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Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval.

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  1. [1] § Materials and Methods › fMRI analysis › Region of interest (ROI) analysis ↔ DataScripts/PosteriorParietalROISubregions_RetrievalOrientation.R, lines 1–58 · score 0.59 · JuBrain, posterior parietal, signal change, subregions, ROI, hemispheres

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The authors' code

R · 1,111 lines · 37 KB · no license · 1 match

  1. #Load libraries
  2. library(readxl)
  3. library(tidyverse)
  4. library(stringr)
  5. library(afex)
  6. library(emmeans)
  7. library(DescTools)
  8. library(patchwork)
  9. library(ggsignif)
  10. library(effectsize)
  11. library(grid)
  13. # STEP 1: Load the new data file
  14. df_wide <- read_excel("~/Library/CloudStorage/[email hidden]/Shared drives/MELab/Projects/RetrievalOrientation/Results/ROI/JuBrain/PosteriorParietalSubregionROISignalChange_RetrievalOrientation.xlsx")
  16. # STEP 2: Convert wide to long format and extract variables
  17. df_long <- df_wide %>%
  18. pivot_longer(
  19. cols = -ID,
  20. names_to = "Condition",
  21. values_to = "Activation"
  22. ) %>%
  23. mutate(
  24. ID = as.factor(ID),
  25. Phase = case_when(
  26. str_detect(Condition, "^Cue") ~ "Cue",
  27. str_detect(Condition, "^Probe") ~ "Probe",
  28. TRUE ~ NA_character_
  29. ),
  30. RetrievalOrientation = case_when(
  31. str_detect(Condition, "OE") ~ "OwnEyes",
  32. str_detect(Condition, "OB") ~ "Observer",
  33. str_detect(Condition, "Ret") ~ "Retrieve",
  34. TRUE ~ NA_character_
  35. ),
  36. Hemisphere = case_when(
  37. str_detect(Condition, "Left") ~ "Left",
  38. str_detect(Condition, "Right") ~ "Right",
  39. TRUE ~ NA_character_
  40. ),
  41. Region = case_when(
  42. str_detect(Condition, "PGa") ~ "PGa",
  43. str_detect(Condition, "PGp") ~ "PGp",
  44. str_detect(Condition, "7A") ~ "7A",
  45. str_detect(Condition, "7M") ~ "7M",
  46. str_detect(Condition, "7P") ~ "7P",
  47. TRUE ~ NA_character_
  48. )
  49. ) %>%
  50. filter(!is.na(Phase)) %>%
  51. mutate(
  52. RetrievalOrientation = factor(RetrievalOrientation, levels = c("OwnEyes", "Observer", "Retrieve")),
  53. Hemisphere = factor(Hemisphere),
  54. Region = factor(Region),
  55. Phase = factor(Phase, levels = c("Cue", "Probe"))
  56. )
  58. view(df_long)
  62. # NEW FUNCTION
  63. run_roi_orientation_interaction <- function(df, hemisphere, phase, roi_group) {
  65. message("\n===== ", hemisphere, " Hemisphere — ", phase, " Phase — ROIs: ", paste(roi_group, collapse = ", "), " =====")
  67. df_subset <- df %>%
  68. filter(Hemisphere == hemisphere, Phase == phase, Region %in% roi_group)
  70. aov_model <- aov_ez(
  71. id = "ID",
  72. dv = "Activation",
  73. within = c("RetrievalOrientation", "Region"),
  74. data = df_subset,
  75. anova_table = list(correction = "GG", es = "pes")
  76. )
  78. print(summary(aov_model))
  80. # Optional: Post hoc for RetrievalOrientation within Region
  81. em <- emmeans(aov_model, ~ RetrievalOrientation | Region)
  82. print("\nEstimated marginal means for RetrievalOrientation:")
  83. print(summary(em))
  85. return(aov_model)
  86. }
  88. ################# Angular Gyrus ############################################
  90. ##### CUE PHASE ##########
  92. ###LEFT AG###
  93. results_left_cue_AG <- run_roi_orientation_interaction(df_long, "Left", "Cue", c("PGp", "PGa"))
  94. eta_squared(results_left_cue_AG, partial = TRUE)
  96. # Region x Retrieval Orientation Interaction
  97. em_intrxn <- emmeans(results_left_cue_AG, ~ RetrievalOrientation | Region)
  98. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  99. print(contrast_results)
  100. em_summary <- summary(em_intrxn)
  101. em_summary$SD <- em_summary$SE * sqrt(30)
  102. print(em_summary)
  105. ### RIGHT AG###
  106. results_right_cue_AG <- run_roi_orientation_interaction(df_long, "Right", "Cue", c("PGp", "PGa"))
  107. eta_squared(results_right_cue_AG, partial = TRUE)
  109. # Region x Retrieval Orientation Interaction
  110. em_intrxn <- emmeans(results_right_cue_AG, ~ RetrievalOrientation | Region)
  111. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  112. print(contrast_results)
  113. em_summary <- summary(em_intrxn)
  114. em_summary$SD <- em_summary$SE * sqrt(30)
  115. print(em_summary)
  117. #Main effect of Region
  118. em_region <- emmeans(results_right_cue_AG, ~ Region)
  119. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  120. print(contrast_region)
  121. em_summary <- summary(em_region)
  122. em_summary$SD <- em_summary$SE * sqrt(30)
  123. print(em_summary)
  126. ##### Probe PHASE ##########
  128. ###LEFT AG###
  129. results_left_probe_AG <- run_roi_orientation_interaction(df_long, "Left", "Probe", c("PGp", "PGa"))
  130. eta_squared(results_left_probe_AG, partial = TRUE)
  132. #Main effect of Region
  133. em_region <- emmeans(results_left_probe_AG, ~ Region)
  134. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  135. print(contrast_region)
  136. em_summary <- summary(em_region)
  137. em_summary$SD <- em_summary$SE * sqrt(30)
  138. print(em_summary)
  140. #Main effect of Retrieval Orientation
  141. em_retorient <- emmeans(results_left_probe_AG, ~ RetrievalOrientation)
  142. contrast_retorient <- contrast(em_retorient, method = "pairwise", adjust = "holm")
  143. print(contrast_retorient)
  144. em_summary <- summary(em_retorient)
  145. em_summary$SD <- em_summary$SE * sqrt(30)
  146. print(em_summary)
  148. # Region x Retrieval Orientation Interaction
  149. em_intrxn <- emmeans(results_left_probe_AG, ~ RetrievalOrientation | Region)
  150. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  151. print(contrast_results)
  152. em_summary <- summary(em_intrxn)
