OSCR

Age-related changes in behavioural and neural variability in a decision-making task.

Code ↔ Paper

16 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 16 matches
  1. [1] § Results ↔ aging_variability/behavior/behavior_01b_plot_main.py, lines 495–542 · score 0.72 · chronometric curve, RT distribution, psychometric curve, Median RT, RT variability, mouse age
  2. [2] § Methods › Quality control › Trials ↔ aging_variability/config.py, lines 10–96 · score 0.70 · 0.08–2 s, probabilityLeft, feedbackType, 0.08 s, movement, events
  3. [3] § Methods › Quality control › Trials ↔ config.py, lines 11–97 · score 0.70 · 0.08–2 s, probabilityLeft, feedbackType, 0.08 s, movement, events
  4. [4] § Results › Large-scale Neuropixels recordings across the mouse lifespan ↔ aging_variability/preprocessing/preprocessing_00_QC_process.py, lines 50–143 · score 0.69 · single unit QC, Error trials, missing events, Brain regions, Atlas, ROIs
  5. [5] § Results › Age effects on neural variability persist after accounting for training duration ↔ aging_variability/neural/neural_07_plot_scatters_slice_org.py, lines 1–17 · score 0.67 · pre stimulus firing, post stimulus firing, Fano factor quenching, contrast modulation, firing rate, metric
  6. [6] § Results › Age effects are robust to video-based movement covariates ↔ aging_variability/utils/data_utils.py, lines 215–248 · score 0.65 · moderate H0, weak H1, moderate H1, qualitative
  7. [7] § Methods › Statistical tests ↔ aging_variability/neural/neural_01_compute_metrics_time_courses.py, lines 709–798 · score 0.64 · probe insertion, Neural yield, Neural metrics, Fano factor, firing rate, window
  8. [8] § Methods › Statistical tests › Neural metrics › Extended models including age and movement ↔ aging_variability/neural/neural_07_plot_scatters_slice_org.py, lines 1–17 · score 0.63 · post stimulus firing, Fano factor quench, contrast modulation, firing rate, pre, metric
  9. [9] § Methods › Statistical tests ↔ aging_variability/neural/neural_07_plot_scatters_slice_org.py, lines 201–297 · score 0.63 · modulation metrics, log transformed, FF quench, slope, median, age
  10. [10] § Methods › Statistical tests ↔ aging_variability/utils/data_utils.py, lines 184–212 · score 0.60 · identity link, Gaussian family, linear, variables, variability
  11. [11] § Results › Increased firing rate in older animals ↔ aging_variability/config.py, lines 10–96 · score 0.59 · causally, firing rates, CP, CA1, DG, SCm
  12. [12] § Results › Increased firing rate in older animals ↔ config.py, lines 11–97 · score 0.59 · causally, firing rates, CP, CA1, DG, SCm
  13. [13] § Methods › Statistical tests › Neural metrics › Extended models including age and movement ↔ aging_variability/neural/neural_06_plot_modulation_timecourses_slice_org.py, lines 133–200 · score 0.57 · neural metrics, 160 ms, stimulus onset, 260 ms, median, windows
  14. [14] § Results › Older mice show more variable behaviour on a standardized decision-making task ↔ aging_variability/behavior/behavior_04_plot_supp_choice_bias.py, lines 1–49 · score 0.55 · choice bias, history dependent, blocks, age related, fit, mice
  15. [15] § Results › Increased firing rate in older animals ↔ aging_variability/neural/neural_06_plot_modulation_timecourses_slice_org.py, lines 43–92 · score 0.54 · baseline corrected, 160 ms, stimulus onset, 260 ms, omnibus, window
  16. [16] § Results › Reduced stimulus-induced variability quenching in older animals ↔ aging_variability/neural/neural_06_plot_modulation_timecourses_slice_org.py, lines 43–92 · score 0.53 · baseline corrected, stimulus onset, 160 ms, 260 ms, windows, modulation

