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Augmenting extinction with counterconditioning strengthens and sustains neural safety representations in PTSD.

Code ↔ Paper

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  1. [1] § Method › Psychophysiology quantification ↔ code/scr_analyses.R, lines 1–55 · score 0.55 · skin conductance response, zero, Raw, scored, SCR, stimulus

Paper

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The authors' code

R · 127 lines · 5.4 KB · no license · 1 match

  1. #####################################################################################
  2. # scr_analyses.R
  3. #
  4. # Cooper et al. (2025). Translational Psychiatry.
  5. #
  6. # Skin conductance response (SCR) models across phases: fear acquisition,
  7. # extinction, renewal24hr, and renewal1mo. Outcome (scrz) is SCR z-scored
  8. # within group prior (see scr.csv); trial numbering in the raw
  9. # data runs continuously across phases, so each model below re-zeroes
  10. # `trial` to that phase's own start and re-derives an early/late half split
  11. # from the corrected count.
  12. #
  13. # INPUTS (read from data/, deposited alongside this script)
  14. # scr.csv -> df.scr (subject, group, task, stim, trial, scrz)
  15. # excluded_subjects.csv -> df.excluded_subjects
  16. #
  17. # OUTPUTS
  18. # This script produces no new files.
  19. #
  20. #####################################################################################
  23. # setup -------------------------------------------------------------------------
  24. library(tidyverse)
  25. library(lme4)
  26. library(easystats)
  27. library(robustlmm)
  28. library(emmeans)
  30. # load data -------------------------------------------------------------------------
  32. df.scr <- read_csv("data/scr.csv")
  33. df.excluded_subjects <- read_csv("data/excluded_subjects.csv")
  36. # acquisition ---------------------------------------------------------------
  38. m1.scr.acq <- df.scr %>%
  39. filter(task=="FC") %>%
  40. filter(!subject %in% df.excluded_subjects$subject) %>%
  41. mutate(half = case_when(trial < 13 ~ "early", # trial numbering starts at 1 for this phase
  42. TRUE ~ "late")) %>%
  43. mutate(stim = fct_relevel(stim,"CC","EXT","CTRL")) %>%
  44. lmer(scrz~stim*group*half+trial+(1|subject)+(1|subject:stim)+(1|subject:half)+(1|trial),data=.)
  46. m1.scr.acq.stdize <- m1.scr.acq %>% datawizard::standardise()
  48. m1.scr.acq %>% parameters(summary=T)
  49. m1.scr.acq.stdize %>% parameters(summary=T)
  51. m1.scr.acq %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  52. m1.scr.acq.stdize %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  54. m1.scr.acq %>% emmeans(pairwise~stim|half|group,adjust="none")
  55. m1.scr.acq.stdize %>% emmeans(pairwise~stim|half|group,adjust="none")
  58. # extinction ------------------------------------------------------------------
  60. m1.scr.ext <- df.scr %>%
  61. filter(task=="SafetyLearning") %>%
  62. filter(!subject %in% df.excluded_subjects$subject) %>%
