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Human risk taking and metabolic state: No credible evidence for desacyl-ghrelin modulation of neural or behavioural effects.

Code ↔ Paper

3 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 3 matches
  1. [1] § Results › Behavioural results ↔ src/visualization/plotAssist.R, lines 252–274 · score 0.61 · extreme evidence, moderate evidence, anecdotal evidence
  2. [2] § Methods › Neuroimaging analysis › Choice effects ↔ src/SPM_functions/hg_secondLevel_flexibleFactorial_choice.m, lines 18–46 · score 0.51 · flexible factorial model, onset
  3. [3] § Methods › Neuroimaging analysis › Choice effects ↔ src/SPM_functions/hn_secondLevel_flexibleFactorial_choice.m, lines 18–46 · score 0.51 · flexible factorial model, onset

Paper

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The authors' code

R · 338 lines · 10 KB · no license · 1 match

  1. # Functions: Plot assist -- Version 6.1
  2. # Last edit: 2026/01/23
  3. # Author: Geysen, Steven (SG)
  4. # Notes: - Function script for plotting
  5. # - Functions:
  6. # * Plot theme for PhD
  7. # * Plot theme for risky choice project
  8. # * Difference plot
  9. # * Connected boxplot
  10. # * Directed Bayes factor
  11. # * Outlier calculation
  12. # * Quantile interval
  13. # * Modified pairs
  14. # - Release notes:
  15. # * Recode condition in prepdata
  16. # To do: - Keep up to date
  17. # - Enable method change in panel_cor
  18. # Comments: SG: Load with ´source('src/visualization/plotAssist.R')´.
  19. # SG: I modified the output of
  20. # PerformanceAnalytics::chart.Correlation so that the fitted
  21. # line is a linear regression instead of the smooth fitted
  22. # line.
  23. # Sources: Marsman and Wagenmakers (2017; https://doi.org/10.1177/0013164416669201 )
  24. # https://www.r-bloggers.com/2022/08/how-to-label-outliers-in-boxplots-in-ggplot2/
  25. # https://www.datamentor.io/r-programming/return-function
  26. # https://rpubs.com/mclaire19/ggplot2-custom-themes
  27. # https://joeystanley.com/blog/custom-themes-in-ggplot2/
  28. # Etz and colleagues (2024; https://doi.apa.org/doi/10.1037/met0000660 )
  29. # https://www.elenadudukina.com/post/iterative-ggplotting/2021-06-20-iterative-plotting/
  30. # Regression lines in pairs plot ( https://stackoverflow.com/a/49289436/21564504 )
  31. # https://github.com/braverock/PerformanceAnalytics/blob/49a93f1ed6e2e159b63bf346672575f3634ed370/R/chart.Correlation.R
  32. # ------------
  36. #### Imports ####
  39. # Libraries
  40. library(bayestestR)
  44. #### Variables ####
  45. ###################
  48. # Figure values
  49. ## Individual point size
  50. ipos <- 4
  51. ## Jitter width
  52. jitw <- 0.1
  53. ## Jitter seed
  54. jits <- 21
  58. #### Functions ####
  59. ###################
  62. theme_phd <- function(media = "txt") {
  63. # https://rpubs.com/mclaire19/ggplot2-custom-themes
  64. # https://joeystanley.com/blog/custom-themes-in-ggplot2/
  66. bsize <- 32
  67. if (media == "ppt") {
  68. bsize <- 48
  69. }
  71. theme_classic(base_size = bsize) %+replace%
  72. theme(
  73. plot.title = element_text(hjust = 0.5),
  74. plot.tag = element_text(),
  75. axis.title.y = element_text(angle = 90, vjust = 0.5),
  76. panel.grid.major = element_blank(),
  77. panel.grid.minor = element_blank(),
  78. panel.background = element_blank(),
  79. axis.line = element_line(colour = "black")
  80. )
  81. }
  84. theme_risky <- function(datalabel) {
  85. # Plot theme for risky choice project
  86. # Standardised colour scheme and legend labels for the different data sets of
  87. # the risky choice project.
  89. bl_colour <- '#1f77b4'
  91. if (datalabel == "hg") {
  92. subtit <- "Study 1"
  93. subtag <- "A"
  94. coco <- "#ff9896"
  95. dico <- "#8F88A5"
  96. blab_short <- "SAT"
  97. colab_short <- "FAS"
