OSCR

Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Data acquisition, processing, and analysis › Data analysis ↔ Scripts/Mediation_OSF.R, lines 136–186 · score 0.63 · SRT social incongruent, latent variable, indirect, SEM, LSAS, ERT
  2. [2] § Methods › Data acquisition, processing, and analysis › Data analysis ↔ Scripts/Correlations_T1_OSF.R, lines 101–176 · score 0.54 · Benjamini Hochberg, threshold, FDR, correlations, variables
  3. [3] § Results › T1 intercorrelations ↔ Scripts/LMM_Analysis_07102025.r, lines 2588–2637 · score 0.52 · ERT_Pos, ERT_Neg, BFNE, DASS, fear, SPAI
  4. [4] § Results › T1 intercorrelations ↔ Scripts/LMM_Analysis_14012026_R1.r, lines 2605–2654 · score 0.52 · ERT_Pos, ERT_Neg, BFNE, DASS, fear, SPAI
  5. [5] § Methods › Data acquisition, processing, and analysis › Data analysis ↔ Scripts/Correlations_T1_OSF.R, lines 2–56 · score 0.51 · HF power, RMSSD, ln, HR

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 177 lines · 4.9 KB · no license · 2 matches

  2. rm(list=ls())
  4. # Load libraries
  5. library(readxl)
  6. library(dplyr)
  7. library(psych)
  8. library(corrplot)
  9. library(writexl)
  10. library(ggplot2)
  12. # 1. Read the merged dataset
  13. file_path <- "Datasets/Aggregated/SPOT_merged_all_data.xlsx"
  14. df_all <- read_excel(file_path)
  16. # 2. Subset to Time = "T1"
  17. df_t1 <- df_all %>% filter(time == "T1")
  19. # 3. Select variables of interest
  20. df_vars <- df_t1 %>%
  21. select(
  22. subject, time, group,
  23. LSAS_Total,
  24. DASS_Total,
  25. BFNE_Score,
  26. SPAI_Score,
  27. ERT_Neg,
  28. ERT_Pos,
  29. starts_with("SRT_mean"),
  30. mood_mean,
  31. starts_with("N400"),
  32. AlphaAsymmetry_EC,
  33. alpha_mean,
  34. cortisol_mean,
  35. meanhr_mean,
  36. ln_rmssd_mean,
  37. ln_hfpowfft_mean
  38. )
  40. # Rename variables
  41. df_vars <- df_vars %>%
  42. rename(
  43. LSAS = LSAS_Total,
  44. DASS = DASS_Total,
  45. BFNE = BFNE_Score,
  46. SPAI = SPAI_Score,
  47. Negative_Affect = mood_mean,
  48. Alpha_Asymmetry = AlphaAsymmetry_EC,
  49. Alpha_Amylase = alpha_mean,
  50. Cortisol = cortisol_mean,
  51. Mean_HR = meanhr_mean,
  52. RMSSD_log = ln_rmssd_mean,
  53. HF_Power_log = ln_hfpowfft_mean
  54. )
  55. df_vars <- df_vars %>%
  56. rename_with(~ gsub("mean_", "", .x), starts_with("SRT_mean_"))
  58. # 4. Keep only numeric columns for correlation
  59. numeric_vars <- df_vars %>%
  60. select(-subject, -time, -group) %>%
  61. mutate_if(is.character, as.numeric)
  63. # Count how many correlation tests (pairwise comparisons) are being FDR-adjusted
  64. k <- ncol(numeric_vars) # number of variables in the correlation set
  65. m <- k * (k - 1) / 2 # number of unique pairwise correlations/tests
  66. cat("FDR multiple-comparisons count:", m, "tests (", k, "variables )\n")
  68. # 5. Compute Pearson correlations
  69. # a) raw p-values
  70. res_raw <- corr.test(
  71. numeric_vars,
  72. use = "pairwise",
  73. method = "pearson",
  74. adjust = "none"
  75. )
  76. # b) FDR-adjusted p
  77. res_fdr <- corr.test(
  78. numeric_vars,
  79. use = "pairwise",
  80. method = "pearson",
  81. adjust = "fdr"
  82. )
  83. # c) Bonferroni-adjusted p
  84. res_bonf <- corr.test(
  85. numeric_vars,
  86. use = "pairwise",
  87. method = "pearson",
  88. adjust = "bonferroni"
  89. )
  91. # 6. Extract matrices
  92. r_matrix <- res_raw$r # same r in all three
  93. p_raw_matrix <- res_raw$p # uncorrected
  94. p_fdr_matrix <- res_fdr$p # FDR corrected
  95. p_bonf_matrix <- res_bonf$p # Bonferroni corrected
  97. # Make adjusted p-value matrices symmetric (corr.test adjusts one triangle)
  98. p_fdr_matrix[lower.tri(p_fdr_matrix)] <- t(p_fdr_matrix)[lower.tri(p_fdr_matrix)]
  99. p_bonf_matrix[lower.tri(p_bonf_matrix)] <- t(p_bonf_matrix)[lower.tri(p_bonf_matrix)]
  101. # Derive the BH-FDR "raw p-value threshold" for q < .05 for THIS set of tests
  102. # (i.e., the largest raw p that still passes Benjamini–Hochberg at alpha = .05)
  103. pvals_unique <- p_raw_matrix[upper.tri(p_raw_matrix)]
  104. pvals_unique <- pvals_unique[!is.na(pvals_unique)]
  105. m_tests <- length(pvals_unique)
  106. alpha_fdr <- 0.05
  108. if (m_tests > 0) {
  109. p_sorted <- sort(pvals_unique)
  110. crit <- (1:m_tests) / m_tests * alpha_fdr
  111. idx_max <- which(p_sorted <= crit)
  113. if (length(idx_max) > 0) {
  114. raw_p_threshold_bh <- p_sorted[max(idx_max)]
  115. cat("BH-FDR raw p-value threshold at alpha =", alpha_fdr, "is:", raw_p_threshold_bh, "\n")
  116. } else {
  117. cat("BH-FDR: no p-values are significant at alpha =", alpha_fdr, "\n")
  118. }
  119. } else {
  120. cat("BH-FDR: no valid p-values to threshold (all NA).\n")
  121. }
  123. # 7. Visualize correlations (FDR-significant only)
  125. # Define where to save
  126. output_dir_base <- "Results/T1_Correlations"
  127. if (!dir.exists(output_dir_base)) {
  128. dir.create(output_dir_base, recursive = TRUE)
  129. }
  131. # Open a file for the corrplot
  132. png(
  133. filename = file.path(output_dir_base, "T1_FDR_correlation_matrix.png"),
  134. width = 1000, # pixels
  135. height = 1000, # pixels
  136. res = 150 # resolution (ppi)
  137. )
  139. #Create plots
  140. corrplot(
  141. r_matrix,
  142. method = "color",
  143. col = colorRampPalette(c("navy", "white", "firebrick3"))(200),
  144. cl.lim = c(-1, 1),
  145. tl.col = "black",
  146. addCoef.col = "grey20",
  147. number.cex = 0.5,
  148. p.mat = p_fdr_matrix,
  149. sig.level = 0.05,
  150. insig = "blank",
  151. title = "T1 Pearson Correlations\n(FDR p < .05 shown)"
  152. )
  155. # close device
  157. # Close the file
  158. dev.off()
  159. # 8. Prepare data.frames with variable names in first column
  160. df_r <- data.frame(Variable = rownames(r_matrix), r_matrix, check.names = FALSE)
  161. df_p_raw <- data.frame(Variable = rownames(p_raw_matrix), p_raw_matrix, check.names = FALSE)
  162. df_p_fdr <- data.frame(Variable = rownames(p_fdr_matrix), p_fdr_matrix, check.names = FALSE)
  163. df_p_bonf <- data.frame(Variable = rownames(p_bonf_matrix), p_bonf_matrix, check.names = FALSE)
  166. # 9. Export all to one Excel workbook with four sheets
  167. output_path <- "Results/T1_correlations.xlsx"
  168. write_xlsx(
  169. list(
  170. Correlations = df_r,
  171. p_raw = df_p_raw,
  172. p_FDR = df_p_fdr,
  173. p_Bonferroni = df_p_bonf
  174. ),
  175. path = output_path
  176. )