  153. em_summary$SD <- em_summary$SE * sqrt(30)
  154. print(em_summary)
  156. ### RIGHT AG###
  157. results_right_probe_AG <- run_roi_orientation_interaction(df_long, "Right", "Probe", c("PGp", "PGa"))
  158. eta_squared(results_right_probe_AG, partial = TRUE)
  160. #Main effect of Region
  161. em_region <- emmeans(results_right_probe_AG, ~ Region)
  162. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  163. print(contrast_region)
  164. em_summary <- summary(em_region)
  165. em_summary$SD <- em_summary$SE * sqrt(30)
  166. print(em_summary)
  168. #Main effect of Retrieval Orientation
  169. em_retorient <- emmeans(results_right_probe_AG, ~ RetrievalOrientation)
  170. contrast_retorient <- contrast(em_retorient, method = "pairwise", adjust = "holm")
  171. print(contrast_retorient)
  172. em_summary <- summary(em_retorient)
  173. em_summary$SD <- em_summary$SE * sqrt(30)
  174. print(em_summary)
  176. ################# Precuneus ############################################
  178. ##### CUE PHASE ##########
  180. ###LEFT PRECUNEUS###
  181. results_left_cue_precuneus <- run_roi_orientation_interaction(df_long, "Left", "Cue", c("7A", "7M", "7P"))
  182. eta_squared(results_left_cue_precuneus, partial = TRUE)
  184. #Main Effect of Region
  185. em_region <- emmeans(results_left_cue_precuneus, ~ Region)
  186. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  187. print(contrast_region)
  188. em_summary <- summary(em_region)
  189. em_summary$SD <- em_summary$SE * sqrt(30)
  190. print(em_summary)
  192. #Main effect of Retrieval Orientation
  193. em_retorient <- emmeans(results_left_cue_precuneus, ~ RetrievalOrientation)
  194. contrast_retorient <- contrast(em_retorient, method = "pairwise", adjust = "holm")
  195. print(contrast_retorient)
  196. em_summary <- summary(em_retorient)
  197. em_summary$SD <- em_summary$SE * sqrt(30)
  198. print(em_summary)
  200. ###RIGHT Precuneus###
  201. results_right_cue_precuneus <- run_roi_orientation_interaction(df_long, "Right", "Cue", c("7A", "7M", "7P"))
  202. eta_squared(results_right_cue_precuneus, partial = TRUE)
  204. #Main effect of Region
  205. em_region <- emmeans(results_right_cue_precuneus, ~ Region)
  206. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  207. print(contrast_region)
  208. em_summary <- summary(em_region)
  209. em_summary$SD <- em_summary$SE * sqrt(30)
  210. print(em_summary)
  212. #Main effect of Retrieval Orientation
  213. em_retorient <- emmeans(results_right_cue_precuneus, ~ RetrievalOrientation)
  214. contrast_retorient <- contrast(em_retorient, method = "pairwise", adjust = "holm")
  215. print(contrast_retorient)
  216. em_summary <- summary(em_retorient)
  217. em_summary$SD <- em_summary$SE * sqrt(30)
  218. print(em_summary)
  219. ############################################
  222. ###################### PROBE PHASE ##########
  224. ###LEFT PRECUNEUS###
  225. results_left_probe_precuneus <- run_roi_orientation_interaction(df_long, "Left", "Probe", c("7A", "7M", "7P"))
  226. eta_squared(results_left_probe_precuneus, partial = TRUE)
  228. #Main effect of Region
  229. em_region <- emmeans(results_left_probe_precuneus, ~ Region)
  230. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  231. print(contrast_region)
  232. em_summary <- summary(em_region)
  233. em_summary$SD <- em_summary$SE * sqrt(30)
  234. print(em_summary)
  236. #Main effect of Retrieval Orientation
  237. em_retorient <- emmeans(results_left_probe_precuneus, ~ RetrievalOrientation)
  238. contrast_retorient <- contrast(em_retorient, method = "pairwise", adjust = "holm")
  239. print(contrast_retorient)
  240. em_summary <- summary(em_retorient)
  241. em_summary$SD <- em_summary$SE * sqrt(30)
  242. print(em_summary)
  244. # Region x Retrieval Orientation Interaction
  245. em_intrxn <- emmeans(results_left_probe_precuneus, ~ RetrievalOrientation | Region)
  246. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  247. print(contrast_results)
  248. em_summary <- summary(em_intrxn)
  249. em_summary$SD <- em_summary$SE * sqrt(30)
  250. print(em_summary)
  252. ###RIGHT PRECUNEUS###
  253. results_right_probe_precuneus <- run_roi_orientation_interaction(df_long, "Right", "Probe", c("7A", "7M", "7P"))
  254. eta_squared(results_right_probe_precuneus, partial = TRUE)
  256. #Main effect of Region
  257. em_region <- emmeans(results_right_probe_precuneus, ~ Region)
  258. contrast_region <- contrast(em_region, method = "pairwise", adjust = "holm")
  259. print(contrast_region)
  260. em_summary <- summary(em_region)
  261. em_summary$SD <- em_summary$SE * sqrt(30)
  262. print(em_summary)
  264. # Region x Retrieval Orientation Interaction
  265. em_intrxn <- emmeans(results_right_probe_precuneus, ~ RetrievalOrientation | Region)
  266. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  267. print(contrast_results)
  268. em_summary <- summary(em_intrxn)
  269. em_summary$SD <- em_summary$SE * sqrt(30)
  270. print(em_summary)
  272. em_intrxn <- emmeans(results_right_probe_precuneus, ~ Region | RetrievalOrientation)
  273. contrast_results <- contrast(em_intrxn, method = "pairwise", adjust = "holm")
  274. print(contrast_results)
  275. ########################################
  278. ######### Figures ################
  280. palette_by_orientation <- c(
  281. "OwnEyes" = "#1F78B4",
  282. "Observer" = "#A6CEE3",
  283. "Retrieve" = "gray45"
  284. )
  287. #### Angular Gyrus #######
  288. plot_AG_by_region_phase <- function(df, region_name, phase_name, title_text, pal, show_y_label = TRUE, y_limits, y_breaks, y_expand) {
  289. df_sub <- df %>%
  290. filter(
  291. Phase == phase_name,
  292. Hemisphere == "Left",
  293. Region == region_name,
  294. RetrievalOrientation %in% c("OwnEyes", "Observer", "Retrieve")
