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Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses.

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The authors' code

R · 225 lines · 6.6 KB · no license

  1. # ==============================================================================
  2. # Analysis code for publication:
  3. # "Predicting stress response trajectories: Differential contributions of limbic
  4. # and prefrontal regions to cortisol and affective responses"
  5. #
  6. # Author: Renée Lipka
  7. # R version: 4.3.2 (2023-10-31)
  8. # ==============================================================================
  10. # ---------------------------------------------
  11. # Load libraries
  12. # ---------------------------------------------
  13. library(haven)
  14. library(dplyr)
  15. library(tidyr)
  16. library(lcmm)
  18. # ---------------------------------------------
  19. # Set working directory and load data
  20. # ---------------------------------------------
  21. setwd("/Users/reneelipka/Documents/PhD/06 GIH projects/Trajectory/01 Data")
  22. data <- read_spss(
  23. "ScanStress_trajectories_structure_betas_relevant_HR_NA_PA_ASI_BDI_TICS_CTQ_LEC.sav"
  24. )
  26. # ==============================================================================
  27. # 1. Preparing data
  28. # ==============================================================================
  30. # Convert to long format
  31. data_long <- data %>%
  32. gather(time, cort, Cort1_minus15:Cort1_plus110, factor_key = TRUE) %>%
  33. mutate(
  34. sex = factor(Geschlecht),
  35. age = Alter,
  36. time_n = case_when(
  37. time == "Cort1_minus15" ~ -15,
  38. time == "Cort1_minus1" ~ -1,
  39. time == "Cort1_plus15" ~ 15,
  40. time == "Cort1_plus30" ~ 30,
  41. time == "Cort1_plus50" ~ 50,
  42. time == "Cort1_plus65" ~ 65,
  43. time == "Cort1_plus80" ~ 80,
  44. time == "Cort1_plus95" ~ 95,
  45. time == "Cort1_plus110" ~ 110
  46. )
  47. ) %>%
  48. select(-Geschlecht, -Alter)
  50. # Standardize predictors
  51. predictors <- c(
  52. "left_amygdala_vol", "right_amygdala_vol",
  53. "left_hippocampus_vol", "right_hippocampus_vol",
  54. "left_mOFC_thick", "right_mOFC_thick",
  55. "left_rACC_thick", "right_rACC_thick"
  56. )
  57. data_long[predictors] <- lapply(data_long[predictors], scale)
  59. # Create averaged left-right structure parameters
  60. structure_params <- list(
  61. amygdala = c("vol", "var2", "delta_var2"),
  62. hippocampus= c("vol", "var2", "delta_var2"),
  63. mOFC = c("thick", "var2", "delta_var2"),
  64. rACC = c("thick", "var2", "delta_var2")
  65. )
  67. for (structure in names(structure_params)) {
  68. for (param in structure_params[[structure]]) {
  69. left_col <- paste0("left_", structure, "_", param)
  70. right_col <- paste0("right_", structure, "_", param)
  71. avg_col <- paste0(structure, "_", param, "_avg")
  73. data_long[[avg_col]] <- rowMeans(
  74. data_long[, c(left_col, right_col)],
  75. na.rm = TRUE
  76. )
  77. }
  78. }
  80. # ---------------------------------------------
  81. # 2. Determining link function
  82. # ---------------------------------------------
  84. # Common formulas
  85. formula_fixed <- cort ~ time_n + sex + age
  86. formula_random <- ~ time_n
  87. subject <- "VPNr"
  88. link_functions <- c("linear", "beta", "splines")
  90. for (link in link_functions) {
  91. model_name <- paste0("m1_", link)
  92. assign(
  93. model_name,
  94. lcmm(
  95. formula_fixed,
  96. random = formula_random,
  97. subject = subject,
  98. ng = 1,
  99. data = data_long,
  100. maxiter = 1000,
  101. link = link
  102. )
  103. )
  104. message("Completed model: ", model_name)
