OSCR

Predicting individual differences of fear and cognitive learning and extinction.

Code ↔ Paper

11 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 11 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Acquisition ↔ scripts/11. pipeline_modelling.r, lines 105–148 · score 0.95 · lACC, lCEB, lPFC, rAMY, rPFC, lAMY
  2. [2] § Results › Acquisition ↔ scripts/12. pipeline_generalisability.r, lines 106–149 · score 0.95 · lACC, lCEB, lPFC, rAMY, rPFC, lAMY
  3. [3] § Methods › Functional connectivity (FC) ↔ scripts/06. pipeline_FC.py, lines 286–391 · score 0.94 · wavelet coherence, Euclidean distance, Manhattan distance, Wasserstein distance, mutual information, cross correlation
  4. [4] § Results › Extinction ↔ scripts/11. pipeline_modelling.r, lines 105–148 · score 0.88 · lCEB, lPFC, rAMY, lAMY, lHIP, rACC
  5. [5] § Results › Extinction ↔ scripts/12. pipeline_generalisability.r, lines 106–149 · score 0.88 · lCEB, lPFC, rAMY, lAMY, lHIP, rACC
  6. [6] § Methods › Structural connectivity (SC) › Learning measures › SCR measures ↔ scripts/09b. pipeline_EDA_DCM.m, lines 17–82 · score 0.68 · CS interval, CS onset, latency, flexible, events, linear
  7. [7] § Methods › Preprocessing of neuroimaging data ↔ scripts/03. pipeline_denoising.py, lines 91–151 · score 0.60 · global signals, Satterthwaite, WM, CSF, denoising, regressors
  8. [8] § Results › Learning measures ↔ scripts/10. pipeline_learning.r, lines 93–168 · score 0.58 · linear mixed, learning scores, behavioural, CS, renewal, extinction
  9. [9] § Methods › Behavioural responses › Predicting individual differences ↔ scripts/12. pipeline_generalisability.r, lines 191–230 · score 0.54 · nested cross validation, fold, trained, split, LASSO, predict
  10. [10] § Methods › ROI extraction ↔ scripts/05. pipeline_SUIT.m, the whole file · a weak match · score 0.54 · cerebellar nuclei, ACPC, FreeSurfer, atlas, T1w, space
  11. [11] § Methods › Behavioural responses › Predicting individual differences ↔ scripts/11. pipeline_modelling.r, lines 191–230 · score 0.54 · nested cross validation, fold, trained, split, LASSO, predict