  295. ) %>%
  296. mutate(
  297. Orientation = factor(RetrievalOrientation, levels = c("OwnEyes", "Observer", "Retrieve"))
  298. )
  300. # summary_df <- df_sub %>%
  301. # group_by(Orientation) %>%
  302. # summarise(
  303. # mean = mean(Activation, na.rm = TRUE),
  304. # se = sd(Activation, na.rm = TRUE) / sqrt(n()),
  305. # n = n(),
  306. # .groups = "drop"
  307. # ) %>%
  308. # mutate(
  309. # ci_lower = mean - 1.96 * se,
  310. # ci_upper = mean + 1.96 * se
  311. # )
  313. # Use your within-subjects variables
  314. df_summary <- Rmisc::summarySEwithin(
  315. data = df_sub,
  316. measurevar = "Activation",
  317. withinvars = "Orientation",
  318. idvar = "ID", # change to your actual subject ID column
  319. na.rm = TRUE,
  320. conf.interval = .95
  321. )
  324. plot <- ggplot(df_summary, aes(x = Orientation, y = Activation, fill = Orientation)) +
  325. geom_col(width = 0.7, color = "black") +
  327. geom_errorbar(
  328. #aes(ymin = ci_lower, ymax = ci_upper),
  329. aes(ymin = Activation - ci, ymax = Activation + ci),
  330. width = 0.2,
  331. size = 0.8,
  332. color = "black"
  333. ) +
  334. geom_hline(yintercept = 0, color = "black", linewidth = 0.8) +
  335. scale_fill_manual(values = pal) +
  336. scale_x_discrete(labels = c("OwnEyes" = "Own Eyes", "Observer" = "Observer", "Retrieve" = "Retrieve")) +
  337. scale_y_continuous(
  338. limits = y_limits,
  339. breaks = y_breaks,
  340. labels = function(x) sprintf("%.2f", x),
  341. expand = y_expand
  342. ) +
  343. labs(
  344. title = title_text,
  345. x = NULL,
  346. y = if (show_y_label) "% Signal Change" else NULL
  347. ) +
  348. theme(
  349. plot.background = element_rect(fill = "white"),
  350. panel.background = element_rect(fill = "white"),
  351. axis.line = element_line(color = "black"),
  352. axis.ticks = element_line(size = 1),
  353. axis.ticks.length = unit(0.25, "cm"),
  354. panel.border = element_rect(colour = "black", fill = NA, size = 1.25),
  355. legend.position = "none",
  356. text = element_text(size = 24, colour = "black", family = "Arial"),
  357. axis.text = element_text(size = 20, colour = "black"),
  358. axis.text.x = element_text(size = 20),
  359. axis.title.x = element_blank(),
  360. strip.text = element_text(size = 26, face = "bold"),
  361. strip.background = element_rect(fill = "white", color = NA),
  362. plot.title = element_text(size = 26, hjust = 0.5),
  363. plot.tag.position = c(0, 0.98), # top-left corner (x = 0 [left], y = 1 [top])
  364. plot.tag = element_text(size = 26), # customize style
  365. plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "lines")
  366. )
  368. if (region_name == "PGp" & phase_name == "Cue") {
  369. plot <- plot +
  370. geom_signif(
  371. comparisons = list(c("OwnEyes", "Retrieve"), c("Observer", "Retrieve")),
  372. annotations = c("*", "*"),
  373. y_position = c(0.12, 0.09),
  374. tip_length = 0.04,
  375. textsize = 8
  376. )
  377. theme(
  378. axis.title.y = element_blank(),
  379. axis.text.y = element_blank(),
  380. axis.ticks.y = element_blank()
  381. )
  383. }
  386. if (region_name == "PGa") {
  387. plot <- plot +
  388. theme(plot.margin = unit(c(1, 0.25, 1, 1), "lines"))
  390. }
  392. if (region_name == "PGp") {
  393. plot <- plot +
  394. theme(
  395. plot.margin = unit(c(1, 1, 1, 0.1), "lines"),
  396. axis.title.y = element_blank(),
  397. axis.text.y = element_blank(),
  398. axis.ticks.y = element_blank()
  399. )
  401. }
  404. if (region_name == "PGp" & phase_name == "Probe") {
  405. plot <- plot +
  406. geom_signif(
  407. comparisons = list(c("OwnEyes", "Observer"), c("Observer", "Retrieve")),
  408. annotations = c("*", "*"),
  409. y_position = c(0.55, 0.60),
  410. tip_length = 0.04,
  411. textsize = 8
  412. )
  413. theme(
  414. axis.title.y = element_blank(),
  415. axis.text.y = element_blank(),
  416. axis.ticks.y = element_blank()
  417. )
  419. }
  422. # Return the final plot
  423. return(plot)
  424. }
  426. # Cue phase plots
  427. p_pga_cue <- plot_AG_by_region_phase(df_long, "PGa", "Cue", "Left PGa", palette_by_orientation,show_y_label = TRUE, y_limits = c(-0.06, 0.15), y_breaks = c(-0.06, -0.03, 0, 0.03, 0.06, 0.09, 0.12, 0.15), y_expand = c(0.01, 0.01))
  428. p_pgp_cue <- plot_AG_by_region_phase(df_long, "PGp", "Cue", "Left PGp", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.06, 0.15), y_breaks = c(-0.06, -0.03, 0, 0.03, 0.06, 0.09, 0.12, 0.15), y_expand = c(0.01, 0.01))
  430. # Probe phase plots
  431. p_pga_probe <- plot_AG_by_region_phase(df_long, "PGa", "Probe", "Left PGa", palette_by_orientation, show_y_label = TRUE, y_limits = c(0, 0.75), y_breaks = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7), y_expand = c(0, 0))
  432. p_pgp_probe <- plot_AG_by_region_phase(df_long, "PGp", "Probe", "Left PGp", palette_by_orientation, show_y_label = FALSE, y_limits = c(0, 0.75), y_breaks = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7), y_expand = c(0, 0))
  435. # Step 0: Add tags to each individual plot
  436. p_pga_cue <- p_pga_cue + labs(tag = "A")
  437. p_pgp_cue <- p_pgp_cue + labs(tag = "B")
  438. p_pga_probe <- p_pga_probe + labs(tag = "C")
  439. p_pgp_probe <- p_pgp_probe + labs(tag = "D")
  441. p_pga_cue <- p_pga_cue +
  442. theme(plot.tag.position = c(0.155, 0.98))
  444. p_pga_probe <- p_pga_probe +
  445. theme(plot.tag.position = c(0.155, 0.98))
  447. p_pgp_cue <- p_pgp_cue +
  448. theme(plot.tag.position = c(0.02, 0.98))
  450. p_pgp_probe <- p_pgp_probe +
  451. theme(plot.tag.position = c(0.02, 0.98))
  454. cue_annotation <- wrap_elements(
  455. full = grid::textGrob(
  456. "Cue Phase: Region x Retrieval Orientation Interaction",
  457. x = unit(0, "npc"), just = "left",
  458. gp = gpar(fontsize = 24, fontface = "italic")
