OSCR

The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development.

Code ↔ Paper

13 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 13 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Precision mapping of hippocampal functional systems ↔ hipp_mapping/mapping.py, lines 69–134 · score 0.82 · trilinear interpolation, metric smoothing, surface mapping, BOLD signals, wb_command, fwhm
  2. [2] § Methods › Precision mapping of hippocampal functional systems ↔ hipp_mapping/main.py, lines 8–28 · score 0.66 · mid thickness surface, wb_command, fwhm, volume, kernel, BOLD signals
  3. [3] § Methods › Neocortical connectivity of the posterior hippocampus ↔ manuscript_analyses.ipynb, lines 193–248 · score 0.65 · odds ratios, logistic regression, classify, coefficients, cluster, anterior
  4. [4] § Results › Neocortical connectivity differs between functional systems along the hippocampal long axis ↔ manuscript_analyses.ipynb, lines 135–189 · score 0.65 · default_c, default_b, control_c, salience, FC, parietal
  5. [5] § Methods › Quantifying topographic and functional specialization ↔ feature_extraction/sharpness.py, the whole file · a weak match · score 0.64 · border vertex, boundary sharpness, BOLD signal, Cohen, correlating
  6. [6] § Methods › Neocortical connectivity of the posterior hippocampus ↔ manuscript_analyses.ipynb, lines 292–315 · score 0.64 · Picture Sequence, control_c, ROI, age adjusted, Pm, RSC
  7. [7] § Methods › Precision mapping of hippocampal functional systems ↔ cortex_mapping/mapping.py, lines 80–173 · score 0.60 · template matching, precision mapping, vector, matrix, profile, vertex
  8. [8] § Results › Neocortical connectivity differs between functional systems along the hippocampal long axis ↔ manuscript_analyses.ipynb, lines 193–248 · score 0.56 · odds ratio, logistic regression, classifiers, anterior, body, network
  9. [9] § Results › Precision-functional mapping of the long-axis systems of the hippocampus ↔ hipp_mapping/mapping.py, lines 69–134 · score 0.56 · trilinear interpolation, Hippocampal vertices, BOLD signal, profile, hippocampal system, template
  10. [10] § Methods › Sensitivity analyses ↔ manuscript_analyses.ipynb, lines 114–132 · score 0.56 · predicting age, body segregation, surface area, regression, anterior, posterior
  11. [11] § Results › The posterior hippocampal system becomes functionally specialized with development ↔ manuscript_analyses.ipynb, lines 114–132 · score 0.54 · linear regression predicting, surface area, sharpness, segregation, age, posterior
  12. [12] § Results › Posterior hippocampal system shows increased connectivity to the control-c/medial parietal network with age and memory ↔ manuscript_analyses.ipynb, lines 252–269 · score 0.51 · Picture Sequence, control_c, age adjusted, NIH, raw, memory
  13. [13] § Methods › Neocortical connectivity of the posterior hippocampus ↔ cortex_mapping/mapping.py, lines 80–173 · score 0.51 · template matching, Dice, thresholded, profile, signal, cortex

