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Bilingualism modulates functional connectivity induced by a domain-general artificial grammar learning task.

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  1. [1] § Method › Statistical analysis ↔ Analysis/EEG/BIFUNC Supplementary Model Comparisons.Rmd, lines 166–181 · score 0.96 · approximate Likelihood Ratio, Akaike Information Criterion, Chi Square, anova.gam, adding handedness, better fit
  2. [2] § Results › Post-task ↔ Analysis/EEG/BIFUNC Pre- & Post-task Statistical Analysis.Rmd, lines 478–496 · score 0.57 · medial frontal, medial occipital, right parietal, post task, pre task, smooths
  3. [3] § Results › Post-task ↔ Analysis/EEG/BIFUNC Pre- & Post-task Statistical Analysis.Rmd, lines 794–836 · score 0.53 · right parietal, LSBQ composite score, post task, pre task, smooth, connection

Paper

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The authors' code

R Markdown · 840 lines · 23 KB · no license · 2 matches

  1. ---
  2. title: "BIFUNC Pre- & Post-task Statistical Analysis"
  3. author: "Alex Sheehan"
  4. date: "2025-09-16"
  5. output:
  6. html_document:
  7. theme: flatly
  8. highlight: tango
  9. toc: TRUE
  10. toc_float:
  11. collapsed: false
  12. smooth_scroll: true
  13. toc_depth: 3
  14. ---
  16. # Set-up
  18. ```{r setup, include=FALSE}
  19. knitr::opts_chunk$set(echo = TRUE,
  20. message = FALSE,
  21. warning = FALSE,
  22. dev = 'png')
  23. options(scipen=999)
  24. ```
  26. ## Load packages
  28. ```{r message=FALSE, warning=FALSE}
  29. library(readxl)
  30. library(mgcv)
  31. library(ggplot2)
  32. library(reshape2)
  33. library(tidyverse)
  34. library(tidygam)
  35. library(gratia)
  36. library(dplyr)
  37. ```
  39. ## Load data
  41. After the first time you run this markdown, this chunk should then find 'Data/projectdata_long.RData' and pre-load it
  43. ```{r}
  44. if (file.exists("Data/projectdata_long.RData")) {
  45. load("Data/projectdata_long.RData")
  46. print("Loaded 'projectdata_long'. Skip straight to 'Functions' section.")
  47. } else {
  48. print("Could not find 'projectdata_long' RData file. Importing original data sheet for wrangling...")
  49. data <- read_excel("Data/BIFUNC_data_GC_matrices.xlsx")
  50. maindata <- data.frame(data)
  51. }
  52. ```
  54. ## Set up data
  56. ```{r message=FALSE}
  57. # "postfib" = Domain-general
  58. # "postflan" = Attentional domain
  59. # "postmulti" = Language domain
  61. if (exists("projectdata_long")) {
  62. print("'projectdata_long' already loaded. Skip straight to 'Functions' section")
  63. } else {
  64. # Check column names
  65. names(maindata)
  66. }
  67. ```
  69. ### Set variable types
  71. ```{r collapse=TRUE}
  72. if (exists("projectdata_long")) {
  73. print("'projectdata_long' already loaded. Skip straight to 'Functions' section")
  74. } else {
  75. # Gender - nominal factor - 2 levels
  76. maindata$gender_quant <- factor(maindata$gender_quant,
  77. levels = c("1", "2"),
  78. labels = c("Female", "Male"))
  79. contrasts(maindata$gender_quant) <- "contr.treatment"
  81. # Subject Education level - ordinal factor - 5 levels
  82. maindata$education_subj_quant <- factor(maindata$education_subj_quant,
  83. levels = c("3", "4", "5"),
  84. labels = c("A-Level",
  85. "Degree/Diploma",
  86. "Postgraduate"),
  87. ordered = TRUE)
  88. contrasts(maindata$education_subj_quant) <- "contr.sum"
  90. # Subjid - factor - random effect
  91. maindata$subjid <- as.factor(maindata$subjid)
  92. }
  93. ```
  95. ### Convert to long format
  97. ```{r}
  98. if (exists("projectdata_long")) {
  99. print("'projectdata_long' has already been made & saved. Skip straight to 'Functions' section")
