OSCR

Effects on hippocampal activity following novel 5-HT4 receptor agonism in unmedicated patients with depression: the RESTAND study.

Code ↔ Paper

6 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 6 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 › fMRI memory encoding task › Main effect of task ↔ Emotional Cognition paper scripts/06_fmri_wholebrain_clusters.R, lines 1–80 · score 0.82 · frontal pole, paracingulate gyrus, frontal gyrus, lateral occipital, temporal, cerebellum
  2. [2] § Materials and methods › Questionnaire measures ↔ Emotional Cognition paper scripts/09_selfreport.R, lines 1–52 · score 0.77 · depressive symptoms, state anxiety, Research Visit, PANAS, STAI, anhedonia
  3. [3] § Materials and methods › Questionnaire measures ↔ Emotional Cognition paper scripts/02_demographic_comparison.R, the whole file · a weak match · score 0.69 · Trait Anxiety, depressive symptoms, Inventory, STAI, anhedonia, BDI
  4. [4] § Results › Auditory verbal learning task ↔ RESTAND AVLT ANALYSIS_forpublication.Rmd, lines 562–578 · score 0.59 · menstrual cramps, long delay, word recall, unblinding, AVLT
  5. [5] § Materials and methods › Statistical analysis › MRI data acquisition and analysis – primary analyses ↔ Emotional Cognition paper scripts/07_ROI_analysis.R, lines 417–490 · score 0.58 · BOLD signal change, ROI, fMRI, FEAT, cluster, baseline
  6. [6] § Results › Auditory verbal learning task ↔ RESTAND AVLT ANALYSIS_forpublication.Rmd, lines 186–222 · score 0.54 · Word recall, Short Delay, Long Delay, block, Scores

