OSCR

Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation.

Code ↔ Paper

21 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 21 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 › Quantification and data analysis › Figure-specific analysis methods ↔ codes/utils/figure4C_G.py, lines 434–535 · score 0.88 · StandardScaler, variance explained, principal component, stim trials, catch trials, pivoted
  2. [2] § Methods › Quantification and data analysis › Gradient boosted decision tree model ↔ codes/utils/figure1KL.py, lines 215–300 · score 0.78 · log loss, Optuna, Hyperparameter, minimized, rounds, optimization
  3. [3] § Methods › Calcium imaging ↔ converters/widefield_to_nwb.py, lines 65–135 · score 0.75 · Hamamatsu Orca, jRGECO1a, dorsal cortex, SynchronousTrigger, excitatory, exposure
  4. [4] § Methods › Quantification and data analysis › Figure-specific analysis methods ↔ codes/utils/figure1KL.py, lines 302–423 · score 0.67 · feature perturbation, SHAP, interventional, interaction, prediction, model
  5. [5] § Methods › Behavioral task and training curriculum ↔ main_control.m, lines 217–309 · score 0.67 · whisker hit, mice licked, response window, carefully, pulse, delivered
  6. [6] § Methods › Quantification and data analysis › Gradient boosted decision tree model ↔ codes/utils/figure1IJ.py, lines 53–97 · score 0.65 · Jaw speed, whisker speed, pupil area, whisker angle, rewarded, mouse
  7. [7] § Methods › Quantification and data analysis › Figure-specific analysis methods ↔ codes/utils/figure4C_G.py, lines 434–535 · score 0.64 · tjM1, tjS1, coefficient, ALM, space, correlation
  8. [8] § Methods › Behavioral task and training curriculum ↔ update_parameters.m, lines 42–153 · score 0.63 · inter trial interval, response window, detection, weight, ms, threshold
  9. [9] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ utils_opto/rescue_gui.m, the whole file · a weak match · score 0.61 · brown noise, pink noise, response window, punished, delivered, background
  10. [10] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ update_parameters.m, lines 1–40 · score 0.61 · brown noise, pink noise, response window, punished, delivered, rewarded
  11. [11] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ codes/utils/figure1IJ.py, lines 12–51 · score 0.60 · Jaw speed, jaw angle, quiet window, whisker speed, baseline, Figure 1
  12. [12] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ utils_plots/plot_performance.m, the whole file · a weak match · score 0.59 · alarm rate, hit rate, Lick rates, reward, auditory, stimulus
  13. [13] § Methods › Quantification and data analysis › Figure-specific analysis methods ↔ main_analysis/model_context_behaviour.py, lines 19–145 · score 0.57 · Pupil area, whisker angle, likelihood, subtracted, filtered, threshold
  14. [14] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ update_parameters.m, lines 287–339 · score 0.56 · background noise, context sounds, context blocks, reward, stimulus, Mice
  15. [15] § Methods › Implantation of headpost and skull preparation ↔ converters/widefield_to_nwb.py, lines 65–135 · score 0.55 · left hemisphere, widefield imaging, dorsal cortex, optically
  16. [16] § Methods › Quantification and data analysis › Seed correlations ↔ main_analysis/figure4_analysis.py, lines 149–172 · score 0.55 · tjM1, tjS1, S2, ALM, ROI, RSC
  17. [17] § Methods › Quantification and data analysis › Figure-specific analysis methods ↔ main_analysis/process_deeplabcut_data.py, lines 50–110 · score 0.54 · Pupil area, whisker angle, likelihood, filtered, threshold, timestamps
  18. [18] § Methods › Quantification and data analysis › Figure-specific analysis methods ↔ main_analysis/figure4_analysis.py, lines 149–172 · score 0.53 · tjM1, tjS1, ALM, ROI, RSC, traces
  19. [19] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ codes/utils/figure1IJ.py, lines 53–97 · score 0.53 · jaw speed, pupil area, whisker angle, licking, Figure 1, mouse
  20. [20] § Methods › Quantification and data analysis › Gradient boosted decision tree model ↔ main_control.m, lines 217–309 · score 0.52 · correct rejection, context block, variables, hit, window, rewarded
  21. [21] § Results › Rapid behavioral adaptation to sensory stimuli in a changing context ↔ codes/utils/figure3_supp.py, lines 520–599 · score 0.52 · whisker speed, jaw opening, Whisker angle, Vertical, traces, whisker trials