  105. }
  107. # Summary
  108. summarytable(
  109. m1_linear,
  110. m1_beta,
  111. m1_splines,
  112. which = c("loglik", "conv", "npm", "AIC")
  113. )
  115. # Plot link functions
  116. col <- rainbow(3)
  117. plot(m1_linear, which="linkfunction", bty='l', ylab="Cortisol",
  118. col=col[1], lwd=2, xlab="Latent process")
  119. plot(m1_beta, which="linkfunction", add=TRUE, col=col[2], lwd=2)
  120. plot(m1_splines, which="linkfunction", add=TRUE, col=col[3], lwd=2)
  121. legend(x="topleft", legend=c("linear", "beta", "splines"), lty=1,
  122. col=col, bty="n", lwd=2)
  124. # ---------------------------------------------
  125. # 3. Cortisol baseline models
  126. # ---------------------------------------------
  128. # Baseline model (1-class)
  129. m1_baseline <- lcmm(
  130. formula_fixed,
  131. random = formula_random,
  132. subject = subject,
  133. ng = 1,
  134. data = data_long,
  135. maxiter = 1000,
  136. link = 'splines'
  137. )
  139. # 2- to 4-class baseline models using gridsearch
  140. for (ng in 2:4) {
  141. model_name <- paste0("m", ng, "_baseline")
  143. assign(
  144. model_name,
  145. gridsearch(
  146. lcmm(
  147. formula_fixed,
  148. random = formula_random,
  149. mixture = formula_random,
  150. subject = subject,
  151. ng = ng,
  152. data = data_long,
  153. link = 'splines'
  154. ),
  155. maxiter = 30,
  156. rep = 100,
  157. minit = m1_baseline
  158. )
  159. )
  161. message("Completed model: ", model_name)
  162. }
  164. # Optional: Summary table for baseline models
  165. # summary_table <- summarytable(m1_baseline, m2_baseline, m3_baseline, m4_baseline,
  166. # which = c("G", "loglik", "conv", "npm", "AIC", "BIC", "SABIC", "entropy", "%class"))
  168. # ---------------------------------------------
  169. # 4. Cortisol brain models
  170. # ---------------------------------------------
  172. # Model configurations for different brain regions
  173. model_configurations <- list(
  174. list(model_extension = "_amy", formula_classmb = ~ amygdala_var2_avg + amygdala_delta_var2_avg + amygdala_vol_avg),
  175. list(model_extension = "_hippo", formula_classmb = ~ hippocampus_var2_avg + hippocampus_delta_var2_avg + hippocampus_vol_avg),
  176. list(model_extension = "_mOFC", formula_classmb = ~ mOFC_var2_avg + mOFC_delta_var2_avg + mOFC_thick_avg),
  177. list(model_extension = "_rACC", formula_classmb = ~ rACC_var2_avg + rACC_delta_var2_avg + rACC_thick_avg)
  178. )
  180. # Common model settings
  181. link <- 'splines'
  182. maxiter <- 30
  183. rep <- 100
  184. minit <- m1_baseline
  185. formula_mixture <- ~ time_n
  187. # Run brain models
  188. for (config in model_configurations) {
  189. model_extension <- config$model_extension
  190. formula_classmb <- config$formula_classmb
  192. for (ng in 2:4) {
  193. model_name <- paste0("m", ng, model_extension)
  195. assign(
  196. model_name,
  197. gridsearch(
  198. lcmm(
  199. formula_fixed,
  200. random = formula_random,
  201. mixture = formula_mixture,
  202. classmb = formula_classmb,
  203. subject = subject,
  204. ng = ng,
  205. data = data_long,
  206. link = link
  207. ),
  208. maxiter = maxiter,
  209. rep = rep,
  210. minit = minit
  211. )
  212. )
  214. message("Completed model: ", model_name)
  215. }
  216. }
  219. # Optional: Summary table for baseline models
  220. # summary_table <- summarytable(m1_baseline, m2_baseline, m3_baseline, m4_baseline,
  221. # m2_amy, m3_amy, m4_amy,
  222. # m2_hippo, m3_hippo, m4_hippo,
  223. # m2_mOFC, m3_mOFC, m4_mOFC,
  224. # m2_rACC, m3_rACC, m4_rACC,
  225. # which = c("G", "loglik", "conv", "npm", "AIC", "BIC", "SABIC", "entropy", "%class"))