Paper

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The authors' code

R Markdown · 706 lines · 20 KB · no license · 2 matches

  1. ---
  2. title: "RESTAND Analysis"
  3. author: "Amy Gillespie, adapted by Angharad de Cates "
  4. date: "`r Sys.Date()`"
  5. output:
  6. html_document: default
  7. keep_md: yes
  8. ---
  10. ```{r setup, include=FALSE}
  11. knitr::opts_chunk$set(dev = "svg",
  12. dpi = 300,
  13. echo = FALSE,
  14. message=FALSE,
  15. cache = TRUE,
  16. fig.width = 10,
  17. fig.height = 10)
  19. library(dplyr)
  20. library(ggplot2)
  21. library(rstatix) #mean_sd summaries
  22. library(tidyr)
  23. library(ggpubr) #ggdensity plots
  24. library(moments) #skewness
  25. library(car) #levenetest
  26. library(ez) #anovas for avlt
  27. library(stringr) #manipulating strings of text in columns
  28. library(tidyverse)
  29. library(effectsize)
  31. #set theme for plots
  33. theme = theme_set(theme_minimal())
  34. theme = theme_update(legend.position="bottom",
  35. panel.grid.major.x=element_blank(),
  36. axis.line.x = element_blank(),
  37. axis.line.y = element_blank(),
  38. axis.ticks.y = element_line(colour="grey"),
  39. axis.title.x = element_text(colour="grey26"),
  40. axis.title.y = element_text(colour="grey26"),
  41. axis.text.x = element_text(colour="grey26"),
  42. axis.text.y = element_text(colour="grey26"),
  43. text=element_text(face="bold", size=11))
  45. figgroupnames_twoway <- c("5HT4", "Placebo")
  46. figgroupcolours_twoway <- c("blue", "#A6A6A6")
  48. figgroupnames <- c("PF-04995274 (5HT4 agonist)", "Citalopram", "Placebo")
  49. figgroupcolours <- c("#D55E00", "#99CCFF", "#A6A6A6")
  51. RESTAND_unblinded_allocations <- read.csv("C:/Users/adecates/OneDrive - Nexus365/Shared Documents/Data/Manual_input_data/RESTAND/RESTAND_unblinded_ids.csv") #subjects IDs & medication groups
  53. #read in datatracker with all ids
  54. datatracker <- read.csv("C:/Users/adecates/OneDrive - Nexus365/Shared Documents/Data/Manual_input_data/RESTAND/RESTAND_final_datatrackerAUG2022.csv")
  56. ```
  58. ## Demographics
  60. ```{r}
  61. Demographics <- read.csv("C:/Users/adecates/OneDrive - Nexus365/Shared Documents/Data/Manual_input_data/RESTAND/RESTAND_demographics.csv")
  63. Demographics <- Demographics[-c(91:92),]
  65. Demographics <- Demographics %>%
  66. select(PID, Age_decimal, Sex, Race, First_language, Medical_history:Caff_perday, BMI, Years_Educ)
  68. Demographics$Sex<- toupper(Demographics$Sex)
  69. Demographics$First_language<- tolower(Demographics$First_language)
  71. Demographics[Demographics == "Missing"] <- NA
  72. Demographics[Demographics == "MISSING"] <- NA
  74. Demographics$Years_Educ <- as.numeric(as.character(Demographics$Years_Educ, na.rm = TRUE))
  75. Demographics$BMI <- as.numeric(as.character(Demographics$BMI, na.rm = TRUE))
  78. RESTAND_Demo <- merge (RESTAND_unblinded_allocations, Demographics, by = "PID")
  80. RESTAND_HAMD <- read.csv("C:/Users/adecates/OneDrive - Nexus365/DPhil work/5HT4_novel/Analysis/AVLT/5HT4 RESTAND_HAMD_forR.csv")
  81. RESTAND_HAMD$HAMD_SV <- as.numeric(as.character(RESTAND_HAMD$HAMD_SV, na.rm = TRUE))
  82. RESTAND_HAMD$HAMD_change <- as.numeric(as.character(RESTAND_HAMD$HAMD_change, na.rm = TRUE))
  84. RESTAND_HAMD <- RESTAND_HAMD %>%
  85. select(PID, Rand_no, HAMD_SV, HAMD_change)
  87. #summary(RESTAND_Demo)
  89. ```
  91. ```{r create RESTAND_participants - core demographic and group allocation file}
  92. RESTAND_participants <- merge(RESTAND_Demo, RESTAND_HAMD, by = c("PID", "Rand_no"), all = TRUE)
  93. RESTAND_participants <- merge(RESTAND_participants, datatracker, by = c("PID", "Rand_no"), all = TRUE)
  95. RESTAND_participants <- RESTAND_participants %>%
  96. select(PID, Rand_no, MedicationGroup, Sex, Age_decimal, Rvday, MR_randcode.x, HAMD_SV, HAMD_change)