Correlations_T1_OSF.R, no license · at the source

Overview

Authors: Elinor Abado1, Marius Kunna2, Felix Würtz2, Lilith Laflör2, Laura Blank2, Simon E Blackwell1, Elske Salemink3, Dirk Adolph2, Fanny Alexandra Dietel4, Oliver T Wolf5, Jürgen Margraf2, Marcella L Woud1,2
ORCID iDs: Elinor Abado, Marius Kunna, Felix Würtz, Lilith Laflör, Laura Blank, Simon E Blackwell, Elske Salemink, Dirk Adolph, Fanny Alexandra Dietel, Oliver T Wolf, Jürgen Margraf, Marcella L Woud
  1. Department of Clinical Psychology and Experimental Psychopathology, Georg-Elias-Müller-Institute of Psychology, University of Göttingen, Göttignen, Germany
  2. Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
  3. Department of Clinical Psychology, University of Utrecht, Utrecht, Netherlands
  4. Department of Clinical Psychology and Psychotherapy, University of Osnabrück, Osnabrück, Germany
  5. Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
Institutions: University of Göttingen (Germany); Ruhr University Bochum (Germany); Utrecht University (Netherlands); Osnabrück University (Germany)
Journal: Translational psychiatry, volume 16, issue 1, article 279
Dates: received 10 October 2025; accepted 13 May 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04122-2 · PMID 42173859 · PMCID PMC13197415 · OpenAlex W4415058941
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/2bv3x_v1
Status: code verified
Categories: human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Evoked potentials, Physiology & signal measures
Keywords: Human behaviour, Physiology
MeSH: Anxiety*, Cognitive Behavioral Therapy*, Phobia, Social*, Adult, Autonomic Nervous System, Fear, Female, Humans, Male, Young Adult (* major topic)
Topic: Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (442163275)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

