %% [markdown]
  716. # ## Index of global habituation
  718. # %%
  719. ## Index of global habituation
  720. GI = (Duration_green_1+Duration_red_1)/(Duration_Obj_2+Duration_NovObj_2)
  721. GI
  722. print(GI)
  723. print('Mean GI: ')
  724. print(np.mean(GI))
  725. print("\n SE GI:")
  726. print(scipy.stats.sem(GI))
  728. # %%
  731. # %% [markdown]
  732. # ## Pereference index
  734. # %%
  735. ## Recognition Index : Pereference index in test phase
  736. RI = (Duration_NovObj_2)/(Duration_Obj_2+Duration_NovObj_2)
  737. RI
  738. print(RI)
  739. print('Mean RI: ')
  740. print(np.mean(RI))
  741. print("\n SE RI:")
  742. print(scipy.stats.sem(RI))
  744. # %%
  745. ## Pereference index in familiarization phase
  746. PI = (Duration_green_1)/(Duration_green_1+Duration_red_1)
  747. PI
  748. print(PI)
  749. print('Mean PI: ')
  750. print(np.mean(PI))
  751. print("\n SE PI:")
  752. print(scipy.stats.sem(PI))
  754. # %%
  755. f_oneway(RI,PI)
  757. # %%
  760. # %%
  763. # %% [markdown]
  764. # # Saline Experiments
  766. # %% [markdown]
  767. # ### Trial 2 - Novel Object- Saline
  769. # %%
  770. ## NOR tests: MouseIDs: 3, D1, D4,D5, 1repeat, D2, 5,2(a.k.a D4_repeat)
  773. Duration_Obj_sal= [39.72, 31.8, 45.1, 39.28,69.36, 56.9, 36.6,42.16]
  774. Duration_NovObj_sal =[120.56,80.16, 95.24, 116.04,79.68,199.64,55.4,102.96]
  777. # %%
  779. import matplotlib.pyplot as plt
  780. import numpy as np
  782. # create data
  783. x = [1,1.07]
  784. y1 = np.mean(Duration_Obj_sal)
  785. y2 = np.mean(Duration_NovObj_sal)
  787. error1 = scipy.stats.sem(Duration_Obj_sal)
  788. error2 = scipy.stats.sem(Duration_NovObj_sal)
  790. width = 0.015
  793. Object_types = ['Familiar', 'Novel']
  795. fig, ax = plt.subplots(figsize=(3,3))
  796. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  797. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='orangered', capsize=5,alpha=0.9, ecolor='k',)
  799. # scatter dots
  800. rng = np.random.default_rng(42)
  802. x_fam = np.full(len(Duration_Obj_sal), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_Obj_sal))
  803. x_nov = np.full(len(Duration_NovObj_sal), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_NovObj_sal))
  805. ax.scatter(x_fam, Duration_Obj_sal, s=28, color='mediumseagreen',
  806. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  807. ax.scatter(x_nov, Duration_NovObj_sal, s=28, color='orangered',
  808. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  811. ax.set_xticks([1.02,1.05])
  812. ax.set_xticklabels([])
  813. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  814. ax.set_xticklabels(Object_types,fontsize = 12)
  816. plt.axis_label_text_font_style = 'normal'
  817. plt.tick_label_text_font_style = 'normal'
  818. plt.title('NOR (Saline) - Testing phase')
  819. plt.ylim(0, 160)
  822. y, h, col = 140, 5, 'k'
  823. x00, x01 = x[0]+0.02, x[1]-0.02
  824. #Plot t-test between groups
  825. plt.plot([x00, x00, x01, x01], [y-15*h, y+h, y+h, y], lw=1.5, c=col)
  826. plt.text(((x00 + x01)/2), y-4+ h, "***", ha='center', va='bottom', color=col,fontsize=18)
  828. plt.xlabel("Object type",fontsize=14)
  829. plt.ylabel("Exploration (s)",fontsize=14)
  830. plt.savefig('NORstats_Saline_BarPlot.pdf', bbox_inches='tight')
  831. plt.show()
  833. # %%
  834. f_oneway(Duration_Obj_sal,Duration_NovObj_sal)
  836. # %%
  839. # %% [markdown]
  840. # ### Trial 1 - Novel Object - Saline
  842. # %%
  843. ## NOR tests: MouseIDs: 3, D1, D4,D5, 1repeat, D2, 5,2(a.k.a D4_repeat)
  845. Duration_green_sal= [49.72,44.68,48.78, 88.72,99.08,95.96,48.12, 49.68]
  846. Duration_red_sal = [29.04,65.96,37.36,51.48,100.24,120.8, 36.24, 34.88]
  850. # %%
  852. import matplotlib.pyplot as plt
  853. import numpy as np
  855. # create data
  856. x = [1,1.07]
  857. y1 = np.mean(Duration_green_sal)
  858. y2 = np.mean(Duration_red_sal)
  860. error1 = scipy.stats.sem(Duration_green_sal)
  861. error2 = scipy.stats.sem(Duration_red_sal)
  863. width = 0.015
  866. Object_types = ['Similar-Left', 'Similar-Right']
  868. fig, ax = plt.subplots(figsize=(3,3))
  869. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  870. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='forestgreen', capsize=5,alpha=0.9, ecolor='k',)
  872. # scatter dots
  873. rng = np.random.default_rng(42)
  875. x_fam = np.full(len(Duration_green_sal), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_green_sal))
  876. x_nov = np.full(len(Duration_red_sal), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_red_sal))
  878. ax.scatter(x_fam, Duration_green_sal, s=28, color='mediumseagreen',
  879. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  880. ax.scatter(x_nov, Duration_red_sal, s=28, color='forestgreen',
  881. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  885. ax.set_xticks([1.02,1.05])
  886. ax.set_xticklabels([])
  887. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  888. ax.set_xticklabels(Object_types,fontsize = 12)
  890. plt.axis_label_text_font_style = 'normal'
  891. plt.tick_label_text_font_style = 'normal'
  892. plt.title('NOR (Saline) - Familiarization phase')
  893. plt.ylim(0, 160)
  895. y, h, col = 80, 5, 'k'
  896. x00, x01 = x[0]+0.02, x[1]-0.02
  897. #Plot t-test between groups
  898. plt.plot([x00, x00, x01, x01], [y, y+h, y+h, y], lw=1.5, c=col)
  899. plt.text(((x00 + x01)/2), y+ 2+h, " $\it{p}$ = 0.686", ha='center', va='bottom', color=col,fontsize=12)
  902. plt.xlabel("Object type",fontsize=14)