  99. ```
  101. #Sex
  102. ```{r}
  103. RESTAND_male_participants <- RESTAND_participants %>%
  104. filter (Sex == "M")
  106. RESTAND_female_participants <- RESTAND_participants %>%
  107. filter (Sex == "F")
  109. ```
  114. ## AVLT
  116. ```{r}
  117. RESTAND_AVLT <- read.csv("C:/Users/adecates/OneDrive - Nexus365/Shared Documents/Data/Manual_input_data/RESTAND/RESTAND_AVLT.csv", header=TRUE)
  119. RESTAND_AVLT_wide <- merge (RESTAND_participants, RESTAND_AVLT, by = "PID")
  121. setwd("C:/Users/adecates/OneDrive - Nexus365/DPhil work/5HT4_novel/Analysis/AVLT")
  122. write.csv(RESTAND_AVLT_wide, "AVLT_full_R_allpts.csv")
  124. RESTAND_AVLT_wide$PID <- as.factor(RESTAND_AVLT_wide$PID) #counts the participant IDs as categorical
  125. RESTAND_AVLT_wide$MedicationGroup <- as.factor(RESTAND_AVLT_wide$MedicationGroup) #counts the drug condition as categorical
  127. RESTAND_AVLT_wide_female <- RESTAND_AVLT_wide %>%
  128. filter(Sex == "F")
  130. RESTAND_AVLT_wide_male <- RESTAND_AVLT_wide %>%
  131. filter(Sex == "M")
  134. RESTAND_AVLT_wide_twoway <- RESTAND_AVLT_wide %>%
  135. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  137. RESTAND_AVLT_wide_twoway_cit_plac <- RESTAND_AVLT_wide %>%
  138. filter(MedicationGroup == "Citalopram"|MedicationGroup == "Placebo")
  140. RESTAND_AVLT_wide_twoway_female <- RESTAND_AVLT_wide_female %>%
  141. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  143. RESTAND_AVLT_wide_twoway_male <- RESTAND_AVLT_wide_male %>%
  144. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  146. RESTAND_AVLT_long <- RESTAND_AVLT_wide %>%
  147. gather(key="Block", value = "Score", T1_correct, T2_correct, T3_correct, T4_correct, T5_correct, ListB_correct, Short_delay, Long_delay)
  151. RESTAND_AVLT_long$Block <- as.character(RESTAND_AVLT_long$Block)
  154. RESTAND_AVLT_long_female <- RESTAND_AVLT_long %>%
  155. filter(Sex == "F")
  157. RESTAND_AVLT_long_male <- RESTAND_AVLT_long %>%
  158. filter(Sex == "M")
  160. RESTAND_AVLT_long_twoway <- RESTAND_AVLT_long %>%
  161. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  163. RESTAND_AVLT_long_twoway_cit_plac <- RESTAND_AVLT_long %>%
  164. filter(MedicationGroup == "Citalopram"|MedicationGroup == "Placebo")
  166. RESTAND_AVLT_long_ListA <- RESTAND_AVLT_long %>%
  167. filter (Block == c("T1_correct", "T2_correct", "T3_correct", "T4_correct", "T5_correct"))
  169. RESTAND_AVLT_long_ListA_twoway <- RESTAND_AVLT_long_twoway %>%
  170. filter (Block == c("T1_correct", "T2_correct", "T3_correct", "T4_correct", "T5_correct"))
  172. RESTAND_AVLT_long_twoway_female <- RESTAND_AVLT_long_female %>%
  173. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  175. RESTAND_AVLT_long_twoway_male <- RESTAND_AVLT_long_male %>%
  176. filter(MedicationGroup == "Active"|MedicationGroup == "Placebo")
  178. RESTAND_AVLT_wide %>%
  179. group_by(MedicationGroup) %>%
  180. get_summary_stats(T1_correct:T5_correct, type = "mean_sd")
  183. ```
  186. ```{r}
  187. # Reorder and rename the levels of the Block variable
  188. RESTAND_AVLT_long_twoway$Block <- factor(
  189. RESTAND_AVLT_long_twoway$Block,
  190. levels = c("T1_correct", "T2_correct", "T3_correct", "T4_correct",
  191. "T5_correct", "ListB_correct", "Short_delay", "Long_delay"),
  192. labels = c("List A Trial1", "List A Trial2", "List A Trial3",
  193. "List A Trial4", "List A Trial5", "List B",
  194. "List A Short Delay", "List A Long Delay")
  195. )
  198. summary_df <- RESTAND_AVLT_long_twoway %>%
  199. group_by(Block, MedicationGroup) %>%
  200. dplyr::summarise(
  201. mean = mean(Score, na.rm = TRUE),
  202. se = sd(Score, na.rm = TRUE) / sqrt(n()),
  203. lower = mean(Score, na.rm = TRUE) - (sd(Score, na.rm = TRUE) / sqrt(n())),
  204. upper = mean(Score, na.rm = TRUE) + (sd(Score, na.rm = TRUE) / sqrt(n())),