Paper

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The authors' code

R · 310 lines · 10 KB · no license · 3 matches

  1. library(dplyr)
  2. library(tidyr)
  3. library(tibble)
  4. library(glmnet)
  5. library(caret)
  6. library(emmeans)
  7. library(car)
  8. require(doMC)
  10. # Set-Up and Configuration
  12. cl <- parallel::makeCluster(24)
  13. doParallel::registerDoParallel(cl)
  15. norm = 1
  17. tasK = "AC"
  19. cutoff = 50
  21. root_dir = "E:"
  22. base_dir = file.path(root_dir, "data")
  23. group_dir = file.path(base_dir, "group")
  24. fig_dir = file.path(root_dir,"figures")
  26. # load helper functions: normData, scale2, composite_score, etc.
  27. source(file.path(base_dir,"scripts","misc_funs.R"))
  29. # Data Loading and Preprocessing
  31. dat_learn_EDA = read.csv(file.path(base_dir, "datasets", "stat-bf_desc-EDA_df.csv"), sep='\t')
  33. dat_learn_beh = read.csv(file.path(base_dir, "datasets", "stat-logit_desc-beh_df.tsv"), sep='\t')
  34. dat_learn = bind_rows(dat_learn_EDA, dat_learn_beh) %>% filter(!is.na(learning))
  36. covariates = c("age","sex")
  38. demographics = read.csv2(file.path(base_dir, "datasets", "demographics.csv"), sep='\t')
  40. comb_AG = gen_combs(unique(dat_learn$AG))
  41. comb_AG = list(
  42. # c("A08"), # PL
  43. # c("A03"), # FLr
  44. # c("A03","A05"), # FLc
  45. # c("A03","A05","A09"),# FLs
  46. # c("A02","A03","A05","A09"), # FLel
  47. # c("A03","A05","A09","A12"), # FLst
  48. # c("A02","A03","A05","A09","A12"), # FL
  49. c("A02","A03","A05","A08","A09","A12")) # All
  52. # dupls = read.csv(file.path(base_dir, "desc-duplicates_df.tsv"), sep='\t')
  53. # excl_subs = dupls[dupls$keep==0,c("participant","AG","study")]
  54. # dat_learn = anti_join(dat_learn, excl_subs, by=c("participant","AG","study"))
  56. dat_learn_corrs = dat_learn %>% group_by(AG,study,task) %>% group_modify(~ normData(.x))
  58. df_learndemo = demographics %>%
  59. right_join(dat_learn, by=c("participant","AG","study","task"), multiple='all')
  61. # dat_excl_conn = read.csv(file.path(base_dir, "desc-ExcludeSubsConn_table.tsv"), sep='\t')
  63. # Connectivity Data Handling
  65. dat_FC = read.csv(file.path(base_dir, "datasets", "desc-FC_df.tsv"), sep='\t')
  66. # dat_FC = subset(dat_FC, !(participant %in% dat_excl_conn[dat_excl_conn$pipeline=="FC",]$participant_id))
  68. dat_DTI = read.csv(file.path(group_dir, "SC", "desc-SC_df.tsv"), sep='\t')
  69. dat_DTI = subset(dat_DTI, !(participant %in% dat_excl_conn[dat_excl_conn$pipeline=="DTI",]$participant_id))
  71. dat_spDCM = read.csv(file.path(group_dir,"EC","desc-EC_df.tsv"), sep='\t')
  72. dat_spDCM = subset(dat_spDCM, !(participant %in% dat_excl_conn[dat_excl_conn$pipeline=="spDCM",]$participant_id))
  74. df_comb = data.frame(); df_coefs = data.frame(); df_pred = data.frame(); df_counts = data.frame()
  76. # Group-wise and Modality-wise Processing
  78. l = list(FC=dat_FC, SC=dat_DTI, EC=dat_spDCM)
  79. l = list(FC=dat_FC)
  80. for (nAG in 1:length(comb_AG)) {
  81. AGs = comb_AG[[nAG]]
  83. for (n in 1:length(l)) {
  85. if (names(l[n]) == 'FC') {
  86. metric = c('corrLW','xcorr','EuclideanDist','ManhattanDist','WassersteinDist','dtw','MI','mscohe','wavcohe')
  87. inv = c('EuclideanDist','ManhattanDist','WassersteinDist','dtw')
  88. absl = c('corrLW')
  89. dat_conn = dat_FC
  90. pair='pair_und'
  91. } else if (names(l[n]) == 'SC') {
  92. metric = 'streamlines'
  93. dat_conn = dat_DTI
  94. pair='pair_und'
  95. } else {
  96. metric = 'spDCM'