Paper

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The authors' code

Python · 301 lines · 15 KB · MIT · 3 matches

  1. import gc
  2. import os
  3. import seaborn as sns
  4. import pandas as pd
  5. import numpy as np
  6. from scipy.stats import ttest_rel
  7. import matplotlib.pyplot as plt
  8. from codes.utils.misc.fig_saving import save_fig
  9. from codes.utils.misc.table_saving import save_table
  12. def plot_baseline_differences(side_dlc_data, top_dlc_data, save_path, supp_save_path, figname,
  13. fig_formats=['png', 'svg']):
  15. s_path = os.path.join(supp_save_path, 'figure1_supp2')
  16. if not os.path.exists(s_path):
  17. os.makedirs(s_path)
  19. print('Data formatting')
  20. uncentered_combined_side_data = side_dlc_data.copy(deep=True)
  21. uncentered_combined_top_data = top_dlc_data.copy(deep=True)
  22. del side_dlc_data, top_dlc_data
  23. gc.collect()
  25. # DATA : processing
  26. uncentered_combined_side_data['jaw_angle'] = 90 - uncentered_combined_side_data['jaw_angle']
  27. uncentered_combined_side_data['trial_count'] = (uncentered_combined_side_data['time'].diff().abs() > 1).cumsum()
  29. trial_counts = uncentered_combined_side_data['trial_count'].to_numpy()
  30. jaw_y = uncentered_combined_side_data['jaw_y'].to_numpy()
  31. jaw_speed_vals = np.empty(len(jaw_y), dtype=jaw_y.dtype)
  32. jaw_speed_vals[0] = np.nan
  33. same_trial = trial_counts[1:] == trial_counts[:-1]
  34. jaw_speed_vals[1:] = np.where(same_trial, np.abs(np.diff(jaw_y)), np.nan)
  35. uncentered_combined_side_data['jaw_speed'] = jaw_speed_vals * 200
  36. del jaw_speed_vals, trial_counts, jaw_y
  37. gc.collect()
  39. uncentered_combined_top_data['whisker_speed'] = uncentered_combined_top_data['whisker_velocity'].abs() * 200
  40. uncentered_combined_top_data['trial_count'] = (uncentered_combined_top_data['time'].diff().abs() > 1).cumsum()
  42. # DATA : time selection (quiet window)
  43. uncentered_combined_side_data = uncentered_combined_side_data[
  44. uncentered_combined_side_data.time < 0].copy()
  45. uncentered_combined_top_data = uncentered_combined_top_data[
  46. uncentered_combined_top_data.time < 0].copy()
  47. gc.collect()
  49. # DATA : correct choice
  50. uncentered_combined_top_data['correct_choice'] = uncentered_combined_top_data['correct_choice'].astype(bool)
  51. uncentered_combined_side_data['correct_choice'] = uncentered_combined_side_data['correct_choice'].astype(bool)
  53. # DATA : readable legends
  54. uncentered_combined_side_data['legend'] = (
  55. uncentered_combined_side_data['context'] + ' - ' +
  56. uncentered_combined_side_data['correct_choice'].map({True: 'correct', False: 'incorrect', 1: 'correct', 0: 'incorrect'})
  57. )
  58. uncentered_combined_side_data['stim_type'] = uncentered_combined_side_data['trial_type'].str.split('_').str[0]
  59. uncentered_combined_side_data = uncentered_combined_side_data.loc[
  60. uncentered_combined_side_data.trial_type.str.contains('trial')]
  62. uncentered_combined_top_data['legend'] = (
  63. uncentered_combined_top_data['context'] + ' - ' +
  64. uncentered_combined_top_data['correct_choice'].map({True: 'correct', False: 'incorrect', 1: 'correct', 0: 'incorrect'})
  65. )
  66. uncentered_combined_top_data['stim_type'] = uncentered_combined_top_data['trial_type'].str.split('_').str[0]
  67. uncentered_combined_top_data = uncentered_combined_top_data.loc[
  68. uncentered_combined_top_data.trial_type.str.contains('trial')]
  70. # DATA : final average and merge side and top
  71. print('Data averaging')
  72. groupby_cols = ['mouse_id', 'session_id', 'context', 'context_background',