  205. .groups = "drop"
  206. )
  208. AVLT_plot_line_2way_updated <- ggplot(summary_df, aes(x = Block, y = mean, color = factor(MedicationGroup), fill = factor(MedicationGroup), group = factor(MedicationGroup))) +
  209. geom_ribbon(aes(ymin = lower, ymax = upper), alpha = 0.2, linetype = 3) + # increased transparency
  210. geom_line(size = 1, linetype = "solid") + # darker/thicker mean line
  211. geom_point(size = 3) +
  212. scale_color_manual(name = "Medication", labels = figgroupnames_twoway, values = figgroupcolours_twoway) +
  213. scale_fill_manual(name = "Medication", labels = figgroupnames_twoway, values = figgroupcolours_twoway) +
  214. coord_cartesian(ylim = c(5, 15)) +
  215. theme() +
  216. labs(title = "AVLT Recall Across Medication Groups",
  217. x = "AVLT Block", y = "Number of Words Recalled", tag = "A")
  219. AVLT_plot_line_2way_updated
  222. ```
  225. ### Number of words recalled - List A immediate (total correct) & score for each block with block
  226. ```{r}
  227. RESTAND_AVLT_wide %>%
  228. group_by(MedicationGroup) %>%
  229. get_summary_stats(Total_correct, type = "mean_sd")
  231. summary(aov(Total_correct ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway))
  234. Twowayaccuracy_block <- lmer(Score ~ MedicationGroup * Block + (1|PID), data = RESTAND_AVLT_long_twoway)
  235. anova(Twowayaccuracy_block)
  237. library(effectsize)
  238. eta_squared(Twowayaccuracy_block, partial = TRUE)
  241. ```
  243. ### Repeat of above main analyses using citVsplac as repeat for supplement
  244. ```{r}
  246. summary(aov(Total_correct ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway_cit_plac))
  249. Twowayaccuracy_block <- lmer(Score ~ MedicationGroup * Block + (1|PID), data = RESTAND_AVLT_long_twoway_cit_plac)
  250. anova(Twowayaccuracy_block)
  252. eta_squared(Twowayaccuracy_block, partial = TRUE)
  255. ```
  258. #### Interaction with sex
  259. ```{r}
  260. Twoway_accuracy_block_sex <- lmer(Score ~ MedicationGroup * Block * Sex + (1|PID), data = RESTAND_AVLT_long_twoway)
  261. anova(Twoway_accuracy_block_sex)
  263. effect_sizes_anova_sex <- eta_squared(Twowayaccuracy_block_sex, partial = TRUE)
  264. print(effect_sizes_anova_sex)
  267. RESTAND_AVLT_wide_twoway %>%
  268. group_by(MedicationGroup, Sex) %>%
  269. get_summary_stats(Total_correct, type = "mean_sd")
  271. ```
  274. ```{r}
  275. # Plotting for sex
  277. # Calculate mean and standard error
  278. RESTAND_AVLT_wide_twoway_plottable <- RESTAND_AVLT_wide_twoway %>%
  279. group_by(MedicationGroup, Sex) %>%
  280. dplyr::summarise(
  281. mean = mean(Total_correct, na.rm = TRUE), # Added na.rm=TRUE
  282. se = sd(Total_correct, na.rm = TRUE) / sqrt(n())
  283. )
  285. # Reshape the data for means
  286. means_wide <- RESTAND_AVLT_wide_twoway_plottable %>%
  287. select(MedicationGroup, Sex, mean) %>%
  288. tidyr::pivot_wider(names_from = MedicationGroup, names_prefix = "Total_correct", values_from = mean)
  290. # Reshape the data for standard errors
  291. se_wide <- RESTAND_AVLT_wide_twoway_plottable %>%
  292. select(MedicationGroup, Sex, se) %>%
  293. tidyr::pivot_wider(names_from = MedicationGroup, names_prefix = "SE", values_from = se)
  295. # Convert to matrix format for plotting
  296. means_matrix <- as.matrix(means_wide[,-1])
  297. se_matrix <- as.matrix(se_wide[,-1])
  298. rownames(means_matrix) <- means_wide$Sex
  300. # Set up the plotting area with space for the legend at the bottom
  301. # par(xpd = TRUE) allows plotting outside the plot area for the legend
  302. par(xpd = TRUE, mar = c(5, 4, 4, 2) + 0.1) # Increase bottom margin for legend
  304. # Create the barplot
  305. bp <- barplot(means_matrix,
  306. beside = TRUE,
  307. legend = FALSE, # Remove default legend
  308. col = c("orange", "darkgreen"),
