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Autonomic and limbic system functioning, early life stress, and depressive symptoms in adolescence.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Statistical analyses ↔ h2_recovery_connectivity_els_cdi.py, lines 1–15 · score 0.82 · way interaction, simple slopes, way model, joint model, RespHRV, SD
  2. [2] § Methods › Statistical analyses ↔ h1_rsphrv_hipcamy_connectivity.py, lines 1–11 · score 0.80 · Benjamini Hochberg FDR, TSST phase, FDR correction, hippocampal amygdala connection, RespHRV, regressed
  3. [3] § Methods › Statistical analyses ↔ h1_rsphrv_hipcamy_connectivity.py, lines 1–11 · score 0.69 · Benjamini Hochberg FDR, hippocampal amygdala connectivity, RespHRV, regression, score
  4. [4] § Results › Hypothesis 2: ELS moderation of the joint association of RespHRV recovery and connectivity with depressive symptoms ↔ h2_recovery_connectivity_els_cdi.py, lines 1–15 · score 0.69 · HIP AMY connectivity, joint model, adversity dimensions, RespHRV, concurrent, interaction

Paper

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The authors' code

Python · 168 lines · 9.7 KB · no license · 2 matches

  1. """
  2. Hypothesis 2: RespHRV recovery × HIP-AMY connectivity × ELS → CDI-S
  3. - Two-way model: recovery × connectivity (no adversity)
  4. - Three-way models: recovery × connectivity × threat (separate)
  5. recovery × connectivity × deprivation (separate)
  6. recovery × connectivity × unpredictability (separate)
  7. - Joint model: all three adversity dimensions simultaneously
  8. - Simple-slopes decomposition at ±1 SD for significant three-way interactions
  9. - Outcome: CDI-S total score (concurrent, matched to TSST timepoint)
  10. - Covariates: age, mean framewise displacement (mFD)
  11. - All continuous predictors z-scored
  12. """
  13. import pandas as pd
  14. import numpy as np
  15. from scipy import stats
  17. # ── Paths ──────────────────────────────────────────────────────────────────────
  18. OD = '/Users/eu/Library/CloudStorage/OneDrive-Stanford/ELS Actigraphy/'
  19. BOX = '/Users/eu/Library/CloudStorage/Box-Box/mooddata_nophi/ELS_RDoC/2. Curated Sheets/'
  20. ELS_D = '/Users/eu/Library/CloudStorage/OneDrive-Stanford/Research Projects/1 - Data/ELS/'
  22. cardiac = pd.read_csv(OD + 'analytic_dataset.csv')
  23. amy = pd.read_csv(ELS_D + '0 - curated/amygda-hippo/amygdala_connectivity.csv')
  24. t1 = pd.read_csv(BOX + 'ELS_T1_Child_Curated.csv').rename(columns={'ELS_ID': 'ID'})
  25. t2 = pd.read_csv(BOX + 'ELS_T2_Curated.csv').rename(columns={'ELS_ID': 'ID'})
  26. threat_df = pd.read_csv(ELS_D + '0 - curated/els/els_threat_score.csv').rename(columns={'ELS_ID': 'ID'})
  27. depri_df = pd.read_csv(ELS_D + '0 - curated/els/els_deprivation.csv').rename(columns={'ELS_ID': 'ID'})
  28. unpred_df = pd.read_csv(ELS_D + '0 - curated/els/els_unpredict_score.csv').rename(columns={'ELS_ID': 'ID'})
  30. # ── Merge at matched timepoints ────────────────────────────────────────────────
  31. brain_col = 'tian_HIP_tail_rh_x_lAMY_lh'
  32. amy_t1 = amy[amy['timepoint'] == 'T1'][['subject_id', 'mean_fd', brain_col]]\
  33. .rename(columns={'subject_id': 'ID'})
  34. amy_t2 = amy[amy['timepoint'] == 'T2'][['subject_id', 'mean_fd', brain_col]]\