Paper

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The authors' code

Python · 457 lines · 18 KB · MIT · 3 matches

  1. """
  2. Figure 3b,d,g
  3. Figure 3 S1b. Regional specificity of pre-stimulus firing rates.
  4. Figure 3 S2. Regional specificity of post-stimulus firing rates.
  5. Figure 3 S4b. Regional specificity of age-related differences in contrast modulation of firing rates.
  7. Figure 4b,d,f,i
  8. Figure 4 S2b. Regional specificity of pre-stimulus mean-subtracted Fano Factors.
  9. Figure 4 S2c. Regional specificity of post-stimulus mean-subtracted Fano Factors.
  10. Figure 4 S3. Regional specificity of mean-subtracted Fano Factor quenching.
  11. Figure 4 S5b Regional specificity in age-related differences in contrast modulation of the Fano Factor.
  13. Outputs:
  14. - Omnibus scatter figs per metric
  15. - Region slice-org figs per metric
  17. """
  19. # %%
  20. # =====================
  21. # Imports (cleaned)
  22. # =====================
  23. import os
  24. import numpy as np
  25. import pandas as pd
  26. import matplotlib.pyplot as plt
  27. import seaborn as sns
  29. from ibl_style.utils import MM_TO_INCH
  30. import figrid as fg
  32. import aging_variability.config as C
  33. from aging_variability.utils.io import read_table, save_figure, get_suffix
  34. from aging_variability.utils.plot_utils import figure_style, create_slice_org_axes, format_bf_annotation
  35. from aging_variability.utils.data_utils import add_age_group
  37. from statsmodels.formula.api import glm
  38. from statsmodels.genmod.families import Gaussian
  39. from aging_variability.utils.io import read_table, save_figure
  40. import logging
  42. log = logging.getLogger(__name__)
  44. # =====================
  45. # Helpers: GLM & age fields
  46. # =====================
  47. def def_glm_formula_plot(metric, mean_subtraction=False, log_transform=False):
  48. """
  49. Return a Patsy formula (no C() to avoid name collision with config alias).
  50. - pooled (across regions): include 'cluster_region' on RHS
  51. - single-region panels: we later strip 'cluster_region'
  52. """
  53. yvar = f'log_{metric}' if log_transform else metric
  54. if metric in ['fr_delta_modulation', 'ff_quench_modulation']:
  55. return f"{yvar} ~ age_years + cluster_region + n_trials"
  56. elif metric == 'ff_quench':
  57. if mean_subtraction:
  58. return f"{yvar} ~ age_years + cluster_region + n_trials"
  59. else:
  60. return f"{yvar} ~ age_years + cluster_region + abs_contrast + n_trials"
  61. else:
  62. if mean_subtraction:
  63. return f"{yvar} ~ age_years + cluster_region + n_trials"
  64. else:
  65. return f"{yvar} ~ age_years + cluster_region + abs_contrast + n_trials"
  68. def strip_region_factor(formula):
  69. """Remove 'cluster_region' term safely from a simple '+ ...' RHS formula."""
  70. out = formula.replace('+ cluster_region', '')
  71. out = out.replace('cluster_region +', '')
  72. out = out.replace('cluster_region', '')
  73. return out
  76. def ensure_age_years(df):
  77. """Ensure an 'age_years' column exists from common age columns."""
  78. if 'age_years' in df.columns:
  79. return df
  80. out = df.copy()
  81. if 'mouse_age' in out.columns:
  82. out['age_years'] = out['mouse_age'] / 365.0
  83. elif 'mouse_age_months' in out.columns:
  84. out['age_years'] = out['mouse_age_months'] / 12.0
  85. elif 'age_at_recording' in out.columns:
  86. out['age_years'] = out['age_at_recording'] / 365.0
  87. else:
  88. raise ValueError("Need one of ['mouse_age','mouse_age_months','age_at_recording'] to compute age_years.")
  89. return out
  92. def ensure_age_months(df):
  93. """Ensure a 'mouse_age_months' column exists (used on x-axis)."""