  903. plt.ylabel("Exploration (s)",fontsize=14)
  904. plt.savefig('NORstatsTrial1_Saline_BarPlot.pdf', bbox_inches='tight')
  905. plt.show()
  907. # %%
  908. f_oneway(Duration_green_sal,Duration_red_sal)
  910. # %%
  913. # %% [markdown]
  914. # # Scopolamine Experiments
  916. # %% [markdown]
  917. # ### Trial 2 - Novel Object - Scopolamine
  919. # %%
  920. ## NOR tests: MouseIDs: 1, 2, D1, D2, D4, D5_repeat
  923. Duration_Obj_s= [63,14.28,31.08,21.84,40.32,71.64]
  924. Duration_NovObj_s =[50.48,14,61.04,19.28,37,91.12]
  927. # %%
  929. import matplotlib.pyplot as plt
  930. import numpy as np
  932. # create data
  933. x = [1,1.07]
  934. y1 = np.mean(Duration_Obj_s)
  935. y2 = np.mean(Duration_NovObj_s)
  937. error1 = scipy.stats.sem(Duration_Obj_s)
  938. error2 = scipy.stats.sem(Duration_NovObj_s)
  940. width = 0.015
  943. Object_types = ['Familiar', 'Novel']
  945. fig, ax = plt.subplots(figsize=(3,3))
  946. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  947. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='orangered', capsize=5,alpha=0.9, ecolor='k',)
  949. # scatter dots
  950. rng = np.random.default_rng(42)
  952. x_fam = np.full(len(Duration_Obj_s), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_Obj_s))
  953. x_nov = np.full(len(Duration_NovObj_s), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_NovObj_s))
  955. ax.scatter(x_fam, Duration_Obj_s, s=28, color='mediumseagreen',
  956. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  957. ax.scatter(x_nov, Duration_NovObj_s, s=28, color='orangered',
  958. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  961. ax.set_xticks([1.02,1.05])
  962. ax.set_xticklabels([])
  963. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  964. ax.set_xticklabels(Object_types,fontsize = 12)
  966. plt.axis_label_text_font_style = 'normal'
  967. plt.tick_label_text_font_style = 'normal'
  968. plt.title('NOR (Scopolamine) - Testing phase')
  969. plt.ylim(0, 160)
  971. y, h, col = 60, 5, 'k'
  972. x00, x01 = x[0]+0.02, x[1]-0.02
  973. #Plot t-test between groups
  974. plt.plot([x00, x00, x01, x01], [y, y+h, y+h, y], lw=1.5, c=col)
  975. plt.text(((x00 + x01)/2), y+ 2+h, " $\it{p}$ = 0.738", ha='center', va='bottom', color=col,fontsize=12)
  977. plt.xlabel("Object type",fontsize=14)
  978. plt.ylabel("Exploration (s)",fontsize=14)
  979. plt.savefig('NORstats_Scopolamine_BarPlot.pdf', bbox_inches='tight')
  980. plt.show()
  982. # %%
  983. f_oneway(Duration_Obj_s,Duration_NovObj_s)
  985. # %%
  988. # %%
  991. # %%
  994. # %% [markdown]
  995. # ### Trial 1 - Similar Objects - Scopolamine
  997. # %%
  998. ## NOR tests: MouseIDs: 1, 2, D1, D2, D4, D5_repeat
  1000. Duration_green_s= [99.64,20.08,25.64,21.56,38.88,78.88]
  1001. Duration_red_s = [95.12,14.6,52.72,33.84,39.12,75.72]
  1005. # %%
  1007. import matplotlib.pyplot as plt
  1008. import numpy as np
  1010. # create data
  1011. x = [1,1.07]
  1012. y1 = np.mean(Duration_green_s)
  1013. y2 = np.mean(Duration_red_s)
  1015. error1 = scipy.stats.sem(Duration_green_s)
  1016. error2 = scipy.stats.sem(Duration_red_s)
  1018. width = 0.015
  1021. Object_types = ['Similar-Left', 'Similar-Right']
  1023. fig, ax = plt.subplots(figsize=(3,3))
  1024. plt.bar(x[0]+0.02, y1, width, yerr=error1, color='mediumseagreen', capsize=5,alpha=0.9, ecolor='k',)
  1025. plt.bar(x[1]-0.02, y2, width, yerr=error2, color='forestgreen', capsize=5,alpha=0.9, ecolor='k',)
  1027. # scatter dots
  1028. rng = np.random.default_rng(42)
  1030. x_fam = np.full(len(Duration_green_s), x[0] + 0.02) + rng.uniform(-0.003, 0.003, len(Duration_green_s))
  1031. x_nov = np.full(len(Duration_red_s), x[1] - 0.02) + rng.uniform(-0.003, 0.003, len(Duration_red_s))
  1033. ax.scatter(x_fam, Duration_green_s, s=28, color='mediumseagreen',
  1034. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  1035. ax.scatter(x_nov, Duration_red_s, s=28, color='forestgreen',
  1036. edgecolor='black', linewidth=0.4, alpha=0.7, zorder=5)
  1038. ax.set_xticks([1.02,1.05])
  1039. ax.set_xticklabels([])
  1040. # ax.yaxis.grid(color = 'grey', linestyle = '--', linewidth = 0.5)
  1041. ax.set_xticklabels(Object_types,fontsize = 12)
  1043. plt.axis_label_text_font_style = 'normal'
  1044. plt.tick_label_text_font_style = 'normal'
  1045. plt.title('NOR (Scopolamine) - Familiarization phase')
  1046. plt.ylim(0, 160)
  1048. y, h, col = 70, 5, 'k'
  1049. x00, x01 = x[0]+0.02, x[1]-0.02
  1050. #Plot t-test between groups
  1051. plt.plot([x00, x00, x01, x01], [y, y+h, y+h, y], lw=1.5, c=col)
  1052. plt.text(((x00 + x01)/2), y+ 2+h, " $\it{p}$ = 0.814", ha='center', va='bottom', color=col,fontsize=12)
  1055. plt.xlabel("Object type",fontsize=14)
  1056. plt.ylabel("Exploration (s)",fontsize=14)
  1057. plt.savefig('NORstatsTrial1_Scopolamine_BarPlot.pdf', bbox_inches='tight')
  1058. plt.show()
  1060. # %%
  1063. # %%
  1064. f_oneway(Duration_green_s,Duration_red_s)
  1066. # %%
  1069. # %% [markdown]
  1070. # # NOR Indices
  1072. # %%
  1073. ## NOR tests: MouseIDs: 1, 2, D1, D2, D4, D5_repeat
  1074. import numpy as np
  1076. Duration_green_1_s= np.array([99.64,20.08,25.64,21.56,38.88,78.88])
  1077. Duration_red_1_s = np.array([95.12,14.6,52.72,33.84,39.12,75.72])
  1079. Duration_Obj_2_s= np.array([63,14.28,31.08,21.84,40.32,71.64])
  1080. Duration_NovObj_2_s =np.array([50.48,14,61.04,19.28,37,91.12])
  1084. # %% [markdown]
  1085. # ## Discrimination index - Scopolamine
