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Proximity in mice induced by an auditory-conditioned stimulus.

Code ↔ Paper

5 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 5 matches
  1. [1] § Results › Auditory conditioned stimulus facilitates proximity between familiar mice ↔ distribution_analysis_and_graphs.ipynb, lines 242–381 · score 0.81 · probability mass, bimodal model, Akaike weights, Gaussian Mixture, ePMF, empirical
  2. [2] § Results › Auditory conditioned stimulus facilitates proximity between familiar mice ↔ distribution_analysis_and_graphs.ipynb, lines 242–381 · score 0.79 · Probability Mass, bimodal model, Akaike weights, Gaussian Mixture, ePMF, smoothed
  3. [3] § Materials and methods › Data analysis ↔ paper_figures.ipynb, lines 700–771 · score 0.69 · Mann Whitney, unpaired comparisons, Shapiro Wilk, CS
  4. [4] § Materials and methods › Data analysis ↔ distribution_analysis_and_graphs.ipynb, lines 1–119 · score 0.67 · Akaike weights, Gaussian Mixture, density, AIC, BIC, GMM
  5. [5] § Results › Oxytocin signaling is required for CS-induced proximity ↔ distribution_analysis_and_graphs.ipynb, lines 1–119 · score 0.57 · Gaussian mixture, ePMF, density, AIC, BIC, bimodality