  94. if 'mouse_age_months' in df.columns:
  95. return df
  96. out = df.copy()
  97. if 'mouse_age' in out.columns:
  98. out['mouse_age_months'] = out['mouse_age'] / 30.0
  99. elif 'age_years' in out.columns:
  100. out['mouse_age_months'] = out['age_years'] * 12.0
  101. elif 'age_at_recording' in out.columns:
  102. out['mouse_age_months'] = out['age_at_recording'] / 30.0
  103. else:
  104. raise ValueError("Need one of ['mouse_age','age_years','age_at_recording'] to compute mouse_age_months.")
  105. return out
  108. def add_log_y_if_needed(df, metric):
  109. """Add log_<metric> if not present; small epsilon to avoid log(0)."""
  110. yvar = f'log_{metric}'
  111. if yvar not in df.columns:
  112. out = df.copy()
  113. out[yvar] = np.log(out[metric] + 1e-6)
  114. return out
  115. return df
  118. def pooled_marginal_line(model, raw_df, xgrid_years, method='median', region_weights=None):
  119. """
  120. Predict a pooled line by marginalizing cluster_region with weights.
  121. Default weights are proportional to sample counts per region.
  122. """
  123. agg = np.nanmedian if method == 'median' else np.nanmean
  124. regions, counts = np.unique(raw_df['cluster_region'].dropna(), return_counts=True)
  126. if region_weights is None:
  127. w = counts / counts.sum()
  128. region_weights = dict(zip(regions, w))
  129. else:
  130. # normalize supplied weights on existing regions
  131. s = sum(region_weights.get(r, 0) for r in regions)
  132. region_weights = {r: region_weights.get(r, 0) / s for r in regions}
  134. base = {'age_years': xgrid_years}
  135. if 'n_trials' in raw_df.columns:
  136. base['n_trials'] = agg(raw_df['n_trials'])
  137. if 'abs_contrast' in raw_df.columns:
  138. base['abs_contrast'] = agg(raw_df['abs_contrast'])
  140. yhats = []
  141. for r in regions:
  142. new = pd.DataFrame(base)
  143. new['cluster_region'] = r
  144. yhats.append(region_weights[r] * model.predict(new).values)
  146. return np.sum(np.stack(yhats, axis=0), axis=0)
  149. def glm_fit_predict_from_raw(raw_df, metric, mean_subtraction=False, log_transform=False,
  150. method='median', pooled=True):
  151. """
  152. Fit GLM and return (xgrid_years, yhat). Behavior:
  153. - pooled=True → include 'cluster_region' and marginalize across it
  154. - pooled=False → strip 'cluster_region' (single-region panel)
  155. """
  156. fam = Gaussian()
  157. formula = def_glm_formula_plot(metric, mean_subtraction, log_transform)
  159. df_fit = ensure_age_years(raw_df)
  160. if log_transform:
  161. df_fit = add_log_y_if_needed(df_fit, metric)
  162. if 'cluster_region' in df_fit.columns:
  163. df_fit = df_fit.copy()
  164. df_fit['cluster_region'] = df_fit['cluster_region'].astype('category')
  166. if pooled:
  167. formula_use = formula
  168. else:
  169. formula_use = strip_region_factor(formula)
  171. # eval_env=0 avoids collisions with user-level names
  172. model = glm(formula=formula_use, data=df_fit, family=fam, eval_env=0).fit()
  174. xgrid = np.linspace(df_fit['age_years'].min(), df_fit['age_years'].max(), 200)
  175. if pooled:
  176. yhat = pooled_marginal_line(model, raw_df=df_fit, xgrid_years=xgrid, method=method, region_weights=None)
  177. else:
  178. # single region: just predict on median covariates of that region-data (no region term present)
  179. base = {'age_years': xgrid}
  180. if 'n_trials' in df_fit.columns:
  181. base['n_trials'] = np.nanmedian(df_fit['n_trials']) if method == 'median' else np.nanmean(df_fit['n_trials'])
  182. if 'abs_contrast' in df_fit.columns and 'abs_contrast' in formula_use:
  183. base['abs_contrast'] = np.nanmedian(df_fit['abs_contrast']) if method == 'median' else np.nanmean(df_fit['abs_contrast'])