  73. 'trial_type', 'correct_choice', 'legend', 'stim_type', 'trial_count']
  75. side_agg = uncentered_combined_side_data.groupby(by=groupby_cols).agg(
  76. {'jaw_y': 'mean', 'jaw_speed': 'mean', 'pupil_area': 'mean'}).reset_index()
  77. del uncentered_combined_side_data
  78. gc.collect()
  80. top_agg = uncentered_combined_top_data.groupby(by=groupby_cols).agg(
  81. {'whisker_angle': 'mean', 'whisker_speed': 'mean'}).reset_index()
  82. del uncentered_combined_top_data
  83. gc.collect()
  85. data = side_agg.merge(top_agg[['trial_count', 'whisker_angle', 'whisker_speed']], on='trial_count')
  86. del side_agg, top_agg
  87. gc.collect()
  89. data = data.melt(
  90. id_vars=['mouse_id', 'session_id', 'context', 'trial_type', 'correct_choice', 'legend', 'stim_type',
  91. 'trial_count'], value_vars=['jaw_y', 'jaw_speed', 'pupil_area', 'whisker_angle', 'whisker_speed'],
  92. var_name='bodypart')
  94. data['correct_choice'] = data.correct_choice.astype(bool)
  95. data['lick'] = data['legend'].map(
  96. {'non-rewarded - incorrect': 1, 'non-rewarded - correct': 0, 'rewarded - correct': 1,
  97. 'rewarded - incorrect': 0}).astype(bool)
  99. # DATA : only whisker trials
  100. data = data[data.stim_type == 'whisker'].copy()
  101. gc.collect()
  103. data['value'] = pd.to_numeric(data['value'], errors='coerce')
  104. n_comparisons = 20
  106. # ── shared stats helper ────────────────────────────────────────────────
  107. def compute_stats(name, correct, incorrect):
  108. cv = correct['value'].values.astype(float)
  109. iv = incorrect['value'].values.astype(float)
  110. diff = cv - iv
  111. std_diff = np.std(diff, ddof=1)
  112. t, p = ttest_rel(cv, iv)
  113. return {
  114. 'dof': correct.mouse_id.unique().shape[0] - 1,
  115. 'mean_correct': correct['value'].mean(),
  116. 'std_correct': correct['value'].std(),
  117. 'mean_incorrect': incorrect['value'].mean(),
  118. 'std_incorrect': incorrect['value'].std(),
  119. 't': t,
  120. 'p': np.round(p, 8),
  121. 'p_corr': p * n_comparisons,
  122. 'alpha': 0.05,
  123. 'alpha_corr': 0.05 / n_comparisons,
  124. 'significant': p * n_comparisons < 0.05,
  125. 'd_prime': abs(correct['value'].mean() - incorrect['value'].mean()) / std_diff,
  126. }
  127. # ───────────────────────────────────────────────────────────────────────
  129. # CONTEXT EFFECT
  130. print(' ')
  131. print('Context effect ... ')
  132. context_data = data.drop(['trial_type', 'correct_choice', 'legend', 'stim_type', 'lick'], axis=1).groupby(
  133. by=['mouse_id', 'session_id', 'context', 'bodypart'], as_index=False).agg('mean')
  134. context_data = context_data.drop('session_id', axis=1).groupby(
  135. by=['mouse_id', 'context', 'bodypart'], as_index=False).agg('mean')
  137. stats = []
  138. for name, group in context_data.groupby(by='bodypart'):
  139. correct = group.loc[group.context == 'rewarded'].dropna()
  140. incorrect = group.loc[group.context == 'non-rewarded'].dropna()
  141. if correct.shape[0] != incorrect.shape[0]:
  142. correct = correct[correct.mouse_id.isin(incorrect.mouse_id)]
  143. row = compute_stats(name, correct, incorrect)
  144. row['bodypart'] = name
  145. stats.append(row)
  146. stats = pd.DataFrame(stats)
  147. save_table(stats, s_path, 'Figure1_supp2BC_context_stats')
  148. print('Stats done')
  150. fig, axes = plt.subplots(1, len(context_data.bodypart.unique()), figsize=(12, 3))
  151. for ax, part in zip(axes.flat, context_data.bodypart.unique()):
  152. subset = context_data[context_data.bodypart == part].dropna()