  1087. # %%
  1088. ## Discrimination index - test phase
  1089. DI_2_s = (Duration_NovObj_2_s - Duration_Obj_2_s)/(Duration_Obj_2_s+Duration_NovObj_2_s)
  1090. DI_2_s
  1091. print(DI_2_s)
  1092. print('Mean DI_2_s: ')
  1093. print(np.mean(DI_2_s))
  1094. print("\n SE DI_2_s:")
  1095. print(scipy.stats.sem(DI_2_s))
  1097. # %%
  1098. ## Discrimination index - familiarization phase
  1099. DI_1_s = (Duration_green_1_s - Duration_red_1_s)/(Duration_green_1_s+Duration_red_1_s)
  1100. DI_1_s
  1102. print(DI_1_s)
  1103. print('Mean DI_1_s: ')
  1104. print(np.mean(DI_1_s))
  1105. print("\n SE DI_1_s:")
  1106. print(scipy.stats.sem(DI_1_s))
  1108. # %%
  1109. f_oneway(DI_1,DI_2)
  1111. # %%
  1114. # %% [markdown]
  1115. # ## Index of global habituation - Scopolamine
  1117. # %%
  1118. ## Index of global habituation
  1119. GI_s = (Duration_green_1_s+Duration_red_1_s)/(Duration_Obj_2_s+Duration_NovObj_2_s)
  1120. GI_s
  1121. print(GI_s)
  1122. print('Mean GI_s: ')
  1123. print(np.mean(GI_s))
  1124. print("\n SE GI_s:")
  1125. print(scipy.stats.sem(GI_s))
  1127. # %%
  1130. # %% [markdown]
  1131. # ## Pereference index - Scopolamine
  1133. # %%
  1134. ## Recognition Index : Pereference index in test phase
  1135. RI_s = (Duration_NovObj_2_s)/(Duration_Obj_2_s+Duration_NovObj_2_s)
  1136. RI_s
  1137. print(RI_s)
  1138. print('Mean RI_s: ')
  1139. print(np.mean(RI_s))
  1140. print("\n SE RI_s:")
  1141. print(scipy.stats.sem(RI_s))
  1143. # %%
  1144. ## Pereference index in familiarization phase
  1145. PI_s = (Duration_green_1_s)/(Duration_green_1_s+Duration_red_1_s)
  1146. PI_s
  1147. print(PI_s)
  1148. print('Mean PI_s: ')
  1149. print(np.mean(PI_s))
  1150. print("\n SE PI_s:")
  1151. print(scipy.stats.sem(PI_s))
  1153. # %%
  1156. # %%
  1157. f_oneway(RI_s,PI_s)
  1159. # %%
  1162. # %% [markdown]
  1163. # ## Plots:
  1165. # %%
  1166. import matplotlib.pyplot as plt
  1167. import numpy as np
  1168. import seaborn as sns
  1169. import scipy.stats
  1170. import matplotlib.patches as mpatches
  1171. import matplotlib.patches as patches
  1173. # create data
  1174. x = np.linspace(1, 12, num=12)
  1175. y1 = np.mean(DI_2)
  1176. y2 = np.mean(DI_1)
  1177. y3 = np.mean(DI_1_s)
  1178. y4 = np.mean(DI_2_s)
  1179. y5 = np.mean(GI)
  1180. y6 = np.mean(GI_s)
  1181. y7 = np.mean(RI)
  1182. y8 = np.mean(RI_s)
  1183. y9 = np.mean(PI)
  1184. y10 = np.mean(PI_s)
  1186. error1 = scipy.stats.sem(DI_2)
  1187. error2 = scipy.stats.sem(DI_1)
  1188. error3 = scipy.stats.sem(DI_1_s)
  1189. error4 = scipy.stats.sem(DI_2_s)
  1190. error5 = scipy.stats.sem(GI)
  1191. error6 = scipy.stats.sem(GI_s)
  1192. error7 = scipy.stats.sem(RI)
  1193. error8 = scipy.stats.sem(RI_s)
  1194. error9 = scipy.stats.sem(PI)
  1195. error10 = scipy.stats.sem(PI_s)
  1197. width = 1
  1198. Object_types = ['$DI$', '$GI$', '$RI$', '$PI$']
  1200. fig, ax = plt.subplots(figsize=(5.25, 5))
  1202. # bars with black error bars
  1203. ax.bar(x[0]-0.05, y1, width, yerr=error1, color='c', capsize=5, alpha=0.9, ecolor='black', label='Saline')
  1204. ax.bar(x[1]+0.05, y4, width, yerr=error4, color='c', capsize=5, alpha=0.9, ecolor='black', hatch='///', label='Scopolamine')
  1206. # ax.bar(x[3]-0.05, y2, width, yerr=error2, color='#EE3B3B', capsize=5, alpha=0.9, ecolor='black')
  1207. # ax.bar(x[4]+0.05, y3, width, yerr=error3, color='#EE3B3B', capsize=5, alpha=0.9, ecolor='black', hatch='///')
  1209. ax.bar(x[3]-0.05, y5, width, yerr=error5, color='orange', capsize=5, alpha=0.9, ecolor='black')
  1210. ax.bar(x[4]+0.05, y6, width, yerr=error6, color='orange', capsize=5, alpha=0.9, ecolor='black', hatch='///')
  1212. ax.bar(x[6]-0.05, y7, width, yerr=error7, color='#CD6090', capsize=5, alpha=0.9, ecolor='black')
  1213. ax.bar(x[7]+0.05, y8, width, yerr=error8, color='#CD6090', capsize=5, alpha=0.9, ecolor='black', hatch='///')
  1215. ax.bar(x[9]-0.05, y9, width, yerr=error9, color='mediumseagreen', capsize=5, alpha=0.9, ecolor='black')
  1216. ax.bar(x[10]+0.05, y10, width, yerr=error10, color='mediumseagreen', capsize=5, alpha=0.9, ecolor='black', hatch='///')
  1218. # scatter dots
  1219. rng = np.random.default_rng(42)
  1221. def add_scatter(ax, xpos, values, color, jitter=0.08):
  1222. vals = np.asarray(values)
  1223. xj = np.full(len(vals), xpos) + rng.uniform(-jitter, jitter, len(vals))
  1224. ax.scatter(
  1225. xj, vals,
  1226. s=30,
  1227. color=color,
  1228. edgecolor='black',
  1229. linewidth=0.4,
  1230. alpha=0.7,
  1231. zorder=5
  1232. )
  1234. add_scatter(ax, x[0]-0.05, DI_2, 'c')
  1235. add_scatter(ax, x[1]+0.05, DI_2_s, 'c')
  1237. # add_scatter(ax, x[3]-0.05, DI_1, '#EE3B3B')
  1238. # add_scatter(ax, x[4]+0.05, DI_1_s, '#EE3B3B')
  1240. add_scatter(ax, x[3]-0.05, GI, 'orange')
  1241. add_scatter(ax, x[4]+0.05, GI_s, 'orange')
  1243. add_scatter(ax, x[6]-0.05, RI, '#CD6090')
  1244. add_scatter(ax, x[7]+0.05, RI_s, '#CD6090')
  1246. add_scatter(ax, x[9]-0.05, PI, 'mediumseagreen')
  1247. add_scatter(ax, x[10]+0.05, PI_s, 'mediumseagreen')
  1249. sns.set(font_scale=1.3)
  1250. ax.set_xticks([1.5, 4.5, 7.5, 10.5])
  1251. ax.set_xticklabels(Object_types, fontsize=12, rotation=30)
  1253. plt.axis_label_text_font_style = 'Times'
  1254. plt.tick_label_text_font_style = 'Times'
  1255. plt.title('')
  1256. plt.ylim(-.2, 1.55)
  1257. plt.rcParams['hatch.linewidth'] = 2
  1259. first_patch = patches.Rectangle((2, 1), 1.4, 13, linewidth=0, label='Saline', alpha=0.4)