  98. blab_long <- "Sated"
  99. colab_long <- "Fasted"
  100. } else if (datalabel == "hn") {
  101. subtit <- "Study 2"
  102. subtag <- "B"
  103. coco <- "#98df8a"
  104. dico <- "#5CAB9F"
  105. blab_short <- "NNS"
  106. colab_short <- "TSD"
  107. # blab_long <- "Night of normal sleep"
  108. blab_long <- "Normal sleep"
  109. # colab_long <- "Total sleep deprivation"
  110. colab_long <- "Sleep deprivation"
  111. }
  112. else{
  113. print("Theme not available.")
  114. }
  116. legend_labels_short <- c(blab_short, colab_short)
  117. legend_labels_long <- c(blab_long, colab_long)
  119. return(
  120. list(
  121. "subtit" = subtit, "subtag" = subtag,
  122. "bl_colour" = bl_colour, "coco" = coco, "dico" = dico,
  123. "legend_labels_short" = legend_labels_short,
  124. "legend_labels_long" = legend_labels_long
  125. )
  126. )
  127. }
  130. diff_plot <- function(diffdata, theta, leanHPD, strictHPD, title) {
  131. # Difference distribution
  132. # Plot difference distribution with dotted zero line and two hyper density
  133. # intervals.
  135. ggplot(data = diffdata, aes(x = .data[[theta]])) +
  136. geom_density(fill = "#69b3a2", alpha = 0.75) +
  137. geom_vline(xintercept = 0, linetype = "dotted") +
  138. geom_segment(
  139. x = leanHPD$CI_low, y = 0, xend = leanHPD$CI_high, yend = 0, size = 2
  140. ) + geom_segment(
  141. x = strictHPD$CI_low, y = 0, xend = strictHPD$CI_high, yend = 0, size = 1
  142. ) + ggtitle(title)
  143. }
  146. connected_boxplot <- function(
  147. plot_data, xcol, ycol, subs, xlabel, ylabel, studylabel,
  148. show_zero = TRUE
  149. ){
  150. # Boxplots with connected dots
  151. # Boxplots with participants connected between conditions. Outliers are not
  152. # plotted. Plotting of the zero line is optional. Default is TRUE.
  154. # Theme
  155. # -----
  156. # Set layout scheme
  157. plotly <- theme_risky(studylabel)
  158. ## Jitter
  159. poji <- position_jitter(width = 0.1, seed = 21)
  161. # Plot
  162. # ----
  163. cboxplot <- ggplot(
  164. data = plot_data, aes(x = {{xcol}}, y = {{ycol}}, fill = {{xcol}})
  165. ) +
  166. geom_boxplot(outlier.shape = NA, linewidth = 1.25) + theme_phd() +
  167. scale_fill_manual(
  168. name = plotly$subtit, values = c(plotly$bl_colour, plotly$coco)
  169. ) +
  170. geom_point(
  171. aes(fill = {{xcol}}, group = {{subs}}), size = 3, position = poji,
  172. shape = 21, alpha = 0.6
  173. ) +
  174. geom_path(
  175. aes(group = {{subs}}), linewidth = 1.25, color = 'gray', alpha = 0.5,
  176. position = poji
  177. ) +
  178. # TODO: Fix title
  179. labs(x = xlabel, y = ylabel)
  180. if (show_zero == TRUE){
  181. cboxplot + geom_hline(
  182. yintercept = 0, linetype = "dashed", lwd = 2.5, alpha = 0.4
  183. )
  184. }
  185. }
  188. cow_label <- function(label, theme = NULL, element = "text", ...){
  189. # https://stackoverflow.com/a/51004900/21564504
  190. if (is.null(theme)){
  191. theme <- ggplot2::theme_get()
  192. }
  193. if (!element %in% names(theme)){
  194. stop("Element must be a valid ggplot theme element name")
  195. }
  197. elements <- ggplot2::calc_element(element, theme)
  199. cowplot::draw_label(
  200. label, fontfamily = elements$family, fontface = elements$face,
  201. colour = elements$color, size = 32, ...
  202. )
  203. }
  206. dir_bf <- function(dif_dist) {
  207. # Directed Bayes factor
  208. # Equation 2 of Marsman and Wagenmakers (2017) by Deniz Tuzsus.
  210. d <- density.default(dif_dist, n = 1024, cut = 3)
  211. xx <- d$x
  212. dx <- xx[2L] - xx[1L]
  213. yy <- d$y
  214. C <- sum(yy) * dx
  215. p_above.unscaled <- sum(yy[xx >= 0]) * dx
  216. p_above.scaled <- p_above.unscaled / C
  217. p_below.unscaled <- sum(yy[xx < 0]) * dx
  218. p_below.scaled <- p_below.unscaled / C
  220. return(round(p_above.scaled/p_below.scaled, 4))
  221. }