  97. dat_conn = dat_spDCM
  98. pair='pair_dir'
  99. }
  101. dat_conn = subset(dat_conn, hemisphere!="bilateral")
  103. df_join = right_join(df_learndemo, dat_conn, by=c("participant","AG","study"), multiple='all')
  105. ####### CHOOSE WHICH AGs TO INCLUDE IN THE ANALYSIS HERE #######
  106. df_join = df_join %>% subset(AG %in% AGs)
  107. if (dim(df_join)[1]==0) {next}
  108. ################################################################
  110. df_sel = df_join %>% group_by(participant, AG, study) %>%
  111. filter(if_all(!!metric, ~ all(!is.na(.x))))
  113. # df_sel will contain the final sample - that is, excluding subs with any NAs
  114. # Calculate the composite score
  115. if (names(l[n]) == 'FC') {
  116. df_sel = composite_score(df_sel, cols=metric, inv=inv, absl=absl, keep_ori="corrLW")
  117. metric = "composite"
  118. }
  120. selvars = unique(c("participant","AG","study","task",pair,metric,"learning",covariates))
  122. # Standardisation of Learning/connectivity Estimates
  123. df = df_sel[selvars] %>% group_by(AG,study,task,across(all_of(pair))) %>% group_modify(~ normData(.x)) %>%
  124. pivot_wider(names_from=all_of(pair), values_from=all_of(metric), values_fn=mean) %>% ungroup()
  126. if (names(l[n]) == 'EC') {
  127. mVp = df_sel %>% group_by(participant, AG, study) %>% mutate(mVp=mean(spDCM_Var)) %>%
  128. select(participant, AG, study, mVp) %>% distinct()
  129. mVp$mVp = 1-range01(mVp$mVp)
  130. df = left_join(df, select(mVp, mVp))
  131. }
  133. df$const <- factor(rep(1, each=length(df$participant)))
  135. df_acq = subset(df, task == "acquisition")
  136. df_ext = subset(df, task == "extinction")
  137. df_ren = subset(df, task == "renewal")
  139. if (tasK=="AC") {
  140. mdf = df_acq
  141. } else if (tasK=="EX") {
  142. mdf = df_ext
  143. } else {
  144. mdf = df_ren
  145. }
  147. pairs = colnames(mdf %>% dplyr::select(starts_with(
  148. c('AMY','CEB','HIP','ACC','PFC','lAMY','lCEB','lHIP','lACC','lPFC','rAMY','rCEB','rHIP','rACC','rPFC'))))
  150. # LASSO Regression Model Setup
  152. y <- mdf$learning
  153. xx <- mdf %>% ungroup() %>% dplyr::select(all_of(c(pairs,covariates)))
  154. x = data.matrix(makeX(xx, na.impute = TRUE))
  155. myalpha = 1
  157. if (!is.null(covariates)) {
  158. force.vars = as.integer(!Reduce('|', lapply(covariates, function(y) startsWith(as.character(colnames(x)), y))))
  159. } else {
  160. force.vars = rep(1, ncol(xx))
  161. }
  163. # Cross-Validation and Lambda Optimization
  165. tymea = "mse"
  166. lambda_max <- max(abs(colSums(x*y,na.rm=T)))/nrow(x)
  167. epsilon <- .0001
  168. K <- 1000
  169. lambdapath <- round(exp(seq(log(lambda_max), log(lambda_max*epsilon), length.out = K)), digits = 10)
  170. lbpath = lambdapath
  172. ls = foreach(i = 1:100, .combine='rbind', .packages="glmnet") %dopar% {
  173. fit <- cv.glmnet(x, y, alpha=myalpha, nfolds=10, standardize=T, penalty.factor=force.vars,
  174. type.measure=tymea, parallel=T, lambda=lbpath)
  175. errors = data.frame(fit$lambda,fit$cvm)
  176. }
  178. ls <- aggregate(ls[, 2], list(ls$fit.lambda), mean)
  180. bestindex = which(ls[2]==min(ls[2]))[1]
  181. lbd = ls[bestindex,1]
  183. if (names(l[n]) == 'EC') {
  184. ws = 1-mdf$mVp
  185. } else {ws = NULL}
  188. # df_res = dataf(x,y,lbd,force.vars,tymea,ws,names(l[n]),df_pred)
  189. # final_fit = rbind(df_comb,df_res)
  191. # Custom Nested Cross-Validation
  192. fit_lasso_cus = function (x, y, lambda, penalty.factor, type.measure, weights) {
  194. results = data.frame()
