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Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study.

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2 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 2 matches
  1. [1] § Materials and Methods › Data Analysis › ROI Analysis ↔ Data_and_scripts/ROI_analysis/ROI_analysis.R, lines 1–47 · score 0.86 · anterior division, frontal pole, fusiform gyrus, cingulate gyrus, frontal medial cortex, parahippocampal gyrus
  2. [2] § Materials and Methods › Data Analysis › ROI Analysis ↔ Data_and_scripts/ROI_analysis/ROI_analysis.R, lines 49–89 · score 0.52 · aov_ez, post hoc, emmeans, mixed, Bonferroni, ANOVAs

Paper

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The authors' code

R · 151 lines · 6.3 KB · no license · 2 matches

  1. ##########################
  2. ## ROI Final Analysis ##
  3. ##########################
  4. # Load necessary libraries
  5. library(tidyverse) # Data manipulation and visualization
  6. library(ggpubr) # Publication-ready plots
  7. library(ggplot2) # Data visualization
  8. library(emmeans) # Estimated marginal means for post-hoc tests
  9. library(rstatix) # Statistical tests and summaries
  10. library(marginaleffects) # Marginal effects analyses
  11. library(afex) # ANOVA and mixed models
  13. # Load data from CSV files
  14. roi_data <- read.csv(".../ROI_analysis_data.csv")
  15. Ids_data <- read.csv(".../IDs_matching.csv", sep = ";")
  16. Participants_data <- read.csv(".../participants_data_final.csv", sep = ",")
  18. # Check number of observations per ROI
  19. table(roi_data$ROI)
  21. # Convert IDs to numeric for joining
  22. roi_data$Sub <- as.numeric(roi_data$Sub)
  23. Ids_data$Analysis_id <- as.numeric(Ids_data$Analysis_id)
  25. # Join ROI data with IDs
  26. roi_combined_with_ids <- roi_data %>%
  27. left_join(Ids_data, by = c("Sub" = "Analysis_id"))
  29. # Join above with participant data
  30. roi_combined_full <- roi_combined_with_ids %>%
  31. left_join(Participants_data, by = "Sub_id")
  33. # Convert variables to factors as appropriate
  34. roi_combined_full$Sub <- as.factor(roi_combined_full$Sub)
  35. roi_combined_full$Group <- as.factor(roi_combined_full$Group)
  36. roi_combined_full$Condition <- as.factor(roi_combined_full$Condition)
  38. ##################
  40. # List of ROIs analyzed
  41. roi_list <- c("Cingulate Gyrus anterior division",
  42. "Frontal Medial Cortex",
  43. "Frontal Pole",
  44. "Left Amygdala",
  45. "Right Amygdala",
  46. "Parahippocampal Gyrus",
  47. "Fusiform gyrus")
  49. # Loop through each ROI and run ANOVA + post hoc tests + plots
  50. for (roi_name in roi_list) {
  52. # Run mixed ANOVA with Condition (within) and Group (between)
  53. anova <- aov_ez(
  54. id = "Sub",
  55. dv = "Mean_Beta",
  56. within = "Condition",
  57. between = "Group",
  58. data = roi_combined_full[roi_combined_full$ROI == roi_name,])
  60. # Print ANOVA results for current ROI
  61. cat(roi_name, "\n")
  62. print(get_anova_table(anova))
  63. cat("\n \n \n")
  65. # If main effect of Condition is significant, run and print post-hoc tests
  66. if (anova$anova_table["Condition","Pr(>F)"] < 0.05) {
  67. posthoc_1 <- emmeans(anova, specs = pairwise ~ Condition, adjust = "bonferroni")
  68. cat("\nPost-hoc tests main effect of Emotion:\n")
  69. print(posthoc_1$contrasts)
  70. cat("\n \n \n")
  71. }
  73. # Prepare data for plotting
  74. roi_data <- roi_combined_full %>%
  75. filter(ROI == roi_name)
  77. # Create barplot for mean activation by condition
  78. p <- ggplot(roi_data, aes(x = Condition, y = Mean_Beta, fill = Condition)) +
  79. stat_summary(fun = mean, geom = "bar", position = position_dodge(0.9), width = 0.7) +