  1260. second_patch = patches.Rectangle((2, 1), 1.4, 13, linewidth=0, label='Scopolamine', hatch='///', alpha=0.4)
  1262. plt.legend(handles=[first_patch, second_patch], loc='upper right')
  1264. plt.xlabel("", fontsize=14)
  1265. plt.ylabel("Index", fontsize=18)
  1267. ax.set_xticklabels(ax.get_xticklabels(), fontsize=14)
  1269. y, h, col = 0.6, 0.04, 'k'
  1270. x00, x01 = x[0]-0.05, x[1]+0.05
  1271. x20, x21 = x[3]-0.05, x[4]+0.05
  1272. x30, x31 = x[6]-0.05, x[7]+0.05
  1273. x40, x41 = x[9]-0.05, x[10]+0.05
  1275. # Plot t-test between groups
  1276. ax.plot([x00, x00, x01, x01], [y-0.05, y-0.05+h, y-0.05+h, y-10*h], lw=1.5, c=col)
  1277. ax.text(((x00 + x01)/2), y-0.07+h, "***", ha='center', va='bottom', color=col, fontsize=14)
  1279. ax.plot([x20, x20, x21, x21], [y+0.804-11*h, y+0.804+h, y+0.804+h, y+0.804], lw=1.5, c=col)
  1280. ax.text(((x20 + x21)/2), y+0.768+h, "**", ha='center', va='bottom', color=col, fontsize=14)
  1282. ax.plot([x30, x30, x31, x31], [y+0.22, y+0.22+h, y+0.22+h, y+0.22-5*h], lw=1.5, c=col)
  1283. ax.text(((x30 + x31)/2), y+0.19+h, "***", ha='center', va='bottom', color=col, fontsize=14)
  1285. ax.plot([x40, x40, x41, x41], [y+0.02, y+0.02+h, y+0.02+h, y+0.02-1.5*h], lw=1.5, c=col)
  1286. ax.text(((x40 + x41)/2), y+0.07+h, "$\it{p}$ = 0.199", ha='center', va='bottom', color=col, fontsize=10)
  1288. plt.savefig('NORstats_SalinevsScopolamine.pdf', bbox_inches='tight')
  1289. plt.show();
  1291. # %%
  1292. f_oneway(DI_2,DI_2_s)
  1294. # %%
  1295. f_oneway(DI_1,DI_1_s)
  1297. # %%
  1298. f_oneway(GI,GI_s)
  1300. # %%
  1301. f_oneway(RI,RI_s)
  1303. # %%
  1304. f_oneway(PI,PI_s)
  1306. # %%
  1309. # %%
  1312. # %% [markdown]
  1313. # ## Correlations
  1315. # %%
  1316. ## NOR tests: 1_Repeat, 2, D1, D2, D4, D5, 3, 5
  1318. DCC_NOR = [0.41,0.151,0.087,0.079,0.728,0.29,0.243,0.394]
  1319. DCC_Cage = [0.965,1.995,1.799,0.515,0.819,1.145,0.807,0.873]
  1321. BR_NOR =[0.9998,0.9093,0.9998,0.91494,0.99867,0.99432,0.9998,0.99801]
  1322. BR_Cage = [0.93821,0.90995,0.87027,0.90995,0.98629,0.90978,0.89195,0.92929]
  1324. SCE_NOR =[0.13,0.018,0.069,0.0518,0.0358,0.014,0.0502,0.011]
  1325. SCE_Cage = [0.2028,0.2346,0.1928,0.2128,0.2289,0.1879,0.4072,0.4409]
  1328. ## NOR tests: MouseIDs: 1, 2, D1, D2, D4, D5_repeat
  1329. DCC_Scopolamine = [0.308,2.948,0.946,0.881,0.774,2.278]
  1330. DCC_Cage=[0.965,1.995,1.799,0.515,0.819,1.145]
  1332. BR_Scopolamine = [0.85568,0.62586,0.93741,0.98936,0.97951,0.6031]
  1333. BR_Cage = [0.93821,0.90995,0.87027,0.90995,0.98629,0.90978]
  1335. SCE_Scopolamine = [0.27286709,0.36076511,0.093427,0.2603256,0.28944217,0.32972748]
  1336. SCE_Cage = [0.2028,0.2346,0.1928,0.2128,0.2289,0.1879]
  1338. # %%
  1339. from numpy.random import randn
  1340. from numpy.random import seed
  1341. from scipy.stats import pearsonr
  1342. from scipy import stats
  1345. # %%
  1346. corr, p_value = pearsonr(DCC_NOR, DI_2)
  1347. print('Pearsons correlation DCC/DI_2: %.3f' % corr)
  1348. print('Pearsons p-value DCC/DI_2: %.8f\n' % p_value)
  1350. corr2,p_value = stats.spearmanr(DCC_NOR, DI_2)
  1351. print('Spearman correlation DCC/DI_2: %.3f' % corr2)
  1352. print('Spearman p-value DCC/DI_2: %.8f\n' % p_value)
  1356. corr, p_value = pearsonr(BR_NOR, DI_2)
  1357. print('Pearsons correlation BR/DI_2: %.3f' % corr)
  1358. print('Pearsons p-value BR/DI_2: %.8f\n' % p_value)
  1360. corr2,p_value = stats.spearmanr(BR_NOR, DI_2)
  1361. print('Spearman correlation DCC/DI_2: %.3f' % corr2)
  1362. print('Spearman p-value BR/DI_2: %.8f\n' % p_value)
  1366. corr, p_value = pearsonr(SCE_NOR, DI_2)
  1367. print('Pearsons correlation SCE/DI_2: %.3f' % corr)
  1368. print('Pearsons p-value SCE/DI_2: %.8f\n' % p_value)
  1370. corr2,p_value = stats.spearmanr(SCE_NOR, DI_2)
  1371. print('Spearman correlation SCE/DI_2: %.3f' % corr2)
  1372. print('Spearman p-value SCE/DI_2: %.8f\n' % p_value)
  1375. # %%
  1376. corr, p_value = pearsonr(DCC_NOR, DI_1)
  1377. print('Pearsons correlation DCC/DI_1: %.3f' % corr)
  1378. print('Pearsons p-value DCC/DI_1: %.8f\n' % p_value)
  1380. corr2,p_value = stats.spearmanr(DCC_NOR, DI_1)
  1381. print('Spearman correlation DCC/DI_1: %.3f' % corr2)
  1382. print('Spearman p-value DCC/DI_1: %.8f\n' % p_value)
  1386. corr, p_value = pearsonr(BR_NOR, DI_1)
  1387. print('Pearsons correlation BR/DI_1: %.3f' % corr)
  1388. print('Pearsons p-value BR/DI_1: %.8f\n' % p_value)
  1390. corr2,p_value = stats.spearmanr(BR_NOR, DI_1)
  1391. print('Spearman correlation DCC/DI_1: %.3f' % corr2)
  1392. print('Spearman p-value BR/DI_1: %.8f\n' % p_value)
  1396. corr, p_value = pearsonr(SCE_NOR, DI_1)
  1397. print('Pearsons correlation SCE/DI_1: %.3f' % corr)
  1398. print('Pearsons p-value SCE/DI_1: %.8f\n' % p_value)
  1400. corr2,p_value = stats.spearmanr(SCE_NOR, DI_1)
  1401. print('Spearman correlation SCE/DI_1: %.3f' % corr2)
  1402. print('Spearman p-value SCE/DI_1: %.8f\n' % p_value)
  1405. # %%
  1406. corr, p_value = pearsonr(DCC_NOR, GI)
  1407. print('Pearsons correlation DCC/GI: %.3f' % corr)
  1408. print('Pearsons p-value DCC/GI: %.8f\n' % p_value)
  1410. corr2,p_value = stats.spearmanr(DCC_NOR, GI)
  1411. print('Spearman correlation DCC/GI: %.3f' % corr2)
  1412. print('Spearman p-value DCC/GI: %.8f\n' % p_value)
  1416. corr, p_value = pearsonr(BR_NOR, GI)
  1417. print('Pearsons correlation BR/GI: %.3f' % corr)
  1418. print('Pearsons p-value BR/GI: %.8f\n' % p_value)