Paper

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The authors' code

Jupyter notebook · 414 lines · 12 KB · no license · 7 matches

  1. # %%
  2. import json
  3. import warnings
  4. import numpy as np
  5. import pandas as pd
  6. import pingouin as pg
  7. import seaborn as sns
  8. import matplotlib.pyplot as plt
  9. %config InlineBackend.figure_format='retina'
  10. warnings.filterwarnings('ignore')
  12. # Load cluster colors for anterior, body, and posterior.
  13. with open('cluster_colors.json', 'r') as f:
  14. cluster_colors = {int(k): tuple(v) for k, v in json.load(f).items()}
  16. # Load data.
  17. df = pd.read_csv('supplementary_data.csv')
  18. display(df,df.columns)
  20. # %%
  21. # Plot changes in surface area with age.
  23. for idx, pos in enumerate(['ant','body','post']):
  25. stats = pg.corr(df['age'], df[f'{pos}_mm'])
  26. r = stats['r'].values[0]
  27. p = stats['p-val'].values[0]
  28. display(stats)
  30. fig, ax = plt.subplots(figsize=(4.5,3.5))
  31. if p < .05:
  32. sns.regplot(x=df['age'], y=df[f'{pos}_mm'], color=cluster_colors[idx+1])
  33. sns.regplot(x=df['age'], y=df[f'{pos}_mm'], color=cluster_colors[idx+1], scatter_kws={'s':0})
  34. ax.text(16.5,25,f'$r$={r:.2f}, $p$={p:1.0e}')
  35. else:
  36. sns.regplot(x=df['age'], y=df[f'{pos}_mm'], color=cluster_colors[idx+1],fit_reg=False)
  37. ax.text(20,25,f'$ns$')
  39. sns.despine()
  40. ax.grid(linestyle='--', alpha=.25, axis='y')
  41. ax.set_ylabel('Surface area [mm$^2$]')
  42. ax.set_xlabel('Age')
  44. ax.set_xticks([6,10,14,18,22])
  45. ax.set_ylim([0, 1350])
  46. plt.tight_layout()
  47. plt.savefig(f'extra/{pos}_surface_area_mm.svg')
  48. plt.show()
  51. # %%
  52. # Plot changes in boundary-sharpness segregation with age.
  54. for idx, pos in enumerate(['ant','body','post']):
  56. print(pos)
  57. stats = pg.corr(df['age'], df[f'{pos}_sharpness'])
  58. r = stats['r'].values[0]
  59. p = stats['p-val'].values[0]
  60. display(stats)
  62. fig, ax = plt.subplots(figsize=(4.5,3.5))
  64. # Add regression line to significant effects.
  65. if p < .05:
  66. sns.regplot(x=df['age'], y=df[f'{pos}_sharpness'], color=cluster_colors[idx+1])
  67. sns.regplot(x=df['age'], y=df[f'{pos}_sharpness'], color=cluster_colors[idx+1], scatter_kws={'s':0})
  68. ax.text(.65,.025,f'$r$={r:.2f}, $p$={p:1.0e}', transform=ax.transAxes)
  69. else:
  70. sns.regplot(x=df['age'], y=df[f'{pos}_sharpness'], color=cluster_colors[idx+1],fit_reg=False)
  71. ax.text(.9,.025,f'$ns$', transform=ax.transAxes)
  73. sns.despine()
  74. ax.grid(linestyle='--', alpha=.25, axis='y')
  75. ax.set_ylabel('Boundary sharpness [$d$]')
  76. ax.set_xlabel('Age')
  77. ax.set_xticks([6,10,14,18,22])
  78. plt.tight_layout()
  79. plt.savefig(f'extra/{pos}_sharpness.svg')
  80. plt.show()
  84. # %%
  85. # Plot changes in segregation with age.
  87. for idx, pos in enumerate(['ant','body','post']):
  89. stats = pg.corr(df['age'], df[f'{pos}_segregation'])
  90. r = stats['r'].values[0]
  91. p = stats['p-val'].values[0]
  92. display(stats)
  94. fig, ax = plt.subplots(figsize=(4.5,3.5))
  95. if p < .05:
  96. sns.regplot(x=df['age'], y=df[f'{pos}_segregation'], color=cluster_colors[idx+1])
  97. sns.regplot(x=df['age'], y=df[f'{pos}_segregation'], color=cluster_colors[idx+1], scatter_kws={'s':0})
  98. ax.text(.65,.025,f'$r$={r:.2f}, $p$={p:1.0e}', transform=ax.transAxes)