  35. .rename(columns={'subject_id': 'ID'})
  36. m1 = cardiac[cardiac['tsst_timepoint'] == 1].merge(amy_t1, on='ID', how='inner')
  37. m2 = cardiac[cardiac['tsst_timepoint'] == 2].merge(amy_t2, on='ID', how='inner')
  38. df = pd.concat([m1, m2], ignore_index=True)
  40. # Concurrent CDI-S
  41. cdi1 = df['ID'].map(t1.drop_duplicates('ID').set_index('ID')['CDI_total.T1'])
  42. cdi2 = df['ID'].map(t2.drop_duplicates('ID').set_index('ID')['CDI_total.T2'])
  43. df['CDI_c'] = np.where(df['tsst_timepoint'] == 1, cdi1, cdi2).astype(float)
  45. # Adversity scores
  46. df['threat'] = df['ID'].map(threat_df.drop_duplicates('ID').set_index('ID')['threat_sumsev'])
  47. df['depri'] = df['ID'].map(depri_df.drop_duplicates('ID').set_index('ID')['deprivation_sumsev'])
  48. df['unpred'] = df['ID'].map(unpred_df.drop_duplicates('ID').set_index('ID')['unpred_sumsev'])
  50. # ── Analytic sample ────────────────────────────────────────────────────────────
  51. cols = ['recovery_RSA', brain_col, 'threat', 'depri', 'unpred',
  52. 'CDI_c', 'age_at_tsst', 'mean_fd']
  53. d = df[cols].apply(pd.to_numeric, errors='coerce').dropna()
  54. N = len(d)
  55. print(f'Analytic N = {N}')
  57. # ── Z-score all continuous predictors ─────────────────────────────────────────
  58. def zscore(x):
  59. return (x - x.mean()) / x.std()
  61. rv = zscore(d['recovery_RSA'].values.astype(float)); rsa_sd = d['recovery_RSA'].std()
  62. bv = zscore(d[brain_col].values.astype(float)); brain_sd = 1.0 # already z-scored → SD=1
  63. tv = zscore(d['threat'].values.astype(float)); threat_sd = 1.0
  64. dv = zscore(d['depri'].values.astype(float)); depri_sd = 1.0
  65. uv = zscore(d['unpred'].values.astype(float)); unpred_sd = 1.0
  66. av = zscore(d['age_at_tsst'].values.astype(float))
  67. fv = zscore(d['mean_fd'].values.astype(float))
  68. Y = d['CDI_c'].values.astype(float)
  70. # ── OLS helper ────────────────────────────────────────────────────────────────
  71. def ols(C, y):
  72. b, _, _, _ = np.linalg.lstsq(C, y, rcond=None)
  73. dof = len(y) - C.shape[1]
  74. mse = np.sum((y - C @ b) ** 2) / dof
  75. cov_m = mse * np.linalg.inv(C.T @ C)
  76. se = np.sqrt(np.diag(cov_m))
  77. t = b / se
  78. p = np.array([float(2 * stats.t.sf(abs(ti), dof)) for ti in t])
  79. return b, se, t, p, dof, cov_m
  81. def print_model(labels, b, se, t, p, dof, title=''):
  82. if title:
  83. print(f'\n{"="*65}')
  84. print(title)
  85. print(f'{"="*65}')
  86. print(f'{"Term":<26} {"β":>8} {"SE":>7} {"t":>7} {"p":>8}')
  87. print('-' * 60)
  88. for lbl, bi, si, ti, pi in zip(labels, b, se, t, p):
  89. sig = ' **' if pi < .01 else (' *' if pi < .05 else (' +' if pi < .10 else ''))
  90. print(f'{lbl:<26} {bi:>+8.3f} {si:>7.3f} {ti:>+7.2f} {pi:>8.4f}{sig}')
  92. def simple_slopes(b, cov_m, dof, adv_sd=1.0):
  93. """
  94. Simple slopes of recovery at brain ±1 SD × adversity ±1 SD.
  95. Model index: 0=int, 1=recov, 2=brain, 3=adv, 4=r×b, 5=r×adv, 6=b×adv, 7=r×b×adv, 8=age, 9=mfd
  96. """
  97. print(f'\n Simple slopes of recovery at brain ±1 SD × adversity ±1 SD:')
  98. print(f' {"Condition":<30} {"slope":>8} {"SE":>7} {"t":>7} {"p":>8}')
  99. print(' ' + '-' * 58)
  100. for b_lbl, bval in [('High connectivity (+1 SD)', 1.0), ('Low connectivity (−1 SD)', -1.0)]:
