OSCR

Language laterality and cognitive skills: does anatomy matter?

Code ↔ Paper

8 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 8 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Cognitive battery ↔ 02_cognitive_FA_figures.R.R, lines 72–158 · score 0.92 · Corsi block, labyrinth navigation, topographic orientation, mental rotation, Arithmetic knowledge, Raven
  2. [2] § Methods › Cognitive battery ↔ 01_cognitive_FA_analyses.R.R, lines 41–107 · score 0.86 · Corsi block, topographic orientation, mental rotation, term memory, Raven, labyrinth
  3. [3] § Results › Principal components of cognitive scores ↔ 02_cognitive_FA_figures.R.R, lines 72–158 · score 0.86 · labyrinth navigation, mental rotation, complex mental, arithmetical facts, general cognitive function, visuospatial processing
  4. [4] § Methods › Diffusion Magnetic Resonance Imaging analysis ↔ GenerateFAvaluesCC.sh, the whole file · a weak match · score 0.74 · Binary masks, FA maps, FSL, streamlines, BCC, diffusion
  5. [5] § Methods › Diffusion Magnetic Resonance Imaging analysis ↔ GenerateFAvaluesAF.sh, the whole file · a weak match · score 0.72 · Binary masks, FA maps, FSL, streamlines, diffusion, volume
  6. [6] § Results › Principal components of cognitive scores ↔ 01_cognitive_FA_analyses.R.R, lines 41–107 · score 0.69 · mental rotation, complex mental, arithmetical facts, Raven, labyrinth, matrices
  7. [7] § Results › Typical and atypical language lateralisation ↔ 02_cognitive_FA_figures.R.R, lines 477–550 · score 0.60 · GCC PC2, SCC PC1, PC scores, R2, FDR, regression
  8. [8] § Methods › Statistical analyses ↔ 02_cognitive_FA_figures.R.R, lines 283–349 · score 0.53 · strongly atypical, PC score, fitted, splenium, genu, models

