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Neural correlates of novel word-form learning in developmental language disorder

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  1. [1] § Results › Neuroimaging › ROI analysis ↔ Codes/nwr_fmri_DLD_frontostriatal_masks_OSF.Rmd, lines 52 to 133 · score 0.50 · percent signal change, CI, masks, SE, hemispheres, interaction · Report a mismatch

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The authors' code

R Markdown · 261 lines · 7.1 KB · no license · 1 match

  1. ---
  2. title: "Frontostriatal ROI analyses for pseudoword learning in DLD"
  3. author: ""
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. editor_options:
  7. chunk_output_type: console
  8. ---
  10. # Overview
  12. This R Markdown reproduces the region-of-interest (ROI) mixed-effects analyses for frontostriatal and inferior frontal masks in the pseudoword-learning fMRI study.
  14. It expects the following CSV files to be stored in a subfolder called `masks/` relative to this `.Rmd` file:
  16. - `masks/Caudate.csv`
  17. - `masks/Putamen.csv`
  18. - `masks/SMA.csv`
  19. - `masks/pOp.csv`
  20. - `masks/pTri.csv`
  22. The script:
  23. 1. loads each ROI dataset,
  24. 2. standardizes factor coding,
  25. 3. fits the mixed-effects model used for that ROI,
  26. 4. outputs model summaries and confidence intervals,
  27. 5. produces descriptive plots for pars opercularis and pars triangularis.
  29. # How to use this file
  31. 1. Place this `.Rmd` file in the same folder as the `masks/` directory.
  32. 2. Make sure the required packages are installed.
  33. 3. Open the file in RStudio.
  34. 4. Knit the document, or run chunks interactively.
  36. ```{r setup, include=FALSE}
  37. knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
  38. ```
  40. # Packages
  42. ```{r packages}
  43. library(readr)
  44. library(dplyr)
  45. library(ggplot2)
  46. library(lme4)
  47. library(lmerTest)
  48. library(multcomp)
  49. library(lsmeans)
  50. ```
  52. # Helper functions
  54. ```{r helpers}
  55. load_roi_data <- function(file, time_levels = c("0", "1", "2", "3"),
  56. hemisphere_levels = NULL,
  57. group_levels = c("TD", "DLD")) {
  58. dat <- read.csv(file, header = TRUE, check.names = TRUE, stringsAsFactors = FALSE)
  59. dat$Time <- factor(dat$Time, levels = time_levels)
  60. dat$Group <- factor(dat$Group, levels = group_levels)
  61. if (!is.null(hemisphere_levels) && "Hemisphere" %in% names(dat)) {
  62. dat$Hemisphere <- factor(dat$Hemisphere, levels = hemisphere_levels)
  63. }
  64. dat
  65. }
  67. count_subjects_by_group <- function(dat) {
  68. dat %>%
  69. group_by(Group) %>%
  70. summarise(unique_subjects = n_distinct(Subject), .groups = "drop")
  71. }
  73. summarise_roi_for_plot <- function(dat, value_col) {
  74. value_sym <- rlang::sym(value_col)
  75. dat %>%
  76. group_by(Time, Group, Hemisphere) %>%
  77. summarise(
  78. Mean = mean(!!value_sym, na.rm = TRUE),
  79. N = sum(!is.na(!!value_sym)),
  80. SD = sd(!!value_sym, na.rm = TRUE),
  81. SE = SD / sqrt(N),
  82. CI_lower = Mean - qt(0.975, df = pmax(N - 1, 1)) * SE,
  83. CI_upper = Mean + qt(0.975, df = pmax(N - 1, 1)) * SE,
  84. .groups = "drop"
  85. ) %>%
  86. mutate(Group_Hemisphere = interaction(Group, Hemisphere, sep = "_"))
  87. }
  89. plot_ifg_roi <- function(plot_data, y_limits = c(-0.1, 0.25), hide_legend = FALSE,
  90. hemisphere_labels = c("TD_LH" = "TD LH",
  91. "TD_RH" = "TD RH",
  92. "DLD_LH" = "DLD LH",
  93. "DLD_RH" = "DLD RH"),
  94. hemisphere_colors = c("TD_LH" = "blue",
  95. "TD_RH" = "#ADD8E6",
  96. "DLD_LH" = "#EFC000FF",
  97. "DLD_RH" = "#F7E7A1")) {
  98. p <- ggplot(plot_data, aes(x = Time, y = Mean, group = Group_Hemisphere, colour = Group_Hemisphere)) +
  99. facet_wrap(~ Group, strip.position = "top") +
  100. theme_classic() +
  101. geom_line(linewidth = 1) +
  102. geom_point(size = 2.2, colour = "black") +
  103. geom_point(size = 2) +
  104. geom_errorbar(aes(ymin = CI_lower, ymax = CI_upper), width = 0.2, linewidth = 0.2, color = "black") +
  105. labs(x = "Pseudoword repetitions", y = "Percent signal change", color = "Hemisphere") +
  106. coord_cartesian(ylim = y_limits) +
