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The human amygdala in threat learning and extinction.

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  1. [1] § MATERIALS AND METHODS › SCR similarity index ↔ stats/learning_models/SCR_autocorrelation.R, lines 48 to 110 · score 0.52 · correlation coefficients, lagged, autocorrelation, SCRs, CS, hippocampus · Report a mismatch

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The authors' code

R · 110 lines · 3.6 KB · MIT · 1 match

  1. if (!requireNamespace("here", quietly = TRUE)) install.packages("here")
  2. source(here::here("stats","learning_models","_setup.R"))
  4. data_acq <- subset(scr_df, PHASE == "acquisition" & TUS == "active" & CS == "threat")
  6. data_acq$CS <- relevel(data_acq$CS, ref = "control")
  7. data_acq$US <- relevel(data_acq$US, ref = "unreinforced")
  9. data_acq <- data_acq %>%
  10. mutate(SUBID = factor(SUBID, levels = unique(SUBID)))
  12. # Create a new column with NA values
  13. data_acq$n_trials_after_US <- NA
  15. # Loop through each unique SUBID
  16. unique_subjects <- unique(data_acq$SUBID)
  18. for (subid in unique_subjects) {
  20. # Subset data for the current subject
  21. subj_data <- data_acq[data_acq$SUBID == subid, ]
  23. # Initialize counters and reset conditions for this subject
  24. counters <- list()
  25. reset_conditions <- list()
  27. for (i in 1:nrow(subj_data)) {
  29. # Create a unique combination key based on TUS and CS values
  30. key <- paste(subj_data$TUS[i], subj_data$CS[i], sep = "_")
  32. # If the key doesn't exist in counters, initialize it
  33. if (!key %in% names(counters)) {
  34. counters[[key]] <- 0
  35. reset_conditions[[key]] <- TRUE
  36. }
  38. # Update the counter for the current key
  39. if (subj_data$US[i] == "reinforced") {
  40. counters[[key]] <- 0
  41. reset_conditions[[key]] <- FALSE
  42. } else if (reset_conditions[[key]]) {
  43. counters[[key]] <- 0
  44. } else {
  45. counters[[key]] <- counters[[key]] + 1
  46. }
  48. # Set the value in n_trials_after_US based on the counter for the current key
  49. subj_data$n_trials_after_US[i] <- counters[[key]]
  50. }
  52. # Update the original data frame with the computed values for this subject
  53. data_acq[data_acq$SUBID == subid, "n_trials_after_US"] <- subj_data$n_trials_after_US
  54. }
  56. # Assign block IDs as before
  57. data_acq <- data_acq %>%
  58. group_by(EXPERIMENT, SUBID) %>%
  59. mutate(
  60. n_trials_after_US_block_ID = cumsum(n_trials_after_US == 0 &
  61. (lag(n_trials_after_US, default = -1) != 0)) + 1
  62. ) %>%
  63. ungroup()
  65. # Group by n_trials_after_US and n_trials_after_US_block_ID
  66. data_acq_blockID_mean <- data_acq %>%
  67. group_by(EXPERIMENT, SUBID, n_trials_after_US, n_trials_after_US_block_ID) %>%
  68. dplyr::summarise(
  69. mean_SCR_sqrt = mean(SCR_sqrt, na.rm = TRUE)
  70. ) %>%
  71. arrange(SUBID, n_trials_after_US_block_ID) %>%
  72. ungroup()
  74. scatter_data <- data_acq_blockID_mean
  76. data_reshaped <- scatter_data %>%
  77. group_by(EXPERIMENT, SUBID, n_trials_after_US_block_ID) %>%
  78. pivot_wider(., names_from = n_trials_after_US, values_from = mean_SCR_sqrt)
  80. data_reshaped <- data_reshaped %>%
  81. dplyr::rename(
  82. mean_SCR_sqrt_0 = `0`,
  83. mean_SCR_sqrt_1 = `1`,
  84. mean_SCR_sqrt_2 = `2`
  85. )
  87. # Calculate Spearman's rank correlation coefficients for each participant
  88. corr_results <- data_reshaped %>%
  89. group_by(EXPERIMENT, SUBID) %>%
  90. dplyr::summarize(
  91. corrcoef_0_1 = cor(mean_SCR_sqrt_0, mean_SCR_sqrt_1, method = "spearman", use = "complete.obs"),
  92. corrcoef_1_2 = cor(mean_SCR_sqrt_1, mean_SCR_sqrt_2, method = "spearman", use = "complete.obs")
  93. ) %>%
  94. ungroup()
  96. # Perform t-tests to compare the correlation coefficients between the two experiments
  97. t_test_0_1 <- t.test(corr_results$corrcoef_0_1 ~ corr_results$EXPERIMENT)
  98. t_test_1_2 <- t.test(corr_results$corrcoef_1_2 ~ corr_results$EXPERIMENT)
  100. t_test_0_1 # CS+US --> CS
  101. t_test_1_2 # CS --> CS
  103. # Compute Cohen's d (pooled SD) for CS+US --> CS effect
  104. n1 <- sum(corr_results$EXPERIMENT == "amygdala")
  105. n2 <- sum(corr_results$EXPERIMENT == "hippocampus")
  107. t_val <- t_test_0_1$statistic[[1]]
  109. d_pooled <- t_val * sqrt(1/n1 + 1/n2) # Cohen's d (pooled SD version)
  110. d_pooled