  80. stat_summary(fun.data = mean_se, geom = "errorbar", position = position_dodge(0.9),
  81. width = 0.2) +
  82. scale_fill_manual(values = c("#FD8C80","#A1D3FF","#FFD0A1","#CBCBCB"), name = "Emotion") +
  83. labs(title = paste("Mean Activation:", roi_name),
  84. x = "Condition",
  85. y = "Mean Activation") +
  86. theme_minimal() +
  87. theme(axis.text.x = element_text(angle = 45, hjust = 1),
  88. legend.position = "top")
  89. print(p)
  91. # If interaction effect Group by Condition is significant, do group plots and post-hoc
  92. if (anova$anova_table["Group:Condition","Pr(>F)"] < 0.05) {
  93. # Bar plot by Group and Condition
  94. p_group <- ggplot(roi_data, aes(x = Group, y = Mean_Beta, fill = Condition)) +
  95. stat_summary(fun = mean, geom = "bar", position = position_dodge(0.9), width = 0.7) +
  96. stat_summary(fun.data = mean_se, geom = "errorbar", position = position_dodge(0.9),
  97. width = 0.2) +
  98. scale_fill_manual(values = c("#FD8C80","#A1D3FF","#FFD0A1","#CBCBCB"), name = "Emotion") +
  99. labs(title = paste("Mean Activation by Group:", roi_name),
  100. x = "Group",
  101. y = "Mean Activation") +
  102. theme_minimal() +
  103. theme(axis.text.x = element_text(angle = 45, hjust = 1),
  104. legend.position = "top")
  106. print(p_group)
  108. # Post hoc tests for Group x Condition interaction
  109. posthoc_2 <- emmeans(anova, specs = pairwise ~ Condition | Group, adjust = 'bonferroni')
  110. posthoc_3 <- emmeans(anova, specs = pairwise ~ Group | Condition, adjust = 'bonferroni')
  111. cat("\nPost-hoc tests for interaction:\n")
  112. print(posthoc_2$contrasts)
  113. print(posthoc_3$contrasts)
  114. }
  115. }
  117. # Additional plotting for selected ROIs side-by-side
  118. library(patchwork)
  120. roi_list <- c("Frontal Medial Cortex", "Parahippocampal Gyrus")
  122. plots <- list()
  124. for (roi_name in roi_list) {
  125. roi_data <- roi_combined_full %>%
  126. filter(ROI == roi_name) %>%
  127. mutate(Group = factor(Group, levels = c("Users", "Non-users")))
  129. p_group <- ggplot(roi_data, aes(x = Group, y = Mean_Beta, fill = Condition)) +
  130. stat_summary(fun = mean, geom = "bar", position = position_dodge(0.9), width = 0.7) +
  131. stat_summary(fun.data = mean_se, geom = "errorbar", position = position_dodge(0.9), width = 0.2, size = 0.5) +
  132. scale_fill_manual(values = c("#FD8C80", "#A1D3FF", "#FFD0A1", "#CBCBCB"), name = "Emotion") +
  133. labs(title = roi_name, x = "Group", y = "Mean Activation") +
  134. theme_minimal(base_size = 24) +
  135. theme(legend.position = "right")
  136. plots[[roi_name]] <- p_group
  137. }
  139. # Add significance annotations to plots
  140. plots[[1]] <- plots[[1]] +
  141. geom_signif(y_position = c(-270), xmin = c(1.65), xmax = c(2.1), annotation = c("***"), tip_length = -0.002, size = 1.4, textsize = 9, vjust = 1.7) +
  142. geom_signif(y_position = c(-239), xmin = c(1.9), xmax = c(2.1), annotation = c("**"), tip_length = -0.002, size = 1.4, textsize = 9, vjust = 1.7)
  144. plots[[2]] <- plots[[2]] +
  145. geom_signif(y_position = c(-255), xmin = c(1.65), xmax = c(2.1), annotation = c("***"), tip_length = -0.002, size = 1.4, textsize = 9, vjust = 1.7) +
  146. geom_signif(y_position = c(-230), xmin = c(1.9), xmax = c(2.1), annotation = c("***"), tip_length = -0.002, size = 1.4, textsize = 9, vjust = 1.7) +
  147. geom_signif(y_position = c(-280), xmin = c(1.65), xmax = c(2.35), annotation = c("*"), tip_length = -0.002, size = 1.4, textsize = 9, vjust = 1.7)
  149. # Combine the two plots side by side and print
  150. combined_plot <- plots[[1]] + plots[[2]] + plot_layout(ncol = 2)
  151. print(combined_plot)