  195. final_df = data.frame()
  196. pred_df = data.frame()
  198. for (i in 1:10) {
  199. cv_folds <- vfold_cv(data.frame(x), v = 10)
  201. for (j in seq_along(cv_folds$splits)) {
  202. split <- cv_folds$splits[[j]]
  204. train_data <- analysis(split)
  205. test_data <- assessment(split)
  207. x_train <- data.matrix(train_data)
  208. y_train <- y[as.integer(rownames(train_data))]
  210. x_test <- data.matrix(test_data)
  211. test_idx <- as.integer(rownames(test_data))
  212. y_test <- y[test_idx]
  214. # Fit model
  215. fit <- glmnet(x_train, y_train, alpha = 1, lambda = lambda, penalty.factor = penalty.factor,
  216. type.measure=type.measure, weights=weights)
  218. # Predict on test set
  219. y_pred <- predict(fit, newx = x_test)
  221. # Save predictions
  222. pred_df = bind_rows(pred_df,
  223. data.frame(
  224. iteration = i,
  225. fold = j,
  226. subject = test_idx,
  227. y_true = y_test,
  228. y_pred = as.vector(y_pred)
  229. )
  230. )
  232. # Save coefficients
  233. coefs = coef(fit)
  234. coef_df = as.data.frame(as.matrix(coefs)) %>%
  235. rownames_to_column("connection") %>%
  236. rename(coefficient = 2) %>%
  237. filter(coefficient != 0) %>%
  238. mutate(iteration = i, fold = j)
  240. final_df <- bind_rows(final_df, coef_df)
  241. }
  242. }
  244. group_df = final_df %>%
  245. group_by(connection) %>%
  246. summarise(
  247. times_selected = n(),
  248. avg_coefficient = mean(coefficient)
  249. ) %>%
  250. arrange(desc(times_selected))
  252. return(group_df)
  254. }
  256. ffit = fit_lasso_cus(x,y,lbpath,force.vars,tymea,ws) %>% filter(times_selected > cutoff)
  257. ffit$mod=names(l[n])
  258. ffit$group=paste(AGs,collapse='_')
  260. # Poisson regression
  262. # Set-Up and Parallelized Simulation
  264. tp = foreach(niter = 1:10, .combine='rbind', .packages=c("dplyr","doMC","tibble","glmnet")) %dopar% {
  266. mdf2 = mdf[sample(nrow(mdf), 80),]
  268. lambda_max <- max(abs(colSums(x*y,na.rm=T)))/nrow(x)
  269. lbpath <- round(exp(seq(log(lambda_max), log(lambda_max*epsilon), length.out = K)), digits = 10)
  271. # LASSO Regression and Feature Selection
  273. ls = foreach(i = 1:100, .combine='rbind', .packages="glmnet") %dopar% {
  274. fit <- cv.glmnet(x, y, alpha=myalpha, nfolds=10, standardize=T, penalty.factor=force.vars,
  275. type.measure=tymea, parallel=T, lambda=lbpath)
  276. errors = data.frame(fit$lambda,fit$cvm)
  277. }
  279. ls <- aggregate(ls[, 2], list(ls$fit.lambda), mean)
  281. bestindex = which(ls[2]==min(ls[2]))[1]
  282. lbd = ls[bestindex,1]
  284. if (names(l[n]) == 'EC') {
  285. ws = 1-mdf2$mVp
  286. } else {ws = NULL}
  288. fit.lasso = glmnet(x=x,y=y,lambda=lbd, alpha=myalpha, penalty.factor=force.vars, type.measure=tymea,
  289. weights=ws)
  290. results = coef(fit.lasso)
  292. df_res = as.data.frame(as.matrix(results)) %>% rownames_to_column(var="connection")
  294. preds = df_res$connection
  296. # Feature Region Analysis
  297. df_tempc = data.frame(G = niter, AMY=length(grep("AMY", preds)), ACC=length(grep("ACC", preds)), HIP=length(grep("HIP", preds)), PFC=length(grep("PFC", preds)),
  298. CEB=length(grep("CEB", preds)), mod=names(l[n]))
  300. }
  301. df_counts = rbind(df_counts,tp)
  302. }
  303. }
  304. # Statistical Comparison
  306. m = df_counts %>% pivot_longer(cols = -c(G,mod)) %>% group_by(G,name)
  308. model <- glmer(value ~ name + (1|G), family=poisson, data=subset(m, mod==mg))
  309. summary(model)
  310. em = emmeans(model, pairwise ~ name, adjust="fdr")