  100. } else {
  101. projectdata_long <- maindata
  103. projectdata_long <- maindata %>%
  104. pivot_longer(
  105. cols = matches("^(pretask|postfib|postflan|postmulti)_"),
  106. names_to = c("condition", "region"),
  107. names_pattern = "([^_]+)_(.*)",
  108. values_to = "connectivity"
  109. )
  111. # Set region to factor
  112. projectdata_long$region <- as.factor(projectdata_long$region)
  114. # Remove LSBQ_comp outlier
  115. projectdata_long <- subset(projectdata_long, subjid != "30")
  117. # Save for quick loading in future
  118. save(projectdata_long, file = "Data/projectdata_long.RData")
  119. }
  120. ```
  123. ## Functions
  125. Make sure to run all 3 of these to avoid errors later in the markdown
  127. ### MC correction function
  129. ```{r}
  130. extract_pre_post_pvals <- function(model, model_id = "model1") {
  131. smry <- summary(model)
  133. if (!is.null(smry$s.table) && "p-value" %in% colnames(smry$s.table)) {
  134. df <- data.frame(
  135. model_id = model_id,
  136. term = rownames(smry$s.table),
  137. p_value = smry$s.table[, "p-value"]
  138. )
  140. rownames(df) <- NULL
  141. df <- df %>%
  142. filter(grepl("^s\\(lsbq_comp):region", term)) %>%
  143. mutate(
  144. region = sub("^s\\(lsbq_comp):region", "", term),
  145. p_adj_fdr = p.adjust(p_value, method = "fdr"),
  146. sig_raw = ifelse(p_value < 0.05, "SIG", "-"),
  147. sig_fdr = ifelse(p_adj_fdr < 0.05, "SIG", "-")
  148. )
  149. return(df)
  150. } else {
  151. return(data.frame(
  152. model_id = character(0),
  153. term = character(0),
  154. p_value = numeric(0),
  155. region = character(0),
  156. p_adj_fdr = numeric(0),
  157. sig_raw = character(0),
  158. sig_fdr = character(0)
  159. ))
  160. }
  161. }
  162. ```
  164. ### Pre-task plots function
  166. ```{r}
  167. generate_pretask_plots <- function(model, term_list, color = "green4") {
  168. plot_list <- list()
  170. for (term in term_list) {
  171. region_label <- gsub("s\\(lsbq_comp):region", "", term)
  172. title_text <- gsub("_", " ", region_label)
  173. title_text <- tools::toTitleCase(title_text)
  175. plot_obj <- draw(model, select = term) +
  176. labs(
  177. title = paste("Pre-task", title_text, "Connectivity"),
  178. y = "Connectivity",
  179. x = "LSBQ Composite Score"
  180. ) +
  181. geom_line(color = color)
  183. plot_obj$layers[[1]]$aes_params$fill <- color
  184. plot_list[[region_label]] <- plot_obj
  185. print(plot_obj)
  186. }
  187. return(plot_list)
  188. }
  189. ```
  191. ### Post-task plots function
  193. ```{r}
  194. generate_posttask_plots <- function(model, term_list, color = "orange2") {
  195. plot_list <- list()
  197. for (term in term_list) {
  198. region_label <- gsub("s\\(lsbq_comp):region", "", term)
  199. title_text <- gsub("_", " ", region_label)
  200. title_text <- tools::toTitleCase(title_text)
  202. plot_obj <- draw(model, select = term) +
  203. labs(
  204. title = paste("Post-task", title_text, "Connectivity"),
  205. y = "Connectivity",
  206. x = "LSBQ Composite Score"
  207. ) +
  208. geom_line(color = color)
  210. plot_obj$layers[[1]]$aes_params$fill <- color
  211. plot_list[[region_label]] <- plot_obj
  212. print(plot_obj)
  213. }
  214. return(plot_list)
  215. }
  216. ```
  218. ### Save GAM checks function
  220. ```{r}
  221. save_gam_check <- function(model, name) {
  222. subfolder <- if (grepl("pretask", name, ignore.case = TRUE)) {
  223. "Checks/gam.check/Pretask"
  224. } else {
  225. "Checks/gam.check/Posttask"
  226. }
  227. dir.create(subfolder, recursive = TRUE, showWarnings = FALSE)
  229. txt <- capture.output(gam.check(model, k.rep = 1000))