  99. else:
  100. sns.regplot(x=df['age'], y=df[f'{pos}_segregation'], color=cluster_colors[idx+1],fit_reg=False)
  101. ax.text(.9,.025,f'$ns$', transform=ax.transAxes)
  103. sns.despine()
  104. ax.grid(linestyle='--', alpha=.25, axis='y')
  105. ax.set_ylabel('Segregation [$d$]')
  106. ax.set_xlabel('Age')
  107. ax.set_xticks([6,10,14,18,22])
  108. plt.tight_layout()
  109. plt.savefig(f'extra/{pos}_segregation.svg')
  110. plt.show()
  114. # %%
  115. # Plot correlation between segregation and surface area.
  117. fig ,ax = plt.subplots(figsize=(6,4))
  119. sns.regplot(x=df['ant_mm'], y=df['ant_segregation'], scatter_kws={'s':5}, color=cluster_colors[1])
  120. sns.regplot(x=df['body_mm'], y=df['body_segregation'], scatter_kws={'s':5}, color=cluster_colors[2])
  121. sns.regplot(x=df['post_mm'], y=df['post_segregation'], scatter_kws={'s':5}, color=cluster_colors[3])
  122. sns.despine()
  124. ax.grid(linestyle='--', alpha=.25)
  125. ax.set_xlabel('Surface area [mm$^2$]')
  126. ax.set_ylabel('Segregation [$d$]')
  127. ax.legend(['_','anterior','_','_','body','_','_','posterior'], frameon=False)
  128. plt.show()
  130. lm = pg.linear_regression(X=df[['post_mm','post_sharpness','post_segregation']], y=df['age'])
  131. print('Linear regression predicting age:')
  132. display(lm)
  135. # %%
  136. networks = {
  137. 1:'visual_a',
  138. 2:'visual_b',
  139. 3:'somatomotor_a',
  140. 4:'somatomotor_b',
  141. 5:'dorsal_attention_a',
  142. 6:'dorsal_attention_b',
  143. 7:'ventral_attention',
  144. 8:'salience',
  145. 9:'limbic_a',
  146. 10:'limbic_b',
  147. 11:'control_c',
  148. 12:'control_a',
  149. 13:'control_b',
  150. 14:'temporal_parietal',
  151. 15:'default_c',
  152. 16:'default_a',
  153. 17:'default_b'
  154. }
  155. network_labels = np.array(list(networks.values()))
  157. def cohens_d(x):
  158. d = (np.mean(x) - 0) / np.std(x, ddof=1)
  159. return np.abs(d)
  163. for idx, pos in enumerate(['ant','body','post']):
  165. fig, ax = plt.subplots(figsize=(3.25, 4))
  167. network_fc = np.array([df[f'{pos}_{network}_FC'].mean() for network in network_labels])
  168. network_fc_err = np.array([df[f'{pos}_{network}_FC'].std() for network in network_labels])
  169. eff_size = np.array([cohens_d(df[f'{pos}_{network}_FC']) for network in network_labels])[np.argsort(network_fc)]
  171. bars = ax.barh(
  172. width=network_fc[np.argsort(network_fc)],
  173. y=network_labels[np.argsort(network_fc)],
  174. color=cluster_colors[idx+1], xerr=network_fc_err[np.argsort(network_fc)]
  175. )
  176. yticks = ax.get_yticklabels()
  179. for idx, bar in enumerate(bars):
  180. if eff_size[idx] < 1.5: bar.set_alpha(.25)
  181. if eff_size[idx] >= 1.5: yticks[idx].set_fontweight('bold')
  183. ax.set_xlim([-1.65,1.65])
  184. ax.set_xlabel('$z$ score')
  185. ax.axvline(0, color='k')
  186. sns.despine()
  187. ax.grid(axis='y', linestyle='--', alpha=.2)
  188. plt.tight_layout()
  189. plt.savefig(f'extra/{pos}_network_barplot.svg')
  193. # %%
  194. from sklearn.utils import resample
  195. from sklearn.linear_model import LogisticRegression
  196. from sklearn.multiclass import OneVsRestClassifier
  199. # Convert dataframe to long-form for one-versus-rest classification.
  200. df_long = df.melt(id_vars='id', var_name='feature', value_name='value')