Cognitive Bias Modification – Interpretation (CBM-I) aims to alter maladaptive interpretations in social anxiety, yet effects are often small and outcome measures are diverse. Although CBM-I has shown promise, its underlying mechanisms remain unclear and integration with psychophysiological and neural measures has been limited. In this randomized controlled trial, eighty-eight participants with high levels of social anxiety completed two lab sessions, an online training in between, and online follow-up. Participants filled out questionnaires, completed interpretation bias tasks, and underwent neuro-psychophysiological assessments. Active CBM-I trained positive resolutions of ambiguous social scenarios, while the sham version used neutral scenarios. The primary outcome, i.e., scores on the Liebowitz Social Anxiety Scale (LSAS), decreased across time in both groups, without group differences. However, the Brief Fear of Negative Evaluation decreased only in the active group. Interpretation bias shifted more strongly toward positive outcomes in the active group. Autonomic measures confirmed sensitivity to stress induction but did not differentiate between conditions. Electrophysiological results paralleled subjective ratings, as participants exhibited ambivalent responses to socially relevant stimuli but clearly differentiated responses toward neutral stimuli. Baseline correlations indicated strong convergence across self-report and interpretation tasks. Mediation analyses showed that reductions in negative interpretations mediated the effect of the training group on LSAS scores at follow-up. These findings identify interpretation bias as a modifiable mechanism underlying social anxiety and underscore its role as a transdiagnostic marker. Targeting interpretation bias through easily accessible and applicable online interventions may strengthen preventive and therapeutic approaches for social anxiety and related disorders.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF e254p

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (6), MATLAB (1)
Size: 56 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), car (4 files), broom (2 files), emmeans (2 files), ggplot2 (2 files), lavaan (2 files), nlme (2 files), EEGLAB (1 file), ERPLAB (1 file), ICLabel (1 file), psych (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/e254p/?view_only=13bc5c6b202b4083a86917100a351fa6

Code availability

This randomized controlled trial (RCT) was preregistered on ClinicalTrials.gov (Identifier: NCT05798078 (https://clinicaltrials.gov/ct2/show/NCT05798078); https://clinicaltrials.gov/study/NCT05798078), and all methods and planned analyses were preregistered on the Open Science Framework (OSF). Scripts used for data analysis can also be found on the OSF (same link as “Data Availability”). Unless stated otherwise, all methodological details are in line with the preregistration.

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;
  • 7 scripts, each with its path and the digest of its content;
  • 5 matches 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.

Data availability

Anonymized data, along with pre-processing and analysis pipelines, are available on the Open Science Framework (OSF): https://osf.io/e254p/?view_only=13bc5c6b202b4083a86917100a351fa6.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 2 keywords, 10 MeSH terms, 1 funder, 51 references.

Cite

This paper

Abado, E., Kunna, M., Würtz, F., Laflör, L., Blank, L., Blackwell, S. E., Salemink, E., Adolph, D., Dietel, F. A., Wolf, O. T., Margraf, J., & Woud, M. L. (2026). Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial. Translational psychiatry, 16(1), 279. https://doi.org/10.1038/s41398-026-04122-2

BibTeX

@article{abado2026multi,
author = {Abado, Elinor and Kunna, Marius and Würtz, Felix and Laflör, Lilith and Blank, Laura and Blackwell, Simon E and Salemink, Elske and Adolph, Dirk and Dietel, Fanny Alexandra and Wolf, Oliver T and Margraf, Jürgen and Woud, Marcella L},
title = {{Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {279},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04122-2},
url = {https://doi.org/10.1038/s41398-026-04122-2},
pmid = {42173859},
pmcid = {PMC13197415}
}

RIS

TY - JOUR
AU - Abado, Elinor
AU - Kunna, Marius
AU - Würtz, Felix
AU - Laflör, Lilith
AU - Blank, Laura
AU - Blackwell, Simon E
AU - Salemink, Elske
AU - Adolph, Dirk
AU - Dietel, Fanny Alexandra
AU - Wolf, Oliver T
AU - Margraf, Jürgen
AU - Woud, Marcella L
TI - Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/22
VL - 16
IS - 1
SP - 279
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04122-2
UR - https://doi.org/10.1038/s41398-026-04122-2
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Multi-session CBM-I for social anxiety: examining psychopathology, cognitive, neural, and psychophysiological effects in a randomized controlled trial",
"container-title": "Translational psychiatry",
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"date-parts": [
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