  107. scale_y_continuous(breaks = seq(-0.05, 0.25, 0.09)) +
  108. scale_color_manual(values = hemisphere_colors, labels = hemisphere_labels) +
  109. scale_x_discrete(labels = c("1st", "2nd", "3rd", "4th")) +
  110. theme(
  111. axis.text.x = element_text(size = 11, color = "grey20", face = "bold"),
  112. axis.text.y = element_text(size = 11, color = "grey20", face = "bold"),
  113. axis.title = element_text(size = 12, face = "bold"),
  114. legend.text = element_text(size = 8, color = "black"),
  115. legend.title = element_text(size = 10, face = "bold"),
  116. plot.title = element_blank(),
  117. strip.text = element_text(size = 14, face = "bold"),
  118. strip.background = element_blank(),
  119. panel.grid.major = element_blank(),
  120. panel.grid.minor = element_blank(),
  121. panel.background = element_blank(),
  122. axis.line = element_line(colour = "black"),
  123. axis.title.x = element_text(family = "sans", size = 12, margin = margin(10, 0, 0, 0)),
  124. axis.title.y = element_text(family = "sans", size = 12, margin = margin(0, 10, 0, 0))
  125. )
  127. if (hide_legend) {
  128. p <- p + theme(legend.position = "none")
  129. }
  131. p
  132. }
  133. ```
  135. # Caudate
  137. ```{r caudate}
  138. Caudate_data <- load_roi_data(
  139. file = file.path("masks", "Caudate.csv"),
  140. hemisphere_levels = c("LH", "RH")
  141. )
  143. count_subjects_by_group(Caudate_data)
  145. summary(model_Caudate <- lmer(
  146. Caudate ~ 1 + Time * Hemisphere * Group + (1 + Time | Subject),
  147. data = Caudate_data,
  148. REML = FALSE
  149. ))
  150. ```
  152. # Putamen
  154. ```{r putamen}
  155. Putamen_data <- load_roi_data(
  156. file = file.path("masks", "Putamen.csv"),
  157. hemisphere_levels = c("LH", "RH")
  158. )
  160. count_subjects_by_group(Putamen_data)
  162. summary(model_Putamen <- lmer(
  163. Putamen ~ 1 + Time * Group * Hemisphere + (1 + Time | Subject),
  164. data = Putamen_data,
  165. REML = TRUE
  166. ))
  167. ```
  169. # Supplementary motor area (SMA)
  171. ```{r sma}
  172. SMA_data <- load_roi_data(
  173. file = file.path("masks", "SMA.csv")
  174. )
  176. count_subjects_by_group(SMA_data)
  178. summary(model_SMA <- lmer(
  179. SMA ~ 1 + Time * Group + (1 | Subject),
  180. data = SMA_data,
  181. REML = TRUE
  182. ))
  183. ```
  185. # Pars opercularis
  187. ```{r pop-model}
  188. pOp_data <- load_roi_data(
  189. file = file.path("masks", "pOp.csv"),
  190. hemisphere_levels = c("LH", "RH")
  191. )
  193. count_subjects_by_group(pOp_data)
  195. summary(model_pOp <- lmer(
  196. pOp ~ 1 + Time * Group * Hemisphere + (1 + Time | Subject),
  197. data = pOp_data,
  198. REML = TRUE
  199. ))
  201. confint(model_pOp)
  203. summary(
  204. glht(model_pOp, lsm(pairwise ~ Group * Hemisphere)),
  205. test = adjusted(type = "bonferroni")
  206. )
  207. ```
  209. ```{r pop-plot}
  210. pOp_plot_data <- summarise_roi_for_plot(pOp_data, "pOp") %>%
  211. mutate(
  212. Group_Hemisphere = case_when(
  213. Group_Hemisphere == "TD_Left" ~ "TD_LH",
  214. Group_Hemisphere == "TD_Right" ~ "TD_RH",
  215. Group_Hemisphere == "DLD_Left" ~ "DLD_LH",
  216. Group_Hemisphere == "DLD_Right" ~ "DLD_RH",
  217. TRUE ~ Group_Hemisphere
  218. )
  219. )
  221. pOp_plot <- plot_ifg_roi(pOp_plot_data, hide_legend = FALSE)
  222. pOp_plot
  223. ```
  225. # Pars triangularis
  227. ```{r ptri-model}
  228. pTri_data <- load_roi_data(
  229. file = file.path("masks", "pTri.csv"),
  230. hemisphere_levels = c("LH", "RH")
  231. )
  233. count_subjects_by_group(pTri_data)
  235. summary(model_pTri <- lmer(
  236. pTri ~ 1 + Time * Group * Hemisphere + (1 + Time | Subject),
  237. data = pTri_data,
  238. REML = TRUE
  239. ))
  241. confint(model_pTri)
  243. summary(
  244. glht(model_pTri, lsm(pairwise ~ Group * Hemisphere)),
  245. test = adjusted(type = "bonferroni")
  246. )
  247. ```
  249. ```{r ptri-plot}
  250. pTri_plot_data <- summarise_roi_for_plot(pTri_data, "pTri")
  252. pTri_plot <- plot_ifg_roi(pTri_plot_data, hide_legend = TRUE)
  253. pTri_plot
  254. ```
  256. # Combined inferior frontal plot
  258. ```{r combined-plot}
  259. Broca_plot <- pTri_plot + pOp_plot
  260. Broca_plot
  261. ```