  153. ax.set_title(f"delta {part} \nn = {stats.loc[(stats.bodypart == part), 'dof'].to_numpy()[0] + 1}")
  154. ax.spines[['top', 'right']].set_visible(False)
  155. sns.pointplot(subset, x='context', y='value', hue='context', legend=False,
  156. order=['non-rewarded', 'rewarded'], palette=['#6E188A', '#348A18'],
  157. estimator='mean', errorbar=('ci', 95), markers='o',
  158. linestyle='none', dodge=True, ax=ax)
  159. pivoted = subset.pivot(index='mouse_id', columns='context', values='value').dropna()
  160. for _, row in pivoted.iterrows():
  161. ax.plot([0.1, 0.9], row.values, color='gray', alpha=0.4, linewidth=3)
  162. if stats.loc[stats.bodypart == part, 'significant'].any():
  163. star_loc = max(ax.get_ylim())
  164. ax.scatter(.5, stats.loc[(stats.bodypart == part), 'significant'].map(
  165. {True: 1}).to_numpy() * star_loc * 0.9, marker='*', s=100, c='k')
  166. ax.margins(x=0.25)
  167. fig.tight_layout()
  168. save_fig(fig, s_path, figname + '_supp2BC_context', fig_formats)
  169. print('Plots done')
  171. # LICK EFFECT
  172. print(' ')
  173. print('Lick effect ...')
  174. lick_data = data.drop(['trial_type', 'correct_choice', 'legend', 'stim_type', 'context'], axis=1).groupby(
  175. by=['mouse_id', 'session_id', 'lick', 'bodypart'], as_index=False).agg('mean')
  176. lick_data = lick_data.drop('session_id', axis=1).groupby(
  177. by=['mouse_id', 'lick', 'bodypart'], as_index=False).agg('mean')
  179. stats = []
  180. for name, group in lick_data.groupby(by='bodypart'):
  181. correct = group.loc[group.lick == True].dropna()
  182. incorrect = group.loc[group.lick == False].dropna()
  183. if correct.shape[0] != incorrect.shape[0]:
  184. correct = correct[correct.mouse_id.isin(incorrect.mouse_id)]
  185. row = compute_stats(name, correct, incorrect)
  186. row['bodypart'] = name
  187. stats.append(row)
  188. stats = pd.DataFrame(stats)
  189. save_table(stats, s_path, 'Figure1_supp2BC_lick_stats')
  190. print('Stats done')
  192. fig, axes = plt.subplots(1, len(lick_data.bodypart.unique()), figsize=(12, 3))
  193. for ax, part in zip(axes.flat, lick_data.bodypart.unique()):
  194. subset = lick_data[lick_data.bodypart == part].dropna()
  195. ax.set_title(f"delta {part} \nn = {stats.loc[(stats.bodypart == part), 'dof'].to_numpy()[0] + 1}")
  196. ax.spines[['top', 'right']].set_visible(False)
  197. sns.pointplot(subset, x='lick', y='value', hue='lick', legend=False,
  198. order=[False, True], palette=['#a0a0a0', '#000000'],
  199. estimator='mean', errorbar=('ci', 95), markers='o',
  200. linestyle='none', dodge=True, ax=ax)
  201. pivoted = subset.pivot(index='mouse_id', columns='lick', values='value').dropna()
  202. for _, row in pivoted.iterrows():
  203. ax.plot([0.1, 0.9], row.values, color='gray', alpha=0.4, linewidth=3)
  204. if stats.loc[stats.bodypart == part, 'significant'].any():
  205. star_loc = max(ax.get_ylim())
  206. ax.scatter(.5, stats.loc[(stats.bodypart == part), 'significant'].map(
  207. {True: 1}).to_numpy() * star_loc * 0.9, marker='*', s=100, c='k')
  208. ax.margins(x=0.25)
  209. fig.tight_layout()
  210. save_fig(fig, s_path, figname + '_supp2BC_lick', fig_formats)
  211. print('Plots done')
  213. # CONTEXT - LICK INTERACTION EFFECT
  214. print(' ')
  215. print('Context - Lick interaction effect ...')
  216. lick_vs_context_data = data.drop(['trial_type', 'correct_choice', 'legend', 'stim_type'], axis=1).groupby(
  217. by=['mouse_id', 'session_id', 'context', 'lick', 'bodypart'], as_index=False).agg('mean')
  218. lick_vs_context_data = lick_vs_context_data.drop('session_id', axis=1).groupby(