  201. df_long[['class', 'sub_feature']] = df_long['feature'].str.extract(r'^(ant|body|post)_(.+)$')
  204. df_long = df_long.drop_duplicates(subset=['id', 'class', 'sub_feature'])
  205. df_wide = df_long.pivot(index=['id', 'class'], columns='sub_feature', values='value').reset_index()
  207. df_wide = df_wide[df_wide['class'].isin(['ant','body','post'])]
  208. features = [f'{label}_FC' for label in network_labels]
  210. X = df_wide[features].to_numpy()
  211. y = df_wide['class']
  213. # Fit base classifier for coefficients.
  214. clf = OneVsRestClassifier(LogisticRegression(penalty='l2', solver='liblinear'))
  215. clf.fit(X, y)
  216. coefs = np.vstack([est.coef_ for est in clf.estimators_])
  218. titles = ['anterior','body','posterior']
  220. for idx, pos in enumerate(['ant','body','post']):
  222. coef = clf.estimators_[idx].coef_[0]
  223. odds_ratios = np.exp(coef)
  224. odds_ratios_sorted = odds_ratios[np.argsort(odds_ratios)]
  225. network_labels_sorted = network_labels[np.argsort(odds_ratios)]
  227. fig, ax = plt.subplots(figsize=(3, 4))
  229. bars = ax.barh(
  230. width=odds_ratios_sorted,
  231. y=network_labels_sorted,
  232. color=cluster_colors[idx+1]
  233. )
  234. yticks = ax.get_yticklabels()
  236. for bar_idx, bar in enumerate(bars):
  237. if odds_ratios_sorted[bar_idx] < 10: bar.set_alpha(.15)
  238. if odds_ratios_sorted[bar_idx] > 10: yticks[bar_idx].set_fontweight('bold')
  240. ax.set_xlim([0,15])
  241. ax.set_xlabel('Odds-ratio')
  242. ax.axvline(0, color='k', alpha=.75)
  243. sns.despine()
  244. ax.grid(axis='y', linestyle='--', alpha=.2)
  245. plt.tight_layout()
  246. plt.savefig(f'extra/{pos}_network_barplot.svg')
  247. print(titles[idx])
  248. display(dict(zip(network_labels_sorted,odds_ratios_sorted)))
  252. # %%
  253. # Plot changes in connectivity of posterior_hipp – control_c network with age and memory.
  255. labels = ['Picture-sequence scores [raw]','Picture-sequence scores [age-adjusted]','Age']
  257. for idx, feature in enumerate(['nih_picseq_raw','nih_picseq_ageadjusted','age']):
  259. display(pg.corr(df[feature], df['post_control_c']))
  261. fig, ax = plt.subplots(figsize=(4,3))
  262. sns.regplot(x=df[feature], y=df['post_control_c'], color=cluster_colors[3], scatter_kws={'s':20})
  263. sns.despine()
  264. ax.axhline(0, linestyle='--', color='k')
  265. ax.set_ylabel('$z$-score')
  266. ax.set_xlabel(labels[idx])
  267. ax.set_title(f'Connectivity with control_c', loc='left')
  268. ax.grid(linestyle='--', alpha=.25)
  269. plt.show()
  272. # %%
  273. # Plot changes in connectivity of posterior_hipp – control_c, versus-anterior/body, network with age and memory.
  275. labels = ['Picture-sequence scores [raw]','Picture-sequence scores [age-adjusted]','Age']
  277. for idx, feature in enumerate(['nih_picseq_raw','nih_picseq_ageadjusted','age']):
  279. display(pg.corr(df[feature], df['post_control_c_OVR']))
  281. fig, ax = plt.subplots(figsize=(4,3))
  282. sns.regplot(x=df[feature], y=df['post_control_c_OVR'], color=cluster_colors[3], scatter_kws={'s':20})
  283. sns.despine()
  284. ax.axhline(0, linestyle='--', color='k')
  285. ax.set_ylabel('$z$-score')
  286. ax.set_xlabel(labels[idx])
  287. ax.set_title(f'Connectivity with control_c', loc='left')
  288. ax.grid(linestyle='--', alpha=.25)
  289. plt.show()
  292. # %%