  309. main = "AVLT Total Correct",
  310. ylim = c(0,100),
  311. ylab = "Total Correct for List A",
  312. names.arg = c("5HT4", "Placebo"))
  314. # Add error bars
  315. for(i in 1:nrow(means_matrix)) {
  316. arrows(bp[i,], means_matrix[i,] - se_matrix[i,],
  317. bp[i,], means_matrix[i,] + se_matrix[i,],
  318. angle=90, code=3, length=0.05)
  319. }
  321. # Add legend below the plot
  322. legend(x = "bottom", # Position the legend at the bottom
  323. inset = -0.2, # Move legend down
  324. legend = rownames(means_matrix), # Sex labels
  325. fill = c("orange", "darkgreen"),
  326. horiz = TRUE, # Horizontal legend
  327. bty = "n") # No box around legend
  329. # Reset plotting parameters
  330. par(mar = c(5, 4, 4, 2))
  331. par(xpd = FALSE)
  334. # Updated above with spread of data
  335. # Calculate mean and standard error
  336. RESTAND_AVLT_wide_twoway_plottable <- RESTAND_AVLT_wide_twoway %>%
  337. group_by(MedicationGroup, Sex) %>%
  338. dplyr::summarise(
  339. mean = mean(Total_correct, na.rm = TRUE),
  340. se = sd(Total_correct, na.rm = TRUE) / sqrt(n())
  341. )
  343. # Reshape the data for means and SEs
  344. means_wide <- RESTAND_AVLT_wide_twoway_plottable %>%
  345. select(MedicationGroup, Sex, mean) %>%
  346. tidyr::pivot_wider(names_from = MedicationGroup, names_prefix = "Total_correct", values_from = mean)
  348. se_wide <- RESTAND_AVLT_wide_twoway_plottable %>%
  349. select(MedicationGroup, Sex, se) %>%
  350. tidyr::pivot_wider(names_from = MedicationGroup, names_prefix = "SE", values_from = se)
  352. # Convert to matrix for plotting
  353. means_matrix <- as.matrix(means_wide[,-1])
  354. se_matrix <- as.matrix(se_wide[,-1])
  355. rownames(means_matrix) <- means_wide$Sex
  357. # Set up plotting area with space for legend
  358. par(xpd = TRUE, mar = c(5, 4, 4, 2) + 0.1) # Adjust bottom margin for legend
  360. # # Create grouped barplot
  361. # bp <- barplot(means_matrix,
  362. # beside = TRUE,
  363. # legend = FALSE, # We'll add custom legend
  364. # col = c("orange", "darkgreen"),
  365. # main = "AVLT Total Correct",
  366. # ylim = c(0, 100),
  367. # ylab = "Total Correct for List A",
  368. # names.arg = c("5HT4", "Placebo"))
  370. # Define lighter fill colors and darker border colors
  371. fill_colors <- c("#FFCC99", "#99CC99") # Light orange, light green
  372. border_colors <- c("#CC6600", "#336600") # Darker orange, dark green
  374. par(lwd=4)
  376. # Set space between groups and bars (space = c(space_between_groups, space_within_group))
  377. # For 2 bars per group: e.g., 0.5 between groups, 0.3 between bars within group
  378. bp <- barplot(
  379. means_matrix,
  380. beside = TRUE,
  381. legend = FALSE,
  382. col = fill_colors, # Lighter fill
  383. border = border_colors, # Darker outline
  384. space = c(0.3, 0.8), # Adds space between groups and between bars in groups
  385. main = "AVLT Total Correct",
  386. ylim = c(0, 100),
  387. ylab = "Total Correct for List A",
  388. names.arg = c("5HT4", "Placebo")
  389. )
  393. # Add error bars (mean ± SE)
  394. for(i in 1:nrow(means_matrix)) {
  395. arrows(bp[i,], means_matrix[i,] - se_matrix[i,],
  396. bp[i,], means_matrix[i,] + se_matrix[i,],
  397. angle=90, code=3, length=0.3, lwd=2)
  398. }
  400. par(lwd=1)
  401. # Subset original data to only desired MedicationGroup levels
  402. RESTAND_sub <- RESTAND_AVLT_wide_twoway %>%
  403. filter(MedicationGroup %in% c("Active", "Placebo"))
  404. # Drop unused factor levels after subsetting
  405. RESTAND_sub$MedicationGroup <- droplevels(RESTAND_sub$MedicationGroup)
  407. # Then get med_levels from this cleaned data
  408. med_levels <- levels(RESTAND_sub$MedicationGroup)
  409. sex_levels <- unique(RESTAND_sub$Sex) # returns character vector of unique Sex values
  411. for (i in seq_along(sex_levels)) {