Paper

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The authors' code

R · 619 lines · 18 KB · MIT · 4 matches

  1. ###############################################
  2. ## 02_cognitive_FA_figures.R
  3. ## Figures 2–4 and supplementary figure
  4. ###############################################
  6. ## ---------------------------
  7. ## Packages
  8. ## ---------------------------
  9. fig_pkgs <- c("ggplot2", "patchwork", "ggstatsplot", "MASS", "ggpubr")
  10. for (p in fig_pkgs) {
  11. if (!requireNamespace(p, quietly = TRUE)) {
  12. install.packages(p)
  13. }
  14. library(p, character.only = TRUE)
  15. }
  17. ## If needed:
  18. ## source("01_cognitive_FA_analyses.R")
  20. ## =====================================================
  21. ## FIGURE 2 – PCA scree plot + loadings
  22. ## =====================================================
  24. loadings <- results_cognitive_fancy_PCA$pc$rotation
  25. eigenvalues <- results_cognitive_fancy_PCA$pc$sdev^2
  27. explained_variance <- eigenvalues / sum(eigenvalues) * 100
  28. cumulative_variance <- cumsum(explained_variance)
  30. scree_data <- data.frame(
  31. PC = 1:length(eigenvalues),
  32. ExplainedVariance = explained_variance,
  33. CumulativeVariance = cumulative_variance
  34. )
  36. scree_plot <- ggplot(scree_data, aes(x = PC)) +
  37. geom_bar(
  38. aes(y = ExplainedVariance),
  39. stat = "identity",
  40. fill = "grey",
  41. color = "black"
  42. ) +
  43. geom_line(
  44. aes(y = CumulativeVariance, group = 1),
  45. color = "red",
  46. size = 1
  47. ) +
  48. geom_point(
  49. aes(y = CumulativeVariance),
  50. color = "red",
  51. size = 2
  52. ) +
  53. labs(
  54. x = "PCs",
  55. y = "Explained Variance (%)",
  56. title = "Explained Variance by PCs"
  57. ) +
  58. scale_x_continuous(
  59. breaks = seq(1, length(scree_data$PC), by = 1),
  60. labels = as.character(seq(1, length(scree_data$PC), by = 1))
  61. ) +
  62. theme_minimal() +
  63. theme(
  64. panel.grid = element_blank(),
  65. axis.line = element_line(color = "black"),
  66. axis.ticks = element_line(color = "black"),
  67. text = element_text(size = 14),
  68. plot.title = element_text(hjust = 0.5, face = "bold"),
  69. axis.title.y = element_text(margin = margin(r = 10))
  70. )
  72. loadings_df <- as.data.frame(loadings)
  73. colnames(loadings_df) <- paste0("PC", 1:ncol(loadings_df))
  74. loadings_df$Variable <- rownames(loadings_df)
  76. loadings_long <- tidyr::pivot_longer(
  77. loadings_df,
  78. cols = dplyr::starts_with("PC"),
  79. names_to = "PC",
  80. values_to = "Loading"
  81. )
  83. selected_pcs <- c("PC1", "PC2", "PC3")
  85. loadings_long_filtered <- loadings_long %>%
  86. dplyr::filter(PC %in% selected_pcs)
  88. rename_labels <- c(
  89. "Vocab_extent" = "Vocabulary Range",
  90. "Verbal_fluency_nouns_per_item" = "Verbal Fluency Task",
  91. "Topographic_orientation_labyrinth_score" = "Labyrinth Navigation",
  92. "Non_verbal_reasoning_Ravens_matrices" = "Raven’s Matrices",
  93. "Mental_rotation" = "Mental Rotation Test",
  94. "Corsi_Block_test_visuospatial_span" = "Corsi Block Test",
  95. "Complex_mental_calculation_score" = "Complex Calculations",
  96. "Auditory_verbal_learning_recalled_words" = "Delayed Recall (Words)",
  97. "Arithmetical_facts_score" = "Arithmetic Knowledge"
  98. )
  100. loadings_long_filtered <- loadings_long_filtered %>%