  293. labels = ['Picture-sequence scores [raw]','Picture-sequence scores [age-adjusted]','Age']
  294. col = (0.4627450980392157, 0.5450980392156862, 0.6862745098039216) # Yeo control_c Color.
  296. for roi in ['POS2','RSC','7Pm']:
  298. print(roi)
  300. for idx, feature in enumerate(['nih_picseq_raw','nih_picseq_ageadjusted','age']):
  302. fig, ax = plt.subplots(figsize=(4,3))
  303. stats = pg.corr(df[feature], df[f'post_{roi}_OVR'])
  304. display(stats)
  305. sns.regplot(x=df[feature], y=df[f'post_{roi}_OVR'], color=col, scatter_kws={'s':20})
  306. sns.despine()
  307. ax.axhline(0, linestyle='--', color='k')
  308. ax.set_ylabel('$z$-score')
  309. ax.set_xlabel(labels[idx])
  310. ax.grid(linestyle='--', alpha=.25)
  311. ax.text(.02,.075,f"$r$={stats['r'].values[0]:.2f}, $p$={stats['p-val'].values[0]:.0e}", transform=ax.transAxes)
  312. plt.show()
  314. print('Mediation analysis:')
  315. display(pg.mediation_analysis(data=df, x='age', m=f'post_{roi}_OVR', y='nih_picseq_ageadjusted'))
  318. # %%
  319. # Replication across sites.
  321. for feature in ['post_mm','post_segregation','post_sharpness']:
  323. print(f'Feature: {feature}')
  325. for site in set(df.site):
  326. print(f'{site}:')
  327. display(pg.corr(
  328. df[df.site == site]['age'],
  329. df[df.site == site][feature])
  330. )
  331. print('\n')
  333. for feature in ['post_control_c_OVR']:
  335. print(f'Feature: {feature}')
  337. for site in set(df.site):
  338. print(f'{site}:')
  339. print('Age:')
  340. display(pg.corr(
  341. df[df.site == site]['age'],
  342. df[df.site == site][feature])
  343. )
  344. print('Age-adjusted memory:')
  345. display(pg.corr(
  346. df[df.site == site]['nih_picseq_raw'],
  347. df[df.site == site][feature])
  348. )
  349. print('Raw-memory:')
  350. display(pg.corr(
  351. df[df.site == site]['nih_picseq_ageadjusted'],
  352. df[df.site == site][feature])
  353. )
  355. print('\n')
  359. display(pg.anova(data=df, dv='age', between='site'))
  360. for site in set(df.site):
  361. print(site)
  362. print(f'{df[df.site == site].age.mean():2.1f} ± {df[df.site == site].age.std():2.1f}')
  364. display(pg.chi2_independence(data=df, x='site', y='sex'))
  367. # %%
  368. # Replication across sites.
  370. for feature in ['post_mm','post_segregation','post_sharpness']:
  372. print(f'Feature: {feature}')
  374. for site in set(df.site):
  375. print(f'{site}:')
  376. display(pg.corr(
  377. df[df.site != site]['age'],
  378. df[df.site != site][feature])
  379. )
  380. print('\n')
  382. for feature in ['post_control_c_OVR']:
  384. print(f'Feature: {feature}')
  386. for site in set(df.site):
  387. print(f'{site}:')
  388. print('Age:')
  389. display(pg.corr(
  390. df[df.site != site]['age'],
  391. df[df.site != site][feature])
  392. )
  393. print('Age-adjusted memory:')
  394. display(pg.corr(
  395. df[df.site != site]['nih_picseq_raw'],
  396. df[df.site != site][feature])
  397. )
  398. print('Raw-memory:')
  399. display(pg.corr(
  400. df[df.site != site]['nih_picseq_ageadjusted'],
  401. df[df.site != site][feature])
  402. )
  404. print('\n')
  408. display(pg.anova(data=df, dv='age', between='site'))
  409. for site in set(df.site):
  410. print(site)
  411. print(f'{df[df.site != site].age.mean():2.1f} ± {df[df.site != site].age.std():2.1f}')
  413. display(pg.chi2_independence(data=df, x='site', y='sex'))