  224. QIofMCMC <- function(sampleVec ,credMass = 0.95) {
  225. # Computes the quantile interval from
  226. # (1-credMass)/2 to 1-(1-credMass)/2, where credMass is the target percentage
  227. # of posterior samples to be included in the interval. The typical value
  228. # credMass=0.95 results in an interval from the 2.5% to the 97.5% quantile.
  229. # -- Etz and colleagues (2024; https://doi.apa.org/doi/10.1037/met0000660 )
  231. alp <- (1 - credMass)/2
  233. return(quantile(sampleVec ,probs = c(alp ,1 - alp)))
  234. }
  237. dbf <- function(diff_data, direction = "pos") {
  238. # Directed Bayes factor
  239. # Equation 2 of Marsman and Wagenmakers (2017) by Ella Brands.
  241. diff_dens <- estimate_density(diff_data)
  242. underzero <- DescTools::AUC(diff_dens$x, diff_dens$y, to = 0)
  243. overzero <- DescTools::AUC(diff_dens$x, diff_dens$y, from = 0)
  245. return(overzero/underzero)
  246. if (direction == "neg") {
  247. return(underzero/overzero)
  248. }
  249. }
  252. interpret_Jeffreys <- function(dBFval) {
  253. # Interpretation of Bayes factor according to Jeffreys (1939)
  254. # Only a guide line and not a strict rule.
  256. if (dBFval < 1/100 | dBFval > 100) {
  257. return("Extreme evidence")
  258. }
  259. if ((1/100 <= dBFval & dBFval < 1/30) | (30 < dBFval & dBFval <= 100)) {
  260. return("Very strong evidence")
  261. }
  262. if ((1/30 <= dBFval & dBFval < 1/10) | (10 <= dBFval & dBFval <= 30)) {
  263. return("Strong evidence")
  264. }
  265. if ((1/10 <= dBFval & dBFval < 1/3) | (3 < dBFval & dBFval <= 10)) {
  266. return("Moderate evidence")
  267. }
  268. if ((1/3 <= dBFval & dBFval < 1) | (1 < dBFval & dBFval <= 3)) {
  269. return("Anecdotal evidence")
  270. }
  271. else {
  272. return("No evidence")
  273. }
  274. }
  277. findoutlier <- function(x) {
  278. # Label outliers in boxplot
  279. # https://www.r-bloggers.com/2022/08/how-to-label-outliers-in-boxplots-in-ggplot2/
  281. return(x < quantile(x, .25) - 1.5*IQR(x) | x > quantile(x, .75) + 1.5*IQR(x))
  282. }
  285. # Modified pairs
  286. ## Assist functions
  287. reglines <- function(x, y, col) abline(lm(y ~ x), col = col)
  289. panel_cor <- function(
  290. ##SG: Font size adjusting to correlation.
  291. x, y, digits = 2, prefix = "", method = "pearson", cex.cor
  292. ) {
  293. usr <- par("usr"); on.exit(par(usr = usr))
  294. par(usr = c(0, 1, 0, 1))
  295. coco <- cor(x, y, use = "pairwise.complete.obs", method = method)
  296. txt <- format(c(coco, 0.123456789), digits = digits)[1]
  297. txt <- paste0(prefix, txt, sep = "")
  298. if(missing(cex.cor)) cex <- .7 / strwidth(txt)
  300. sigtest <- cor.test(as.numeric(x), as.numeric(y), method = method)
  301. sigcor <- symnum(
  302. sigtest$p.value, corr = FALSE, na = FALSE,
  303. cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
  304. symbols = c("***", "**", "*", ".", " ")
  305. )
  306. # text(.5, .5, txt, cex = cex * (abs(coco) + .3) / 1.05)
  307. text(.5, .5, txt, cex = cex)
  308. text(.8, .8, sigcor, cex = cex, col = 2)
  309. }
  311. panel_hist <- function(x) {
  312. par(new = TRUE)
  313. hist(
  314. x, col = "lightgray", probability = TRUE, axes = FALSE, main = "",
  315. breaks = "FD"
  316. )
  317. lines(density(x, na.rm = TRUE), col = "red", lwd = 1)
  318. rug(x)
  319. }
  321. panel_lm <- function(
  322. x, y, col = par("col"), bg = NA, pch = par("pch"), cex = 1,
  323. col_smooth = "red", span = 2/3, iter = 3
  324. ) {
  325. points(x, y , pch = pch, col = col, bg = bg, cex = cex)
  326. reglines(x, y, col_smooth)
  327. }
  329. mod_pairs <- function(dataset, maintitle = "") {
  330. pairs(
  331. dataset, gap = 0, lower.panel = panel_lm, upper.panel = panel_cor,
  332. diag.panel = panel_hist, main = maintitle
  333. )
  334. }
  338. # ------------------------------------------------------------------------- End