  101. dplyr::mutate(Variable = rename_labels[Variable])
  103. desired_order <- c(
  104. "Vocabulary Range",
  105. "Verbal Fluency Task",
  106. "Delayed Recall (Words)",
  107. "Mental Rotation Test",
  108. "Corsi Block Test",
  109. "Labyrinth Navigation",
  110. "Raven’s Matrices",
  111. "Arithmetic Knowledge",
  112. "Complex Calculations"
  113. )
  115. loadings_long_filtered <- loadings_long_filtered %>%
  116. dplyr::mutate(Variable = factor(Variable, levels = rev(desired_order))) %>%
  117. dplyr::mutate(
  118. PC = factor(
  119. PC,
  120. levels = c("PC1", "PC2", "PC3"),
  121. labels = c(
  122. "PC1: Generall Cognitive Function",
  123. "PC2: Visuospatial Processing and Memory",
  124. "PC3: Mathematics"
  125. )
  126. )
  127. )
  129. loadings_plot <- ggplot(
  130. loadings_long_filtered,
  131. aes(x = Loading, y = Variable, fill = Loading > 0)
  132. ) +
  133. geom_bar(stat = "identity", color = "black", width = 0.8) +
  134. facet_wrap(~PC, scales = "free_x", ncol = 3) +
  135. scale_fill_manual(values = c("TRUE" = "black", "FALSE" = "grey")) +
  136. labs(
  137. x = "Loadings",
  138. y = "",
  139. title = "Loadings for Selected Principal Components"
  140. ) +
  141. theme_minimal() +
  142. theme(
  143. panel.grid = element_blank(),
  144. axis.line.y = element_line(color = "black"),
  145. axis.line.x = element_line(color = "black"),
  146. axis.ticks = element_line(color = "black"),
  147. axis.text.y = element_text(size = 10),
  148. axis.text.x = element_text(size = 10),
  149. strip.text = element_text(size = 14, face = "bold"),
  150. plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
  151. legend.position = "none"
  152. )
  154. combined_plot_F2 <- (scree_plot | loadings_plot) +
  155. patchwork::plot_layout(widths = c(0.75, 2)) +
  156. patchwork::plot_annotation(tag_levels = "A")
  158. combined_plot_F2
  160. ## =====================================================
  161. ## FIGURE 4 – Cross-dominance (PC2, BCC_3)
  162. ## =====================================================
  164. PC2_crossLat <- long_data_PC_CC_FA_small_parts %>%
  165. dplyr::filter(CC_part == "BCC_3", Principal_Component == "PC2")
  167. lm_model <- lm(Value ~ Sexe + Edinburg.Score + age_IRM_Anat, data = PC2_crossLat)
  168. PC2_crossLat$residuals <- lm_model$residuals
  170. lm_crosslat <- lm(residuals ~ crossed_dominance, data = PC2_crossLat)
  171. anova(lm_crosslat)
  173. ## Posthoc grouping variable for Figure 4B
  174. PC2_crossLat$posthoc_cross <- ifelse(
  175. PC2_crossLat$crossed_dominance == "consistent",
  176. PC2_crossLat$laterality_frontal_0.1,
  177. ifelse(
  178. PC2_crossLat$crossed_dominance == "weak_lat" &
  179. PC2_crossLat$laterality_frontal_0.1 != "biLat",
  180. PC2_crossLat$laterality_frontal_0.1,
  181. ifelse(
  182. PC2_crossLat$crossed_dominance == "weak_lat" &
  183. PC2_crossLat$laterality_frontal_0.1 == "biLat",
  184. PC2_crossLat$laterality_temporal_0.1,
  185. ifelse(
  186. PC2_crossLat$crossed_dominance == "crossed_dominance",
  187. "crossed",
  188. NA
  189. )
  190. )
  191. )
  192. )
  194. lm_crosslat <- lm(residuals ~ posthoc_cross, data = PC2_crossLat)
  195. anova(lm_crosslat)
  197. filtered_data <- PC2_crossLat %>%
  198. dplyr::group_by(posthoc_cross) %>%
  199. dplyr::filter(dplyr::n() >= 3) %>%
  200. dplyr::ungroup()
  202. plot_a <- ggstatsplot::ggbetweenstats(
