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Age-related differences in hippocampal network engagement during safety processing in adolescents.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 3 matches
  1. [1] § Results › Post-hoc ROI and Connectivity Results ↔ Code/data_analysis.Rmd, lines 367–430 · score 0.52 · protection accuracy, vmPFC, median, SE, anterior, models
  2. [2] § Methods › Task Design ↔ Code/data_analysis.ipynb, lines 51–66 · score 0.51 · fist, goose, gun, cat, grizzly, stick
  3. [3] § Methods › Task Design ↔ Code/data_analysis.ipynb, lines 51–66 · score 0.51 · fist, goose, gun, cat, grizzly, stick

Paper

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The authors' code

Jupyter notebook · 113 lines · 5.1 KB · no license · 2 matches

  1. # %%
  2. # Setup
  3. import pandas as pd # version 2.1.1
  4. import numpy as np
  5. import os
  6. import matplotlib.pyplot as plt
  7. import seaborn as sns # version 0.13.2
  8. import scipy # version 1.12.0
  9. from scipy.optimize import curve_fit
  10. import scipy.stats as ss
  11. import itertools
  12. from patsy import dmatrices
  13. import statsmodels.api as sm
  14. import statsmodels.formula.api as smf
  17. file_path = './ado_behavioral_data.csv'
  18. all_data = pd.read_csv(file_path)
  20. palette = {'safety': 'cornflowerblue', 'threat': 'lightcoral'}
  21. palette2 = {'safety': 'lightsalmon', 'threat': 'plum'}
  23. # %%
  24. fig, ax = plt.subplots(figsize = (6, 4))
  25. plot_data = all_data.groupby(['participant_num','first_condition','first_win_prob'], as_index=False)['first_win'].mean()
  26. sns.lineplot(data=plot_data, x='first_win_prob', y='first_win', hue='first_condition', err_style='bars', palette=palette)
  27. plt.show()
  30. g = all_data.groupby(['participant_num', 'first_condition'], as_index=False)['first_win'].mean()
  31. print(ss.ttest_rel(g.loc[g.first_condition=='safety']['first_win'], g.loc[g.first_condition=='threat']['first_win']))
  33. # %%
  34. # Heat map
  35. def plot_heat_map(all_data):
  36. fig, axes = plt.subplots(1, 2, figsize=(12, 4))
  37. g_sf = all_data.loc[all_data['first_condition']=='safety'].groupby(['first_win_prob', 'second_win_prob'],
  38. as_index=False)['second_win'].mean()
  39. g_sf = g_sf.pivot(index="first_win_prob", columns="second_win_prob", values="second_win")
  40. sns.heatmap(g_sf, ax=axes[0])
  41. axes[0].set_title('safety first')
  43. g_tf = all_data.loc[all_data['first_condition']=='threat'].groupby(['first_win_prob', 'second_win_prob'],
  44. as_index=False)['second_win'].mean()
  45. g_tf = g_tf.pivot(index="first_win_prob", columns="second_win_prob", values="second_win")
  46. sns.heatmap(g_tf, ax=axes[1])
  47. axes[1].set_title('threat first')
  49. plot_heat_map(all_data)
  51. # %%
  52. mean_first_win_by_stim = all_data.groupby(['participant_num', 'first_safety_value'])['first_win'].mean().reset_index(name='mean_first_win_by_stim')
  53. mean_second_win_by_stim = all_data.groupby(['participant_num', 'second_safety_value'])['second_win'].mean().reset_index(name='mean_second_win_by_stim')
  55. mean_win_by_stim = pd.merge(mean_first_win_by_stim, mean_second_win_by_stim, \