  230. writeLines(txt, file.path(subfolder, paste0(name, "_gam.check.txt")))
  232. pdf(file.path(subfolder, paste0(name, "_gam.check.pdf")),
  233. width = 8.27, height = 11.69)
  234. lines_per_page <- 70
  235. total_lines <- length(txt)
  236. pages <- ceiling(total_lines / lines_per_page)
  238. for (i in seq_len(pages)) {
  239. start_line <- (i - 1) * lines_per_page + 1
  240. end_line <- min(i * lines_per_page, total_lines)
  242. plot.new()
  243. text(0, 1, paste(txt[start_line:end_line], collapse = "\n"),
  244. adj = c(0, 1), family = "mono", cex = 0.6)
  245. }
  246. gam.check(model, k.rep = 1000)
  247. dev.off()
  248. }
  249. ```
  253. # Models
  255. ## Load Pre-fitted
  257. After running this markdown for the first time, the chunk below should then find the listed models & summaries
  258. This avoids having to re-fit everything each time the markdown is loaded
  260. ```{r}
  261. gam_files <- c(
  262. "Models/lsbq_pretask.RData",
  263. "Models/lsbq_posttask.RData",
  264. "Summaries/lsbq_pretask_summ.RData",
  265. "Summaries/lsbq_posttask_summ.RData"
  266. )
  268. for (f in gam_files) {
  269. if (file.exists(f)) {
  270. load(f)
  271. print(paste("Loaded:", f))
  272. } else {
  273. print(paste("No previously-fitted GAM or summary found at", f))
  274. }
  275. }
  277. # Clean up env
  278. rm(f)
  279. rm(gam_files)
  280. ```
  283. ## Fitting
  285. ### Pre-task
  287. #### Construct & FDR correct
  289. ```{r}
  290. if (!exists("lsbq_pretask")) {
  292. lsbq_pretask <- bam(connectivity ~
  293. s(lsbq_comp, by = region, k = 15, sp = 0.02) + # k = 15 means k-value (no. of basis functions) = 14
  294. region +
  295. s(taskorder, bs = "re") +
  296. s(subjid, bs = "re") +
  297. age + lang2_aoa + education_subj_quant + gender_quant,
  298. data = projectdata_long %>%
  299. filter(condition == "pretask"),
  300. method = "fREML", discrete = TRUE, nthreads = 21)
  301. save(lsbq_pretask, file = "Models/lsbq_pretask.RData")
  302. lsbq_pretask_summ <- summary(lsbq_pretask)
  303. save(lsbq_pretask_summ, file = "Summaries/lsbq_pretask_summ.RData")
  304. print(lsbq_pretask_summ)
  306. pretask_pvals <- extract_pre_post_pvals(lsbq_pretask, model_id = "lsbq_pretask")
  308. write.csv(
  309. pretask_pvals,
  310. file = "MC_corrections/lsbq_pretask_pvals_fdr.csv",
  311. row.names = FALSE
  312. )
  314. } else {
  316. print("lsbq_pretask model already exists in environment; skipping re-fit & printing summary...")
  318. if (exists("lsbq_pretask_summ")) {
  320. print(lsbq_pretask_summ)
  321. pretask_pvals <- extract_pre_post_pvals(lsbq_pretask, model_id = "lsbq_pretask")
  323. } else {
  325. print("lsbq_pretask_summ object not found, saving for later retrieval & printing summary...")
  326. lsbq_pretask_summ <- summary(lsbq_pretask)
  327. save(lsbq_pretask_summ, file = "Summaries/lsbq_pretask_summ.RData")
  328. print(lsbq_pretask_summ)
  330. pretask_pvals <- extract_pre_post_pvals(lsbq_pretask, model_id = "lsbq_pretask")
  332. if (file.exists("MC_corrections/lsbq_pretask_pvals_fdr.csv")) {
  334. print("Pre-task FDR-corrected CSV file already exists; skipping saving...")
  336. } else {
  338. write.csv(
  339. pretask_pvals,
  340. file = "MC_corrections/lsbq_pretask_pvals_fdr.csv",
  341. row.names = FALSE
  342. )
  344. }
  345. }
  346. }
  347. ```
  349. ##### Corrected p-values
  351. ```{r}
  352. print(pretask_pvals[, c("region", "p_adj_fdr", "sig_fdr")])
  353. ```
  355. #### Plot
  357. ```{r}
  358. # List significant terms post-FDR correction
  359. pretask_sig_mods <- list(
  360. "s(lsbq_comp):regionleft_central_right_central",
  361. "s(lsbq_comp):regionleft_central_right_parietal",