  1420. corr2,p_value = stats.spearmanr(BR_NOR, GI)
  1421. print('Spearman correlation DCC/GI: %.3f' % corr2)
  1422. print('Spearman p-value BR/GI: %.8f\n' % p_value)
  1426. corr, p_value = pearsonr(SCE_NOR, GI)
  1427. print('Pearsons correlation SCE/GI: %.3f' % corr)
  1428. print('Pearsons p-value SCE/GI: %.8f\n' % p_value)
  1430. corr2,p_value = stats.spearmanr(SCE_NOR, GI)
  1431. print('Spearman correlation SCE/GI: %.3f' % corr2)
  1432. print('Spearman p-value SCE/GI: %.8f\n' % p_value)
  1435. # %%
  1436. corr, p_value = pearsonr(DCC_NOR, RI)
  1437. print('Pearsons correlation DCC/RI: %.3f' % corr)
  1438. print('Pearsons p-value DCC/RI: %.8f\n' % p_value)
  1440. corr2,p_value = stats.spearmanr(DCC_NOR, RI)
  1441. print('Spearman correlation DCC/RI: %.3f' % corr2)
  1442. print('Spearman p-value DCC/RI: %.8f\n' % p_value)
  1446. corr, p_value = pearsonr(BR_NOR, RI)
  1447. print('Pearsons correlation BR/RI: %.3f' % corr)
  1448. print('Pearsons p-value BR/RI: %.8f\n' % p_value)
  1450. corr2,p_value = stats.spearmanr(BR_NOR, RI)
  1451. print('Spearman correlation DCC/RI: %.3f' % corr2)
  1452. print('Spearman p-value BR/RI: %.8f\n' % p_value)
  1456. corr, p_value = pearsonr(SCE_NOR, RI)
  1457. print('Pearsons correlation SCE/RI: %.3f' % corr)
  1458. print('Pearsons p-value SCE/RI: %.8f\n' % p_value)
  1460. corr2,p_value = stats.spearmanr(SCE_NOR, RI)
  1461. print('Spearman correlation SCE/RI: %.3f' % corr2)
  1462. print('Spearman p-value SCE/RI: %.8f\n' % p_value)
  1465. # %%
  1468. # %%
  1471. # %%
  1474. # %%
  1475. import seaborn as sns
  1477. # ax = sns.scatterplot(y="branching_ratio", x="hit_miss_ratio", data=sorted_all_data_df)
  1478. sns.lmplot(y="branching_ratio", x="hit_miss_ratio", hue="activity_status",data=sorted_all_data_df)
  1479. sns.set(style="darkgrid")
  1480. plt.grid(False)
  1481. plt.savefig('Results/Statistical Analysis/'+'Pearson_Corr_BR-HMR_act&rest.pdf', bbox_inches='tight')
  1483. sns.set(font_scale = 1.5)
  1484. b = sns.lmplot(y="branching_ratio", x="hit_miss_ratio", data=sorted_all_data_df,line_kws={'color': 'red'})
  1485. sns.set(style="darkgrid")
  1486. plt.grid(False)
  1488. corr, p_value= stats.pearsonr(sorted_all_data_df['branching_ratio'],sorted_all_data_df['hit_miss_ratio'])
  1489. plt.ylabel('BR', fontsize = 22)
  1490. plt.xlabel('H/M ratio', fontsize = 22)
  1491. # plt.setp(ax.get_yticklabels(), fontsize=20)
  1492. # plt.setp(ax.get_xticklabels(), fontsize=20)
  1496. # plt.text(1.2,0.8, "Pearson Correlation: %.3f \n p-value: %.6f" % (corr,p_value), horizontalalignment='left', size='medium', color='black', weight='semibold')
  1497. plt.savefig('Paper Plots/'+'Pearson_Corr_BR-HMR.pdf', bbox_inches='tight')
  1498. print('p-value',p_value)
  1499. print('Correlation',corr)
  1502. # %%
  1505. # %%
  1508. # %% [markdown]
  1509. # ## Number of neurons saline and scopolamine
  1511. # %%
  1512. ##Saline:
  1513. x = [578,203,483,269,221,174,195,203]
  1514. ##Scopolamine:
  1515. y =[179,432,192,175,285,444]
  1516. ##Cage:
  1517. y2 =[224,114,251,197,549,170,176,158]
  1519. # %%
  1520. from scipy import stats, optimize, interpolate
  1521. from scipy.stats import alexandergovern
  1522. from scipy.stats import f_oneway
  1524. # %%
  1525. alexandergovern(x,y)
  1527. # %%
  1528. f_oneway(x,y)
  1530. # %%
  1533. # %% [markdown]
  1534. # # Review round
  1536. # %% [markdown]
  1537. # ## Linking behaviour and avalanche statistics:
  1539. # %%
  1540. import pandas as pd
  1541. import numpy as np
  1543. avalanche_rows = [
  1544. (1, "Cage", 2.944741, 1.826018, 2.353877, 1.388832, 0.015756, 0.965045, 0.93821, 0.2028),
  1545. (1, "NOR (Saline)", 2.937048, 2.031536, 1.875380, 1.465491, 0.008734, 0.409889, 0.9998, 0.13),
  1546. (1, "NOR (Scopolamine)", 2.127443, 1.848198, 1.324312, 1.631867, 0.007362, 0.307555, 0.85568, 0.2729),
  1548. (2, "Cage", 2.437292, 1.402012, 3.564297, 1.569605, 0.005116, 1.994692, 0.90995, 0.2346),
  1549. (2, "NOR (Saline)", 2.924021, 2.274226, 1.509953, 1.359075, 0.006379, 0.150878, 0.9093, 0.0690),
  1550. (2, "NOR (Scopolamine)", 2.550383, 1.385671, 3.994948, 1.047235, 0.007634, 2.947714, 0.62686, 0.3608),
  1552. (3, "Cage", 1.993352, 1.446372, 2.217301, 1.410267, 0.011417, 0.807034, 0.89195, 0.4072),
  1553. (3, "NOR (Saline)", 2.406970, 1.830398, 1.683970, 1.440837, 0.015476, 0.243133, 0.9998, 0.0502),
  1555. (4, "Cage", 2.334646, 1.495112, 2.676519, 1.803963, 0.014538, 0.872556, 0.92929, 0.4409),
  1556. (4, "NOR (Saline)", 2.841140, 2.009749, 1.823365, 1.429635, 0.008229, 0.393730, 0.99801, 0.0110),
  1558. (5, "Cage", 2.085834, 1.333674, 3.240000, 1.440826, 0.015182, 1.799174, 0.87027, 0.1928),
  1559. (5, "NOR (Saline)", 2.444311, 1.925183, 1.561678, 1.475117, 0.020117, 0.086561, 0.9998, 0.0690),
  1560. (5, "NOR (Scopolamine)", 2.566835, 1.700068, 2.238118, 1.292573, 0.011734, 0.945546, 0.93741, 0.0934),
  1562. (6, "Cage", 2.337229, 1.682043, 1.956755, 1.441831, 0.005785, 0.514924, 0.90995, 0.2128),
  1563. (6, "NOR (Saline)", 2.397312, 1.992004, 1.409372, 1.488863, 0.012023, 0.079491, 0.91494, 0.0518),
  1564. (6, "NOR (Scopolamine)", 3.058842, 1.864875, 2.376188, 1.495326, 0.006791, 0.880862, 0.98936, 0.2603),
  1566. (7, "Cage", 2.420109, 1.589785, 2.392807, 1.573986, 0.010804, 0.818821, 0.98629, 0.2289),
  1567. (7, "NOR (Saline)", 2.327177, 1.581250, 2.283317, 1.555539, 0.006043, 0.727777, 0.99867, 0.0358),
  1568. (7, "NOR (Scopolamine)", 2.864331, 1.777053, 2.395078, 1.621442, 0.006387, 0.773636, 0.97951, 0.2894),