  219. by=['mouse_id', 'context', 'lick', 'bodypart'], as_index=False).agg('mean')
  220. lick_vs_context_data['legend'] = (
  221. lick_vs_context_data['context'] + ' - ' +
  222. lick_vs_context_data['lick'].map({True: 'lick', False: 'no-lick'})
  223. )
  225. del data
  226. gc.collect()
  228. stats = []
  229. for name, group in lick_vs_context_data.groupby(by=['bodypart', 'context']):
  230. correct = group.loc[group.lick == True].dropna()
  231. incorrect = group.loc[group.lick == False].dropna()
  232. if correct.shape[0] != incorrect.shape[0]:
  233. correct = correct[correct.mouse_id.isin(incorrect.mouse_id)]
  234. row = compute_stats(name, correct, incorrect)
  235. row['bodypart'] = name[0]
  236. row['context'] = name[1]
  237. stats.append(row)
  238. stats = pd.DataFrame(stats)
  239. save_table(stats, save_path, 'Figure1IJ_stats')
  240. print('Stats done')
  242. palette = {'non-rewarded - no-lick': '#C5A2D0',
  243. 'non-rewarded - lick': '#6E188A',
  244. 'rewarded - no-lick': '#ADD0A2',
  245. 'rewarded - lick': '#348A18'}
  246. legend_order = ['non-rewarded - no-lick', 'non-rewarded - lick', 'rewarded - no-lick', 'rewarded - lick']
  248. reference = 'non-rewarded - no-lick'
  249. norm_df = []
  250. for i, row in lick_vs_context_data.iterrows():
  251. mouse_id = row.mouse_id
  252. bodypart = row.bodypart
  253. ref_val = lick_vs_context_data.loc[
  254. (lick_vs_context_data.mouse_id == mouse_id) &
  255. (lick_vs_context_data.bodypart == bodypart) &
  256. (lick_vs_context_data.legend == reference), 'value'].to_numpy()
  257. if len(ref_val) > 0:
  258. row = row.copy()
  259. row['value'] = float(row['value']) - float(ref_val[0])
  260. norm_df += [row]
  261. norm_df = pd.DataFrame(norm_df)
  262. norm_df['value'] = pd.to_numeric(norm_df['value'], errors='coerce')
  263. lick_vs_context_data = norm_df
  265. fig, axes = plt.subplots(1, len(lick_vs_context_data.bodypart.unique()), figsize=(12, 3))
  266. for ax, part in zip(axes.flat, lick_vs_context_data.bodypart.unique()):
  267. ax.set_title(f"delta {part} \nn = {stats.loc[(stats.bodypart == part), 'dof'].unique()[0] + 1}")
  268. ax.margins(x=0.25)
  269. ax.spines[['top', 'right']].set_visible(False)
  271. subset = lick_vs_context_data[lick_vs_context_data.bodypart == part].dropna()
  272. subset = subset.copy()
  273. subset['legend'] = subset['context'] + ' - ' + subset['lick'].map({True: 'lick', False: 'no-lick'})
  275. sns.pointplot(subset, x='legend', y='value', hue='legend',
  276. order=legend_order, hue_order=legend_order, palette=palette,
  277. estimator='mean', errorbar=('ci', 95), markers='o',
  278. linestyle='none', dodge=False, ax=ax)
  279. legend = ax.get_legend()
  280. if legend is not None:
  281. legend.set_visible(False)
  282. ax.set_xlabel('')
  283. ax.set_xticklabels([])
  285. for c in lick_vs_context_data.context.unique():
  286. no_lick_label = f"{c} - no-lick"
  287. lick_label = f"{c} - lick"
  288. pivoted = (subset.loc[subset.context == c]
  289. .pivot(index='mouse_id', columns='lick', values='value')
  290. .dropna())
  291. for _, row in pivoted.iterrows():
  292. ax.plot([no_lick_label, lick_label], row.values, color='gray', alpha=0.4, linewidth=3)
  293. is_significant = stats.loc[
  294. (stats.bodypart == part) & (stats.context == c), 'significant'].values
  295. if len(is_significant) > 0 and is_significant[0]:
  296. ax.annotate('*', xy=(lick_label, max(ax.get_ylim()) * 0.9),
  297. ha='center', va='bottom', fontsize=14, color='k')
  299. fig.tight_layout()
  300. save_fig(fig, save_path, figname + 'IJ', fig_formats)
  301. print('Plots done')