  362. "s(lsbq_comp):regionmedial_occipital_medial_frontal",
  363. "s(lsbq_comp):regionright_central_medial_occipital",
  364. "s(lsbq_comp):regionright_temporal_right_parietal"
  365. )
  367. # Plot preliminary visualisations
  368. pretask_plots <- generate_pretask_plots(lsbq_pretask, pretask_sig_mods)
  369. ```
  372. ### Post-task
  374. #### Construct & FDR correct
  376. ```{r}
  377. if (!exists("lsbq_posttask")) {
  379. lsbq_posttask <- bam(connectivity ~
  380. s(lsbq_comp, by = region, k = 15, sp = 0.02) + # k = 15 means k-value (no. of basis functions) = 14
  381. region +
  382. s(taskorder, bs = "re") +
  383. s(subjid, bs = "re") +
  384. age + lang2_aoa + education_subj_quant + gender_quant,
  385. data = projectdata_long %>%
  386. filter(condition == "postfib"),
  387. method = "fREML", discrete = TRUE, nthreads = 21)
  388. save(lsbq_posttask, file = "Models/lsbq_posttask.RData")
  389. lsbq_posttask_summ <- summary(lsbq_posttask)
  390. save(lsbq_posttask_summ, file = "Summaries/lsbq_posttask_summ.RData")
  391. print(lsbq_posttask_summ)
  393. posttask_pvals <- extract_pre_post_pvals(lsbq_posttask, model_id = "lsbq_posttask")
  395. write.csv(
  396. posttask_pvals,
  397. file = "MC_corrections/lsbq_posttask_pvals_fdr.csv",
  398. row.names = FALSE
  399. )
  401. } else {
  403. print("lsbq_posttask model already exists in environment; skipping re-fit & printing summary...")
  405. if (exists("lsbq_posttask_summ")) {
  407. print(lsbq_posttask_summ)
  408. posttask_pvals <- extract_pre_post_pvals(lsbq_posttask, model_id = "lsbq_posttask")
  410. } else {
  412. print("lsbq_posttask_summ object not found, saving for later retrieval & printing summary...")
  413. lsbq_posttask_summ <- summary(lsbq_posttask)
  414. save(lsbq_posttask_summ, file = "Summaries/lsbq_posttask_summ.RData")
  415. print(lsbq_posttask_summ)
  417. posttask_pvals <- extract_pre_post_pvals(lsbq_posttask, model_id = "lsbq_posttask")
  419. if (file.exists("MC_corrections/lsbq_posttask_pvals_fdr.csv")) {
  421. print("Post-task FDR-corrected CSV file already exists; skipping saving...")
  423. } else {
  425. write.csv(
  426. posttask_pvals,
  427. file = "MC_corrections/lsbq_posttask_pvals_fdr.csv",
  428. row.names = FALSE
  429. )
  431. }
  432. }
  433. }
  434. ```
  436. ##### Corrected p-values
  438. ```{r}
  439. print(posttask_pvals[, c("region", "p_adj_fdr", "sig_fdr")])
  440. ```
  442. #### Plot
  444. ```{r}
  445. # List significant terms post-FDR correction
  446. posttask_sig_mods <- list(
  447. "s(lsbq_comp):regionleft_central_left_temporal",
  448. "s(lsbq_comp):regionleft_central_medial_occipital",
  449. "s(lsbq_comp):regionleft_central_right_parietal",
  450. "s(lsbq_comp):regionleft_temporal_left_parietal",
  451. "s(lsbq_comp):regionmedial_frontal_left_central",
  452. "s(lsbq_comp):regionmedial_frontal_left_temporal",
  453. "s(lsbq_comp):regionmedial_frontal_medial_occipital"
  454. )
  456. # Plot preliminary visualisations
  457. posttask_plots <- generate_posttask_plots(lsbq_posttask, posttask_sig_mods)
  458. ```
  462. # Model Checks
  464. ## Pre-task
  466. ```{r}
  467. save_gam_check(lsbq_pretask, "lsbq_pretask")
  468. ```
  470. ## Post-task
  472. ```{r}
  473. save_gam_check(lsbq_posttask, "lsbq_posttask")
  474. ```
  478. # Individual plots
  480. Running the chunks below will overwrite any plots already generated & saved
  482. ## Pre-task
  484. ```{r}
  485. # Show index numbers of terms in model
  486. invisible(lapply(seq_along(lsbq_pretask$smooth), function(i) {
  487. cat(paste0("[", i, "] ", lsbq_pretask$smooth[[i]]$label, "\n"))
  488. }))
  490. # # Sig. terms & index numbers