  412. for (j in seq_along(med_levels)) {
  413. # Filter raw data for this group
  414. ind <- which(RESTAND_sub$Sex == sex_levels[i] & RESTAND_sub$MedicationGroup == med_levels[j])
  415. y_vals <- RESTAND_sub$Total_correct[ind]
  416. x_pos <- bp[i, j]
  417. point_colors <- c("Active" = "darkblue", "Placebo" = "darkgrey")
  418. # Add horizontal jitter to avoid overlapping points
  419. x_jitter <- jitter(rep(x_pos, length(y_vals)), amount = 0.2)
  420. points(x_jitter, y_vals, pch = 21, bg = point_colors[med_levels[j]], col = "black", cex = 0.8)
  421. }
  422. }
  424. # Add legend below the plot
  425. legend(x = "bottom",
  426. inset = -0.2,
  427. legend = rownames(means_matrix),
  428. fill = c("orange", "darkgreen"),
  429. horiz = TRUE,
  430. bty = "n")
  432. # Reset par settings
  433. par(mar = c(5, 4, 4, 2))
  434. par(xpd = FALSE)
  436. ```
  440. #### Women only for Total Correct
  441. ```{r}
  443. summary(aov(Total_correct ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway_female))
  445. ```
  447. #### Men only for Total Correct
  448. ```{r}
  450. summary(aov(Total_correct ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway_male))
  452. ```
  455. #### Sex and medication group when model block
  456. ```{r}
  459. Twoway_main_sex <- lmer(Score ~ MedicationGroup * Block * Sex + (1|PID), data = RESTAND_AVLT_long_twoway)
  460. anova(Twoway_main_sex)
  462. effect_sizes_Twoway_main_sex <- eta_squared(Twoway_main_sex, partial = TRUE)
  463. print(effect_sizes_Twoway_main_sex)
  467. ```
  469. #### Women only
  470. ```{r}
  472. Twoway_main_sex_female <- lmer(Score ~ MedicationGroup * Block + (1|PID), data = RESTAND_AVLT_long_ListA_twoway_female)
  473. anova(Twoway_main_sex_female)
  475. effect_sizes_Twoway_main_sex_female <- eta_squared(Twoway_main_sex_female, partial = TRUE)
  476. print(effect_sizes_Twoway_main_sex_female)
  478. RESTAND_AVLT_wide_female %>%
  479. group_by(MedicationGroup,) %>%
  480. get_summary_stats(T1_correct:T5_correct, type = "mean_sd")
  483. ```
  485. #### Men only (small n)
  486. ```{r}
  489. RESTAND_AVLT_wide_male %>%
  490. group_by(MedicationGroup,) %>%
  491. get_summary_stats(T1_correct:T5_correct, type = "mean_sd")
  493. ```
  496. #### Age and medication group when model block
  497. ```{r}
  499. Twoway_main_age <- lmer(Score ~ MedicationGroup * Block * Age_decimal + (1|PID), data = RESTAND_AVLT_long_twoway)
  500. anova(Twoway_main_sex)
  502. effect_sizes_Twoway_main_age <- eta_squared(Twoway_main_age, partial = TRUE)
  503. print(effect_sizes_Twoway_main_age)
  507. ```
  510. #### HAMD baseline, and Age + HAMD baseline, and HAM change and medication group when model block
  511. ```{r}
  513. Twoway_main_HAMDbaseline <- lmer(Score ~ MedicationGroup * Block * HAMD_SV + (1|PID), data = RESTAND_AVLT_long_twoway)
  514. anova(Twoway_main_HAMDbaseline)
  516. effect_sizes_Twoway_main_HAMDbaseline <- eta_squared(Twoway_main_HAMDbaseline, partial = TRUE)
  517. print(effect_sizes_Twoway_main_HAMDbaseline)
  520. Twoway_main_HAMDbaseline_age <- lmer(Score ~ MedicationGroup * Block * Age_decimal * HAMD_SV + (1|PID), data = RESTAND_AVLT_long_twoway)
  521. anova(Twoway_main_HAMDbaseline_age)
  523. effect_sizes_Twoway_main_HAMDbaseline_age <- eta_squared(Twoway_main_HAMDbaseline_age, partial = TRUE)
  524. print(effect_sizes_Twoway_main_HAMDbaseline_age)
  527. Twoway_main_HAMDchange <- lmer(Score ~ MedicationGroup * Block * HAMD_change + (1|PID), data = RESTAND_AVLT_long_twoway)
  528. anova(Twoway_main_HAMDchange)
  530. effect_sizes_Twoway_main_HAMDchange <- eta_squared(Twoway_main_HAMDchange, partial = TRUE)
  531. print(effect_sizes_Twoway_main_HAMDchange)
  533. ```
  536. ### Number of words recalled - List A short delay
  537. ```{r}