  184. new = pd.DataFrame(base)
  185. yhat = model.predict(new).values
  187. return xgrid, yhat
  190. def get_vmin_vmax(metric):
  191. """Per-metric y-limits (kept identical to your previous settings)."""
  192. ranges = {
  193. 'log_pre_fr': (0.5, 4.5), 'log_post_fr': (0.5, 4),
  194. 'fr_delta_modulation': (-10, 20),
  195. 'log_pre_ff': (-0.75, 0.75), 'log_post_ff': (-0.75, 0.75),
  196. 'ff_quench': (-0.6, 0.4), 'ff_quench_modulation': (-1.5, 1.2)
  197. }
  198. return ranges.get(metric, (-1, 1))
  201. # =====================
  202. # Plotting: pooled omnibus scatter
  203. # =====================
  204. def plot_scatter_pooled(df, permut_df, BF_df, y_col='pre_fr', estimator='mean',
  205. granularity='probe_level', ylim=(None, None),
  206. save=True, mean_subtraction=False):
  207. """
  208. Pooled scatter (across regions) with a marginal GLM fit line when BF supports H1.
  209. Text annotation uses unified format_bf_annotation.
  210. """
  211. figure_style()
  212. raw_df = df.copy()
  214. # Deduplicate for modulation metrics when not mean-subtracted
  215. if not mean_subtraction and y_col in ['fr_delta_modulation', 'ff_quench_modulation']:
  216. raw_df = raw_df.drop(columns=['signed_contrast', 'abs_contrast'], errors='ignore')
  217. raw_df = raw_df.drop_duplicates(subset=['uuids', 'session_pid', 'mouse_age_months', 'age_group', y_col])
  219. # Ensure axes age columns
  220. raw_df = ensure_age_years(ensure_age_months(raw_df))
  222. # stats
  223. slope_age = permut_df['observed_val'].values[0]
  224. p_perm = permut_df['p_perm'].values[0]
  225. BF_conclusion = BF_df.loc[BF_df['metric'] == y_col, 'BF10_age_category'].values[0]
  226. BF10 = BF_df.loc[BF_df['metric'] == y_col, 'BF10_age'].values[0]
  228. fig, ax = plt.subplots(1, 1, figsize=(2.36, 2.36))
  230. # scatter (size encodes #neurons per session)
  231. if y_col in ['pre_fr', 'post_fr', 'pre_ff', 'post_ff']:
  232. log_y = True
  233. raw_df[f'log_{y_col}'] = np.log(raw_df[y_col] + 1e-6)
  234. agg_df = (raw_df
  235. .groupby(['session_pid', 'mouse_age_months', 'age_group'])
  236. .agg(number_neurons=('uuids', 'nunique'),
  237. **{f'log_{y_col}': (f'log_{y_col}', 'mean')})
  238. ).reset_index()
  239. sns.scatterplot(x='mouse_age_months', y=f'log_{y_col}', data=agg_df,
  240. hue='age_group', marker='.', legend=False, s=agg_df['number_neurons'],
  241. palette=C.PALETTE, ax=ax)
  242. vmin, vmax = get_vmin_vmax(f'log_{y_col}')
  243. else:
  244. log_y = False
  245. agg_df = (raw_df
  246. .groupby(['session_pid', 'mouse_age_months', 'age_group'])
  247. .agg(number_neurons=('uuids', 'nunique'),
  248. **{y_col: (y_col, estimator)})
  249. ).reset_index()
  250. agg_df = agg_df.dropna(subset=['mouse_age_months', y_col])
  251. sns.scatterplot(x='mouse_age_months', y=y_col, data=agg_df,
  252. hue='age_group', marker='.', legend=False, s=agg_df['number_neurons'],
  253. palette=C.PALETTE, ax=ax)
  254. vmin, vmax = get_vmin_vmax(y_col)
  256. # fit line only if BF supports H1
  257. if BF_conclusion in ('strong H1', 'moderate H1'):
  258. xgrid_years, yhat = glm_fit_predict_from_raw(
  259. raw_df=raw_df,
  260. metric=y_col,
  261. mean_subtraction=mean_subtraction,
  262. log_transform=log_y,
  263. method='median',
  264. pooled=True
  265. )
  266. ax.plot(xgrid_years * 12.0, yhat, color='gray')
  268. # y axis config
  269. ax.set_ylim(vmin, vmax)
  270. if log_y:
  271. if 'fr' in y_col:
  272. tick_vals = [1, 2, 4, 8, 16, 32, 64]
  273. elif 'ff' in y_col:
  274. tick_vals = [0.5, 1, 2, 4]
  275. tick_pos = np.log(tick_vals)
  276. ax.set_yticks(tick_pos)
  277. ax.set_yticklabels([str(v) for v in tick_vals])
  278. ax.set_ylim(np.log(min(tick_vals)), np.log(max(tick_vals)))