figure1IJ.py at commit 91f04fd, under MIT · at the source

Overview

Authors: Pol Bech1, Robin F Dard1, Jules Lebert1, Lana Smith1, Axel Bisi1, Anthony Renard1, Sylvain Crochet1, Carl CH Petersen1
ORCID iDs: Robin F Dard, Sylvain Crochet, Carl CH Petersen
  1. Laboratory of Sensory Processing, Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland
Institutions: École Polytechnique Fédérale de Lausanne (Switzerland)
Journal: eLife, volume 14, article RP109717
Dates: published online 29 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.109717 · PMID 42372004 · PMCID PMC13313688 · OpenAlex W7117558464
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: whisker sensory perception, context-dependent sensory processing, somatosensory cortex, frontal cortex, retrosplenial cortex, optical imaging, Mouse
MeSH: Gyrus Cinguli*, Motor Cortex*, Somatosensory Cortex*, Animals, Brain Mapping, Goals, Mice, Neurons, Optogenetics, Vibrissae (* major topic)
Journal subjects: Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (31003A_182010, 310030_219343, TMAG-3_209271)
Citations: not cited yet (Europe PMC); 116 references in the paper

Abstract

The ability to dynamically adjust a behavioral response to a stimulus depending on context is of critical importance for animals. To investigate the neural basis supporting context-dependent sensory processing, we developed a behavioral task in which mice changed their response to a single whisker deflection according to a continuously present contextual cue. Through unbiased optogenetic inactivation mapping, we found that neuronal activity in sensory and motor cortices contributed to task execution and, interestingly, we uncovered an unexpected role of the retrosplenial cortex (RSC) for contextual integration. Widefield calcium imaging revealed that the RSC was the first dorsal cortical area to show context discrimination in response to whisker stimulation, followed by the whisker motor cortex. Finally, we combined optogenetic inactivation with calcium imaging to define causal context-dependent changes in sensorimotor processing. Our cortex-wide mapping experiments thus begin to define key cortical nodes for context-dependent sensorimotor transformation and highlight an important contribution of RSC.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 21 matches between paragraphs and lines of code.

LSENS-BMI-EPFL/behavior_control

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 832408ee6f7c4bc4e6e4de6290ca4f04e1ee5a1f, 28 May 2026
Languages: MATLAB (40)
Size: 54 files, 40 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Tools: Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
41 files
At the source: github.com/LSENS-BMI-EPFL/behavior_control

LSENS-BMI-EPFL/NWB_converter

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 96afc9b049aa887597325d3451eda3216ecf31a7, 27 September 2026
Languages: Python (32), Jupyter (2)
Size: 39 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (environment.yml, pyproject.toml), 2 notebooks
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (26 files), pandas (16 files), Matplotlib (12 files), Neurodata Without Borders (PyNWB, MatNWB) (12 files), SciPy (8 files), imageio (3 files), Pillow (3 files), OpenCV (2 files), h5py (1 file), Kilosort (1 file), scikit-image (1 file), tifffile (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
35 files
At the source: github.com/LSENS-BMI-EPFL/NWB_converter

LSENS-BMI-EPFL/Bech_Dard_process_NWB

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 212a63103528cdeafb6ebcf90a50235cd2290082, 30 June 2026
Languages: Python (16)
Size: 59 files, 16 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (pyproject.toml)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (11 files), pandas (11 files), SciPy (3 files), Matplotlib (2 files), h5py (1 file), Numba (1 file), Neurodata Without Borders (PyNWB, MatNWB) (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/LSENS-BMI-EPFL/Bech_Dard_process_NWB