  459. )
  460. )
  462. probe_annotation <- wrap_elements(
  463. full = grid::textGrob(
  464. "Probe Phase: Region x Retrieval Orientation Interaction",
  465. x = unit(0, "npc"), just = "left",
  466. gp = gpar(fontsize = 24, fontface = "italic")
  467. )
  468. )
  470. combined_plot <- wrap_plots(
  471. cue_annotation,
  472. (p_pga_cue | p_pgp_cue),
  473. probe_annotation,
  474. (p_pga_probe | p_pgp_probe),
  475. ncol = 1,
  476. heights = c(0.08, 1, 0.08, 1)
  477. )
  478. combined_plot
  480. ggsave(
  481. filename = "Figure_LeftAG_Subregions_Combined.png", # or .tiff, .pdf
  482. plot = combined_plot,
  483. path = "~/Library/CloudStorage/[email hidden]/Shared drives/MELab/Projects/RetrievalOrientation/Results/ROI/",
  484. width = 12, # in inches
  485. height = 12, # in inches
  486. dpi = 600 # high-res for publication
  487. )
  490. ### Right AG ########
  492. plot_AG_by_region_phase <- function(df, region_name, phase_name, title_text, pal, show_y_label = TRUE, y_limits, y_breaks, y_expand) {
  493. df_sub <- df %>%
  494. filter(
  495. Phase == phase_name,
  496. Hemisphere == "Right",
  497. Region == region_name,
  498. RetrievalOrientation %in% c("OwnEyes", "Observer", "Retrieve")
  499. ) %>%
  500. mutate(
  501. Orientation = factor(RetrievalOrientation, levels = c("OwnEyes", "Observer", "Retrieve"))
  502. )
  504. # summary_df <- df_sub %>%
  505. # group_by(Orientation) %>%
  506. # summarise(
  507. # mean = mean(Activation, na.rm = TRUE),
  508. # se = sd(Activation, na.rm = TRUE) / sqrt(n()),
  509. # n = n(),
  510. # .groups = "drop"
  511. # ) %>%
  512. # mutate(
  513. # ci_lower = mean - 1.96 * se,
  514. # ci_upper = mean + 1.96 * se
  515. # )
  517. # Use your within-subjects variables
  518. df_summary <- Rmisc::summarySEwithin(
  519. data = df_sub,
  520. measurevar = "Activation",
  521. withinvars = "Orientation",
  522. idvar = "ID", # change to your actual subject ID column
  523. na.rm = TRUE,
  524. conf.interval = .95
  525. )
  528. plot <- ggplot(df_summary, aes(x = Orientation, y = Activation, fill = Orientation)) +
  529. geom_col(width = 0.7, color = "black") +
  531. geom_errorbar(
  532. #aes(ymin = ci_lower, ymax = ci_upper),
  533. aes(ymin = Activation - ci, ymax = Activation + ci),
  534. width = 0.2,
  535. size = 0.8,
  536. color = "black"
  537. ) +
  538. geom_hline(yintercept = 0, color = "black", linewidth = 0.8) +
  539. scale_fill_manual(values = pal) +
  540. scale_x_discrete(labels = c("OwnEyes" = "Own Eyes", "Observer" = "Observer", "Retrieve" = "Retrieve")) +
  541. scale_y_continuous(
  542. limits = y_limits,
  543. breaks = y_breaks,
  544. labels = function(x) sprintf("%.2f", x),
  545. expand = y_expand
  546. ) +
  547. labs(
  548. title = title_text,
  549. x = NULL,
  550. y = if (show_y_label) "% Signal Change" else NULL
  551. ) +
  552. theme(
  553. plot.background = element_rect(fill = "white"),
  554. panel.background = element_rect(fill = "white"),
  555. axis.line = element_line(color = "black"),
  556. axis.ticks = element_line(size = 1),
  557. axis.ticks.length = unit(0.25, "cm"),
  558. panel.border = element_rect(colour = "black", fill = NA, size = 1.25),
  559. legend.position = "none",
  560. text = element_text(size = 24, colour = "black", family = "Arial"),
  561. axis.text = element_text(size = 20, colour = "black"),
  562. axis.text.x = element_text(size = 20),
  563. axis.title.x = element_blank(),
  564. strip.text = element_text(size = 26, face = "bold"),
  565. strip.background = element_rect(fill = "white", color = NA),
  566. plot.title = element_text(size = 26, hjust = 0.5),
  567. plot.tag.position = c(0, 0.98), # top-left corner (x = 0 [left], y = 1 [top])
  568. plot.tag = element_text(size = 26), # customize style
  569. plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "lines")
  570. )
  572. if (region_name == "PGp" & phase_name == "Cue") {
  573. plot <- plot +
  575. theme(
  576. axis.title.y = element_blank(),
  577. axis.text.y = element_blank(),
  578. axis.ticks.y = element_blank()
  579. )
  581. }
  584. if (region_name == "PGa") {
  585. plot <- plot +
  586. theme(plot.margin = unit(c(1, 0.25, 1, 1), "lines"))
  588. }
  590. if (region_name == "PGp") {
  591. plot <- plot +
  592. theme(
  593. plot.margin = unit(c(1, 1, 1, 0.1), "lines"),
  594. axis.title.y = element_blank(),
  595. axis.text.y = element_blank(),
  596. axis.ticks.y = element_blank()
  597. )
  599. }
  602. if (phase_name == "Probe") {
  603. ob_row <- df_summary[df_summary$Orientation == "Observer", ]
  605. if (nrow(ob_row) > 0) {
  606. y_value <- ob_row$Activation
  607. y_ci <- ob_row$ci
  609. # Place asterisk above or below depending on direction
  610. y_star <- if (y_value >= 0) y_value + y_ci + 0.03 else y_value - y_ci - 0.05
  612. plot <- plot +
  613. annotate("text", x = "Observer", y = y_star, label = "*", size = 10)
  614. }
  615. }
  618. # Return the final plot
  619. return(plot)
  620. }
  622. # Cue phase plots
  623. p_pga_cue <- plot_AG_by_region_phase(df_long, "PGa", "Cue", "Right PGa: Cue", palette_by_orientation,show_y_label = TRUE, y_limits = c(-0.09, 0.09), y_breaks = c(-0.09, -0.06, -0.03, 0, 0.03, 0.06, 0.09), y_expand = c(0.01, 0.01))
  624. p_pgp_cue <- plot_AG_by_region_phase(df_long, "PGp", "Cue", "Right PGp: Cue", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.09, 0.09), y_breaks = c(-0.09, -0.06, -0.03, 0, 0.03, 0.06, 0.09), y_expand = c(0.01, 0.01))
  626. # Probe phase plots
  627. p_pga_probe <- plot_AG_by_region_phase(df_long, "PGa", "Probe", "Right PGa: Probe", palette_by_orientation, show_y_label = TRUE, y_limits = c(-0.2, 0.4), y_breaks = c(-0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4), y_expand = c(0.01, 0.01))