Paper

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The authors' code

Jupyter notebook · 384 lines · 13 KB · no license · 4 matches

  1. # %%
  2. # Combined code for ECDF, EPMF, and bimodality test. EPMF bins are set at 30.Enter the file name into the file path at 1.SETUP
  5. import os
  6. # Fix for Windows MKL memory leak/UserWarning
  7. os.environ["OMP_NUM_THREADS"] = "1"
  9. import pandas as pd
  10. import numpy as np
  11. import matplotlib.pyplot as plt
  12. from sklearn.mixture import GaussianMixture
  13. from scipy.stats import levene
  14. import warnings
  16. # Silence the specific KMeans warning if it still persists
  17. warnings.filterwarnings("ignore", category=UserWarning, module="sklearn.cluster._kmeans")
  19. # --- 1. SETUP ---
  20. file_path = r"W:\alexei\jupyter\graphs_jupyter\unfamiliar_vs_familiar.xlsx"
  21. df = pd.read_excel(file_path)
  22. base_name = os.path.splitext(os.path.basename(file_path))[0]
  23. output_dir = os.path.dirname(file_path)
  25. control = df["control"].dropna().values
  26. treatment = df["treatment"].dropna().values
  27. datasets = {"Control": control, "Treatment": treatment}
  29. # Illustrator-ready settings
  30. plt.rcParams['svg.fonttype'] = 'none'
  31. plt.rcParams['pdf.fonttype'] = 42
  33. # --- 2. ANALYTICS FUNCTIONS ---
  34. def ecdf(data):
  35. x = np.sort(data)
  36. y = np.arange(1, len(x) + 1) / len(x)
  37. return x, y
  39. def calculate_weights(ics):
  40. ics = np.array(ics)
  41. delta_ic = ics - np.min(ics)
  42. weights = np.exp(-0.5 * delta_ic)
  43. return weights / np.sum(weights)
  45. # --- 3. EXECUTION & PLOTTING ---
  46. fig, axes = plt.subplots(1, 3, figsize=(18, 5))
  48. # Common Bins for ePMF (not Freedman-Diaconis rule but a chosen number)
  49. all_data = np.concatenate([control, treatment])
  50. bins = np.histogram_bin_edges(all_data, bins=30)
  52. aic_results = []
  53. bic_results = []
  55. print(f"=== Analysis for {base_name} ===\n")
  57. # Run Levene's Test for Variance
  58. stat, p_val = levene(control, treatment)
  59. print(f"Levene’s Test for Equal Variance: p = {p_val:.4f}")
  60. if p_val < 0.05:
  61. print("Result: Significant difference in variance detected.\n")
  62. else:
  63. print("Result: No significant difference in variance.\n")
  65. for i, (name, data) in enumerate(datasets.items()):
  66. data_reshaped = data.reshape(-1, 1)
  68. # Panel 1: ECDF
  69. x, y = ecdf(data)
  70. axes[0].step(x, y, where="post", label=name, lw=2)
  72. # Panel 2: ePMF (Binned Density)
  73. counts, edges = np.histogram(data, bins=bins)
  74. pmf = counts / counts.sum()
  75. centers = (edges[:-1] + edges[1:]) / 2
  76. axes[1].plot(centers, pmf, marker='o', label=name, lw=2)
  78. # Model Comparison (GMM)
  79. m1 = GaussianMixture(n_components=1, random_state=0).fit(data_reshaped)
  80. m2 = GaussianMixture(n_components=2, random_state=0).fit(data_reshaped)
  82. aics = [m1.aic(data_reshaped), m2.aic(data_reshaped)]
  83. bics = [m1.bic(data_reshaped), m2.bic(data_reshaped)]
  85. aic_w = calculate_weights(aics)
  86. bic_w = calculate_weights(bics)
  87. aic_results.append(aic_w)
  88. bic_results.append(bic_w)
  90. print(f"[{name}] Bimodal Evidence: AIC Weight {aic_w[1]:.1%}, BIC Weight {bic_w[1]:.1%}")
  92. # --- 4. FINALIZE VISUALS ---
  93. axes[0].set_title("ECDF (Cumulative)")
  94. axes[0].set_ylabel("Cumulative Probability")
  95. axes[0].legend()
  97. axes[1].set_title("ePMF (Density)")
  98. axes[1].set_ylabel("Probability")
  99. axes[1].legend()
  101. # Panel 3: Akaike Weight Bar Chart
  102. labels = ["Control", "Treatment"]
  103. x_pos = np.arange(len(labels))