LSENS-BMI-EPFL/Bech_Dard_plot_figures

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 91f04fd782e609e53e800f33009c9b1f815ddf18, 30 June 2026
Languages: Python (49)
Size: 54 files, 49 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (29 files), Matplotlib (26 files), NumPy (25 files), seaborn (20 files), SciPy (10 files), scikit-learn (3 files), scikit-image (2 files), SHAP (2 files), XGBoost (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
51 files
At the source: github.com/LSENS-BMI-EPFL/Bech_Dard_plot_figures

Zenodo 17424306

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5281/zenodo.17424306

Code availability

Code for data acquisition and behavior control is available on Github (https://github.com/LSENS-BMI-EPFL/behavior_control, copy archived at Bech et al., 2026a). All the code used to preprocess and convert the data into NWB format is available on Github (https://github.com/LSENS-BMI-EPFL/NWB_converter, copy archived at Bech et al., 2026b). All the code used for the primary analysis of the NWB dataset and generation of intermediate data is available on Github (https://github.com/LSENS-BMI-EPFL/Bech_Dard_process_NWB, copy archived at Bech et al., 2026c). The code used to generate figure panels from intermediate data is available both on Github (https://github.com/LSENS-BMI-EPFL/Bech_Dard_plot_figures) and Zenodo at 10.5281/zenodo.17424306 (https://doi.org/10.5281/zenodo.17424306).

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 139 scripts, each with its path and the digest of its content;
  • 21 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

Datasets cited

Data availability

For each imaging session, imaging data, behavioral data, cortical region contours, and calcium traces were combined into a single NWB file. NWB offers a common format for sharing and analyzing neurophysiology data (Rübel et al., 2022). Subsequently, we developed open-source Python scripts to analyze data in the NWB format. The full dataset in NWB format is available on the DANDI archive (https://doi.org/10.48324/dandi.001847/0.260610.1400) and processed data is available on Zenodo at https://doi.org/10.5281/zenodo.17424306.

The following datasets were generated:

BechP DardRF LebertJ SmithL BisiA RenardA CrochetS PetersenCCH 2026Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformationDANDI10.48324/dandi.001847/0.260610.1400PMC1331368842372004

BechP DardRF LebertJ SmithL BisiA RenardA CrochetS PetersenCCH 2026Data set for "Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation"Zenodo10.5281/zenodo.17424306PMC1331368842372004

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 7 keywords, 10 MeSH terms, 1 funder, 112 references.

Cite

This paper

Bech, P., Dard, R. F., Lebert, J., Smith, L., Bisi, A., Renard, A., Crochet, S., & Petersen, C. C. (2026). Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation. eLife, 14, RP109717. https://doi.org/10.7554/elife.109717

BibTeX

@article{bech2026retrosplenial,
author = {Bech, Pol and Dard, Robin F and Lebert, Jules and Smith, Lana and Bisi, Axel and Renard, Anthony and Crochet, Sylvain and Petersen, Carl CH},
title = {{Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation}},
journal = {eLife},
year = {2026},
month = jun,
volume = {14},
pages = {RP109717},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.109717},
url = {https://doi.org/10.7554/elife.109717},
pmid = {42372004},
pmcid = {PMC13313688}
}

RIS

TY - JOUR
AU - Bech, Pol
AU - Dard, Robin F
AU - Lebert, Jules
AU - Smith, Lana
AU - Bisi, Axel
AU - Renard, Anthony
AU - Crochet, Sylvain
AU - Petersen, Carl CH
TI - Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/06/29
VL - 14
SP - RP109717
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.109717
UR - https://doi.org/10.7554/elife.109717
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.109717",
"type": "article-journal",
"title": "Retrosplenial cortex enables context-dependent goal-directed sensorimotor transformation",
"container-title": "eLife",
"author": [
{
"family": "Bech",
"given": "Pol"
},
{
"family": "Dard",
"given": "Robin F"
},
{
"family": "Lebert",
"given": "Jules"
},
{
"family": "Smith",
"given": "Lana"
},
{
"family": "Bisi",
"given": "Axel"
},
{
"family": "Renard",
"given": "Anthony"
},
{
"family": "Crochet",
"given": "Sylvain"
},
{
"family": "Petersen",
"given": "Carl CH"
}
],
"container-title-short": "Elife",
"volume": "14",
"page": "RP109717",
"DOI": "10.7554/elife.109717",
"PMID": "42372004",
"PMCID": "PMC13313688",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.109717",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
29
]
]
}
}

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

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