  203. data = PC2_crossLat,
  204. x = grouped_dominance,
  205. y = residuals,
  206. pairwise.display = "all",
  207. type = "parametric",
  208. p.adjust.method = "fdr",
  209. pairwise.comparisons.label = "p.signif",
  210. ggtheme = ggplot2::theme_classic()
  211. ) +
  212. labs(
  213. x = NULL,
  214. y = "Residuals",
  215. title = "Spatial Attention Skills by Cross Laterality Groups"
  216. ) +
  217. theme(
  218. text = element_text(size = 14, face = "bold"),
  219. axis.title.x = element_text(margin = margin(t = 10)),
  220. axis.title.y = element_text(margin = margin(r = 10)),
  221. plot.title = element_text(hjust = 0.5)
  222. ) +
  223. scale_x_discrete(labels = c(
  224. "consistent" = "Consistently lateralised",
  225. "crossed_dominance" = "Crossed dominance"
  226. )) +
  227. scale_fill_gradient() +
  228. annotate(
  229. "text",
  230. x = 2.5,
  231. y = max(PC2_crossLat$Value) * 0.95,
  232. label = "ANCOVA pFDR = 0.013",
  233. size = 5,
  234. fontface = "bold",
  235. color = "red",
  236. hjust = 1
  237. )
  239. plot_b <- ggstatsplot::ggbetweenstats(
  240. data = filtered_data,
  241. x = posthoc_cross,
  242. y = residuals,
  243. pairwise.display = "all",
  244. type = "parametric",
  245. p.adjust.method = "fdr",
  246. pairwise.comparisons.label = "p.signif",
  247. ggtheme = ggplot2::theme_classic()
  248. ) +
  249. labs(
  250. x = NULL,
  251. y = "Residuals",
  252. title = "Post-Hoc Analysis"
  253. ) +
  254. theme(
  255. text = element_text(size = 14, face = "bold"),
  256. axis.title.x = element_text(margin = margin(t = 10)),
  257. axis.title.y = element_text(margin = margin(r = 10)),
  258. plot.title = element_text(hjust = 0.5)
  259. ) +
  260. scale_x_discrete(labels = c(
  261. "crossed" = "Crossed dominance",
  262. "leftLat" = "Left-leaning",
  263. "rightLat" = "Right-leaning"
  264. )) +
  265. scale_fill_gradient()
  267. combined_plot_F4 <- (plot_a | plot_b) +
  268. patchwork::plot_annotation(tag_levels = "A") +
  269. theme(plot.tag = element_text(size = 14, face = "bold"))
  271. combined_plot_F4
  273. ggplot2::ggsave(
  274. filename = "/Volumes/LaCie/iMac/Documents/Liverpool/Thesis/Official_Writing/Chapter_9_(cognitive)/Paper_draft2/Preprint/Figure4.tiff",
  275. plot = combined_plot_F4,
  276. width = 378,
  277. height = 193,
  278. units = "mm",
  279. dpi = 600,
  280. compression = "lzw"
  281. )
  283. ## =====================================================
  284. ## FIGURE 3 – Regression plots (Splenium PC1, Genu PC2)
  285. ## =====================================================
  287. # Splenium: SCC_3, PC1, Strong-Atypical
  288. Regression_SCC_PC1 <- long_data_PC_CC_FA_small_parts %>%
  289. dplyr::filter(
  290. CC_part == "SCC_3",
  291. Principal_Component == "PC1",
  292. laterality == "Strong-Atypical"
  293. )
  295. lm_model_a <- lm(Value ~ Sexe + age_IRM_Anat, data = Regression_SCC_PC1)
  296. Regression_SCC_PC1$residuals_a <- residuals(lm_model_a)
  298. lm_model_b <- lm(Value ~ Sexe + age_IRM_Anat + Edinburg.Score, data = Regression_SCC_PC1)
  299. Regression_SCC_PC1$residuals_b <- residuals(lm_model_b)
  301. lm_model_c <- lm(Value ~ mean, data = Regression_SCC_PC1)
  303. new_data <- data.frame(
  304. mean = seq(min(Regression_SCC_PC1$mean), max(Regression_SCC_PC1$mean), length.out = nrow(Regression_SCC_PC1)),