plotAssist.R, no license · at the source

Overview

Authors: Steven Geysen1, Angela M Brands1, Heidrun Schultz2,3, Julian Koenig4, Marc Tittgemeyer5,6,7, Jan Peters1
ORCID iDs: Steven Geysen, Angela M Brands, Heidrun Schultz, Julian Koenig, Marc Tittgemeyer, Jan Peters
  1. Biological Psychology, Department of Psychology, University of Cologne, Cologne, Germany
  2. Cognitive Neuroscience Lab, Department of Liberal Arts and Sciences, University of Technology Nuremberg, Nuremberg, Germany
  3. Research Group Milestones of Early Cognitive Development, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  4. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  5. Max Planck Institute for Metabolism Research, Cologne, Germany
  6. Cologne Excellence Cluster on Ageing and Aging Associated Diseases (CECAD), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  7. Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute, Modern Diet and Physiology Research Centre, McGill University, Montreal, Canada
Institutions: University of Cologne (Germany); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); University of Technology Nuremberg; University Hospital Cologne (Germany); Montreal Neurological Institute and Hospital (Canada); Max Planck Institute for Metabolism Research (Germany); Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (Germany); McGill University (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1330
Dates: received 24 October 2025; accepted 13 July 2026; published online 6 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1330 · PMID 42569362 · PMCID PMC13449932 · OpenAlex W7169525628
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: fMRI, hierarchical Bayesian modelling, hunger, probability discounting, risky decision making, sleep deprivation
Topic: Regulation of Appetite and Obesity (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (TR-CRC134, TR-CRC 134, Project C05); Universität zu Köln
Citations: not cited yet (Europe PMC); 201 references in the paper