  628. p_pgp_probe <- plot_AG_by_region_phase(df_long, "PGp", "Probe", "Right PGp: Probe", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.2, 0.4), y_breaks = c(-0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4), y_expand = c(0.01, 0.01))
  631. # Step 0: Add tags to each individual plot
  632. p_pga_cue <- p_pga_cue + labs(tag = "A")
  633. p_pgp_cue <- p_pgp_cue + labs(tag = "B")
  634. p_pga_probe <- p_pga_probe + labs(tag = "C")
  635. p_pgp_probe <- p_pgp_probe + labs(tag = "D")
  637. p_pga_cue <- p_pga_cue +
  638. theme(plot.tag.position = c(0.155, 0.98))
  640. p_pga_probe <- p_pga_probe +
  641. theme(plot.tag.position = c(0.155, 0.98))
  643. p_pgp_cue <- p_pgp_cue +
  644. theme(plot.tag.position = c(0.02, 0.98))
  646. p_pgp_probe <- p_pgp_probe +
  647. theme(plot.tag.position = c(0.02, 0.98))
  650. cue_annotation <- wrap_elements(
  651. full = grid::textGrob(
  652. "Cue Phase: Main Effect of Region (PGp > PGa)",
  653. x = unit(0, "npc"), just = "left",
  654. gp = gpar(fontsize = 24, fontface = "italic")
  655. )
  656. )
  658. probe_annotation <- wrap_elements(
  659. full = grid::textGrob(
  660. "Probe Phase: Main Effect of Retrieval Orientation (OB > OE & Retrieve)",
  661. x = unit(0, "npc"), just = "left",
  662. gp = gpar(fontsize = 24, fontface = "italic")
  663. )
  664. )
  666. combined_plot <- wrap_plots(
  667. cue_annotation,
  668. (p_pga_cue | p_pgp_cue),
  669. probe_annotation,
  670. (p_pga_probe | p_pgp_probe),
  671. ncol = 1,
  672. heights = c(0.08, 1, 0.08, 1)
  673. )
  674. combined_plot
  676. ggsave(
  677. filename = "Figure_RightAG_Subregions_Combined.png",
  678. plot = combined_plot,
  679. path = "~/Library/CloudStorage/[email hidden]/Shared drives/MELab/Projects/RetrievalOrientation/Results/ROI/",
  680. width = 12, # in inches
  681. height = 12, # in inches
  682. dpi = 600 # high-res for publication
  683. )
  686. ######## Precuneus #########
  688. plot_precuneus_by_region_phase <- function(df, region_name, phase_name, title_text, pal, show_y_label = TRUE, y_limits, y_breaks, y_expand) {
  689. df_sub <- df %>%
  690. filter(
  691. Phase == phase_name,
  692. Hemisphere == "Left",
  693. Region == region_name,
  694. RetrievalOrientation %in% c("OwnEyes", "Observer", "Retrieve")
  695. ) %>%
  696. mutate(
  697. Orientation = factor(RetrievalOrientation, levels = c("OwnEyes", "Observer", "Retrieve"))
  698. )
  700. # summary_df <- df_sub %>%
  701. # group_by(Orientation) %>%
  702. # summarise(
  703. # mean = mean(Activation, na.rm = TRUE),
  704. # se = sd(Activation, na.rm = TRUE) / sqrt(n()),
  705. # n = n(),
  706. # .groups = "drop"
  707. # ) %>%
  708. # mutate(
  709. # ci_lower = mean - 1.96 * se,
  710. # ci_upper = mean + 1.96 * se
  711. # )
  713. # Use your within-subjects variables
  714. df_summary <- Rmisc::summarySEwithin(
  715. data = df_sub,
  716. measurevar = "Activation",
  717. withinvars = "Orientation",
  718. idvar = "ID", # change to your actual subject ID column
  719. na.rm = TRUE,
  720. conf.interval = .95
  721. )
  724. plot <- ggplot(df_summary, aes(x = Orientation, y = Activation, fill = Orientation)) +
  725. geom_col(width = 0.7, color = "black") +
  727. geom_errorbar(
  728. #aes(ymin = ci_lower, ymax = ci_upper),
  729. aes(ymin = Activation - ci, ymax = Activation + ci),
  730. width = 0.2,
  731. size = 0.8,
  732. color = "black"
  733. ) +
  734. geom_hline(yintercept = 0, color = "black", linewidth = 0.8) +
  735. scale_fill_manual(values = pal) +
  736. scale_x_discrete(labels = c("OwnEyes" = "Own Eyes", "Observer" = "Observer", "Retrieve" = "Retrieve")) +
  737. scale_y_continuous(
  738. limits = y_limits,
  739. breaks = y_breaks,
  740. labels = function(x) sprintf("%.2f", x),
  741. expand = y_expand
  742. ) +
  743. labs(
  744. title = title_text,
  745. x = NULL,
  746. y = if (show_y_label) "% Signal Change" else NULL
  747. ) +
  748. theme(
  749. plot.background = element_rect(fill = "white"),
  750. panel.background = element_rect(fill = "white"),
  751. axis.line = element_line(color = "black"),
  752. axis.ticks = element_line(size = 1),
  753. axis.ticks.length = unit(0.25, "cm"),
  754. panel.border = element_rect(colour = "black", fill = NA, size = 1.25),
  755. legend.position = "none",
  756. text = element_text(size = 24, colour = "black", family = "Arial"),
  757. axis.text = element_text(size = 20, colour = "black"),
  758. axis.text.x = element_text(size = 20),
  759. axis.title.x = element_blank(),
  760. strip.text = element_text(size = 26, face = "bold"),
  761. strip.background = element_rect(fill = "white", color = NA),
  762. plot.title = element_text(size = 26, hjust = 0.5),
  763. plot.tag.position = c(0, 0.98), # top-left corner (x = 0 [left], y = 1 [top])
  764. plot.tag = element_text(size = 26), # customize style
  765. plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "lines")
  766. )
  768. if (region_name == "7P" & phase_name == "Probe") {
  769. plot <- plot +
  770. geom_signif(
  771. comparisons = list(c("Observer", "OwnEyes"), c("Observer", "Retrieve")),
  772. annotations = c("*", "*"),
  773. y_position = c(0.55, 0.48),
  774. tip_length = 0.04,
  775. textsize = 8
  776. )
  778. }
  780. if (region_name == "7M") {
  781. plot <- plot +
  782. theme(
  783. plot.margin = unit(c(1, 0.3, 1, 0.3), "lines"),
  784. axis.title.y = element_blank(),
  785. axis.text.y = element_blank(),
  786. axis.ticks.y = element_blank()
  787. )
  789. }
  791. if (region_name == "7P") {
  792. plot <- plot +
  793. theme(
  794. plot.margin = unit(c(1, 1, 1, 0.3), "lines"),
  795. axis.title.y = element_blank(),
  796. axis.text.y = element_blank(),
  797. axis.ticks.y = element_blank()
  798. )
  800. }
  802. if (region_name == "7P") {
  803. plot <- plot +
  804. theme(