  305. Sexe = "F",
  306. age_IRM_Anat = mean(Regression_SCC_PC1$age_IRM_Anat, na.rm = TRUE),
  307. Edinburg.Score = mean(Regression_SCC_PC1$Edinburg.Score, na.rm = TRUE)
  308. )
  310. predictions_a <- predict(lm_model_a, newdata = new_data, interval = "confidence")
  311. new_data$predicted_a <- predictions_a[, "fit"]
  312. new_data$lower_ci_a <- predictions_a[, "lwr"]
  313. new_data$upper_ci_a <- predictions_a[, "upr"]
  315. predictions_b <- predict(lm_model_b, newdata = new_data, interval = "confidence")
  316. new_data$predicted_b <- predictions_b[, "fit"]
  317. new_data$lower_ci_b <- predictions_b[, "lwr"]
  318. new_data$upper_ci_b <- predictions_b[, "upr"]
  320. plot_aS <- ggplot(Regression_SCC_PC1, aes(x = mean, y = residuals_a)) +
  321. geom_point(size = 3, colour = "darkblue") +
  322. stat_smooth(
  323. method = "lm",
  324. formula = y ~ x,
  325. se = TRUE,
  326. colour = "red",
  327. fill = "pink",
  328. linetype = "dashed",
  329. size = 1
  330. ) +
  331. labs(
  332. title = "Mean FA in Splenium vs General Cognitive Function\n(Adjusted for Age and Sex)",
  333. x = "Mean FA (Splenium)",
  334. y = "Working Memory PC Score"
  335. ) +
  336. annotate(
  337. "text",
  338. x = 0.64,
  339. y = -1,
  340. label = paste0("R² = -0.93", "\nP = 0.002", "\npFDR = 0.035"),
  341. size = 5,
  342. hjust = 1,
  343. colour = "black"
  344. ) +
  345. theme_classic() +
  346. theme(
  347. text = element_text(size = 14),
  348. plot.title = element_text(hjust = 0.5, face = "bold")
  349. )
  351. plot_bS <- ggplot(Regression_SCC_PC1, aes(x = mean, y = residuals_b)) +
  352. geom_point(size = 3, colour = "darkblue") +
  353. stat_smooth(
  354. method = "lm",
  355. formula = y ~ x,
  356. se = TRUE,
  357. colour = "red",
  358. fill = "pink",
  359. linetype = "dashed",
  360. size = 1
  361. ) +
  362. labs(
  363. title = "Mean FA in Splenium vs General Cognitive Function\n(Adjusted for Age, Sex and Handedness)",
  364. x = "Mean FA (Splenium)",
  365. y = "Working Memory PC Score"
  366. ) +
  367. annotate(
  368. "text",
  369. x = 0.64,
  370. y = -1,
  371. label = paste0("R² = -0.94", "\nP = 0.004", "\npFDR = 0.062"),
  372. size = 5,
  373. hjust = 1,
  374. colour = "black"
  375. ) +
  376. theme_classic() +
  377. theme(
  378. text = element_text(size = 14),
  379. plot.title = element_text(hjust = 0.5, face = "bold")
  380. )
  382. plot_cS <- ggplot(Regression_SCC_PC1, aes(x = mean, y = Value)) +
  383. geom_point(size = 3, colour = "darkblue") +
  384. stat_smooth(
  385. method = "lm",
  386. formula = y ~ x,
  387. se = TRUE,
  388. colour = "red",
  389. fill = "pink",
  390. linetype = "dashed",
  391. size = 1
  392. ) +
  393. labs(
  394. title = "Mean FA in Splenium vs General Cognitive Function\n(No Covariates)",
  395. x = "Mean FA (Splenium)",
  396. y = "Working Memory PC Score"
  397. ) +
  398. annotate(
  399. "text",
  400. x = 0.64,
  401. y = -1,
  402. label = paste0("R² = -0.36", "\nP = 0.0529", "\npFDR = 0.69"),
  403. size = 5,
  404. hjust = 1,
  405. colour = "black"
  406. ) +
  407. theme_classic() +
  408. theme(
  409. text = element_text(size = 14),
  410. plot.title = element_text(hjust = 0.5, face = "bold")
  411. )
  413. # Genu: GCC_3, PC2, Strong-Atypical
  414. Regression_GCC_PC2 <- long_data_PC_CC_FA_small_parts %>%
  415. dplyr::filter(
  416. CC_part == "GCC_3",