  56. left_on=['participant_num','first_safety_value'], right_on=['participant_num','second_safety_value'], how='left')
  58. title = {-7:"Grizzly", -6:"Lion", -2:"Goose", -1:"Cat", 1:"Fist", 2:"Stick", 6:"Gun", 7:"Grenade"}
  59. mean_win_by_stim['title'] = mean_win_by_stim['first_safety_value'].replace(title)
  61. condition = {-7:"threat", -6:"threat", -2:"threat", -1:"threat", 1:"safety", 2:"safety", 6:"safety", 7:"safety"}
  62. mean_win_by_stim['condition'] = mean_win_by_stim['first_safety_value'].replace(condition)
  64. mean_win_by_stim['mean_total_win_by_stim'] = (mean_win_by_stim['mean_first_win_by_stim'] + mean_win_by_stim['mean_second_win_by_stim'])/2
  66. print(mean_win_by_stim)
  68. # %%
  69. fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), sharey=True)
  71. # First stimulus
  72. sns.scatterplot(ax=ax1, data=mean_win_by_stim, x='first_safety_value', y='mean_first_win_by_stim', hue='condition', palette=palette)
  74. # Regression plots for first stimulus
  75. sns.regplot(ax=ax1, data=mean_win_by_stim[mean_win_by_stim['condition'] == 'safety'], x='first_safety_value', y='mean_first_win_by_stim', ci=None, color='cornflowerblue')
  76. sns.regplot(ax=ax1, data=mean_win_by_stim[mean_win_by_stim['condition'] == 'threat'], x='first_safety_value', y='mean_first_win_by_stim', ci=None, color='lightcoral')
  78. # Second stimulus
  79. sns.scatterplot(ax=ax2, data=mean_win_by_stim, x='second_safety_value', y='mean_second_win_by_stim', hue='condition', palette=palette)
  81. # Regression plots for second stimulus
  82. sns.regplot(ax=ax2, data=mean_win_by_stim[mean_win_by_stim['condition'] == 'safety'], x='second_safety_value', y='mean_second_win_by_stim', ci=None, color='cornflowerblue')
  83. sns.regplot(ax=ax2, data=mean_win_by_stim[mean_win_by_stim['condition'] == 'threat'], x='second_safety_value', y='mean_second_win_by_stim', ci=None, color='lightcoral')
  85. plt.show()
  87. # %%
  88. df = mean_win_by_stim[mean_win_by_stim['condition'] == 'safety']
  89. model = smf.ols('mean_first_win_by_stim ~ first_safety_value', data=df).fit()
  90. print(model.summary())
  91. p_value = model.pvalues['first_safety_value']
  92. print(f"Exact p-value: {p_value:.3e}")
  94. # %%
  95. df = mean_win_by_stim[mean_win_by_stim['condition'] == 'threat']
  96. model = smf.ols('mean_first_win_by_stim ~ first_safety_value', data=df).fit()
  97. print(model.summary())
  98. p_value = model.pvalues['first_safety_value']
  99. print(f"Exact p-value: {p_value:.3e}")
  101. # %%
  102. df = mean_win_by_stim[mean_win_by_stim['condition'] == 'safety']
  103. model = smf.ols('mean_second_win_by_stim ~ second_safety_value', data=df).fit()
  104. print(model.summary())
  105. p_value = model.pvalues['second_safety_value']
  106. print(f"Exact p-value: {p_value:.3e}")
  108. # %%
  109. df = mean_win_by_stim[mean_win_by_stim['condition'] == 'threat']
  110. model = smf.ols('mean_second_win_by_stim ~ second_safety_value', data=df).fit()
  111. print(model.summary())
  112. p_value = model.pvalues['second_safety_value']
  113. print(f"Exact p-value: {p_value:.3e}")