  1570. (8, "Cage", 2.387429, 1.528691, 2.611235, 1.465911, 0.010931, 1.145325, 0.90978, 0.1879),
  1571. (8, "NOR (Saline)", 2.499902, 1.847468, 1.768339, 1.478169, 0.007238, 0.290170, 0.99432, 0.0140),
  1572. (8, "NOR (Scopolamine)", 2.513659, 1.430313, 3.505688, 1.227273, 0.018508, 2.278416, 0.6031, 0.3297),
  1573. ]
  1575. avalanche_df = pd.DataFrame(
  1576. avalanche_rows,
  1577. columns=["MouseID","Condition","alpha_duration","tau_size","beta_pred","beta_fitted","KS","DCC","BR","SCE"]
  1578. )
  1580. # Normalize condition labels for merging later
  1581. avalanche_df["ConditionKey"] = avalanche_df["Condition"].replace({
  1582. "NOR (Saline)": "Saline",
  1583. "NOR (Scopolamine)": "Scopolamine",
  1584. "Cage": "Cage"
  1585. })
  1587. avalanche_df.head()
  1590. # %%
  1591. import numpy as np
  1592. import pandas as pd
  1594. # ---------------------------
  1595. # SALINE (Mouse order: 3,5,7,8,1,6,4,2)
  1596. # ---------------------------
  1597. mouse_saline = np.array([3,5,7,8,1,6,4,2])
  1599. Duration_green_1 = np.array([45.72,49.68,43.78,81.72,98.08,96.96,41.12,46.68])
  1600. Duration_red_1 = np.array([28.04,62.96,39.36,54.48,104.24,126.8,33.24,35.88])
  1602. Duration_Obj_2 = np.array([33.72,32.8,42.4,37.28,68.36,57.0,34.6,42.56]) # familiar in trial 2
  1603. Duration_NovObj_2 = np.array([120.56,87.16,98.24,186.04,76.68,197.64,50.4,108.96])
  1605. # Trial 2 (test): novelty preference
  1606. DI_2 = (Duration_NovObj_2 - Duration_Obj_2) / (Duration_Obj_2 + Duration_NovObj_2)
  1607. RI = Duration_NovObj_2 / (Duration_Obj_2 + Duration_NovObj_2)
  1609. # Trial 1 (familiarization): object-color preference (should be ~0 on average; useful sanity check)
  1610. DI_1 = (Duration_green_1 - Duration_red_1) / (Duration_green_1 + Duration_red_1)
  1611. PI = Duration_green_1 / (Duration_green_1 + Duration_red_1)
  1613. # Global habituation (trial1 total / trial2 total)
  1614. GI = (Duration_green_1 + Duration_red_1) / (Duration_Obj_2 + Duration_NovObj_2)
  1617. behav_saline = pd.DataFrame({
  1618. "MouseID": mouse_saline,
  1619. "ConditionKey": "Saline",
  1620. "DI": DI_2,
  1621. "DI_fam": DI_1,
  1622. "GI": GI,
  1623. "RI": RI,
  1624. "PI": PI,
  1625. "T1_total": Duration_green_1 + Duration_red_1,
  1626. "T2_total": Duration_Obj_2 + Duration_NovObj_2,
  1627. "T2_novel": Duration_NovObj_2,
  1628. "T2_familiar": Duration_Obj_2
  1629. })
  1632. # ---------------------------
  1633. # SCOPOLAMINE (Mouse order: 1,2,5,6,7,8)
  1634. # ---------------------------
  1635. mouse_scop = np.array([1,2,5,6,7,8])
  1637. Duration_green_1_s = np.array([99.64,20.08,25.64,21.56,38.88,78.88])
  1638. Duration_red_1_s = np.array([95.12,14.6,52.72,33.84,39.12,75.72])
  1640. Duration_Obj_2_s = np.array([63.0,14.28,31.08,21.84,40.32,71.64])
  1641. Duration_NovObj_2_s = np.array([50.48,14.0,61.04,19.28,37.0,91.12])
  1643. DI_2_s = (Duration_NovObj_2_s - Duration_Obj_2_s) / (Duration_Obj_2_s + Duration_NovObj_2_s)
  1644. RI_s = Duration_NovObj_2_s / (Duration_Obj_2_s + Duration_NovObj_2_s)
  1645. DI_1_s = (Duration_green_1_s - Duration_red_1_s) / (Duration_green_1_s + Duration_red_1_s)
  1646. PI_s = Duration_green_1_s / (Duration_green_1_s + Duration_red_1_s)
  1647. GI_s = (Duration_green_1_s + Duration_red_1_s) / (Duration_Obj_2_s + Duration_NovObj_2_s)
  1649. behav_scop = pd.DataFrame({
  1650. "MouseID": mouse_scop,
  1651. "ConditionKey": "Scopolamine",
  1652. "DI": DI_2_s,
  1653. "DI_fam": DI_1_s,
  1654. "GI": GI_s,
  1655. "RI": RI_s,
  1656. "PI": PI_s,
  1657. "T1_total": Duration_green_1_s + Duration_red_1_s,
  1658. "T2_total": Duration_Obj_2_s + Duration_NovObj_2_s,
  1659. "T2_novel": Duration_NovObj_2_s,
  1660. "T2_familiar": Duration_Obj_2_s
  1661. })
  1663. behav_df = pd.concat([behav_saline, behav_scop], ignore_index=True).sort_values(["ConditionKey","MouseID"])
  1664. behav_df
  1667. # %%
  1668. merged = avalanche_df.merge(behav_df, on=["MouseID","ConditionKey"], how="inner")
  1670. # keep only NOR conditions
  1671. merged_nor = merged[merged["ConditionKey"].isin(["Saline","Scopolamine"])].copy()
  1673. print("Merged rows:", len(merged_nor))
  1674. merged_nor[["MouseID","ConditionKey","DCC","BR","SCE","tau_size","alpha_duration","DI","GI","RI","PI"]].sort_values(["ConditionKey","MouseID"])
  1677. # %%
  1678. import matplotlib.pyplot as plt
  1679. from scipy.stats import spearmanr, pearsonr
  1681. def scatter_stats(df, x, y, title):
  1682. d = df[[x,y]].dropna()
  1683. n = len(d)
  1684. if n < 3:
  1685. print(f"{title}: not enough data (n={n})")
  1686. return
  1688. r_s, p_s = spearmanr(d[x], d[y])
  1689. r_p, p_p = pearsonr(d[x], d[y])
  1691. # best-fit line (simple linear fit)
  1692. m, b = np.polyfit(d[x].astype(float), d[y].astype(float), 1)
  1693. xs = np.linspace(d[x].min(), d[x].max(), 200)
  1694. ys = m*xs + b
  1696. plt.figure(figsize=(5,4))
  1697. plt.scatter(d[x], d[y], alpha=0.85)
  1698. plt.plot(xs, ys)
  1699. plt.title(title)
  1700. plt.xlabel(x)
  1701. plt.ylabel(y)
  1702. plt.tight_layout()
  1703. plt.show()
  1705. print(f"{title} | n={n}")
  1706. print(f" Spearman r={r_s:.3f}, p={p_s:.3g}")
  1707. print(f" Pearson r={r_p:.3f}, p={p_p:.3g}")
  1708. print("")
  1710. # Choose behavior metric(s) to relate to avalanches
  1711. behav_metrics = ["DI", "RI", "GI", "PI"] # PI/DI_fam are more "control" measures
  1712. aval_metrics = ["DCC", "SCE", "BR", "tau_size", "alpha_duration"]