  279. ax.set_ylabel(y_col)
  280. else:
  281. ax.set_ylabel(y_col)
  283. # unified stats annotation
  284. txt = format_bf_annotation(slope_age, p_perm, BF10, BF_conclusion,
  285. beta_label="age", big_bf=100)
  286. ax.text(0.05, 1.0, txt, transform=ax.transAxes, fontsize=5, va='top',
  287. bbox=dict(facecolor='white', alpha=0.5))
  289. ax.set_xlabel('Age (months)')
  290. ax.set_title(f'Omnibus test: {y_col}')
  291. ax.set_xticks([5, 10, 15, 20])
  292. sns.despine(offset=2, trim=False, ax=ax)
  293. plt.tight_layout()
  295. if save:
  296. fname = C.FIGPATH / f'Omnibus_{y_col}_{get_suffix(mean_subtraction)}_scatter.pdf'
  297. save_figure(fig, fname, add_timestamp=True)
  300. # =====================
  301. # Plotting: per-region slice-org panels
  302. # =====================
  303. def plot_scatter_by_region(df, permut_df, BF_df, y_col='pre_fr', estimator='mean',
  304. granularity='probe_level', save=True, mean_subtraction=False):
  305. """
  306. Slice-org figure with one small panel per region.
  307. Uses unified format_bf_annotation and single-region GLM line.
  308. """
  309. fig, axs = create_slice_org_axes(fg, MM_TO_INCH)
  310. figure_style()
  312. raw_df = ensure_age_years(ensure_age_months(df.copy()))
  314. for region in C.ROIS:
  315. ax = axs[region]
  316. sub_df = raw_df[raw_df['cluster_region'] == region].copy()
  317. if sub_df.empty:
  318. continue
  320. # deduplicate where necessary
  321. if not mean_subtraction and y_col in ['fr_delta_modulation', 'ff_quench_modulation']:
  322. sub_df = sub_df.drop(columns=['signed_contrast', 'abs_contrast'], errors='ignore')
  323. sub_df = sub_df.drop_duplicates(subset=['uuids', 'session_pid', 'mouse_age_months', 'age_group', y_col])
  325. # stats for this region
  326. sub_perm = permut_df[permut_df['cluster_region'] == region]
  327. sub_bf = BF_df[BF_df['cluster_region'] == region]
  328. if sub_perm.empty or sub_bf.empty:
  329. continue
  330. slope_age = sub_perm['observed_val'].values[0]
  331. p_perm = sub_perm['p_perm'].values[0]
  332. BF_conclusion = sub_bf['BF10_age_category'].values[0]
  333. BF10 = sub_bf['BF10_age'].values[0]
  335. # scatter (size encodes #neurons per session)
  336. if y_col in ['pre_fr', 'post_fr', 'pre_ff', 'post_ff']:
  337. log_y = True
  338. sub_df[f'log_{y_col}'] = np.log(sub_df[y_col] + 1e-6)
  339. agg_df = (sub_df
  340. .groupby(['session_pid', 'mouse_age_months', 'age_group'])
  341. .agg(number_neurons=('uuids', 'nunique'),
  342. **{f'log_{y_col}': (f'log_{y_col}', 'mean')})
  343. ).reset_index()
  344. agg_df = agg_df.dropna(subset=['mouse_age_months', f'log_{y_col}'])
  345. sns.scatterplot(x='mouse_age_months', y=f'log_{y_col}', data=agg_df,
  346. hue='age_group', marker='.', legend=False, s=agg_df['number_neurons'],
  347. palette=C.PALETTE, ax=ax)
  348. vmin, vmax = get_vmin_vmax(f'log_{y_col}')
  349. else:
  350. log_y = False
  351. agg_df = (sub_df
  352. .groupby(['session_pid', 'mouse_age_months', 'age_group'])
  353. .agg(number_neurons=('uuids', 'nunique'),
  354. **{y_col: (y_col, estimator)})
  355. ).reset_index()
  356. agg_df = agg_df.dropna(subset=['mouse_age_months', y_col])
  357. sns.scatterplot(x='mouse_age_months', y=y_col, data=agg_df,
  358. hue='age_group', marker='.', legend=False, s=agg_df['number_neurons'],
  359. palette=C.PALETTE, ax=ax)
  360. vmin, vmax = get_vmin_vmax(y_col)
  362. # fit line only if BF supports H1
  363. if BF_conclusion in ('strong H1', 'moderate H1'):
  364. xgrid_years, yhat = glm_fit_predict_from_raw(
  365. raw_df=sub_df,
  366. metric=y_col,
  367. mean_subtraction=mean_subtraction,
  368. log_transform=log_y,
  369. method='median',