Abstract

Risk taking has often been proposed to depend on homeostatic systems. However, empirical evidence remains inconsistent, and the underlying mechanisms remain debated. Changes in risky decision-making, which are associated with homeostatic alterations, may be affected by underlying ghrelin signaling, a peptide hormone that also interacts with dopaminergic functions. The deacylated form of ghrelin has recently received increased attention. Across two studies, we examined whether experimental interventions known to alter desacyl-ghrelin levels influence risky choice in healthy male participants. In study 1, participants underwent a brief fasting period (N =37 ; N =26 for fMRI analyses), whereas in study 2, participants experienced one night of total sleep deprivation (N =40 ; N =36 for fMRI analyses). Risky decision making was assessed using a standard choice paradigm, and behaviour was analysed using computational modelling. Across both studies, we found no credible effects of the experimental manipulations on the proportion of risky choices. Computational modelling indicated that the standard prospect theory (which describes how participants may overestimate small probabilities and underestimate large ones), without accounting for choice repetition, best described the observed behaviour. However, model parameters were not systematically influenced by the experimental manipulations, and changes in desacyl-ghrelin levels were not robustly associated with decision parameters. Functional MRI analyses revealed no effects of state manipulation on neural representations of subjective value or choice in a priori defined regions of interest, including the ventromedial prefrontal cortex, ventral striatum, posterior cingulate cortex, anterior cingulate cortex, and anterior insula. Overall, these findings suggest that state-dependent influences on risky decision making, and their modulation by desacyl-ghrelin, may be weaker than previously thought.

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 3 matches between paragraphs and lines of code.

OSF gcjeq

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (23), Stan (17), Python (5), R (5)
Size: 57 files, 50 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: environment (setup.py)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: SPM (20 files), Stan (17 files), Statistics and Machine Learning Toolbox (6 files), pandas (3 files), reshape2 (2 files), tidyverse (2 files), cowplot (1 file), easystats (1 file), ggplot2 (1 file), NumPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
50 files
At the source: osf.io/gcjeq

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;
  • 50 scripts, each with its path and the digest of its content;
  • 3 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 and Code Availability

The data and the code for this project are publicly available on OSF (https://osf.io/gcjeq).

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 3, 28 September 2026

  • Funding: added Deutsche Forschungsgemeinschaft: TR-CRC134, TR-CRC 134, Project C05; Universität zu Köln

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 6 keywords, 195 references.

Cite

This paper

Geysen, S., Brands, A. M., Schultz, H., Koenig, J., Tittgemeyer, M., & Peters, J. (2026). Human risk taking and metabolic state: No credible evidence for desacyl-ghrelin modulation of neural or behavioural effects. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1330. https://doi.org/10.1162/imag.a.1330

BibTeX

@article{geysen2026human,
author = {Geysen, Steven and Brands, Angela M and Schultz, Heidrun and Koenig, Julian and Tittgemeyer, Marc and Peters, Jan},
title = {{Human risk taking and metabolic state: No credible evidence for desacyl-ghrelin modulation of neural or behavioural effects}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1330},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1330},
url = {https://doi.org/10.1162/imag.a.1330},
pmid = {42569362},
pmcid = {PMC13449932}
}

RIS

TY - JOUR
AU - Geysen, Steven
AU - Brands, Angela M
AU - Schultz, Heidrun
AU - Koenig, Julian
AU - Tittgemeyer, Marc
AU - Peters, Jan
TI - Human risk taking and metabolic state: No credible evidence for desacyl-ghrelin modulation of neural or behavioural effects
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/06
VL - 4
SP - IMAG.a.1330
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1330
UR - https://doi.org/10.1162/imag.a.1330
LA - en
ER -

CSL-JSON

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"title": "Human risk taking and metabolic state: No credible evidence for desacyl-ghrelin modulation of neural or behavioural effects",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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