  539. anova_SD <- aov(Short_delay ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway)
  541. summary(anova_SD)
  543. effect_sizes_anova_SD <- eta_squared(anova_SD, partial = TRUE)
  544. print(effect_sizes_anova_SD)
  548. ```
  551. #### Interaction between medication group and sex
  552. ```{r}
  554. RESTAND_AVLT_wide_twoway %>%
  555. group_by(MedicationGroup, Sex) %>%
  556. get_summary_stats(Short_delay, type = "mean_sd")
  558. ```
  562. ### Number of words recalled - List A long delay
  563. ```{r}
  565. #FILTER DOWN EXCLUDE 187
  566. RESTAND_AVLT_wide_longdelay_twoway <- RESTAND_AVLT_wide_twoway %>%
  567. filter (PID != "UMD187") # RESTAND_exclusiondecisions.csv – it was because she had a 1+ hour gap mid research visit due to menstrual cramps, so a lot longer delay than intended, and we decided to exclude before unblinding
  569. anova_LD <- aov(Long_delay ~ MedicationGroup, data = RESTAND_AVLT_wide_longdelay_twoway)
  570. summary(anova_LD)
  572. effect_sizes_anova_LD <- eta_squared(anova_LD, partial = TRUE)
  573. print(effect_sizes_anova_LD)
  575. RESTAND_AVLT_wide_longdelay_twoway %>%
  576. group_by(MedicationGroup) %>%
  577. get_summary_stats(Long_delay, type = "mean_sd")
  578. ```
  581. #### Interaction between medication and sex
  582. ```{r}
  586. RESTAND_AVLT_wide_longdelay_twoway %>%
  587. group_by(MedicationGroup, Sex) %>%
  588. get_summary_stats(Long_delay, type = "mean_sd")
  590. ```
  593. ### Intrusions
  594. ```{r}
  596. RESTAND_AVLT_wide %>%
  597. group_by(MedicationGroup) %>%
  598. get_summary_stats(Intrusions, type = "mean_sd")
  601. summary(aov(Intrusions ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway))
  604. ```
  606. #### Interaction between medication and sex
  607. ```{r}
  609. RESTAND_AVLT_wide_twoway %>%
  610. group_by(MedicationGroup, Sex) %>%
  611. get_summary_stats(Intrusions, type = "mean_sd")
  614. ```
  617. ### Repetitions
  618. ```{r}
  620. RESTAND_AVLT_wide %>%
  621. group_by(MedicationGroup) %>%
  622. get_summary_stats(Repetitions, type = "mean_sd")
  624. summary(aov(Repetitions ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway))
  626. ```
  628. #### Interaction between medication group with sex
  629. ```{r}
  631. RESTAND_AVLT_wide_twoway %>%
  632. group_by(MedicationGroup, Sex) %>%
  633. get_summary_stats(Repetitions, type = "mean_sd")
  636. ```
  639. ### Number of words recalled - List B immediate
  640. ```{r}
  642. RESTAND_AVLT_wide %>%
  643. group_by(MedicationGroup) %>%
  644. get_summary_stats(ListB_correct, type = "mean_sd")
  645. #
  647. anova_listB <- aov(ListB_correct ~ MedicationGroup, data = RESTAND_AVLT_wide_twoway)
  648. summary(anova_listB)
  650. effect_sizes_anova_listB <- eta_squared(anova_listB, partial = TRUE)
  651. print(effect_sizes_anova_listB)
  653. ```
  655. #### Interaction between medication and sex
  656. ```{r}
  660. RESTAND_AVLT_wide_twoway %>%
  661. group_by(MedicationGroup, Sex) %>%
  662. get_summary_stats(ListB_correct, type = "mean_sd")
  665. ```
  669. ### Recognition - hits
  670. ```{r}
  672. RESTAND_AVLT_wide_longdelay_twoway %>%
  673. group_by(MedicationGroup, Sex) %>%
  674. get_summary_stats(Recog_hits, type = "mean_sd")
  677. summary(aov(Recog_hits ~ MedicationGroup, data = RESTAND_AVLT_wide_longdelay_twoway))
  682. ```
  685. ### Recognition - false alarms
  686. ```{r}
  688. RESTAND_AVLT_wide_longdelay_twoway %>%
  689. group_by(MedicationGroup) %>%
  690. get_summary_stats(Recog_falsepos, type = "mean_sd")
  692. summary(aov(Recog_falsepos ~ MedicationGroup, data = RESTAND_AVLT_wide_longdelay_twoway))
  694. ```
  696. #### Interaction between medication and sex
  697. ```{r}
  699. RESTAND_AVLT_wide_longdelay_twoway %>%
  700. group_by(MedicationGroup, Sex) %>%
  701. get_summary_stats(Recog_falsepos, type = "mean_sd")
  704. ```