  63. mutate(trial = trial - 24, # raw trial numbering continues from acquisition (1-24); reset to phase-local count
  64. half = case_when(trial < 13 ~ "early",
  65. TRUE ~ "late")) %>%
  66. mutate(stim = fct_relevel(stim,"CC","EXT","CTRL")) %>%
  67. lmer(scrz~stim*group*half+trial+(1|subject)+(1|subject:stim)+(1|subject:half)+(1|trial),data=.)
  69. m1.scr.ext.stdize <- m1.scr.ext %>% datawizard::standardise()
  71. m1.scr.ext %>% parameters(summary=T)
  72. m1.scr.ext.stdize %>% parameters(summary=T)
  74. m1.scr.ext %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  75. m1.scr.ext.stdize %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  77. m1.scr.ext %>% emmeans(pairwise~stim|half|group,adjust="none")
  78. m1.scr.ext.stdize %>% emmeans(pairwise~stim|half|group,adjust="none")
  81. # renewal24hr -------------------------------------------------------------
  83. m1.scr.ren1 <- df.scr %>%
  84. filter(task=="24hrRNWL") %>%
  85. filter(!subject %in% df.excluded_subjects$subject) %>%
  86. mutate(trial = trial - 48, # raw trial numbering continues from acquisition+extinction (1-48); reset to phase-local count
  87. half = case_when(trial < 5 ~ "early", # renewal phases are shorter, so the early/late split point differs from acq/ext
  88. TRUE ~ "late")) %>%
  89. mutate(stim = fct_relevel(stim,"CC","EXT","CTRL")) %>%
  90. lmer(scrz~stim*group*half+trial+(1|subject)+(1|subject:stim)+(1|subject:half)+(1|trial),data=.)
  92. m1.scr.ren1.stdize <- m1.scr.ren1 %>% datawizard::standardise()
  94. m1.scr.ren1 %>% parameters(summary=T)
  95. m1.scr.ren1.stdize %>% parameters(summary=T)
  97. m1.scr.ren1 %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  98. m1.scr.ren1.stdize %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  100. m1.scr.ren1 %>% emmeans(pairwise~stim|half|group,adjust="none")
  101. m1.scr.ren1.stdize %>% emmeans(pairwise~stim|half|group,adjust="none")
  104. # renewal1mo --------------------------------------------------------------
  106. m1.scr.ren2 <- df.scr %>%
  107. filter(str_detect(task,"month")) %>%
  108. filter(!subject %in% df.excluded_subjects$subject) %>%
  109. mutate(trial = trial - 56, # raw trial numbering continues from acquisition+extinction+renewal24hr (1-56); reset to phase-local count
  110. half = case_when(trial < 5 ~ "early",
  111. TRUE ~ "late")) %>%
  112. mutate(stim = fct_relevel(stim,"CC","EXT","CTRL")) %>%
  113. lmer(scrz~stim*group*half+trial+(1|subject)+(1|subject:stim)+(1|subject:half)+(1|trial),data=.)
  115. m1.scr.ren2.stdize <- m1.scr.ren2 %>% datawizard::standardise()
  117. m1.scr.ren2 %>% parameters(summary=T)
  118. m1.scr.ren2.stdize %>% parameters(summary=T)
  120. m1.scr.ren2 %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  121. m1.scr.ren2.stdize %>% emmeans(pairwise~stim*group*half) %>% joint_tests(by="half")
  123. m1.scr.ren2 %>% emmeans(pairwise~group|half,adjust="none")
  124. m1.scr.ren2.stdize %>% emmeans(pairwise~group|half,adjust="none")
  126. m1.scr.ren2 %>% emmeans(pairwise~stim|half|group,adjust="none")
  127. m1.scr.ren2.stdize %>% emmeans(pairwise~stim|half|group,adjust="none")