  104. width = 0.35
  105. axes[2].bar(x_pos - width/2, [w[1] for w in aic_results], width, label='AIC (Generous)', color='skyblue')
  106. axes[2].bar(x_pos + width/2, [w[1] for w in bic_results], width, label='BIC (Strict)', color='coral')
  107. axes[2].set_title("Confidence in Bimodality")
  108. axes[2].set_xticks(x_pos)
  109. axes[2].set_xticklabels(labels)
  110. axes[2].set_ylim(0, 1.1)
  111. axes[2].axhline(0.95, color='red', linestyle='--', alpha=0.5, label="95% Confidence")
  112. axes[2].legend()
  114. plt.tight_layout()
  115. output_path = os.path.join(output_dir, f"{base_name}_Full_Analysis")
  116. plt.savefig(output_path + ".svg")
  117. plt.savefig(output_path + ".pdf")
  118. print(f"\nFiles saved to: {output_dir}")
  119. plt.show()
  121. # %%
  122. # Combined code for ECDF, EPMF, and bimodality test. EPMF bins are set per Freedman-Diaconis rule. Enter the file name into the file path at 1.SETUP
  126. import os
  127. # Fix for Windows MKL memory leak/UserWarning
  128. os.environ["OMP_NUM_THREADS"] = "1"
  130. import pandas as pd
  131. import numpy as np
  132. import matplotlib.pyplot as plt
  133. from sklearn.mixture import GaussianMixture
  134. from scipy.stats import levene
  135. import warnings
  137. # Silence the specific KMeans warning if it still persists
  138. warnings.filterwarnings("ignore", category=UserWarning, module="sklearn.cluster._kmeans")
  140. # --- 1. SETUP ---
  141. file_path = r"W:\alexei\jupyter\graphs_jupyter\unfamiliar_vs_familiar.xlsx"
  142. df = pd.read_excel(file_path)
  143. base_name = os.path.splitext(os.path.basename(file_path))[0]
  144. output_dir = os.path.dirname(file_path)
  146. control = df["control"].dropna().values
  147. treatment = df["treatment"].dropna().values
  148. datasets = {"Control": control, "Treatment": treatment}
  150. # Illustrator-ready settings
  151. plt.rcParams['svg.fonttype'] = 'none'
  152. plt.rcParams['pdf.fonttype'] = 42
  154. # --- 2. ANALYTICS FUNCTIONS ---
  155. def ecdf(data):
  156. x = np.sort(data)
  157. y = np.arange(1, len(x) + 1) / len(x)
  158. return x, y
  160. def calculate_weights(ics):
  161. ics = np.array(ics)
  162. delta_ic = ics - np.min(ics)
  163. weights = np.exp(-0.5 * delta_ic)
  164. return weights / np.sum(weights)
  166. # --- 3. EXECUTION & PLOTTING ---
  167. fig, axes = plt.subplots(1, 3, figsize=(18, 5))
  169. # Common Bins for ePMF (Freedman-Diaconis rule)
  170. all_data = np.concatenate([control, treatment])
  171. bins = np.histogram_bin_edges(all_data, bins='fd')
  173. aic_results = []
  174. bic_results = []
  176. print(f"=== Analysis for {base_name} ===\n")
  178. # Run Levene's Test for Variance
  179. stat, p_val = levene(control, treatment)
  180. print(f"Levene’s Test for Equal Variance: p = {p_val:.4f}")
  181. if p_val < 0.05:
  182. print("Result: Significant difference in variance detected.\n")
  183. else:
  184. print("Result: No significant difference in variance.\n")
  186. for i, (name, data) in enumerate(datasets.items()):
  187. data_reshaped = data.reshape(-1, 1)
  189. # Panel 1: ECDF
  190. x, y = ecdf(data)
  191. axes[0].step(x, y, where="post", label=name, lw=2)
  193. # Panel 2: ePMF (Binned Density)
  194. counts, edges = np.histogram(data, bins=bins)
  195. pmf = counts / counts.sum()
  196. centers = (edges[:-1] + edges[1:]) / 2
  197. axes[1].plot(centers, pmf, marker='o', label=name, lw=2)
  199. # Model Comparison (GMM)
  200. m1 = GaussianMixture(n_components=1, random_state=0).fit(data_reshaped)
  201. m2 = GaussianMixture(n_components=2, random_state=0).fit(data_reshaped)
  203. aics = [m1.aic(data_reshaped), m2.aic(data_reshaped)]