  101. for a_lbl, aval in [('High adversity (+1 SD)', adv_sd), ('Low adversity (−1 SD)', -adv_sd)]:
  102. ss = float(b[1] + b[4]*bval + b[5]*aval + b[7]*bval*aval)
  103. g = np.array([1.0, bval, aval, bval*aval])
  104. idx = [1, 4, 5, 7]
  105. var = float(g @ cov_m[np.ix_(idx, idx)] @ g)
  106. tv = ss / np.sqrt(var)
  107. pv = float(2 * stats.t.sf(abs(tv), dof))
  108. sig = ' **' if pv < .01 else (' *' if pv < .05 else (' +' if pv < .10 else ''))
  109. cond = f'{b_lbl}, {a_lbl}'
  110. print(f' {cond:<30} {ss:>+8.3f} {np.sqrt(var):>7.3f} {tv:>+7.2f} {pv:>8.4f}{sig}')
  112. # ══════════════════════════════════════════════════════════════════════════════
  113. # Model 0: Two-way (recovery × connectivity, no adversity)
  114. # ══════════════════════════════════════════════════════════════════════════════
  115. C0 = np.column_stack([np.ones(N), rv, bv, rv*bv, av, fv])
  116. lb0 = ['intercept', 'recovery', 'connectivity', 'recovery×connectivity', 'age', 'mFD']
  117. b0, se0, t0, p0, dof0, _ = ols(C0, Y)
  118. print_model(lb0, b0, se0, t0, p0, dof0,
  119. title='Model 0: Recovery × Connectivity → CDI-S (two-way, no adversity)')
  121. # ══════════════════════════════════════════════════════════════════════════════
  122. # Models 1–3: Separate three-way models
  123. # ══════════════════════════════════════════════════════════════════════════════
  124. adversity_vars = [
  125. ('Threat', tv, 'threat'),
  126. ('Deprivation', dv, 'depri'),
  127. ('Unpredictability', uv, 'unpred'),
  128. ]
  130. for name, av_vec, _ in adversity_vars:
  131. C = np.column_stack([np.ones(N), rv, bv, av_vec,
  132. rv*bv, rv*av_vec, bv*av_vec, rv*bv*av_vec,
  133. av, fv])
  134. lb = ['intercept', 'recovery', 'connectivity', name,
  135. 'recov×conn', f'recov×{name}', f'conn×{name}',
  136. f'recov×conn×{name}', 'age', 'mFD']
  137. b_, se_, t_, p_, dof_, cov_ = ols(C, Y)
  138. print_model(lb, b_, se_, t_, p_, dof_,
  139. title=f'Model: Recovery × Connectivity × {name} → CDI-S')
  140. # Decompose if three-way is significant
  141. if p_[7] < .10:
  142. simple_slopes(b_, cov_, dof_)
  144. # ══════════════════════════════════════════════════════════════════════════════
  145. # Model 4: Joint model — all three adversity dimensions simultaneously
  146. # ══════════════════════════════════════════════════════════════════════════════
  147. # Predictors: recovery, brain, threat, depri, unpred,
  148. # r×b, r×t, r×d, r×u, b×t, b×d, b×u,
  149. # r×b×t, r×b×d, r×b×u,
  150. # age, mfd
  151. C_joint = np.column_stack([
  152. np.ones(N),
  153. rv, bv, tv, dv, uv,
  154. rv*bv, rv*tv, rv*dv, rv*uv,
  155. bv*tv, bv*dv, bv*uv,
  156. rv*bv*tv, rv*bv*dv, rv*bv*uv,
  157. av, fv
  158. ])
  159. lb_joint = [
  160. 'intercept', 'recovery', 'connectivity', 'threat', 'deprivation', 'unpredictability',
  161. 'recov×conn', 'recov×threat', 'recov×depri', 'recov×unpred',
  162. 'conn×threat', 'conn×depri', 'conn×unpred',
  163. 'recov×conn×threat', 'recov×conn×depri', 'recov×conn×unpred',
  164. 'age', 'mFD'
  165. ]
  166. b_j, se_j, t_j, p_j, dof_j, cov_j = ols(C_joint, Y)
  167. print_model(lb_joint, b_j, se_j, t_j, p_j, dof_j,
  168. title='Joint Model: Recovery × Connectivity × [Threat + Deprivation + Unpredictability] → CDI-S')