11. pipeline_modelling.r at commit fee4711, no license · at the source

Overview

Authors: C. A. Gomes1,2,3,4, D. R. Bach5,6, A. Razi5,7,8,9, G. Batsikadze2,3,4, S. Elsenbruch10, H. Engler3,11, T. M. Ernst2,3,4, M. C. Fellner1, C. Fraenz12, E. Genç12, A. Klass13, F. Labrenz10, S. Lissek13, C. J. Merz14, D. Metzen15, A. Nostadt13,16, R. J. Pawlik2,3, J. E. Schneider1,17, M. Tegenthoff13, A. Thieme2,3
and 7 other authorsO. T. Wolf14, O. Güntürkün4,18,19, H. H. Quick4,20, R. Kumsta17, D. Timmann2,3,4, T. Spisak3, N. Axmacher1,4
ORCID iDs: C. A. Gomes, D. R. Bach, A. Razi, G. Batsikadze, S. Elsenbruch, H. Engler, T. M. Ernst, M. C. Fellner, F. Labrenz, S. Lissek, C. J. Merz, A. Nostadt, J. E. Schneider, A. Thieme, O. T. Wolf, O. Güntürkün, R. Kumsta, T. Spisak, N. Axmacher
20 affiliations
  1. Department of Neuropsychology, Ruhr University Bochum,Bochum, Germany
  2. Department of Neurology, University Hospital Essen,Essen, Germany
  3. Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen,Essen, Germany
  4. Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen,Essen, Germany
  5. Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London,London, UK
  6. University of Bonn, Transdisciplinary Research Area “Life & Health”, Centre for Artificial Intelligence and Neuroscience,Bonn, Germany
  7. Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University,Clayton, Australia
  8. Monash Biomedical Imaging, Monash University,Clayton, Australia
  9. CIFAR Azrieli Global Scholars Program, CIFAR,Toronto, Canada
  10. Department of Medical Psychology & Medical Sociology, Ruhr University Bochum,Bochum, Germany
  11. Institute of Medical Psychology and Behavioral Immunobiology, University Hospital Essen,Essen, Germany
  12. Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors at the Technical University of Dortmund (IfADo),Dortmund, Germany
  13. Department of Neurology, BG University Hospital Bergmannsheil, Ruhr University Bochum,Bochum, Germany
  14. Department of Cognitive Psychology, Ruhr University Bochum,Bochum, Germany
  15. Institute of Psychology, Department of Educational Sciences and Psychology, TU Dortmund University,Dortmund, Germany
  16. Department of Psychiatry and Psychotherapy, Center for Mind, Brain and Behavior, Philipps-Universität Marburg,Marburg, Germany
  17. Genetic Psychology Lab, Ruhr University Bochum,Bochum, Germany
  18. Department of Biopsychology, Ruhr University Bochum,Bochum, Germany
  19. Research Center One Health Ruhr, Bochum, Germany
  20. High Field and Hybrid MR Imaging, University Hospital Essen,Essen, Germany
Institutions: Ruhr University Bochum (Germany); Essen University Hospital (Germany); University of Duisburg-Essen (Germany); University College London (United Kingdom); University of Bonn (Germany); Monash University (Australia); Canadian Institute for Advanced Research (Canada); Institut für Medizinische Psychologie (Germany); TU Dortmund University (Germany); Philipps University of Marburg (Germany)
Journal: Nature communications, volume 17, issue 1, article 3780
Dates: received 24 July 2025; accepted 30 March 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71830-0 · PMID 42026055 · PMCID PMC13109375 · OpenAlex W4410092460
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.05.04.651880
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Extinction, Human behaviour
MeSH: Cognition*, Extinction, Psychological*, Fear*, Individuality*, Learning*, Adult, Amygdala, Brain, Brain Mapping, Female, Gyrus Cinguli, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Prefrontal Cortex, Young Adult (* major topic)
Topic: Cultural Differences and Values (Social Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (DFG SFB 1280 “Extinction Learning” (Project Nr. 316803389))
Citations: not cited yet (Europe PMC); 89 references in the paper