SCR_autocorrelation.R at commit 221cad6, under MIT · at the source

Overview

Authors: Sjoerd Meijer1, Eleonora Carpino1,2,3,4, Benjamin R. Kop1, Jesse Lam1, Lycia D. de Voogd1,5,6,7, Karin Roelofs1,7, Lennart Verhagen1
ORCID iDs: Sjoerd Meijer, Benjamin R. Kop, Jesse Lam, Lycia D. de Voogd, Karin Roelofs, Lennart Verhagen
  1. Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, Netherlands
  2. Department of Experimental Psychology, University of Oxford, Oxford, UK
  3. Brain Research and Imaging Centre, Faculty of Health, University of Plymouth, Plymouth, UK
  4. School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
  5. Institute of Psychology, Leiden University, Leiden, Netherlands
  6. Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands
  7. Behavioral Science Institute (BSI), Radboud University, Nijmegen, Netherlands
Institutions: Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands); University of Oxford (United Kingdom); University of Plymouth (United Kingdom); Leiden University (Netherlands)
Journal: Science advances, volume 12, issue 13, article eaea8233
Dates: received 22 July 2025; accepted 13 January 2026; published online 25 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aea8233 · PMID 41880511 · PMCID PMC13015908 · OpenAlex W7140309761
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, Smoothing, state filtering, decompositions
MeSH: Amygdala*, Extinction, Psychological*, Fear*, Learning*, Adult, Conditioning, Classical, Female, Galvanic Skin Response, Hippocampus, Humans, Male, Memory, Young Adult (* major topic)
Journal subjects: Neuroscience, Cognitive Neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HORIZON EUROPE European Innovation Council (101071008); European Research Council (ERC_CoG_772337, MediCoDe); Dutch Research Council (18919)
Citations: cited by 3 papers (Europe PMC); 116 references in the paper

Abstract

Here, we resolve the long-standing but unconfirmed hypothesis that the human amygdala is essential for rapidly acquiring cued-conditioned threat responses. We provide causal evidence for the amygdala’s contribution to forming threat memories that are resistant to extinction. Using transcranial ultrasound stimulation (TUS), a noninvasive technique that modulates deep brain structures with high spatial and temporal precision, we targeted the bilateral amygdala during Pavlovian threat conditioning in healthy adults. Linear mixed-effects models and computational modeling of trial-level skin conductance responses revealed that amygdala-TUS (experiment I, n = 25), but not hippocampus-TUS (experiment II, n = 25), selectively slowed initial threat acquisition, augmented subsequent extinction, and modulated declarative memory of retrospective threat probability. These findings demonstrate that the human amygdala drives an emotional learning state—learning fast, forgetting slow. Our study shows the potential of TUS for targeted neuromodulation of human deep brain structures implicated in conditions such as posttraumatic stress disorder, where pathological threat memories persist despite therapy.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

meijer-s/FEARTUS

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 221cad6a38e80a6f45f291ab29fff59fcca4d120, 11 December 2025
Languages: MATLAB (251), R (32), C (12), Shell (2), C/C++ (2)
Size: 612 files, 299 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: SPM (12 files), tidyverse (10 files), Image Processing Toolbox (6 files), GIfTI library for MATLAB (4 files), FreeSurfer (3 files), cowplot (2 files), easystats (2 files), lme4 (2 files), Stan (2 files), Connectome Workbench (2 files), brms (1 file), broom (1 file), emmeans (1 file), FSL (1 file), ggplot2 (1 file), lmerTest (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
301 files
At the source: github.com/meijer-s/FEARTUS

Zenodo 17899009

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (12 files), tidyverse (10 files), Image Processing Toolbox (6 files), GIfTI library for MATLAB (4 files), FreeSurfer (3 files), cowplot (2 files), easystats (2 files), lme4 (2 files), Stan (2 files), Connectome Workbench (2 files), brms (1 file), broom (1 file), emmeans (1 file), FSL (1 file), ggplot2 (1 file), lmerTest (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
301 files
At the source: doi.org/10.5281/zenodo.17899009

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

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Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper, the Supplementary Materials, or in the following archives. All analysis codes are available on GitHub (https://github.com/meijer-s/FEARTUS) and archived at the time of publication on Zenodo (https://doi.org/10.5281/zenodo.17899009). Behavioral data are openly available on Zenodo (https://doi.org/10.5281/zenodo.17897100). This study did not generate new materials.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 13 MeSH terms, 3 funders, 110 references.

Cite

This paper

Meijer, S., Carpino, E., Kop, B. R., Lam, J., de Voogd, L. D., Roelofs, K., & Verhagen, L. (2026). The human amygdala in threat learning and extinction. Science advances, 12(13), eaea8233. https://doi.org/10.1126/sciadv.aea8233

BibTeX

@article{meijer2026human,
author = {Meijer, Sjoerd and Carpino, Eleonora and Kop, Benjamin R. and Lam, Jesse and de Voogd, Lycia D. and Roelofs, Karin and Verhagen, Lennart},
title = {{The human amygdala in threat learning and extinction}},
journal = {Science advances},
year = {2026},
month = mar,
volume = {12},
number = {13},
pages = {eaea8233},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aea8233},
url = {https://doi.org/10.1126/sciadv.aea8233},
pmid = {41880511},
pmcid = {PMC13015908}
}

RIS

TY - JOUR
AU - Meijer, Sjoerd
AU - Carpino, Eleonora
AU - Kop, Benjamin R.
AU - Lam, Jesse
AU - de Voogd, Lycia D.
AU - Roelofs, Karin
AU - Verhagen, Lennart
TI - The human amygdala in threat learning and extinction
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/03/25
VL - 12
IS - 13
SP - eaea8233
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aea8233
UR - https://doi.org/10.1126/sciadv.aea8233
LA - en
ER -

CSL-JSON

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