  204. bics = [m1.bic(data_reshaped), m2.bic(data_reshaped)]
  206. aic_w = calculate_weights(aics)
  207. bic_w = calculate_weights(bics)
  208. aic_results.append(aic_w)
  209. bic_results.append(bic_w)
  211. print(f"[{name}] Bimodal Evidence: AIC Weight {aic_w[1]:.1%}, BIC Weight {bic_w[1]:.1%}")
  213. # --- 4. FINALIZE VISUALS ---
  214. axes[0].set_title("ECDF (Cumulative)")
  215. axes[0].set_ylabel("Cumulative Probability")
  216. axes[0].legend()
  218. axes[1].set_title("ePMF (Density)")
  219. axes[1].set_ylabel("Probability")
  220. axes[1].legend()
  222. # Panel 3: Akaike Weight Bar Chart
  223. labels = ["Control", "Treatment"]
  224. x_pos = np.arange(len(labels))
  225. width = 0.35
  226. axes[2].bar(x_pos - width/2, [w[1] for w in aic_results], width, label='AIC (Generous)', color='skyblue')
  227. axes[2].bar(x_pos + width/2, [w[1] for w in bic_results], width, label='BIC (Strict)', color='coral')
  228. axes[2].set_title("Confidence in Bimodality")
  229. axes[2].set_xticks(x_pos)
  230. axes[2].set_xticklabels(labels)
  231. axes[2].set_ylim(0, 1.1)
  232. axes[2].axhline(0.95, color='red', linestyle='--', alpha=0.5, label="95% Confidence")
  233. axes[2].legend()
  235. plt.tight_layout()
  236. output_path = os.path.join(output_dir, f"{base_name}_Full_Analysis")
  237. plt.savefig(output_path + ".svg")
  238. plt.savefig(output_path + ".pdf")
  239. print(f"\nFiles saved to: {output_dir}")
  240. plt.show()
  242. # %%
  243. # Combined code for ECDF, EPMF, KS test, and bimodality test. EPMF is slidng window.The ePMF graph has a single shared x_grid
  244. # Enter the file name into the file path at 1.SETUP.
  245. # Set sliding window as % of total: in 3 Execution and plotting, Shared window width for PMF comparison (7.5% of range)
  246. # - 2. ANALYTICS FUNCTIONS --- n points recommended 200
  247. # The sliding window counts are normalized by the sum of all window counts (pmf / np.sum(pmf)) - see x_grid definition
  249. import os
  250. # Fix for Windows MKL memory leak/UserWarning
  251. os.environ["OMP_NUM_THREADS"] = "1"
  253. import pandas as pd
  254. import numpy as np
  255. import matplotlib.pyplot as plt
  256. from sklearn.mixture import GaussianMixture
  257. from scipy.stats import levene, ks_2samp
  258. import warnings
  260. # Silence specific KMeans warnings
  261. warnings.filterwarnings("ignore", category=UserWarning, module="sklearn.cluster._kmeans")
  263. # --- 1. SETUP ---
  264. # Update this path to your local file location
  265. file_path = r"W:\alexei\jupyter\graphs_jupyter\OT_antag_vs_saline_relative.xlsx"
  266. df = pd.read_excel(file_path)
  267. base_name = os.path.splitext(os.path.basename(file_path))[0]
  268. output_dir = os.path.dirname(file_path)
  270. control = df["control"].dropna().values
  271. treatment = df["treatment"].dropna().values
  272. datasets = {"Control": control, "Treatment": treatment}
  274. # Illustrator-ready font settings
  275. plt.rcParams['svg.fonttype'] = 'none'
  276. plt.rcParams['pdf.fonttype'] = 42
  278. # --- 2. ANALYTICS FUNCTIONS ---
  279. def ecdf(data):
  280. """Computes the Empirical Cumulative Distribution Function."""
  281. x = np.sort(data)
  282. y = np.arange(1, len(x) + 1) / len(x)
  283. return x, y
  285. def sliding_window_epmf(data, window_width, n_points=200):
  286. """Computes a smoothed ePMF using a sliding window across the global data range."""
  287. all_vals = np.concatenate([control, treatment])
  288. # use the line below to plot ePMF considering data ranges of each group separately
  289. # x_grid = np.linspace(np.min(data), np.max(data), n_points)