  805. plot.margin = unit(c(1, 1, 1, 0.3), "lines"),
  806. axis.title.y = element_blank(),
  807. axis.text.y = element_blank(),
  808. axis.ticks.y = element_blank()
  809. )
  811. }
  813. if (phase_name == "Cue") {
  814. oe_row <- df_summary[df_summary$Orientation == "OwnEyes", ]
  816. if (nrow(oe_row) > 0) {
  817. y_value <- oe_row$Activation
  818. y_ci <- oe_row$ci
  820. # Place asterisk above or below depending on direction
  821. y_star <- if (y_value >= 0) y_value + y_ci + 0.03 else y_value - y_ci - 0.06
  823. plot <- plot +
  824. annotate("text", x = "OwnEyes", y = y_star, label = "*", size = 10)
  825. }
  826. }
  828. # Return the final plot
  829. return(plot)
  830. }
  832. # Cue phase plots
  833. p_7A_cue <- plot_precuneus_by_region_phase(df_long, "7A", "Cue", "Left 7A", palette_by_orientation, show_y_label = TRUE, y_limits = c(-0.3, 0.5), y_breaks = c(-0.30, -0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50), y_expand = c(0.01, 0.01))
  834. p_7M_cue <- plot_precuneus_by_region_phase(df_long, "7M", "Cue","Left 7M", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.3, 0.5), y_breaks = c(-0.30,-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50), y_expand = c(0.01, 0.01))
  835. p_7P_cue <- plot_precuneus_by_region_phase(df_long, "7P", "Cue","Left 7P", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.3, 0.5), y_breaks = c(-0.30,-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50), y_expand = c(0.01, 0.01))
  837. # Probe phase plots
  838. p_7A_probe <- plot_precuneus_by_region_phase(df_long, "7A","Probe", "Left 7A", palette_by_orientation, show_y_label = TRUE, y_limits = c(-0.2, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  839. p_7M_probe <- plot_precuneus_by_region_phase(df_long, "7M", "Probe","Left 7M", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.2, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  840. p_7P_probe <- plot_precuneus_by_region_phase(df_long, "7P", "Probe","Left 7P", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.2, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  843. # Step 0: Add tags to each individual plot
  844. p_7A_cue <- p_7A_cue + labs(tag = "A")
  845. p_7M_cue <- p_7M_cue + labs(tag = "B")
  846. p_7P_cue <- p_7P_cue + labs(tag = "C")
  847. p_7A_probe <- p_7A_probe + labs(tag = "D")
  848. p_7M_probe <- p_7M_probe + labs(tag = "E")
  849. p_7P_probe <- p_7P_probe + labs(tag = "F")
  851. p_7A_cue <- p_7A_cue +
  852. theme(plot.tag.position = c(0.18, 0.98))
  854. p_7A_probe <- p_7A_probe +
  855. theme(plot.tag.position = c(0.18, 0.98))
  857. p_7M_cue <- p_7M_cue +
  858. theme(plot.tag.position = c(0.02, 0.98))
  860. p_7M_probe <- p_7M_probe +
  861. theme(plot.tag.position = c(0.02, 0.98))
  863. p_7P_cue <- p_7P_cue +
  864. theme(plot.tag.position = c(0.02, 0.98))
  866. p_7P_probe <- p_7P_probe +
  867. theme(plot.tag.position = c(0.02, 0.98))
  869. cue_annotation <- wrap_elements(
  870. full = grid::textGrob(
  871. "Cue Phase: Main Effect of Retrieval Orientation (OE > Retrieve)",
  872. x = unit(0, "npc"), just = "left",
  873. gp = gpar(fontsize = 24, fontface = "italic")
  874. )
  875. )
  877. probe_annotation <- wrap_elements(
  878. full = grid::textGrob(
  879. "Probe Phase: Region x Retrieval Interaction",
  880. x = unit(0, "npc"), just = "left",
  881. gp = gpar(fontsize = 24, fontface = "italic")
  882. )
  883. )
  885. combined_plot <- wrap_plots(
  886. cue_annotation,
  887. (p_7A_cue | p_7M_cue | p_7P_cue),
  888. probe_annotation,
  889. (p_7A_probe | p_7M_probe | p_7P_probe),
  890. ncol = 1,
  891. heights = c(0.08, 1, 0.08, 1)
  892. )
  893. combined_plot
  895. ggsave(
  896. filename = "Figure_LeftPrecuneus_Subregions_Combined_barplot_nowinsorize.png", # or .tiff, .pdf
  897. plot = combined_plot,
  898. path = "~/Library/CloudStorage/[email hidden]/Shared drives/MELab/Projects/RetrievalOrientation/Results/ROI/",
  899. width = 16, # in inches
  900. height = 12, # in inches
  901. dpi = 600 # high-res for publication
  902. )
  907. ### Right Precuneus
  908. plot_precuneus_by_region_phase <- function(df, region_name, phase_name, title_text, pal, show_y_label = TRUE, y_limits, y_breaks, y_expand) {
  909. df_sub <- df %>%
  910. filter(
  911. Phase == phase_name,
  912. Hemisphere == "Right",
  913. Region == region_name,
  914. RetrievalOrientation %in% c("OwnEyes", "Observer", "Retrieve")
  915. ) %>%
  916. mutate(
  917. Orientation = factor(RetrievalOrientation, levels = c("OwnEyes", "Observer", "Retrieve"))
  918. )
  920. # summary_df <- df_sub %>%
  921. # group_by(Orientation) %>%
  922. # summarise(
  923. # mean = mean(Activation, na.rm = TRUE),
  924. # se = sd(Activation, na.rm = TRUE) / sqrt(n()),
  925. # n = n(),
  926. # .groups = "drop"
  927. # ) %>%
  928. # mutate(
  929. # ci_lower = mean - 1.96 * se,
  930. # ci_upper = mean + 1.96 * se
  931. # )
  933. # Use your within-subjects variables
  934. df_summary <- Rmisc::summarySEwithin(
  935. data = df_sub,
  936. measurevar = "Activation",
  937. withinvars = "Orientation",
  938. idvar = "ID", # change to your actual subject ID column
  939. na.rm = TRUE,
  940. conf.interval = .95
  941. )
  944. plot <- ggplot(df_summary, aes(x = Orientation, y = Activation, fill = Orientation)) +
  945. geom_col(width = 0.7, color = "black") +
  947. geom_errorbar(
  948. #aes(ymin = ci_lower, ymax = ci_upper),
  949. aes(ymin = Activation - ci, ymax = Activation + ci),
  950. width = 0.2,
  951. size = 0.8,
  952. color = "black"
  953. ) +
  954. geom_hline(yintercept = 0, color = "black", linewidth = 0.8) +
  955. scale_fill_manual(values = pal) +
  956. scale_x_discrete(labels = c("OwnEyes" = "Own Eyes", "Observer" = "Observer", "Retrieve" = "Retrieve")) +
  957. scale_y_continuous(
  958. limits = y_limits,