  1714. for cond in ["Saline", "Scopolamine"]:
  1715. dfc = merged_nor[merged_nor["ConditionKey"]==cond].copy()
  1716. for bx in behav_metrics:
  1717. for ay in ["DCC","SCE","BR"]:
  1718. scatter_stats(dfc, bx, ay, title=f"{cond}: {ay} vs {bx}")
  1721. # %%
  1722. COLORS = {
  1723. "Saline": {
  1724. "scatter": "#4C9F9F", # muted teal
  1725. "line": "#2F6F6F"
  1726. },
  1727. "Scopolamine": {
  1728. "scatter": "#C96A4A", # muted rust
  1729. "line": "#8C3F28"
  1730. }
  1731. }
  1734. # %%
  1735. import numpy as np
  1736. import matplotlib.pyplot as plt
  1737. import matplotlib as mpl
  1738. from scipy.stats import spearmanr
  1740. # Clean white background everywhere
  1741. plt.rcParams.update({
  1742. "figure.facecolor": "white",
  1743. "axes.facecolor": "white",
  1744. "savefig.facecolor": "white"
  1745. })
  1748. # Hard reset to default matplotlib style
  1749. plt.style.use("default")
  1750. mpl.rcdefaults()
  1752. # Explicit global spine defaults
  1753. mpl.rcParams.update({
  1754. "axes.edgecolor": "black",
  1755. "axes.linewidth": 1.0,
  1756. "axes.spines.top": True,
  1757. "axes.spines.bottom": True,
  1758. "axes.spines.left": True,
  1759. "axes.spines.right": True,
  1760. "axes.facecolor": "white",
  1761. "figure.facecolor": "white",
  1762. "savefig.facecolor": "white",
  1763. })
  1766. COLORS = {
  1767. "Saline": {
  1768. "scatter": "#4C9F9F", # muted teal
  1769. "line": "#2F6F6F"
  1770. },
  1771. "Scopolamine": {
  1772. "scatter": "#C96A4A", # muted rust
  1773. "line": "#8C3F28"
  1774. }
  1775. }
  1777. def make_behavior_avalanche_subplot_figure(
  1778. merged_nor,
  1779. xcol,
  1780. conditions=("Saline", "Scopolamine"),
  1781. ycols=("DCC", "SCE", "BR", "tau_size", "alpha_duration"),
  1782. ylabels=("DCC", "Shape collapse error (SCE)", "Branching ratio (BR)",
  1783. r"Avalanche size exponent $\tau$", r"Avalanche duration exponent $\alpha$"),
  1784. figsize=(16, 7),
  1785. savepath_pdf="Fig_behavior_vs_avalanche.pdf",
  1786. dpi=300
  1787. ):
  1789. missing = [c for c in ["ConditionKey", xcol, *ycols] if c not in merged_nor.columns]
  1790. if missing:
  1791. raise ValueError(f"merged_nor missing columns: {missing}")
  1793. n_rows = len(conditions)
  1794. n_cols = len(ycols)
  1796. fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize, sharex="col")
  1798. if n_rows == 1:
  1799. axes = np.expand_dims(axes, axis=0)
  1800. if n_cols == 1:
  1801. axes = np.expand_dims(axes, axis=1)
  1803. for r, cond in enumerate(conditions):
  1804. dfc = merged_nor[merged_nor["ConditionKey"] == cond].copy()
  1805. color_scatter = COLORS[cond]["scatter"]
  1806. color_line = COLORS[cond]["line"]
  1808. for c, (ycol, ylabel) in enumerate(zip(ycols, ylabels)):
  1809. ax = axes[r, c]
  1811. d = dfc[[xcol, ycol]].dropna()
  1812. x = d[xcol].astype(float).to_numpy()
  1813. y = d[ycol].astype(float).to_numpy()
  1815. # Scatter
  1816. ax.scatter(
  1817. x, y,
  1818. s=45,
  1819. alpha=0.85,
  1820. color=color_scatter,
  1821. edgecolor="none"
  1822. )
  1824. # Regression line + stats
  1825. if len(d) >= 3 and np.unique(x).size >= 2:
  1826. m, b = np.polyfit(x, y, 1)
  1827. xs = np.linspace(np.min(x), np.max(x), 200)
  1828. ax.plot(xs, m * xs + b, color=color_line, linewidth=2)
  1830. rs, ps = spearmanr(x, y)
  1831. ax.text(
  1832. 0.04, 0.96,
  1833. f"Spearman r={rs:.2f}\np={ps:.2g}\nn={len(d)}",
  1834. transform=ax.transAxes,
  1835. va="top",
  1836. ha="left",
  1837. fontsize=12
  1838. )
  1839. else:
  1840. ax.text(
  1841. 0.04, 0.96,
  1842. f"n={len(d)}",
  1843. transform=ax.transAxes,
  1844. va="top",
  1845. ha="left",
  1846. fontsize=14
  1847. )
  1849. # Titles / labels
  1850. if r == 0:
  1851. ax.set_title(ylabel, fontsize=14)
  1852. if c == 0:
  1853. ax.set_ylabel(cond, fontsize=14)
  1855. # Axes styling: keep all borders
  1856. for spine in ax.spines.values():
  1857. spine.set_visible(True)
  1858. spine.set_linewidth(1.0)
  1860. ax.tick_params(axis="both", which="major", labelsize=12)
  1861. ax.grid(False)
  1863. # X-axis label only on bottom row
  1864. for ax in axes[-1, :]:
  1865. ax.set_xlabel(xcol, fontsize=14)
  1867. plt.tight_layout()
  1868. fig.savefig(savepath_pdf, bbox_inches="tight")
  1869. plt.show()
  1871. print(f"Saved: {savepath_pdf}")
  1875. # ---- Run it (behavioural metric vs DCC/SCE/BR/tau/alpha; Saline vs Scopolamine) ----
  1876. for beh_metric in {"PI","GI","RI","DI"}:
  1877. metric = beh_metric
  1878. make_behavior_avalanche_subplot_figure(
  1879. merged_nor,
  1880. xcol=metric,
  1881. figsize=(16, 6),
  1882. savepath_pdf=f"Fig_{metric}_vs_avalanchemetrics.pdf"
  1883. )
  1886. # %%
  1889. # %%

Paper_Plotting_NOR &Criticality_Stats.ipynb, no license · at the source

Overview

Authors: Forough Habibollahi1,2,3, Dechuan Sun3,4, Anthony N. Burkitt1,5, Chris French3,6
ORCID iDs: Anthony N. Burkitt, Chris French
  1. Department of Biomedical Engineering, The University of Melbourne,Melbourne, VIC Australia
  2. Cortical Labs Pty Ltd, Melbourne, VIC Australia
  3. Neural Dynamics Laboratory, Department of Medicine, The University of Melbourne,Melbourne, VIC Australia
  4. Department of Electrical and Electronic Engineering, The University of Melbourne,Melbourne, VIC Australia
  5. Graeme Clark Institute for Biomedical Engineering, The University of Melbourne,Melbourne, VIC Australia
  6. Neurology Department, Royal Melbourne Hospital,Melbourne, VIC Australia