Abstract

The ability to acquire new information and to modify previously learned knowledge are critical in an ever-changing world. However, the efficacy of learning is notably variable among individuals, with extinction learning being the epitome of such variability. Abundant studies have identified a core network of brain regions including the amygdala, hippocampus, dorsal anterior cingulate cortex (ACC), ventromedial prefrontal cortex (PFC) and, more recently, the cerebellum, as key players in learning and extinction. Yet, the precise interactions within this network and their relationship to individual learning abilities and extinction have remained largely unexplored. In the present study, we examined how functional (FC), effective (EC), and structural (SC) connectivity patterns in the core learning network allow the prediction of individual differences in the efficacy of learning, extinction, and renewal. Analysing a large dataset of over 500 participants across a multitude of paradigms, our results revealed that FC predicted better acquisition, with a central role of ACC and hippocampus, whereas SC, involving ACC and amygdala, predicted higher levels of extinction learning. EC results suggested a predominantly inhibitory coupling among core learning network nodes, with paradigm-specific EC connectivity patterns predicting learning. Our predictions not only generalised between fear and cognitive predictive learning paradigms but were also successful in predicting learning from task-related FC and simulated data. Together, these results describe the multimodal neural determinants of learning, extinction, and renewal, and may inform individualised interventions for affective disorders based on neural connectivity patterns.

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

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caadgomes/extinction-learning-study

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: fee4711f283aecc8a70bc17eb87db3336a2a5874, 28 February 2026
Languages: Python (7), MATLAB (5), R (5)
Size: 73 files, 17 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (7 files), NumPy (6 files), NiBabel (5 files), Nipype (5 files), FreeSurfer (4 files), FSL (4 files), tidyverse (4 files), Nilearn (3 files), SciPy (3 files), SPM (3 files), AFNI (2 files), ANTs (2 files), car (2 files), caret (2 files), emmeans (2 files), glmnet (2 files), scikit-learn (2 files), Brain Connectivity Toolbox (1 file), broom (1 file), ggplot2 (1 file), lmerTest (1 file), MNE-Python (1 file), MRtrix3 (1 file), pydicom (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
18 files
At the source: github.com/caadgomes/extinction-learning-study

Code availability

The code for data preprocessing and analysis is available at https://github.com/caadgomes/extinction-learning-study.

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

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Data

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Data availability

Public sharing of the raw data is not permitted because participant consent and ethics approvals do not allow unrestricted public data sharing, and the datasets are subject to institutional and legal data-sharing agreements across the contributing institutions. Raw data are available from the corresponding author upon reasonable request and subject to approval by the contributing institutions and a data-sharing agreement where required. Access will be granted to qualified researchers for non-commercial scientific research purposes; decisions are typically made within 4–6 weeks. Source data are provided with this paper.

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Recorded: type, language, journal, volume, issue, pages, dates, 27 authors, 2 keywords, 17 MeSH terms, 1 funder, 87 references.