  290. x_grid = np.linspace(np.min(all_vals), np.max(all_vals), n_points)
  291. pmf = []
  292. for x in x_grid:
  293. count = np.sum((data >= x - window_width/2) & (data < x + window_width/2))
  294. pmf.append(count)
  295. pmf = np.array(pmf)
  296. return x_grid, pmf / np.sum(pmf)
  298. def calculate_weights(ics):
  299. """Calculates Akaike weights from AIC or BIC values."""
  300. ics = np.array(ics)
  301. delta_ic = ics - np.min(ics)
  302. weights = np.exp(-0.5 * delta_ic)
  303. return weights / np.sum(weights)
  305. # --- 3. EXECUTION & PLOTTING ---
  306. fig, axes = plt.subplots(1, 3, figsize=(18, 5))
  308. aic_results = []
  309. bic_results = []
  311. print(f"=== Statistical Summary for {base_name} ===\n")
  313. # A. Levene's Test (Checks if the 'spread' or variance changed)
  314. l_stat, l_p = levene(control, treatment)
  315. print(f"Levene’s Test (Variance Difference): p = {l_p:.4f}")
  317. # B. Kolmogorov-Smirnov Test (Checks if the overall distribution changed)
  318. ks_stat, ks_p = ks_2samp(control, treatment)
  319. print(f"KS Test (Distributional Shift): D = {ks_stat:.4f}, p = {ks_p:.4e}")
  320. if ks_p < 0.05:
  321. print("Result: Significant global shift between Control and Treatment.\n")
  322. else:
  323. print("Result: No significant global shift detected.\n")
  325. # Shared window width for PMF comparison (10 % of range)
  326. all_data = np.concatenate([control, treatment])
  327. w_width = (np.max(all_data) - np.min(all_data)) * 0.10
  329. for i, (name, data) in enumerate(datasets.items()):
  330. data_reshaped = data.reshape(-1, 1)
  332. # Panel 1: ECDF
  333. x_ec, y_ec = ecdf(data)
  334. axes[0].step(x_ec, y_ec, where="post", label=name, lw=2)
  336. # Panel 2: Sliding Window ePMF
  337. x_pmf, y_pmf = sliding_window_epmf(data, window_width=w_width)
  338. axes[1].plot(x_pmf, y_pmf, label=name, lw=2)
  340. # GMM Model Comparison for Bimodality
  341. m1 = GaussianMixture(n_components=1, random_state=0).fit(data_reshaped)
  342. m2 = GaussianMixture(n_components=2, random_state=0).fit(data_reshaped)
  344. aics = [m1.aic(data_reshaped), m2.aic(data_reshaped)]
  345. bics = [m1.bic(data_reshaped), m2.bic(data_reshaped)]
  347. aic_w = calculate_weights(aics)
  348. bic_w = calculate_weights(bics)
  349. aic_results.append(aic_w)
  350. bic_results.append(bic_w)
  352. print(f"[{name}] Bimodal Evidence: AIC {aic_w[1]:.1%}, BIC {bic_w[1]:.1%}")
  354. # --- 4. FORMATTING & SAVING ---
  355. axes[0].set_title("ECDF (Basis for KS Test)")
  356. axes[0].set_ylabel("Cumulative Probability")
  357. axes[0].legend()
  359. axes[1].set_title(f"Sliding Window ePMF (Width={w_width:.2f})")
  360. axes[1].set_ylabel("Probability Mass")
  361. axes[1].legend()
  363. # Panel 3: Akaike/BIC Weight Bar Chart
  364. labels = list(datasets.keys())
  365. x_pos = np.arange(len(labels))
  366. width = 0.35
  367. axes[2].bar(x_pos - width/2, [w[1] for w in aic_results], width, label='AIC Weight', color='skyblue')
  368. axes[2].bar(x_pos + width/2, [w[1] for w in bic_results], width, label='BIC Weight', color='coral')
  369. axes[2].set_title("Evidence for Bimodal Model")
  370. axes[2].set_xticks(x_pos)
  371. axes[2].set_xticklabels(labels)
  372. axes[2].set_ylim(0, 1.1)
  373. axes[2].axhline(0.95, color='red', linestyle='--', alpha=0.6, label="95% Threshold")
  374. axes[2].legend()
  376. plt.tight_layout()
  377. output_path = os.path.join(output_dir, f"{base_name}_Integrated_Analysis")
  378. plt.savefig(output_path + ".svg")
  379. plt.savefig(output_path + ".pdf")
  380. print(f"\nSaved analysis plots to: {output_dir}")
  381. plt.show()
  383. # %%