Cort_traj_model_estimation.R, no license · at the source

Overview

Authors: Renée Lipka1,2,3,4, Ludwig Kreuzpointner5, Christoph Bärtl1,4, Marina Giglberger5, Julian Konzok5, Hannah L. Peter5, Nina Speicher5, Lea Waller1,4, Brigitte M. Kudielka5, Stefan Wüst5, Henrik Walter1,4, Gina-Isabelle Henze1,4
ORCID iDs: Renée Lipka, Ludwig Kreuzpointner, Christoph Bärtl, Marina Giglberger, Julian Konzok, Nina Speicher, Lea Waller, Brigitte M. Kudielka, Stefan Wüst, Henrik Walter, Gina-Isabelle Henze
  1. Department of Psychiatry and Neurosciences CCM, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin,Berlin, Germany
  2. Department of Psychiatry and Neuroscience CBF, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin,Berlin, Germany
  3. Berlin School of Mind and Brain, Humboldt Universität zu Berlin,Berlin, Germany
  4. German Center for Mental Health (DZPG), Partner Site Berlin - Potsdam,Berlin, Germany
  5. Institute of Psychology, University of Regensburg,Regensburg, Germany
Institutions: Humboldt-Universität zu Berlin (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); University of Regensburg (Germany)
Journal: Translational psychiatry, volume 16, issue 1, article 310
Dates: received 15 November 2025; accepted 22 May 2026; published online 12 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04140-0 · PMID 42285949 · PMCID PMC13263346 · OpenAlex W4416332069
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.11.14.688580
Status: code verified
Categories: human (organism), systems (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Statistics, fMRI & imaging
Keywords: Molecular neuroscience, Predictive markers, Human behaviour
MeSH: Affect*, Amygdala*, Hippocampus*, Hydrocortisone*, Prefrontal Cortex*, Stress, Psychological*, Adult, Female, Humans, Limbic System, Magnetic Resonance Imaging, Male, Saliva, Young Adult (* major topic)
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (513531314); School of Mind and Brain (Stipend)
Citations: not cited yet (Europe PMC); 90 references in the paper

Abstract

Why do individuals respond differently to stress? Since rodent studies indicated that stress regulation relies on limbic and medial prefrontal cortex (mPFC) outputs, we aimed to investigate whether data from these regions could also predict cortisol and affect trajectories following psychosocial stress in humans. In this pre-registered study, 281 healthy adults (145 female) were exposed to ScanSTRESS. Repeated assessments of salivary cortisol and negative affect were used to identify response trajectories (i.e. groups of participants) using latent class mixture modelling (LCMM). LCMMs without brain predictors were compared to LCMMs including structural (volume, thickness) and functional (activation, exposure-time effect) predictors from the amygdala, hippocampus, or mPFC regions, using common fit indices including the Akaike Information Criterion. Results showed that cortisol LCMMs without brain predictors exhibited a single mean trajectory, indicative of homogeneous cortisol responses across the sample. Adding brain predictors resulted in three to four response trajectories, depending on region and outcome. Within identified models, cortisol ‘hyper-response’ trajectories were predicted by larger amygdala and hippocampus volumes. Cortisol ‘non-responses’ were predicted by greater amygdala activation and volume. ‘Elevated baseline’ cortisol was predicted by higher hippocampal activation. mPFC markers did not predict cortisol trajectories, however, medial orbitofrontal cortex parameters identified negative affect response profiles mirroring measures of long-term stress exposure and affect. Together, our findings suggest dissociated roles of limbic and mPFC regions in stress regulation: While limbic structures predicted cortisol responses, the mPFC shaped affective experience.

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

Repository

Its files are read in the Code ↔ Paper reader above.

OSF gk6w9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/gk6w9/

Code availability

The R code of lcmm analyses is provided here: https://osf.io/gk6w9/.

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;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Datasets cited

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. A repository of studies that have already used and published the data is available here: https://osf.io/echja/.

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, 12 authors, 3 keywords, 14 MeSH terms, 2 funders, 85 references.

Cite

This paper

Lipka, R., Kreuzpointner, L., Bärtl, C., Giglberger, M., Konzok, J., Peter, H. L., Speicher, N., Waller, L., Kudielka, B. M., Wüst, S., Walter, H., & Henze, G.-I. (2026). Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses. Translational psychiatry, 16(1), 310. https://doi.org/10.1038/s41398-026-04140-0

BibTeX

@article{lipka2026predicting,
author = {Lipka, Renée and Kreuzpointner, Ludwig and Bärtl, Christoph and Giglberger, Marina and Konzok, Julian and Peter, Hannah L. and Speicher, Nina and Waller, Lea and Kudielka, Brigitte M. and Wüst, Stefan and Walter, Henrik and Henze, Gina-Isabelle},
title = {{Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {310},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04140-0},
url = {https://doi.org/10.1038/s41398-026-04140-0},
pmid = {42285949},
pmcid = {PMC13263346}
}

RIS

TY - JOUR
AU - Lipka, Renée
AU - Kreuzpointner, Ludwig
AU - Bärtl, Christoph
AU - Giglberger, Marina
AU - Konzok, Julian
AU - Peter, Hannah L.
AU - Speicher, Nina
AU - Waller, Lea
AU - Kudielka, Brigitte M.
AU - Wüst, Stefan
AU - Walter, Henrik
AU - Henze, Gina-Isabelle
TI - Predicting stress response trajectories: Differential contributions of limbic and prefrontal regions to cortisol and affective responses
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/12
VL - 16
IS - 1
SP - 310
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04140-0
UR - https://doi.org/10.1038/s41398-026-04140-0
LA - en
ER -

CSL-JSON

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