  491. # s(lsbq_comp):regionleft_central_right_central - [5]
  492. # s(lsbq_comp):regionleft_central_right_parietal - [6]
  493. # s(lsbq_comp):regionmedial_occipital_medial_frontal - [32]
  494. # s(lsbq_comp):regionright_central_medial_occipital - [40]
  495. # s(lsbq_comp):regionright_temporal_right_parietal - [56]
  496. ```
  498. ### Left Central to Right Central
  500. ```{r}
  501. plt_lsbq_pretask_lefcen_rigcen <- draw(lsbq_pretask, select = 5) +
  502. labs(title = "Left Central to Right Central",
  503. subtitle = NULL) +
  504. ylab("Granger Causality") +
  505. xlab("LSBQ Composite Score") +
  506. geom_line(color = "green4")
  507. plt_lsbq_pretask_lefcen_rigcen$layers[[1]]$aes_params$fill <- "green4"
  508. print(plt_lsbq_pretask_lefcen_rigcen)
  510. ggsave(
  511. "Pre-task - Left Central to Right Central.svg",
  512. device = "svg",
  513. path = "Plots/SVG/Pretask")
  515. ggsave(
  516. "Pre-task - Left Central to Right Central.png",
  517. device = "png",
  518. path = "Plots/PNG/Pretask")
  519. ```
  521. ### Left Central to Right Parietal
  523. ```{r}
  524. plt_lsbq_pretask_lefcen_rigpar <- draw(lsbq_pretask, select = 6) +
  525. labs(title = "Left Central to Right Parietal",
  526. subtitle = NULL) +
  527. ylab("Granger Causality") +
  528. xlab("LSBQ Composite Score") +
  529. geom_line(color = "green4")
  530. plt_lsbq_pretask_lefcen_rigpar$layers[[1]]$aes_params$fill <- "green4"
  531. print(plt_lsbq_pretask_lefcen_rigpar)
  533. ggsave(
  534. "Pre-task - Left Central to Right Parietal.svg",
  535. device = "svg",
  536. path = "Plots/SVG/Pretask")
  538. ggsave(
  539. "Pre-task - Left Central to Right Parietal.png",
  540. device = "png",
  541. path = "Plots/PNG/Pretask")
  542. ```
  544. ### Medial Occipital to Medial Frontal
  546. ```{r}
  547. plt_lsbq_pretask_medocc_medfro <- draw(lsbq_pretask, select = 32) +
  548. labs(title = "Medial Occipital to Medial Frontal",
  549. subtitle = NULL) +
  550. ylab("Granger Causality") +
  551. xlab("LSBQ Composite Score") +
  552. geom_line(color = "green4")
  553. plt_lsbq_pretask_medocc_medfro$layers[[1]]$aes_params$fill <- "green4"
  554. print(plt_lsbq_pretask_medocc_medfro)
  556. ggsave(
  557. "Pre-task - Medial Occipital to Medial Frontal.svg",
  558. device = "svg",
  559. path = "Plots/SVG/Pretask")
  561. ggsave(
  562. "Pre-task - Medial Occipital to Medial Frontal.png",
  563. device = "png",
  564. path = "Plots/PNG/Pretask")
  565. ```
  567. ### Right Central to Medial Occipital
  569. ```{r}
  570. plt_lsbq_pretask_rigcen_medocc <- draw(lsbq_pretask, select = 40) +
  571. labs(title = "Right Central to Medial Occipital",
  572. subtitle = NULL) +
  573. ylab("Granger Causality") +
  574. xlab("LSBQ Composite Score") +
  575. geom_line(color = "green4")
  576. plt_lsbq_pretask_rigcen_medocc$layers[[1]]$aes_params$fill <- "green4"
  577. print(plt_lsbq_pretask_rigcen_medocc)
  579. ggsave(
  580. "Pre-task - Right Central to Medial Occipital.svg",
  581. device = "svg",
  582. path = "Plots/SVG/Pretask")
  584. ggsave(
  585. "Pre-task - Right Central to Medial Occipital.png",
  586. device = "png",
  587. path = "Plots/PNG/Pretask")
  588. ```
  590. ### Right Temporal to Right Parietal
  592. ```{r}
  593. plt_lsbq_pretask_rigtem_rigpar <- draw(lsbq_pretask, select = 56) +
  594. labs(title = "Right Temporal to Right Parietal",
  595. subtitle = NULL) +
  596. ylab("Granger Causality") +
  597. xlab("LSBQ Composite Score") +
  598. geom_line(color = "green4")
  599. plt_lsbq_pretask_rigtem_rigpar$layers[[1]]$aes_params$fill <- "green4"
  600. print(plt_lsbq_pretask_rigtem_rigpar)
  602. ggsave(
  603. "Pre-task - Right Temporal to Right Parietal.svg",