  417. Principal_Component == "PC2",
  418. laterality == "Strong-Atypical"
  419. )
  421. lm_model_a <- lm(Value ~ Sexe + age_IRM_Anat, data = Regression_GCC_PC2)
  422. Regression_GCC_PC2$residuals_a <- residuals(lm_model_a)
  424. lm_model_b <- lm(Value ~ Sexe + age_IRM_Anat + Edinburg.Score, data = Regression_GCC_PC2)
  425. Regression_GCC_PC2$residuals_b <- residuals(lm_model_b)
  427. lm_model_c <- lm(Value ~ mean, data = Regression_GCC_PC2)
  429. new_data <- data.frame(
  430. mean = seq(min(Regression_GCC_PC2$mean), max(Regression_GCC_PC2$mean), length.out = nrow(Regression_GCC_PC2)),
  431. Sexe = "F",
  432. age_IRM_Anat = mean(Regression_GCC_PC2$age_IRM_Anat, na.rm = TRUE),
  433. Edinburg.Score = mean(Regression_GCC_PC2$Edinburg.Score, na.rm = TRUE)
  434. )
  436. predictions_a <- predict(lm_model_a, newdata = new_data, interval = "confidence")
  437. new_data$predicted_a <- predictions_a[, "fit"]
  438. new_data$lower_ci_a <- predictions_a[, "lwr"]
  439. new_data$upper_ci_a <- predictions_a[, "upr"]
  441. predictions_b <- predict(lm_model_b, newdata = new_data, interval = "confidence")
  442. new_data$predicted_b <- predictions_b[, "fit"]
  443. new_data$lower_ci_b <- predictions_b[, "lwr"]
  444. new_data$upper_ci_b <- predictions_b[, "upr"]
  446. plot_aG <- ggplot(Regression_GCC_PC2, aes(x = mean, y = residuals_a)) +
  447. geom_point(size = 3, colour = "darkblue") +
  448. stat_smooth(
  449. method = "lm",
  450. formula = y ~ x,
  451. se = TRUE,
  452. colour = "red",
  453. fill = "pink",
  454. linetype = "dashed",
  455. size = 1
  456. ) +
  457. labs(
  458. title = "Mean FA in Genu vs Spatial Attention\n(Adjusted for Age and Sex)",
  459. x = "Mean FA (Genu)",
  460. y = "Spatial Attention PC Score"
  461. ) +
  462. annotate(
  463. "text",
  464. x = max(Regression_GCC_PC2$mean) * 0.99,
  465. y = -1,
  466. label = paste0("R² = 0.87", "\nP = 0.0009", "\npFDR = 0.04"),
  467. size = 5,
  468. hjust = 1,
  469. colour = "black"
  470. ) +
  471. theme_classic() +
  472. theme(
  473. text = element_text(size = 14),
  474. plot.title = element_text(hjust = 0.5, face = "bold")
  475. )
  477. plot_bG <- ggplot(Regression_GCC_PC2, aes(x = mean, y = residuals_b)) +
  478. geom_point(size = 3, colour = "darkblue") +
  479. stat_smooth(
  480. method = "lm",
  481. formula = y ~ x,
  482. se = TRUE,
  483. colour = "red",
  484. fill = "pink",
  485. linetype = "dashed",
  486. size = 1
  487. ) +
  488. labs(
  489. title = "Mean FA in Genu vs Spatial Attention\n(Adjusted for Age, Sex, Handedness)",
  490. x = "Mean FA (Genu)",
  491. y = "Spatial Attention PC Score)"
  492. ) +
  493. annotate(
  494. "text",
  495. x = max(Regression_GCC_PC2$mean) * 0.99,
  496. y = -1,
  497. label = paste0("R² = 0.86", "\nP = 0.006", "\npFDR = 0.06"),
  498. size = 5,
  499. hjust = 1,
  500. colour = "black"
  501. ) +
  502. theme_classic() +
  503. theme(
  504. text = element_text(size = 14),
  505. plot.title = element_text(hjust = 0.5, face = "bold")
  506. )
  508. plot_cG <- ggplot(Regression_GCC_PC2, aes(x = mean, y = Value)) +
  509. geom_point(size = 3, colour = "darkblue") +
  510. stat_smooth(
  511. method = "lm",
  512. formula = y ~ x,
  513. se = TRUE,
  514. colour = "red",
  515. fill = "pink",
  516. linetype = "dashed",
  517. size = 1
  518. ) +
  519. labs(