ROI_analysis.R, no license · at the source

Overview

Authors: Paweł Orłowski1,2, Aleksandra Domagalik1, Michał Bola1
ORCID iDs: Paweł Orłowski, Aleksandra Domagalik, Michał Bola
  1. Centre for Brain Research Jagiellonian University Kraków Poland
  2. Doctoral School in the Social Sciences Jagiellonian University Kraków Poland
Institutions: Jagiellonian University (Poland)
Journal: Human brain mapping, volume 47, issue 5, article e70522
Dates: received 22 October 2025; accepted 26 March 2026; published online 9 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/hbm.70522 · PMID 41954041 · PMCID PMC13063118 · OpenAlex W7152618435
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging
Keywords: classic psychedelics, emotional reactivity, emotions, fMRI, long‐term effects, naturalistic use
MeSH: Cerebral Cortex*, Emotions*, Facial Recognition*, Hallucinogens*, Adult, Cross-Sectional Studies, Facial Expression, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: Narodowe Centrum Nauki (2020/39/O/HS6/01545)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

Classic psychedelics profoundly alter emotional states, inducing intense acute experiences lasting hours, followed by subtler, longer‐lasting changes in emotional reactivity that can persist for weeks. While experimental and clinical studies document these prolonged effects, the highly context‐dependent nature of psychedelic experiences leaves open the question of whether naturalistic, nonclinical use similarly modulates emotional processing. To investigate this, we conducted a preregistered, cross‐sectional fMRI study comparing experienced psychedelic users (≥ 10 lifetime uses; N = 33) with closely matched nonusers (N = 34). Participants performed an emotional face recognition task, and we examined behavioral performance and neural responses to angry, happy, and fearful facial expressions. Behavioral results revealed that psychedelic users recognized angry expressions more quickly and accurately, indicating enhanced processing efficiency for threat‐related stimuli. Consistent with this, whole‐brain fMRI analyses showed reduced activation to anger in key limbic and salience network regions. Psychedelic users also exhibited heightened responses to happy expressions in parietal and sensorimotor cortices—aligning with prior clinical observations—as well as increased precuneus activation to fearful expressions. Region‐of‐interest analyses further demonstrated reduced differentiation between emotional categories in two default mode network nodes: the frontal medial cortex and parahippocampal gyrus. These findings provide a nuanced characterization of neurofunctional changes in emotional processing linked to repeated naturalistic psychedelic use. By bridging clinical and real‐world contexts, this work deepens our understanding of the potential long‐term consequences of psychedelics and complements existing evidence from controlled therapeutic settings.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF 46gmu

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 9 files, 1 script
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: afex (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), patchwork (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/46gmu

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

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;
  • 1 script, each with its path and the digest of its content;
  • 2 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 Statement

The statistical models of the whole‐brain analysis as well as the data and scripts used for the ROI analyses are accessible in the OSF repository (https://osf.io/46gmu). Data from earlier stages of the analysis is available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY-NC), 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, 3 authors, 6 keywords, 12 MeSH terms, 1 funder, 46 references.

Cite

This paper

Orłowski, P., Domagalik, A., & Bola, M. (2026). Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study. Human brain mapping, 47(5), e70522. https://doi.org/10.1002/hbm.70522

BibTeX

@article{orowski2026investigating,
author = {Orłowski, Paweł and Domagalik, Aleksandra and Bola, Michał},
title = {{Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study}},
journal = {Human brain mapping},
year = {2026},
month = apr,
volume = {47},
number = {5},
pages = {e70522},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70522},
url = {https://doi.org/10.1002/hbm.70522},
pmid = {41954041},
pmcid = {PMC13063118}
}

RIS

TY - JOUR
AU - Orłowski, Paweł
AU - Domagalik, Aleksandra
AU - Bola, Michał
TI - Investigating Emotional Reactivity in Experienced Users of Psychedelics: A Cross-Sectional fMRI Study
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/04/01
VL - 47
IS - 5
SP - e70522
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70522
UR - https://doi.org/10.1002/hbm.70522
LA - en
ER -

CSL-JSON

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