  604. device = "svg",
  605. path = "Plots/SVG/Pretask")
  607. ggsave(
  608. "Pre-task - Right Temporal to Right Parietal.png",
  609. device = "png",
  610. path = "Plots/PNG/Pretask")
  611. ```
  614. ## Post-task
  616. ```{r}
  617. # Show index numbers of terms in model
  618. invisible(lapply(seq_along(lsbq_posttask$smooth), function(i) {
  619. cat(paste0("[", i, "] ", lsbq_posttask$smooth[[i]]$label, "\n"))
  620. }))
  622. # s(lsbq_comp):regionleft_central_left_temporal - [2]
  623. # s(lsbq_comp):regionleft_central_medial_occipital - [4]
  624. # s(lsbq_comp):regionleft_central_right_parietal - [6]
  625. # s(lsbq_comp):regionleft_temporal_left_parietal - [16]
  626. # s(lsbq_comp):regionmedial_frontal_left_central - [22]
  627. # s(lsbq_comp):regionmedial_frontal_left_temporal - [24]
  628. # s(lsbq_comp):regionmedial_frontal_medial_occipital - [25]
  629. ```
  631. ### Left Central to Left Temporal
  633. ```{r}
  634. plt_lsbq_posttask_lefcen_leftem <- draw(lsbq_posttask, select = 2) +
  635. labs(title = "Left Central to Left Temporal",
  636. subtitle = NULL) +
  637. ylab("Granger Causality") +
  638. xlab("LSBQ Composite Score") +
  639. geom_line(color = "orange2")
  640. plt_lsbq_posttask_lefcen_leftem$layers[[1]]$aes_params$fill <- "orange2"
  641. print(plt_lsbq_posttask_lefcen_leftem)
  643. ggsave(
  644. "Post-task - Left Central to Left Temporal.svg",
  645. device = "svg",
  646. path = "Plots/SVG/Posttask")
  648. ggsave(
  649. "Post-task - Left Central to Left Temporal.png",
  650. device = "png",
  651. path = "Plots/PNG/Posttask")
  652. ```
  654. ### Left Central to Medial Occipital
  656. ```{r}
  657. plt_lsbq_posttask_lefcen_medocc <- draw(lsbq_posttask, select = 4) +
  658. labs(title = "Left Central to Medial Occipital",
  659. subtitle = NULL) +
  660. ylab("Granger Causality") +
  661. xlab("LSBQ Composite Score") +
  662. geom_line(color = "orange2")
  663. plt_lsbq_posttask_lefcen_medocc$layers[[1]]$aes_params$fill <- "orange2"
  664. print(plt_lsbq_posttask_lefcen_medocc)
  666. ggsave(
  667. "Post-task - Left Central to Medial Occipital.svg",
  668. device = "svg",
  669. path = "Plots/SVG/Posttask")
  671. ggsave(
  672. "Post-task - Left Central to Medial Occipital.png",
  673. device = "png",
  674. path = "Plots/PNG/Posttask")
  675. ```
  677. ### Left Central to Right Parietal
  679. ```{r}
  680. plt_lsbq_posttask_lefcen_rigpar <- draw(lsbq_posttask, select = 6) +
  681. labs(title = "Left Central to Right Parietal",
  682. subtitle = NULL) +
  683. ylab("Granger Causality") +
  684. xlab("LSBQ Composite Score") +
  685. geom_line(color = "orange2")
  686. plt_lsbq_posttask_lefcen_rigpar$layers[[1]]$aes_params$fill <- "orange2"
  687. print(plt_lsbq_posttask_lefcen_rigpar)
  689. ggsave(
  690. "Post-task - Left Central to Right Parietal.svg",
  691. device = "svg",
  692. path = "Plots/SVG/Posttask")
  694. ggsave(
  695. "Post-task - Left Central to Right Parietal.png",
  696. device = "png",
  697. path = "Plots/PNG/Posttask")
  698. ```
  700. ### Left Temporal to Left Parietal
  702. ```{r}
  703. plt_lsbq_posttask_leftem_lefpar <- draw(lsbq_posttask, select = 16) +
  704. labs(title = "Left Temporal to Left Parietal",
  705. subtitle = NULL) +
  706. ylab("Granger Causality") +
  707. xlab("LSBQ Composite Score") +
  708. geom_line(color = "orange2")
  709. plt_lsbq_posttask_leftem_lefpar$layers[[1]]$aes_params$fill <- "orange2"
  710. print(plt_lsbq_posttask_leftem_lefpar)
  712. ggsave(
  713. "Post-task - Left Temporal to Left Parietal.svg",
  714. device = "svg",
  715. path = "Plots/SVG/Posttask")
  717. ggsave(
  718. "Post-task - Left Temporal to Left Parietal.png",