RESTAND AVLT ANALYSIS_forpublication.Rmd, no license · at the source

Overview

Authors: Angharad N de Cates1,2, Amy L Gillespie2,3, Jessica Scaife2, Marieke A G Martens2, James Carson2, Beata R Godlewska2,3, Wendy Howard2, Anutra Guru2, Philip J Cowen2,3, Catherine J Harmer2,3,4, Susannah E Murphy2,3
ORCID iDs: Angharad N de Cates, Amy L Gillespie, Jessica Scaife, Marieke A G Martens, Beata R Godlewska, Philip J Cowen, Catherine J Harmer
  1. Institute for Mental Health, University of Birmingham, Edgbaston, Birmingham, UK
  2. University Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
  3. Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
  4. Oxford Centre for Human Brain Activity, Oxford Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
Institutions: Warneford Hospital (United Kingdom); University of Oxford (United Kingdom); University of Birmingham (United Kingdom); Oxford Health NHS Foundation Trust (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: Translational psychiatry, volume 16, issue 1, article 405
Dates: received 30 September 2025; accepted 22 May 2026; published online 17 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04141-z · PMID 42309998 · PMCID PMC13458156 · OpenAlex W7165055504
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), depression (population)
Methods: Statistics, fMRI & imaging
Keywords: Clinical pharmacology, Depression
MeSH: Hippocampus*, Major Depressive Disorder*, Serotonin 5-HT4 Receptor Agonists*, Adult, Cognitive Enhancement, Double-Blind Method, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Short-Term, Middle Aged, Verbal Learning, Young Adult (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Medical Research Council (MR/S035591/1); Wellcome Trust (216430/Z/19/Z); DH | NIHR | Research Trainees Coordinating Centre
Citations: cited by 2 papers (Europe PMC); 58 references in the paper

Abstract

Cognitive impairment is a common but under-treated feature of Major Depressive Disorder (MDD). Preclinical and early human studies suggest that 5-HT4 receptor (5-HT4R) agonists rapidly improve learning and memory, consistent with this receptor’s role in hippocampal neuroplasticity. However, their effects in clinically depressed patients, remain unexplored. In this double-blind, randomised experimental medicine study, 52 right-handed, unmedicated individuals with MDD received 6–9 days of the 5-HT4R agonist PF-04995274 (15 mg, once daily) or placebo. Participants subsequently underwent fMRI scanning during a memory encoding task and completed behavioural measures of auditory verbal learning and spatial working memory. Compared to placebo, PF-04995274 significantly increased activity in the hippocampus (ROI analysis) in response to novel versus familiar images, particularly in the left hemisphere. Whole brain analysis also revealed greater activation in the left inferior parietal lobule, a key region for memory processing. In contrast with previous studies using the 5-HT4R agonist prucalopride, PF-04995274 had notably limited effects on behavioural measures of memory. The results demonstrate that short term 5-HT4R agonism enhances hippocampal and parietal activity during memory encoding in patients with depression. This replicates and extends previous findings in healthy volunteers using prucalopride, and is consistent with preclinical evidence establishing a key role for 5-HT4Rs in hippocampal-dependent learning and memory. This translational evidence supports a role for 5-HT4R activation in modulating memory-related brain circuits in MDD and previously identified beneficial effects of another 5-HT4 receptor agonist, prucalopride, on cognitive performance.

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OSF v7kgs

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (16)
Size: 16 files, 16 scripts
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Found in: “Code availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
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Analysis code is available at the Open Science Framework (https://osf.io/v7kgs/?view_only=1cd5bb7f964f445397ba16dc72cfb812).

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 14 MeSH terms, 3 funders, 50 references.

Cite

This paper

de Cates, A. N., Gillespie, A. L., Scaife, J., Martens, M. A. G., Carson, J., Godlewska, B. R., Howard, W., Guru, A., Cowen, P. J., Harmer, C. J., & Murphy, S. E. (2026). Effects on hippocampal activity following novel 5-HT4 receptor agonism in unmedicated patients with depression: the RESTAND study. Translational psychiatry, 16(1), 405. https://doi.org/10.1038/s41398-026-04141-z

BibTeX

@article{decates2026effects,
author = {de Cates, Angharad N and Gillespie, Amy L and Scaife, Jessica and Martens, Marieke A G and Carson, James and Godlewska, Beata R and Howard, Wendy and Guru, Anutra and Cowen, Philip J and Harmer, Catherine J and Murphy, Susannah E},
title = {{Effects on hippocampal activity following novel 5-HT4 receptor agonism in unmedicated patients with depression: the RESTAND study}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {405},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04141-z},
url = {https://doi.org/10.1038/s41398-026-04141-z},
pmid = {42309998},
pmcid = {PMC13458156}
}

RIS

TY - JOUR
AU - de Cates, Angharad N
AU - Gillespie, Amy L
AU - Scaife, Jessica
AU - Martens, Marieke A G
AU - Carson, James
AU - Godlewska, Beata R
AU - Howard, Wendy
AU - Guru, Anutra
AU - Cowen, Philip J
AU - Harmer, Catherine J
AU - Murphy, Susannah E
TI - Effects on hippocampal activity following novel 5-HT4 receptor agonism in unmedicated patients with depression: the RESTAND study
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/17
VL - 16
IS - 1
SP - 405
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04141-z
UR - https://doi.org/10.1038/s41398-026-04141-z
LA - en
ER -

CSL-JSON

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