  370. pooled=False # single-region panels
  371. )
  372. ax.plot(xgrid_years * 12.0, yhat, color='gray', lw=0.8)
  374. # axes cosmetics
  375. ax.set_ylim(vmin, vmax)
  376. if log_y:
  377. if 'fr' in y_col:
  378. tick_vals = [1, 3, 9, 27]
  379. elif 'ff' in y_col:
  380. tick_vals = [0.5, 1, 2, 4]
  381. tick_pos = np.log(tick_vals)
  382. ax.set_yticks(tick_pos)
  383. ax.set_yticklabels([str(v) for v in tick_vals])
  384. ax.set_ylim(np.log(min(tick_vals)), np.log(max(tick_vals)))
  385. ax.set_ylabel(y_col)
  386. else:
  387. ax.set_ylabel(y_col)
  389. # unified annotation
  390. txt = format_bf_annotation(slope_age, p_perm, BF10, BF_conclusion,
  391. beta_label="age", big_bf=100)
  392. ax.text(0.05, 1.25, txt, transform=ax.transAxes, fontsize=4, va='top', linespacing=0.8)
  394. sns.despine(offset=2, trim=False, ax=ax)
  395. if region in ['ACB', 'OLF', 'MBm', 'PO']:
  396. ax.set_xticks([5, 10, 15, 20])
  397. else:
  398. ax.set_xticks([])
  399. ax.set_xlabel(" ")
  400. ax.set_ylabel(" ")
  402. fig.suptitle(f'{granularity} level: {y_col}', font="Arial", fontsize=8)
  403. fig.supxlabel('Age (months)', font="Arial", fontsize=8).set_y(0.35)
  405. if save:
  406. fname = C.FIGPATH / f"supp_slice_org_{y_col}_{granularity}_{get_suffix(mean_subtraction)}_{estimator}_age_relationship_{C.ALIGN_EVENT}_2025.pdf"
  407. save_figure(fig, fname, add_timestamp=True)
  410. # =====================
  411. # Main
  412. # =====================
  413. def main(mean_subtraction=False):
  414. """
  415. Load neural metrics → add age_group (via utils) + ensure age fields →
  416. read cached permutation/BF tables → draw pooled + per-region panels.
  417. """
  418. if mean_subtraction:
  419. metrics_path = C.DATAPATH / "neural_metrics_summary_meansub.parquet"
  420. selected_metrics = C.METRICS_WITH_MEANSUB
  421. else:
  422. metrics_path = C.DATAPATH / "neural_metrics_summary_conditions.parquet"
  423. selected_metrics = C.METRICS_WITHOUT_MEANSUB
  425. print("Loading extracted neural metrics summary...")
  426. neural_metrics = read_table(metrics_path)
  427. neural_metrics = add_age_group(neural_metrics)
  428. # ensure x/y age fields exist even if upstream files omit them
  429. neural_metrics = ensure_age_months(ensure_age_years(neural_metrics))
  431. for metric, est in selected_metrics:
  432. df_permut_path_pooled = C.RESULTSPATH / f"Omnibus_{metric}_{C.N_PERMUT_NEURAL_OMNIBUS}permutation_{C.ALIGN_EVENT}_{C.TRIAL_TYPE}_{get_suffix(mean_subtraction)}.csv"
  433. df_permut_path_region = C.RESULTSPATH / f"Regional_{metric}_{C.N_PERMUT_NEURAL_OMNIBUS}permutation_{C.ALIGN_EVENT}_{C.TRIAL_TYPE}_{get_suffix(mean_subtraction)}.csv"
  434. df_BF_path_pooled = C.RESULTSPATH / f"omnibus_{get_suffix(mean_subtraction)}BFs_{C.ALIGN_EVENT}_{C.TRIAL_TYPE}.csv"
  435. df_BF_path_region = C.RESULTSPATH / f"regional_{get_suffix(mean_subtraction)}BFs_{C.ALIGN_EVENT}_{metric}_{C.TRIAL_TYPE}.csv"
  437. df_permut_pooled = read_table(df_permut_path_pooled)
  438. df_permut_region = read_table(df_permut_path_region)
  439. df_BF_pooled = read_table(df_BF_path_pooled)
  440. df_BF_region = read_table(df_BF_path_region)
  442. print(f"Plotting {metric}...")
  443. plot_scatter_pooled(neural_metrics, df_permut_pooled, df_BF_pooled,
  444. y_col=metric, estimator=est, granularity='probe_level',
  445. save=True, mean_subtraction=mean_subtraction)
  446. plot_scatter_by_region(neural_metrics, df_permut_region, df_BF_region,
  447. y_col=metric, estimator=est, granularity='probe_level',
  448. save=True, mean_subtraction=mean_subtraction)
  451. if __name__ == "__main__":
  452. from aging_variability.utils.io import setup_logging
  453. setup_logging()
  456. main(mean_subtraction=True)