  719. device = "png",
  720. path = "Plots/PNG/Posttask")
  721. ```
  723. ### Medial Frontal to Left Central
  725. ```{r}
  726. plt_lsbq_posttask_medfro_lefcen <- draw(lsbq_posttask, select = 22) +
  727. labs(title = "Medial Frontal to Left Central",
  728. subtitle = NULL) +
  729. ylab("Granger Causality") +
  730. xlab("LSBQ Composite Score") +
  731. geom_line(color = "orange2")
  732. plt_lsbq_posttask_medfro_lefcen$layers[[1]]$aes_params$fill <- "orange2"
  733. print(plt_lsbq_posttask_medfro_lefcen)
  735. ggsave(
  736. "Post-task - Medial Frontal to Left Central.svg",
  737. device = "svg",
  738. path = "Plots/SVG/Posttask")
  740. ggsave(
  741. "Post-task - Medial Frontal to Left Central.png",
  742. device = "png",
  743. path = "Plots/PNG/Posttask")
  744. ```
  746. ### Medial Frontal to Left Temporal
  748. ```{r}
  749. plt_lsbq_posttask_medfro_leftem <- draw(lsbq_posttask, select = 24) +
  750. labs(title = "Medial Frontal to Left Temporal",
  751. subtitle = NULL) +
  752. ylab("Granger Causality") +
  753. xlab("LSBQ Composite Score") +
  754. geom_line(color = "orange2")
  755. plt_lsbq_posttask_medfro_leftem$layers[[1]]$aes_params$fill <- "orange2"
  756. print(plt_lsbq_posttask_medfro_leftem)
  758. ggsave(
  759. "Post-task - Medial Frontal to Left Temporal.svg",
  760. device = "svg",
  761. path = "Plots/SVG/Posttask")
  763. ggsave(
  764. "Post-task - Medial Frontal to Left Temporal.png",
  765. device = "png",
  766. path = "Plots/PNG/Posttask")
  767. ```
  769. ### Medial Frontal to Medial Occipital
  771. ```{r}
  772. plt_lsbq_posttask_medfro_medocc <- draw(lsbq_posttask, select = 25) +
  773. labs(title = "Medial Frontal to Medial Occipital",
  774. subtitle = NULL) +
  775. ylab("Granger Causality") +
  776. xlab("LSBQ Composite Score") +
  777. geom_line(color = "orange2")
  778. plt_lsbq_posttask_medfro_medocc$layers[[1]]$aes_params$fill <- "orange2"
  779. print(plt_lsbq_posttask_medfro_medocc)
  781. ggsave(
  782. "Post-task - Medial Frontal to Medial Occipital.svg",
  783. device = "svg",
  784. path = "Plots/SVG/Posttask")
  786. ggsave(
  787. "Post-task - Medial Frontal to Medial Occipital.png",
  788. device = "png",
  789. path = "Plots/PNG/Posttask")
  790. ```
  794. # Common connections between conditions
  796. ## Left Central to Right Parietal
  798. ```{r}
  799. lsbq_prepost <- compare_smooths(
  800. lsbq_pretask, lsbq_posttask,
  801. select = "s(lsbq_comp):regionleft_central_right_parietal"
  802. )
  804. lsbq_prepost$.model <- factor(lsbq_prepost$.model,
  805. levels = c("lsbq_pretask", "lsbq_posttask"),
  806. labels = c("Pre-task", "Post-task"))
  808. plt_lsbq_prepost_lefcen_rigpar <- draw(lsbq_prepost) +
  809. labs(title = "Left Central to Right Parietal",
  810. subtitle = "Pre-task and Post-task Comparison",
  811. color = "Condition") +
  812. scale_color_manual(labels = c("Pre-task", "Post-task"),
  813. values = c("green4", "orange2")) +
  814. scale_fill_manual(values = c("green4", "orange2")) +
  815. ylab("Granger Causality") +
  816. xlab("LSBQ Composite Score") +
  817. guides(fill = "none") +
  818. geom_rug(data = subset(projectdata_long, condition == "pretask"),
  819. mapping = aes(x = lsbq_comp),
  820. color = scales::alpha("grey40", 0.5),
  821. linewidth = 0.25,
  822. inherit.aes = FALSE,
  823. show.legend = FALSE,
  824. sides = "b")
  825. print(plt_lsbq_prepost_lefcen_rigpar)
  827. ggsave(
  828. "Pre-post Comparison - Left Central to Right Parietal.svg",
  829. device = "svg",
  830. path = "Plots/SVG/Both")
  832. ggsave(
  833. "Pre-post Comparison - Left Central to Right Parietal.png",
  834. device = "png",
  835. path = "Plots/PNG/Both")
  836. ```