data_analysis.ipynb, no license · at the source

Overview

Authors: Yubing Zhang1, Madeline K Coates1, Marta I Garrido1,2, Sarah M Tashjian1
ORCID iDs: Sarah M Tashjian
  1. School of Psychological Sciences, The University of Melbourne, Melbourne, VIC 3010, Australia
  2. Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Institutions: The University of Melbourne (Australia)
Journal: Developmental cognitive neuroscience, volume 81, article 101791
Dates: received 19 June 2025; accepted 26 July 2026; published online 28 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.dcn.2026.101791 · PMID 42531887 · PMCID PMC13452386 · OpenAlex W7171521240
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Statistics, Machine learning, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Adolescence, FMRI, Hippocampus, Safety, Threat
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain and Behavior Research Foundation
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Adolescence is a period characterized by exploration, altered risk-taking, and increased vulnerability to mental health disorders. These phenomena may reflect underlying challenges in safety evaluation. Successfully navigating adolescence may therefore be related to the maturation of neural circuits that support safety evaluation, yet how these mechanisms function during development remains unclear. Using 7-Tesla functional magnetic resonance imaging, we recorded neural response in 33 adolescents (MAge = 14.88 years, 19 females) during evaluation of threat (external cues that signal potential danger) and protection (resources available to an agent that increase safety). Our findings reveal age-related differences in neural recruitment during accurate estimation of protection, such that younger adolescents (12–14 years) exhibited greater hippocampal engagement, whereas older adolescents (15–17 years) exhibited a more integrated circuit involving the hippocampus and anterior ventromedial prefrontal cortex (vmPFC). Our results also provide insight into how competition between threat and protection is resolved within the visual cortex during adolescent safety evaluation, demonstrating enhanced perceptual sensitivity to protection signals compared to threat. Behavioral analysis across a broader developmental spectrum (N = 63, MAge = 24.18 years, range 12–40 years, 34 females, including adults from prior work) revealed a quadratic association between age and protection estimation accuracy, with lower accuracy in mid-to-late adolescence relative to early adolescence and adulthood. Together, our behavioral and neural results indicate adolescence is an important developmental period for safety processing, particularly with respect to accurately estimating safety.

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 3 matches between paragraphs and lines of code.

OSF v3kpx

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (4), Jupyter (1), R (1)
Size: 49 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (5 files), PsychoPy (4 files), ggplot2 (1 file), Matplotlib (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file), statsmodels (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
6 files
At the source: doi.org/10.17605/OSF.IO/V3KPX

The paper's code and data availability statement is in the Data section.

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;
  • 6 scripts, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

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

Data availability

Task code and behavioral data are available through the Open Science Framework (OSF; https://doi.org/10.17605/OSF.IO/V3KPX). Neuroimaging data are available through the Science Data Bank (https://doi.org/10.57760/sciencedb.26549).

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

Versions

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Version 2, 28 September 2026

  • Funding: added Brain and Behavior Research Foundation

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 95 references.

Cite

This paper

Zhang, Y., Coates, M. K., Garrido, M. I., & Tashjian, S. M. (2026). Age-related differences in hippocampal network engagement during safety processing in adolescents. Developmental cognitive neuroscience, 81, 101791. https://doi.org/10.1016/j.dcn.2026.101791

BibTeX

@article{zhang2026age,
author = {Zhang, Yubing and Coates, Madeline K and Garrido, Marta I and Tashjian, Sarah M},
title = {{Age-related differences in hippocampal network engagement during safety processing in adolescents}},
journal = {Developmental cognitive neuroscience},
year = {2026},
month = jul,
volume = {81},
pages = {101791},
publisher = {Elsevier},
issn = {1878-9293},
doi = {10.1016/j.dcn.2026.101791},
url = {https://doi.org/10.1016/j.dcn.2026.101791},
pmid = {42531887},
pmcid = {PMC13452386}
}

RIS

TY - JOUR
AU - Zhang, Yubing
AU - Coates, Madeline K
AU - Garrido, Marta I
AU - Tashjian, Sarah M
TI - Age-related differences in hippocampal network engagement during safety processing in adolescents
T2 - Developmental cognitive neuroscience
J2 - Dev Cogn Neurosci
PY - 2026
DA - 2026/07/28
VL - 81
SP - 101791
SN - 1878-9293
PB - Elsevier
DO - 10.1016/j.dcn.2026.101791
UR - https://doi.org/10.1016/j.dcn.2026.101791
LA - en
ER -

CSL-JSON

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