scr_analyses.R, no license · at the source

Overview

Authors: Samuel E Cooper1,2,3, Nicole E Keller4, Elizabeth A Bauer1, Sydney R Lambert1, Augustin C Hennings5, Ameera A Azar1, Sophia A Bibb6, Charles B Nemeroff1,3, Josh M Cisler1,2,3,7, Jarrod A Lewis-Peacock1,2,7,8,9, Joseph E Dunsmoor1,2,7,8,9
ORCID iDs: Samuel E Cooper, Nicole E Keller, Charles B Nemeroff, Jarrod A Lewis-Peacock, Joseph E Dunsmoor
  1. Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, Austin, Texas USA
  2. Interdisciplinary Neuroscience Program, University of Texas at Austin, Austin, Texas USA
  3. Institute for Early Life Adversity Research, Dell Medical School, University of Texas at Austin, Austin, Texas USA
  4. Exponent, Inc, Denver, Colorado, USA
  5. Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey USA
  6. Neuroscience Graduate Program, Ohio State University, Columbus, Ohio USA
  7. Department of Psychology, University of Texas at Austin, Austin, Texas USA
  8. Center for Learning and Memory, University of Texas at Austin, Austin, Texas USA
  9. Department of Neuroscience, University of Texas at Austin, Austin, Texas USA
Institutions: The University of Texas at Austin (United States); Exponent (United States) (United States); Princeton University (United States); The Ohio State University (United States)
Journal: Translational psychiatry, volume 16, issue 1, article 303
Dates: received 18 November 2025; accepted 6 March 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-03966-y · PMID 42026031 · PMCID PMC13237184 · OpenAlex W4410426999
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/3b82c_v1
Status: code verified
Categories: human (organism), other condition (population)
Methods: Statistics, Connectivity, fMRI & imaging, Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning
Keywords: Psychiatric disorders, Learning and memory, Human behaviour
MeSH: Conditioning, Classical*, Extinction, Psychological*, Gyrus Cinguli*, Prefrontal Cortex*, Stress Disorders, Post-Traumatic*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Anesthesia and Neurotoxicity Research (Developmental Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01 MH117293, R01 MH125886, R33 MH108753, R01 MH129042, T32 MH106454, R01 MH119132); NIAAA NIH HHS (R01 AA030740, R01 AA030038); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (K23MH138889)
Citations: cited by 2 papers (Europe PMC); 109 references in the paper
Research resources: RRID:SCR_021898

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

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OSF rfzt6

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (7)
Size: 18 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (7 files), easystats (6 files), emmeans (5 files), lme4 (5 files), car (2 files), ggpubr (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
8 files
At the source: osf.io/rfzt6/

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

Tracing map

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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;
  • 7 scripts, each with its path and the digest of its content;
  • 1 match 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

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Read it in the paper: doi.org/10.1038/s41398-026-03966-y.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 11 MeSH terms, 3 funders, 94 references, 1 RRID.

Cite

This paper

Cooper, S. E., Keller, N. E., Bauer, E. A., Lambert, S. R., Hennings, A. C., Azar, A. A., Bibb, S. A., Nemeroff, C. B., Cisler, J. M., Lewis-Peacock, J. A., & Dunsmoor, J. E. (2026). Augmenting extinction with counterconditioning strengthens and sustains neural safety representations in PTSD. Translational psychiatry, 16(1), 303. https://doi.org/10.1038/s41398-026-03966-y

BibTeX

@article{cooper2026augmenting,
author = {Cooper, Samuel E and Keller, Nicole E and Bauer, Elizabeth A and Lambert, Sydney R and Hennings, Augustin C and Azar, Ameera A and Bibb, Sophia A and Nemeroff, Charles B and Cisler, Josh M and Lewis-Peacock, Jarrod A and Dunsmoor, Joseph E},
title = {{Augmenting extinction with counterconditioning strengthens and sustains neural safety representations in PTSD}},
journal = {Translational psychiatry},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {303},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-03966-y},
url = {https://doi.org/10.1038/s41398-026-03966-y},
pmid = {42026031},
pmcid = {PMC13237184}
}

RIS

TY - JOUR
AU - Cooper, Samuel E
AU - Keller, Nicole E
AU - Bauer, Elizabeth A
AU - Lambert, Sydney R
AU - Hennings, Augustin C
AU - Azar, Ameera A
AU - Bibb, Sophia A
AU - Nemeroff, Charles B
AU - Cisler, Josh M
AU - Lewis-Peacock, Jarrod A
AU - Dunsmoor, Joseph E
TI - Augmenting extinction with counterconditioning strengthens and sustains neural safety representations in PTSD
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/04/23
VL - 16
IS - 1
SP - 303
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-03966-y
UR - https://doi.org/10.1038/s41398-026-03966-y
LA - en
ER -

CSL-JSON

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