manuscript_analyses.ipynb at commit 5fd2858, no license · at the source

Overview

Authors: Jonah Kember1, Ying He1, Zeus Gracia-Tabuenca1,2, Alexander Barnett1, Xiaoqian J. Chai1
ORCID iDs: Jonah Kember, Zeus Gracia-Tabuenca, Alexander Barnett
  1. McGill University, Department of Neurology and Neurosurgery, McGill University,Montréal, QC Canada
  2. Department of Statistical Methods, University of Zaragoza, C. de Pedro Cerbuna, 12,Zaragoza, Spain
Institutions: McGill University (Canada); Universidad de Zaragoza (Spain)
Journal: Nature communications, volume 17, issue 1, article 7699
Dates: received 24 July 2025; accepted 3 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74572-1 · PMID 42315845 · PMCID PMC13434241 · OpenAlex W7165141859
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity, Statistics, fMRI & imaging
Keywords: Hippocampus, Neuronal development
MeSH: Hippocampus*, Adolescent, Brain Mapping, Child, Child, Preschool, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: McGill University; Canada Research Chairs; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada (RGPIN 2020, rgpin-2020-05520); Fonds de recherche du Québec – Nature et technologies (283571)
Citations: cited by 1 paper (Europe PMC); 45 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.

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JonahKember/precision_mapping

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Languages: Python (16)
Size: 45 files, 16 scripts
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Tools: NiBabel (9 files), NumPy (9 files), pandas (7 files), SciPy (6 files), Connectome Workbench (4 files)
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At the source: github.com/JonahKember/precision_mapping

Zenodo 20086215

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codeocean:7323987

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jonahkember/posterior_hippocampus_development

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Commit: 5fd2858ca8979891216f518b45751d4517e037a0, 7 May 2026
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Availability: 1 check, the latest on 27 September 2026: the link answers
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At the source: github.com/JonahKember/posterior_hippocampus_development/tree/v1

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Read it in the paper: doi.org/10.1038/s41467-026-74572-1.

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • 13 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.

Code and data availability statement

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

Read it in the paper: doi.org/10.1038/s41467-026-74572-1.

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 2, 28 September 2026

  • Funding: added McGill University; Canada Research Chairs; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada: RGPIN 2020, rgpin-2020-05520; Fonds de recherche du Québec – Nature et technologies: 283571

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 11 MeSH terms, 44 references.

Cite

This paper

Kember, J., He, Y., Gracia-Tabuenca, Z., Barnett, A., & Chai, X. J. (2026). The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development. Nature communications, 17(1), 7699. https://doi.org/10.1038/s41467-026-74572-1

BibTeX

@article{kember2026hippocampus,
author = {Kember, Jonah and He, Ying and Gracia-Tabuenca, Zeus and Barnett, Alexander and Chai, Xiaoqian J.},
title = {{The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7699},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74572-1},
url = {https://doi.org/10.1038/s41467-026-74572-1},
pmid = {42315845},
pmcid = {PMC13434241}
}

RIS

TY - JOUR
AU - Kember, Jonah
AU - He, Ying
AU - Gracia-Tabuenca, Zeus
AU - Barnett, Alexander
AU - Chai, Xiaoqian J.
TI - The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/18
VL - 17
IS - 1
SP - 7699
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74572-1
UR - https://doi.org/10.1038/s41467-026-74572-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74572-1",
"type": "article-journal",
"title": "The hippocampus becomes topographically and functionally specialized along the longitudinal axis with development",
"container-title": "Nature communications",
"author": [
{
"family": "Kember",
"given": "Jonah"
},
{
"family": "He",
"given": "Ying"
},
{
"family": "Gracia-Tabuenca",
"given": "Zeus"
},
{
"family": "Barnett",
"given": "Alexander"
},
{
"family": "Chai",
"given": "Xiaoqian J."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7699",
"DOI": "10.1038/s41467-026-74572-1",
"PMID": "42315845",
"PMCID": "PMC13434241",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74572-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
18
]
]
}
}

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

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