BIFUNC Pre- & Post-task Statistical Analysis.Rmd, no license · at the source

Overview

Authors: Alex Sheehan1, Doug Saddy1, Diego Krivochen2, Shruti Gupta1,3, Mickey Sibsey1, Christos Pliatsikas1,4
ORCID iDs: Alex Sheehan, Doug Saddy
  1. School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK
  2. School of Languages, Linguistics, Literatures and, Cultures, Faculty of Arts, University of Calgary, Calgary, AB Canada
  3. King’s College Hospital NHS Foundation Trust, London, UK
  4. Facultad de Lenguas y Educación, Centro de Ciencia Cognitiva, Universidad Antonio de Nebrija, Madrid, Spain
Institutions: University of Reading (United Kingdom); University of Calgary (Canada); King's College Hospital NHS Foundation Trust (United Kingdom); Universidad Nebrija (Spain)
Journal: Scientific reports, volume 16, issue 1, article 12756
Dates: received 16 July 2025; accepted 24 February 2026; published online 9 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-42094-x · PMID 41803385 · PMCID PMC13096162 · OpenAlex W7134265266
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/da746
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging
Keywords: Neuroscience, Psychology
MeSH: Brain*, Learning*, Multilingualism*, Adult, Brain Mapping, Electroencephalography, Female, Humans, Male, Reaction Time, Young Adult (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Economic and Social Research Council (2604285)
Citations: not cited yet (Europe PMC); 98 references in the paper

Abstract

Bilingualism is associated with distinct patterns of resting-state functional brain connectivity – a consequence of ongoing language control demands that do not apply to monolinguals. However, it is not well understood how these patterns affect, and are affected by, brain activation for domain-general cognitively demanding tasks. Here, we employ a novel task-driven resting-state electroencephalography design including an implicit Lindenmayer grammar learning task, which tracks aperiodic and hierarchical dependencies, to determine task-related functional connectivity changes in bilinguals. Quantified bilingual experience was used as a predictor of directional effects, using Generalised Additive Models to account for non-linear patterns. Our results revealed post-task alterations to connectivity involving increased involvement of occipital regions, reduced involvement of frontal and central regions, and faster reaction times to stimuli at higher levels of bilingual experience. Crucially, the regions implicated post-task appear to reflect task-relevant regions which are involved in the language and executive control networks, reflecting greater short-term task-driven flexibility. These findings have important implications for our understanding of how domain-general processing and connectivity are shaped by linguistic experience.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-42094-x.

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 4qkfd

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 11 files, 3 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (3 files), mgcv (3 files), reshape2 (3 files), tidyverse (3 files), broom (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/4qkfd/

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

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The data sheets, code, and task used for this manuscript are available in an Open Science Framework repository at https://osf.io/4qkfd/.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 11 MeSH terms, 1 funder, 66 references.

Cite

This paper

Sheehan, A., Saddy, D., Krivochen, D., Gupta, S., Sibsey, M., & Pliatsikas, C. (2026). Bilingualism modulates functional connectivity induced by a domain-general artificial grammar learning task. Scientific reports, 16(1), 12756. https://doi.org/10.1038/s41598-026-42094-x

BibTeX

@article{sheehan2026bilingualism,
author = {Sheehan, Alex and Saddy, Doug and Krivochen, Diego and Gupta, Shruti and Sibsey, Mickey and Pliatsikas, Christos},
title = {{Bilingualism modulates functional connectivity induced by a domain-general artificial grammar learning task}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {12756},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-42094-x},
url = {https://doi.org/10.1038/s41598-026-42094-x},
pmid = {41803385},
pmcid = {PMC13096162}
}

RIS

TY - JOUR
AU - Sheehan, Alex
AU - Saddy, Doug
AU - Krivochen, Diego
AU - Gupta, Shruti
AU - Sibsey, Mickey
AU - Pliatsikas, Christos
TI - Bilingualism modulates functional connectivity induced by a domain-general artificial grammar learning task
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/09
VL - 16
IS - 1
SP - 12756
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-42094-x
UR - https://doi.org/10.1038/s41598-026-42094-x
LA - en
ER -

CSL-JSON

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9
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]
}
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