Cite

This paper

Gomes, C. A., Bach, D. R., Razi, A., Batsikadze, G., Elsenbruch, S., Engler, H., Ernst, T. M., Fellner, M. C., Fraenz, C., Genç, E., Klass, A., Labrenz, F., Lissek, S., Merz, C. J., Metzen, D., Nostadt, A., Pawlik, R. J., Schneider, J. E., Tegenthoff, M., . . . Axmacher, N. (2026). Predicting individual differences of fear and cognitive learning and extinction. Nature communications, 17(1), 3780. https://doi.org/10.1038/s41467-026-71830-0

BibTeX

@article{gomes2026predicting,
author = {Gomes, C. A. and Bach, D. R. and Razi, A. and Batsikadze, G. and Elsenbruch, S. and Engler, H. and Ernst, T. M. and Fellner, M. C. and Fraenz, C. and Genç, E. and Klass, A. and Labrenz, F. and Lissek, S. and Merz, C. J. and Metzen, D. and Nostadt, A. and Pawlik, R. J. and Schneider, J. E. and Tegenthoff, M. and Thieme, A. and Wolf, O. T. and Güntürkün, O. and Quick, H. H. and Kumsta, R. and Timmann, D. and Spisak, T. and Axmacher, N.},
title = {{Predicting individual differences of fear and cognitive learning and extinction}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {3780},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71830-0},
url = {https://doi.org/10.1038/s41467-026-71830-0},
pmid = {42026055},
pmcid = {PMC13109375}
}

RIS

TY - JOUR
AU - Gomes, C. A.
AU - Bach, D. R.
AU - Razi, A.
AU - Batsikadze, G.
AU - Elsenbruch, S.
AU - Engler, H.
AU - Ernst, T. M.
AU - Fellner, M. C.
AU - Fraenz, C.
AU - Genç, E.
AU - Klass, A.
AU - Labrenz, F.
AU - Lissek, S.
AU - Merz, C. J.
AU - Metzen, D.
AU - Nostadt, A.
AU - Pawlik, R. J.
AU - Schneider, J. E.
AU - Tegenthoff, M.
AU - Thieme, A.
AU - Wolf, O. T.
AU - Güntürkün, O.
AU - Quick, H. H.
AU - Kumsta, R.
AU - Timmann, D.
AU - Spisak, T.
AU - Axmacher, N.
TI - Predicting individual differences of fear and cognitive learning and extinction
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/23
VL - 17
IS - 1
SP - 3780
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71830-0
UR - https://doi.org/10.1038/s41467-026-71830-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71830-0",
"type": "article-journal",
"title": "Predicting individual differences of fear and cognitive learning and extinction",
"container-title": "Nature communications",
"author": [
{
"family": "Gomes",
"given": "C. A."
},
{
"family": "Bach",
"given": "D. R."
},
{
"family": "Razi",
"given": "A."
},
{
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"given": "G."
},
{
"family": "Elsenbruch",
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},
{
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},
{
"family": "Ernst",
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},
{
"family": "Fellner",
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},
{
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"given": "C."
},
{
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},
{
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},
{
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"given": "F."
},
{
"family": "Lissek",
"given": "S."
},
{
"family": "Merz",
"given": "C. J."
},
{
"family": "Metzen",
"given": "D."
},
{
"family": "Nostadt",
"given": "A."
},
{
"family": "Pawlik",
"given": "R. J."
},
{
"family": "Schneider",
"given": "J. E."
},
{
"family": "Tegenthoff",
"given": "M."
},
{
"family": "Thieme",
"given": "A."
},
{
"family": "Wolf",
"given": "O. T."
},
{
"family": "Güntürkün",
"given": "O."
},
{
"family": "Quick",
"given": "H. H."
},
{
"family": "Kumsta",
"given": "R."
},
{
"family": "Timmann",
"given": "D."
},
{
"family": "Spisak",
"given": "T."
},
{
"family": "Axmacher",
"given": "N."
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "3780",
"DOI": "10.1038/s41467-026-71830-0",
"PMID": "42026055",
"PMCID": "PMC13109375",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71830-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
23
]
]
}
}

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