  959. breaks = y_breaks,
  960. labels = function(x) sprintf("%.2f", x),
  961. expand = y_expand
  962. ) +
  963. labs(
  964. title = title_text,
  965. x = NULL,
  966. y = if (show_y_label) "% Signal Change" else NULL
  967. ) +
  968. theme(
  969. plot.background = element_rect(fill = "white"),
  970. panel.background = element_rect(fill = "white"),
  971. axis.line = element_line(color = "black"),
  972. axis.ticks = element_line(size = 1),
  973. axis.ticks.length = unit(0.25, "cm"),
  974. panel.border = element_rect(colour = "black", fill = NA, size = 1.25),
  975. legend.position = "none",
  976. text = element_text(size = 24, colour = "black", family = "Arial"),
  977. axis.text = element_text(size = 20, colour = "black"),
  978. axis.text.x = element_text(size = 20),
  979. axis.title.x = element_blank(),
  980. strip.text = element_text(size = 26, face = "bold"),
  981. strip.background = element_rect(fill = "white", color = NA),
  982. plot.title = element_text(size = 26, hjust = 0.5),
  983. plot.tag.position = c(0, 0.98), # top-left corner (x = 0 [left], y = 1 [top])
  984. plot.tag = element_text(size = 26), # customize style
  985. plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "lines")
  986. )
  988. if (region_name == "7A") {
  989. plot <- plot +
  990. theme(plot.margin = unit(c(1, 0.3, 1, 1), "lines"))
  992. }
  994. if (region_name == "7M") {
  995. plot <- plot +
  996. theme(
  997. plot.margin = unit(c(1, 0.3, 1, 0.3), "lines"),
  998. axis.title.y = element_blank(),
  999. axis.text.y = element_blank(),
  1000. axis.ticks.y = element_blank()
  1001. )
  1003. }
  1005. if (region_name == "7P") {
  1006. plot <- plot +
  1007. theme(
  1008. plot.margin = unit(c(1, 1, 1, 0.3), "lines"),
  1009. axis.title.y = element_blank(),
  1010. axis.text.y = element_blank(),
  1011. axis.ticks.y = element_blank()
  1012. )
  1014. }
  1016. if (phase_name == "Cue") {
  1017. oe_row <- df_summary[df_summary$Orientation == "OwnEyes", ]
  1019. if (nrow(oe_row) > 0) {
  1020. y_value <- oe_row$Activation
  1021. y_ci <- oe_row$ci
  1023. # Place asterisk above or below depending on direction
  1024. y_star <- if (y_value >= 0) y_value + y_ci + 0.03 else y_value - y_ci - 0.07
  1026. plot <- plot +
  1027. annotate("text", x = "OwnEyes", y = y_star, label = "*", size = 10)
  1028. }
  1029. }
  1031. # Return the final plot
  1032. return(plot)
  1033. }
  1035. # Cue phase plots
  1036. p_7A_cue <- plot_precuneus_by_region_phase(df_long, "7A", "Cue", "Right 7A", palette_by_orientation, show_y_label = TRUE, y_limits = c(-0.20, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  1037. p_7M_cue <- plot_precuneus_by_region_phase(df_long, "7M", "Cue","Right 7M", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.20, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  1038. p_7P_cue <- plot_precuneus_by_region_phase(df_long, "7P", "Cue","Right 7P", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.20, 0.70), y_breaks = c(-0.20, -0.10, 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70), y_expand = c(0.01, 0.01))
  1040. # Probe phase plots
  1041. p_7A_probe <- plot_precuneus_by_region_phase(df_long, "7A","Probe", "Right 7A", palette_by_orientation, show_y_label = TRUE, y_limits = c(-0.40, 0.80), y_breaks = c(-0.40, -0.20, 0, 0.20, 0.40, 0.60, 0.80), y_expand = c(0.01, 0.01))
  1042. p_7M_probe <- plot_precuneus_by_region_phase(df_long, "7M", "Probe","Right 7M", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.40, 0.80), y_breaks = c(-0.40, -0.20, 0, 0.20, 0.40, 0.60, 0.80), y_expand = c(0.01, 0.01))
  1043. p_7P_probe <- plot_precuneus_by_region_phase(df_long, "7P", "Probe","Right 7P", palette_by_orientation, show_y_label = FALSE, y_limits = c(-0.40, 0.80), y_breaks = c(-0.40, -0.20, 0, 0.20, 0.40, 0.60, 0.80), y_expand = c(0.01, 0.01))
  1046. # Step 0: Add tags to each individual plot
  1047. p_7A_cue <- p_7A_cue + labs(tag = "A")
  1048. p_7M_cue <- p_7M_cue + labs(tag = "B")
  1049. p_7P_cue <- p_7P_cue + labs(tag = "C")
  1050. p_7A_probe <- p_7A_probe + labs(tag = "D")
  1051. p_7M_probe <- p_7M_probe + labs(tag = "E")
  1052. p_7P_probe <- p_7P_probe + labs(tag = "F")
  1054. p_7A_cue <- p_7A_cue +
  1055. theme(plot.tag.position = c(0.18, 0.98))
  1057. p_7A_probe <- p_7A_probe +
  1058. theme(plot.tag.position = c(0.18, 0.98))
  1060. p_7M_cue <- p_7M_cue +
  1061. theme(plot.tag.position = c(0.02, 0.98))
  1063. p_7M_probe <- p_7M_probe +
  1064. theme(plot.tag.position = c(0.02, 0.98))
  1066. p_7P_cue <- p_7P_cue +
  1067. theme(plot.tag.position = c(0.02, 0.98))
  1069. p_7P_probe <- p_7P_probe +
  1070. theme(plot.tag.position = c(0.02, 0.98))
  1072. cue_annotation <- wrap_elements(
  1073. full = grid::textGrob(
  1074. "Cue Phase: Main Effect of Retrieval Orientation (Own Eyes > Retrieve)",
  1075. x = unit(0, "npc"), just = "left",
  1076. gp = gpar(fontsize = 24, fontface = "italic")
  1077. )
  1078. )
  1080. probe_annotation <- wrap_elements(
  1081. full = grid::textGrob(
  1082. "Probe Phase: Main Effect of Region (7M > 7A & 7P)",
  1083. x = unit(0, "npc"), just = "left",
  1084. gp = gpar(fontsize = 24, fontface = "italic")
  1085. )
  1086. )
  1088. combined_plot <- wrap_plots(
  1089. cue_annotation,
  1090. (p_7A_cue | p_7M_cue | p_7P_cue),
  1091. probe_annotation,
  1092. (p_7A_probe | p_7M_probe | p_7P_probe),
  1093. ncol = 1,
  1094. heights = c(0.08, 1, 0.08, 1)
  1095. )
  1096. combined_plot
  1098. ggsave(
  1099. filename = "Figure_RightPrecuneus_Subregions_Combined.png", # or .tiff, .pdf
  1100. plot = combined_plot,
  1101. path = "~/Library/CloudStorage/[email hidden]/Shared drives/MELab/Projects/RetrievalOrientation/Results/",
  1102. height = 12, # in inches
  1103. dpi = 600 # high-res for publication
  1104. )