Institutions: The University of Melbourne (Australia); The Royal Melbourne Hospital (Australia)
Journal: Communications biology, volume 9, issue 1, article 890
Dates: received 6 November 2025; accepted 15 April 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10132-z · PMID 42045604 · PMCID PMC13332206 · OpenAlex W7155913018
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/uemwd
Status: code verified
Categories: mouse (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, Machine learning, Spectral & time-frequency, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Dynamical systems, Spatial memory, Hippocampus, Alzheimer's disease
MeSH: CA1 Region, Hippocampal*, Cognition*, Hippocampus*, Memory Disorders*, Animals, Male, Mice, Mice, Inbred C57BL, Scopolamine (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Royal Melbourne Hospital (RMH)
Citations: cited by 3 papers (Europe PMC); 73 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

OSF uemwd

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Jupyter (2)
Size: 5 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files), Pingouin (1 file), scikit-posthocs (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/uemwd/overview?view_only=b75cffc098944aae80262e32245e1127

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-10132-z.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s42003-026-10132-z.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 4 keywords, 9 MeSH terms, 1 funder, 68 references.

Cite

This paper

Habibollahi, F., Sun, D., Burkitt, A. N., & French, C. (2026). Hippocampal criticality tracks cognitive demand and shifts with memory impairment. Communications biology, 9(1), 890. https://doi.org/10.1038/s42003-026-10132-z

BibTeX

@article{habibollahi2026hippocampal,
author = {Habibollahi, Forough and Sun, Dechuan and Burkitt, Anthony N. and French, Chris},
title = {{Hippocampal criticality tracks cognitive demand and shifts with memory impairment}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {890},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10132-z},
url = {https://doi.org/10.1038/s42003-026-10132-z},
pmid = {42045604},
pmcid = {PMC13332206}
}

RIS

TY - JOUR
AU - Habibollahi, Forough
AU - Sun, Dechuan
AU - Burkitt, Anthony N.
AU - French, Chris
TI - Hippocampal criticality tracks cognitive demand and shifts with memory impairment
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/28
VL - 9
IS - 1
SP - 890
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10132-z
UR - https://doi.org/10.1038/s42003-026-10132-z
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-10132-z",
"type": "article-journal",
"title": "Hippocampal criticality tracks cognitive demand and shifts with memory impairment",
"container-title": "Communications biology",
"author": [
{
"family": "Habibollahi",
"given": "Forough"
},
{
"family": "Sun",
"given": "Dechuan"
},
{
"family": "Burkitt",
"given": "Anthony N."
},
{
"family": "French",
"given": "Chris"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "890",
"DOI": "10.1038/s42003-026-10132-z",
"PMID": "42045604",
"PMCID": "PMC13332206",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10132-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
28
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.isci.2026.117212 [code]
Critical neuronal avalanches arise from excitation-inhibition balanced spontaneous activity.
Journal: iScience
In common: 15 references
[2] doi:10.1371/journal.pcbi.1014521 [code]
Whisker stimulation reinforces a resting-state network in the barrel cortex: Nested oscillations and avalanches.
Journal: PLoS computational biology
In common: 10 references
[3] doi:10.1038/s41467-026-74466-2 [code]
Neuromorphic hierarchical modular reservoirs.
Journal: Nature communications
In common: Pingouin, seaborn, pandas, 3 other tools, 4 references
[4] doi:10.1162/imag.a.1252 [code]
Does the brain's E:I balance really shape long-range temporal correlations? Lessons learned from 3T MRI.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: seaborn, pandas, SciPy, 2 other tools, 5 references
[5] doi:10.1162/imag.a.1348 [code]
The role of high-amplitude bursts of high-gamma activity in naturalistic speech and music listening.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: seaborn, pandas, SciPy, 2 other tools, cognitive, 4 references
[6] doi:10.1016/j.isci.2026.117375 [code]
Motor priming is associated with widespread recruitment into neural ensembles and more rapid ensemble transitions.
Journal: iScience
In common: scikit-posthocs, Pingouin, seaborn, 4 other tools, 1 reference
[7] doi:10.1126/sciadv.aea1037 [code]
Distinct cortical spatial representations learned along disparate visual pathways.
Journal: Science advances
In common: seaborn, pandas, SciPy, 2 other tools, author Anthony N. Burkitt
[8] doi:10.1038/s41593-026-02362-5 [code]
Replay of procedural memory is independent of the hippocampus.
Journal: Nature neuroscience
In common: scikit-posthocs, Pingouin, seaborn, 4 other tools, cognitive, mouse
[9] doi:10.1093/bioinformatics/btag570 [code]
CASCADE: criticality avalanche spike cross-platform analysis detection engine, a multi-manufacturer MEA bash analysis pipeline.
Journal: Bioinformatics (Oxford, England)
In common: pandas, SciPy, Matplotlib, 1 other tool, 4 references
[10] doi:10.1162/imag.a.1234 [code]
Multiscale parcellation of dynamic causal models of the brain.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: 6 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.