neural_07_plot_scatters_slice_org.py at commit 7202a80, under MIT · at the source

Overview

Authors: Fenying Zang1, Anup Khanal2,3, Sonja Förster1, International Brain Laboratory4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24, Anne K Churchland2,3, Anne E Urai1,3
ORCID iDs: Fenying Zang, Anne K Churchland, Anne E Urai
24 affiliations
  1. Cognitive Psychology Unit, Institute of Psychology, Leiden University, Leiden, The Netherlands
  2. Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA USA
  3. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY USA
  4. Zuckerman Institute, Columbia University, New York, NY USA
  5. Department of Basic Neuroscience, University of Geneva, Geneva, Switzerland
  6. Gatsby Computational Neuroscience Unit, University College London, London, UK
  7. Center for Neural Science, New York University, New York, NY USA
  8. Champalimaud Center for the Unknown, Lisboa, Portugal
  9. Princeton Neuroscience Institute, Princeton University, Princeton, NJ USA
  10. Institute of Neurology, University College London, London, UK
  11. Department of Biological Structure, University of Washington, Seattle, WA USA
  12. Department of Applied Physics, Stanford University, Stanford, CA USA
  13. William James Center for Research (WJCR), ISPA - Instituto Universitário, Lisbon, Portugal
  14. Max Planck Institute for Biological Cybernetics, Tübingen, Germany
  15. Sainsbury-Wellcome Centre, University College London, London, UK
  16. Institute of Ophthalmology, University College London, London, UK
  17. Department of Molecular and Cell Biology, University of California, Berkeley, CA USA
  18. Département D’études Cognitives, École Normale Supérieure, Paris, France
  19. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA USA
  20. Wolfson Institute of Biomedical Research, University College London, London, UK
  21. Center for Computational Neuroscience, University of Washington, Seattle, WA USA
  22. Department of Physiology, University of Yamanashi, Kofu, Yamanashi, Japan
  23. Department of Neuroscience, University of Minnesota, Minneapolis, MN USA
  24. The Allen Institute for Neural Dynamics, Seattle, WA USA
Institutions: Leiden University (Netherlands); University of California, Los Angeles (United States); Cold Spring Harbor Laboratory (United States)
Journal: Nature communications, volume 17, issue 1, article 8156
Dates: received 15 September 2025; accepted 28 May 2026; published online 30 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74227-1 · PMID 42380131 · PMCID PMC13458552 · OpenAlex W7166668010
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), cognitive (subfield)
Methods: Statistics, fMRI & imaging, Single-unit activity, calcium imaging, Connectivity, Physiology & signal measures
Keywords: Cognitive ageing, Decision
MeSH: Aging*, Behavior, Animal*, Brain*, Decision Making*, Neurons*, Animals, Male, Mice, Mice, Inbred C57BL, Reaction Time (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Wellcome Trust (216324); Dutch Research Council (NWO) (VI.Veni.212.184)
Citations: cited by 1 paper (Europe PMC); 103 references in the paper