PosteriorParietalROISubregions_RetrievalOrientation.R, no license · at the source

Overview

Authors: Selen Küçüktaş1, Peggy L St Jacques1
ORCID iDs: Selen Küçüktaş, Peggy L St Jacques
  1. Department of Psychology, University of Alberta, Edmonton, AB, Canada
Institutions: University of Alberta (Canada)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1313
Dates: received 30 September 2025; accepted 27 June 2026; published online 27 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1313 · PMID 42524184 · PMCID PMC13409283 · OpenAlex W4411509751
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.20.660447
Status: code verified
Categories: cognitive (subfield)
Methods: Statistics, fMRI & imaging
Keywords: angular gyrus, autobiographical memory, precuneus, retrieval orientation, visual perspective
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: University of Alberta; Canada Research Chairs (RGPIN-2019-06080); Natural Sciences and Engineering Research Council of Canada (rgpin-2019-06080, DGECR‐2019‐00407, RGPIN-2019)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Autobiographical memory (AM) retrieval involves goal-directed and reconstructive processes that unfold over time. A key feature of this process is the visual perspective adopted during remembering, which shapes subjective memory experience. Using fMRI, we cued participants to retrieve AMs from an own eyes, observer, or natural perspective followed by an event probe. Our design temporally isolates preparatory (cue phase) and reconstructive (probe phase) mechanisms to identify the neural signatures of retrieval orientation, the strategic use of cues to optimize retrieval. Whole-brain and ROI analyses revealed that the angular gyrus (AG) and precuneus support perspective-guided retrieval in distinct ways. During the cue phase, PGp showed greater activity for instructed perspectives than natural retrieval, consistent with preparatory perspective selection. During the probe phase, observer-perspective retrieval elicited greater activity in AG and precuneus, supporting sustained perspective maintenance. Brain–behavior models linked PGp and 7P activity to greater vividness and perspective stability, while precuneus (7M) activity was negatively associated with emotional intensity, especially in the observer condition. These findings reveal phase- and subregion-specific contributions of posterior parietal cortex to the subjective qualities of memory. AG subregions support goal-directed perspective selection and implementation, while precuneus subregions flexibly modulate phenomenological features during memory reconstruction.

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 1 match between paragraphs and lines of code.

OSF 4785r

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 12 files, 3 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (3 files), patchwork (3 files), tidyverse (3 files), afex (2 files), easystats (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/4785r/

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;
  • 3 scripts, each with its path and the digest of its content;
  • 1 match 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

Data and code supporting the findings of this study are publicly available via the Open Science Framework: https://osf.io/4785r/.

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 2, 28 September 2026

  • Authors: added Selen Küçüktaş (0000-0001-5735-5302); removed Selen Küçüktaş
  • Funding: added University of Alberta; Canada Research Chairs: RGPIN-2019-06080; Natural Sciences and Engineering Research Council of Canada: rgpin-2019-06080, DGECR‐2019‐00407, RGPIN-2019

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 62 references.

Cite

This paper

Küçüktaş, S., & St Jacques, P. L. (2026). Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1313. https://doi.org/10.1162/imag.a.1313

BibTeX

@article{kucuktas2026functional,
author = {Küçüktaş, Selen and St Jacques, Peggy L},
title = {{Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1313},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1313},
url = {https://doi.org/10.1162/imag.a.1313},
pmid = {42524184},
pmcid = {PMC13409283}
}

RIS

TY - JOUR
AU - Küçüktaş, Selen
AU - St Jacques, Peggy L
TI - Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/27
VL - 4
SP - IMAG.a.1313
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1313
UR - https://doi.org/10.1162/imag.a.1313
LA - en
ER -

CSL-JSON

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"title": "Functional specialization of angular gyrus and precuneus subregions for perspective-guided autobiographical memory retrieval",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
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"family": "Küçüktaş",
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"given": "Peggy L"
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],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1313",
"DOI": "10.1162/imag.a.1313",
"PMID": "42524184",
"PMCID": "PMC13409283",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1313",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
27
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]
}
}

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