  520. title = "Mean FA in Genu vs Spatial Attention\n(No Covariates)",
  521. x = "Mean FA (Genu)",
  522. y = "Spatial Attention PC Score"
  523. ) +
  524. annotate(
  525. "text",
  526. x = max(Regression_GCC_PC2$mean) * 0.99,
  527. y = -1,
  528. label = paste0("R² = 0.54", "\nP = 0.01", "\npFDR = 0.438"),
  529. size = 5,
  530. hjust = 1,
  531. colour = "black"
  532. ) +
  533. theme_classic() +
  534. theme(
  535. text = element_text(size = 14),
  536. plot.title = element_text(hjust = 0.5, face = "bold")
  537. )
  539. combined_plot_F3 <- (plot_aS | plot_bS) /
  540. (plot_aG | plot_bG) +
  541. patchwork::plot_annotation(tag_levels = "A")
  543. combined_plot_F3
  545. ## =====================================================
  546. ## SUPPLEMENTARY FIGURE – regression plots by laterality
  547. ## =====================================================
  549. Regression_SCC_PC1_all <- long_data_PC_CC_FA_small_parts %>%
  550. dplyr::filter(CC_part == "SCC_3", Principal_Component == "PC1")
  552. compute_residuals_by_group <- function(data, formula) {
  553. data %>%
  554. dplyr::group_by(laterality) %>%
  555. dplyr::mutate(residuals = residuals(lm(formula, data = dplyr::cur_data_all()))) %>%
  556. dplyr::ungroup()
  557. }
  559. data_age_sex <- compute_residuals_by_group(
  560. Regression_SCC_PC1_all,
  561. Value ~ Sexe + age_IRM_Anat
  562. )
  564. plot_a_PC1_lat <- ggplot(data_age_sex, aes(x = mean, y = residuals, colour = laterality)) +
  565. geom_point(size = 3, alpha = 0.7) +
  566. geom_smooth(
  567. method = "lm",
  568. aes(fill = laterality),
  569. alpha = 0.2,
  570. se = TRUE
  571. ) +
  572. scale_color_brewer(palette = "Set1", name = "Laterality Group") +
  573. scale_fill_brewer(palette = "Set1", name = "Laterality Group") +
  574. labs(
  575. title = "Working Memory vs Mean FA in Splenium\n (Age, Sex)",
  576. x = "Mean FA (Splenium)",
  577. y = "Residuals (Working Memory PC)"
  578. ) +
  579. theme_classic() +
  580. theme(
  581. text = element_text(size = 14),
  582. plot.title = element_text(hjust = 0.5, face = "bold"),
  583. legend.position = "top"
  584. )
  586. Regression_GCC_PC2_all <- long_data_PC_CC_FA_small_parts %>%
  587. dplyr::filter(CC_part == "GCC_3", Principal_Component == "PC2")
  589. data_age_sex_GCC <- Regression_GCC_PC2_all %>%
  590. dplyr::group_by(laterality) %>%
  591. dplyr::mutate(residuals = residuals(lm(Value ~ Sexe + age_IRM_Anat, data = dplyr::cur_data_all()))) %>%
  592. dplyr::ungroup()
  594. plot_a_PC2_lat <- ggplot(data_age_sex_GCC, aes(x = mean, y = residuals, colour = laterality)) +
  595. geom_point(size = 3, alpha = 0.7) +
  596. geom_smooth(
  597. method = "lm",
  598. aes(fill = laterality),
  599. alpha = 0.2,
  600. se = TRUE
  601. ) +
  602. scale_color_brewer(palette = "Set1", name = "Laterality Group") +
  603. scale_fill_brewer(palette = "Set1", name = "Laterality Group") +
  604. labs(
  605. title = "Spatial Attention vs Mean FA in Genu\n(Age, Sex)",
  606. x = "Mean FA (Genu)",
  607. y = "Residuals (Spatial Attention PC)"
  608. ) +
  609. theme_classic() +
  610. theme(
  611. text = element_text(size = 14),
  612. plot.title = element_text(hjust = 0.5, face = "bold"),
  613. legend.position = "top"
  614. )
  616. combined_plot_supp <- (plot_a_PC1_lat | plot_a_PC2_lat) +
  617. patchwork::plot_annotation(tag_levels = "A")
  619. combined_plot_supp