distribution_analysis_and_graphs.ipynb at commit 1ceada1, no license · at the source

Overview

Authors: Wataru Ito1, Alexei Morozov1,2
ORCID iDs: Wataru Ito, Alexei Morozov
  1. Fralin Biomedical Research Institute Center for Neurobiology Research at Virginia Tech Carilion,Roanoke, VA USA
  2. Department of Psychiatry and Behavioral Medicine, Virginia Tech Carilion School of Medicine,Roanoke, VA USA
Institutions: Virginia Tech (United States)
Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, volume 51, issue 10, pages 1836-1843
Dates: received 3 February 2026; accepted 1 July 2026; published online 15 July 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41386-026-02492-1 · PMID 42457949 · PMCID PMC13486637 · OpenAlex W7168429684
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Statistics, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Limbic system, Social neuroscience
MeSH: Conditioning, Classical*, Fear*, Social Behavior*, Acoustic Stimulation, Animals, Basolateral Nuclear Complex, Female, Hippocampus, Male, Mice, Mice, Inbred C57BL, Receptors, Oxytocin (* major topic)
Topic: Neuroendocrine regulation and behavior (Social Psychology, Psychology), according to OpenAlex
Funding: National Institute of Mental Health (R21MH137592)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Social affiliation promotes survival and well-being across species, and proximity to conspecifics is a necessary precondition for affiliative contact. While innate threats reliably increase proximity among conspecifics, whether learned threats have the same effect remains unknown. Here, we report that an auditory conditioned stimulus (CS) induces proximity in mice. In same-sex dyads, fear-conditioned mice increased proximity during CS presentation, independent of freezing levels. This CS-evoked proximity required familiarity between partners and intact basolateral amygdala-to-ventral hippocampus inputs, demonstrated by DREADD-mediated suppression. It also required oxytocin receptor signaling, demonstrated by systemic administration of the antagonist L368,899. These findings suggest that learned and innate threats engage shared neural circuitry for social proximity.

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 5 matches between paragraphs and lines of code.

wataruito/Codes_in_Threat-induced_proximity_Ito_et_al

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1ceada176731bb006722dd31c0f8362a9b841cff, 9 July 2026
Languages: Jupyter (2)
Size: 8 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (2 files), SciPy (2 files), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/wataruito/Codes_in_Threat-induced_proximity_Ito_et_al

Code availability

All primary data, including video files, are available from the authors upon reasonable request. Analysis code and example datasets are available at https://github.com/wataruito/Codes_in_Threat-induced_proximity_Ito_et_al.

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

Tracing map

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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

All primary data, including video files, are available from the authors upon reasonable request. Analysis code and example datasets will be provided as part of a replication package upon publication.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 2 keywords, 12 MeSH terms, 1 funder, 62 references.

Cite

This paper

Ito, W., & Morozov, A. (2026). Proximity in mice induced by an auditory-conditioned stimulus. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 51(10), 1836-1843. https://doi.org/10.1038/s41386-026-02492-1

BibTeX

@article{ito2026proximity,
author = {Ito, Wataru and Morozov, Alexei},
title = {{Proximity in mice induced by an auditory-conditioned stimulus}},
journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology},
year = {2026},
month = jul,
volume = {51},
number = {10},
pages = {1836--1843},
publisher = {Nature Publishing Group},
issn = {0893-133X},
doi = {10.1038/s41386-026-02492-1},
url = {https://doi.org/10.1038/s41386-026-02492-1},
pmid = {42457949},
pmcid = {PMC13486637}
}

RIS

TY - JOUR
AU - Ito, Wataru
AU - Morozov, Alexei
TI - Proximity in mice induced by an auditory-conditioned stimulus
T2 - Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
J2 - Neuropsychopharmacology
PY - 2026
DA - 2026/07/15
VL - 51
IS - 10
SP - 1836
EP - 1843
SN - 0893-133X
PB - Nature Publishing Group
DO - 10.1038/s41386-026-02492-1
UR - https://doi.org/10.1038/s41386-026-02492-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41386-026-02492-1",
"type": "article-journal",
"title": "Proximity in mice induced by an auditory-conditioned stimulus",
"container-title": "Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology",
"author": [
{
"family": "Ito",
"given": "Wataru"
},
{
"family": "Morozov",
"given": "Alexei"
}
],
"container-title-short": "Neuropsychopharmacology",
"volume": "51",
"issue": "10",
"page": "1836-1843",
"DOI": "10.1038/s41386-026-02492-1",
"PMID": "42457949",
"PMCID": "PMC13486637",
"ISSN": "0893-133X",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41386-026-02492-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
15
]
]
}
}

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