Abstract

Age-related cognitive decline in learning and decision-making may arise from increased variability of neural responses. Here, we investigated how ageing affects behavioural and neural variability by recording >18,000 neurons across 16 brain regions (including cortex, hippocampus, thalamus, midbrain, and basal ganglia) in younger and older mice performing a visual decision-making task. Older mice showed more variable response times, reproducing a common finding in human ageing studies. Ageing globally increased firing rates and post-stimulus neural variability (quantified using the Fano factor), and decreased variability quenching–the reduction in neural variability upon stimulus presentation. Older animals showed higher overall firing rates across areas of visual and motor cortex, striatum, midbrain, and hippocampus, but lower firing rates in thalamic areas. Age-related attenuation in stimulus-induced variability quenching was most prominent in visual and motor cortex, striatum, and thalamic area. These findings show how large-scale neural recordings can help uncover regional specificity of ageing effects in single neurons, ultimately improving our understanding of the neural basis of age-related cognitive decline.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

Fenying-Zang/Ageing_behavioral_and_neural_variability

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7202a808febdf696cf270ef42193042f34d6efb4, 27 August 2026
Languages: Python (33), Jupyter (1)
Size: 496 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (pyproject.toml, requirements.txt), 1 notebook
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: pandas (26 files), NumPy (19 files), Matplotlib (11 files), seaborn (11 files), statsmodels (8 files), Pingouin (2 files), SciPy (2 files), BayesFactor (1 file), rpy2 (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/Fenying-Zang/Ageing_behavioral_and_neural_variability

Code availability

Data analyses were performed using custom Python scripts in a reproducible Python environment. All code required to reproduce the figures and analyses is available at https://github.com/Fenying-Zang/Ageing_behavioral_and_neural_variability, and has been archived on Zenodo101.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 34 scripts, each with its path and the digest of its content;
  • 16 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 data analysed in this study are publicly available through the International Brain Laboratory website (https://www.internationalbrainlab.com/data), under the tag “2025_Q3_Zang_et_al_Aging”. Instructions for accessing these data are provided in the GitHub repository associated with this study (https://github.com/Fenying-Zang/Ageing_behavioral_and_neural_variability). The analysis results underlying the figures are also available in the same repository.

Data analyses were performed using custom Python scripts in a reproducible Python environment. All code required to reproduce the figures and analyses is available at https://github.com/Fenying-Zang/Ageing_behavioral_and_neural_variability, and has been archived on Zenodo101.

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

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 10 MeSH terms, 2 funders, 96 references.

Cite

This paper

Zang, F., Khanal, A., Förster, S., International Brain Laboratory, Churchland, A. K., & Urai, A. E. (2026). Age-related changes in behavioural and neural variability in a decision-making task. Nature communications, 17(1), 8156. https://doi.org/10.1038/s41467-026-74227-1

BibTeX

@article{zang2026age,
author = {Zang, Fenying and Khanal, Anup and Förster, Sonja and {International Brain Laboratory} and Churchland, Anne K and Urai, Anne E},
title = {{Age-related changes in behavioural and neural variability in a decision-making task}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {8156},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74227-1},
url = {https://doi.org/10.1038/s41467-026-74227-1},
pmid = {42380131},
pmcid = {PMC13458552}
}

RIS

TY - JOUR
AU - Zang, Fenying
AU - Khanal, Anup
AU - Förster, Sonja
AU - International Brain Laboratory
AU - Churchland, Anne K
AU - Urai, Anne E
TI - Age-related changes in behavioural and neural variability in a decision-making task
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/30
VL - 17
IS - 1
SP - 8156
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74227-1
UR - https://doi.org/10.1038/s41467-026-74227-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74227-1",
"type": "article-journal",
"title": "Age-related changes in behavioural and neural variability in a decision-making task",
"container-title": "Nature communications",
"author": [
{
"family": "Zang",
"given": "Fenying"
},
{
"family": "Khanal",
"given": "Anup"
},
{
"family": "Förster",
"given": "Sonja"
},
{
"literal": "International Brain Laboratory"
},
{
"family": "Churchland",
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],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "8156",
"DOI": "10.1038/s41467-026-74227-1",
"PMID": "42380131",
"PMCID": "PMC13458552",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74227-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
30
]
]
}
}

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