02_cognitive_FA_figures.R.R at commit 0a03324, under MIT · at the source

Overview

Authors: Ieva Andrulyte1, Laure Zago2, Gael Jobard2, Herve Lemaitre2, Francesca M Branzi3, Francois Rheault4,5, Laurent Petit2,5, Simon S Keller1
ORCID iDs: Ieva Andrulyte, Laure Zago, Gael Jobard, Herve Lemaitre, Francesca M Branzi, Francois Rheault, Laurent Petit, Simon S Keller
  1. The BRAIN Lab, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, L7 8TX, United Kingdom
  2. Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS - CEA – Université de Bordeaux, Bordeaux F-33000, France
  3. Department of Psychological Sciences, Institute of Population Health, University of Liverpool, Eleanor Rathbone Building, Bedford Street South, Liverpool, L69 7ZA, United Kingdom
  4. Sherbrooke Connectivity and Imaging Lab (SCIL), Faculté des Sciences, Université de Sherbrooke, 2500 Bd. de l’Université, Sherbrooke, J1K2R1, QC, Canada
  5. IRP OpTeam, Neurodegeneratives Diseases Institute, UMR 5293, Team 5 - CEA - CNRS - Université de Bordeaux, France and Université de Sherbrooke, Canada
Institutions: University of Liverpool (United Kingdom); Centre National de la Recherche Scientifique (France); Université de Bordeaux (France); Commissariat à l'Énergie Atomique et aux Énergies Alternatives (France); Institut des Maladies Neurodégénératives (France); Université de Sherbrooke (Canada)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 6, article bhag067
Dates: received 14 December 2025; accepted 4 May 2026; published online 25 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag067 · PMID 42348842 · PMCID PMC13298645 · OpenAlex W4414045202
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.06.674566
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics, fMRI & imaging
Keywords: arcuate fasciculus, cognitive function, corpus callosum, diffusion MRI, language lateralisation, white matter
MeSH: Brain*, Cognition*, Functional Laterality*, Language*, White Matter*, Adult, Anisotropy, Attention, Brain Mapping, Diffusion Magnetic Resonance Imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Young Adult (* major topic)
Topic: Hemispheric Asymmetry in Neuroscience (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ieva Andrulyte-BBSRC DTP training (BB/T008695/1); Biotechnology and Biological Sciences Research Council (BB/T008695/1)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Brain anatomy, particularly white matter microstructure, is thought to play a critical role in the relationships between cognitive function and language lateralisation. This study investigates whether white matter microstructural parameters of the arcuate fasciculus and corpus callosum is associated with cognitive performance across distinct language lateralisation groups. Neuroimaging and cognitive data from 279 healthy adults were sourced from the BIL&GIN database. Participants completed a sentence production functional magnetic resonance imaging (fMRI) task, diffusion MRI, and cognitive tasks assessing verbal, visuospatial, and arithmetic skills. Significant positive associations were observed in strongly atypical individuals between fractional anisotropy in the splenium and working memory (R2 = 0.96, pFDR = 0.042) and between fractional anisotropy (FA) in the genu and visuospatial attention (R2 = 0.92, pFDR = 0.042). ANCOVA revealed that cross-dominant individuals had significantly lower visuospatial attention scores compared to consistently lateralized individuals (pFDR = 0.02). These findings challenge the notion that atypical lateralisation is inherently maladaptive and suggest that white matter pathways may serve as an alternative mechanism for supporting cognitive function in individuals with rightward language dominance. Furthermore, the results highlight the cognitive disadvantages of crossed dominance, implicating disrupted interhemispheric communication as a potential underlying mechanism.

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 8 matches between paragraphs and lines of code.

andrulyte/language-laterality-cognition

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0a03324e928a0f1d66134e773c48bf566c5926db, 14 December 2025
Languages: R (3), Shell (3)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (2 files), ggplot2 (2 files), tidyverse (2 files), broom (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/andrulyte/language-laterality-cognition

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

Tracing map

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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;
  • 6 scripts, each with its path and the digest of its content;
  • 8 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data availability

All analysis scripts used in the present study are openly available on GitHub: https://github.com/andrulyte/language-laterality-cognition.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 6 keywords, 17 MeSH terms, 2 funders, 98 references.

Cite

This paper

Andrulyte, I., Zago, L., Jobard, G., Lemaitre, H., Branzi, F. M., Rheault, F., Petit, L., & Keller, S. S. (2026). Language laterality and cognitive skills: does anatomy matter? Cerebral cortex (New York, N.Y. : 1991), 36(6), bhag067. https://doi.org/10.1093/cercor/bhag067

BibTeX

@article{andrulyte2026language,
author = {Andrulyte, Ieva and Zago, Laure and Jobard, Gael and Lemaitre, Herve and Branzi, Francesca M and Rheault, Francois and Petit, Laurent and Keller, Simon S},
title = {{Language laterality and cognitive skills: does anatomy matter?}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = jun,
volume = {36},
number = {6},
pages = {bhag067},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag067},
url = {https://doi.org/10.1093/cercor/bhag067},
pmid = {42348842},
pmcid = {PMC13298645}
}

RIS

TY - JOUR
AU - Andrulyte, Ieva
AU - Zago, Laure
AU - Jobard, Gael
AU - Lemaitre, Herve
AU - Branzi, Francesca M
AU - Rheault, Francois
AU - Petit, Laurent
AU - Keller, Simon S
TI - Language laterality and cognitive skills: does anatomy matter?
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/06/01
VL - 36
IS - 6
SP - bhag067
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag067
UR - https://doi.org/10.1093/cercor/bhag067
LA - en
ER -

CSL-JSON

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