h2_recovery_connectivity_els_cdi.py at commit ef9d4a5, no license · at the source

Overview

Authors: Eugenia Giampetruzzi1, Chase Antonacci1, Jean Rachel Bahk1, Jessica L Buthmann1, Yoonji Lee1, Katharina Kircanski2, Ian H Gotlib1
ORCID iDs: Eugenia Giampetruzzi, Jessica L Buthmann
  1. Department of Psychology, 450 Jane Stanford Way, Building 420, Stanford, CA, 94305, USA
  2. Department of Psychology, University of Southern California, 3620 S. McClintock Ave, Los Angeles, CA, 90089, USA
Institutions: Stanford University (United States); University of Southern California (United States)
Journal: Neurobiology of stress, volume 44, article 100847
Dates: received 13 May 2026; accepted 14 August 2026; published online 15 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.ynstr.2026.100847 · PMID 42656666 · PMCID PMC13507736 · OpenAlex W7203543546
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), depression (population), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging, Evoked potentials, Physiology & signal measures
Keywords: Early life stress, Heart rate variability, Hippocampus, Amygdala, Adolescent depression, Neurovisceral integration
Topic: Heart Rate Variability and Autonomic Control (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: National Institute of Mental Health; Division of Graduate Education
Citations: not cited yet (Europe PMC); 75 references in the paper
Research resources: RRID:SCR_016216

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.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

eugiampetruzzi/autonomic-limbic

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ef9d4a5e95ff1c90d74278a867e63c86181c5a08, 5 May 2026
Languages: Python (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Statistical analyses”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), SciPy (2 files)
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/eugiampetruzzi/autonomic-limbic

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  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Read it in the paper: doi.org/10.1016/j.ynstr.2026.100847.

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Version 2, 28 September 2026

  • Funding: added National Institute of Mental Health; Division of Graduate Education

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 73 references, 1 RRID.

Cite

This paper

Giampetruzzi, E., Antonacci, C., Bahk, J. R., Buthmann, J. L., Lee, Y., Kircanski, K., & Gotlib, I. H. (2026). Autonomic and limbic system functioning, early life stress, and depressive symptoms in adolescence. Neurobiology of stress, 44, 100847. https://doi.org/10.1016/j.ynstr.2026.100847

BibTeX

@article{giampetruzzi2026autonomic,
author = {Giampetruzzi, Eugenia and Antonacci, Chase and Bahk, Jean Rachel and Buthmann, Jessica L and Lee, Yoonji and Kircanski, Katharina and Gotlib, Ian H},
title = {{Autonomic and limbic system functioning, early life stress, and depressive symptoms in adolescence}},
journal = {Neurobiology of stress},
year = {2026},
month = aug,
volume = {44},
pages = {100847},
publisher = {Elsevier},
issn = {2352-2895},
doi = {10.1016/j.ynstr.2026.100847},
url = {https://doi.org/10.1016/j.ynstr.2026.100847},
pmid = {42656666},
pmcid = {PMC13507736}
}

RIS

TY - JOUR
AU - Giampetruzzi, Eugenia
AU - Antonacci, Chase
AU - Bahk, Jean Rachel
AU - Buthmann, Jessica L
AU - Lee, Yoonji
AU - Kircanski, Katharina
AU - Gotlib, Ian H
TI - Autonomic and limbic system functioning, early life stress, and depressive symptoms in adolescence
T2 - Neurobiology of stress
J2 - Neurobiol Stress
PY - 2026
DA - 2026/08/15
VL - 44
SP - 100847
SN - 2352-2895
PB - Elsevier
DO - 10.1016/j.ynstr.2026.100847
UR - https://doi.org/10.1016/j.ynstr.2026.100847
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.ynstr.2026.100847",
"type": "article-journal",
"title": "Autonomic and limbic system functioning, early life stress, and depressive symptoms in adolescence",
"container-title": "Neurobiology of stress",
"author": [
{
"family": "Giampetruzzi",
"given": "Eugenia"
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{
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"container-title-short": "Neurobiol Stress",
"volume": "44",
"page": "100847",
"DOI": "10.1016/j.ynstr.2026.100847",
"PMID": "42656666",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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15
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]
}
}

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