nwr_fmri_DLD_frontostriatal_masks_OSF.Rmd, no license · at the source

Overview

Authors: Nilgoun Bahar1,2, Gabriel J. Cler1,3, Salomi S. Asaridou1, Harriet J. Smith1,4, Hanna E. Willis1,5, Máiréad P. Healy1,6, Sana Chughtai4, Meron Haile1, Saloni Krishnan4, Kate E. Watkins1
ORCID iDs: Nilgoun Bahar, Gabriel J. Cler, Salomi S. Asaridou, Harriet J. Smith, Hanna E. Willis, Máiréad P. Healy, Sana Chughtai, Saloni Krishnan, Kate E. Watkins
  1. Department of Experimental Psychology & Oxford Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
  2. Department of Neurology, Dyslexia Center, University of California at San Francisco, San Francisco, USA
  3. Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
  4. Division of Psychology and Language Sciences, University College London, London, UK
  5. Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
  6. Department of Psychology, University of Cambridge, Cambridge, UK
Institutions: University of Oxford (United Kingdom); University of California, San Francisco (United States); University of Washington (United States); University College London (United Kingdom); University of Cambridge (United Kingdom)
Journal: bioRxiv (preprint)
Dates: published online 31 March 2026
Type: Preprint
License: CC BY
Identifiers: DOI 10.64898/2026.03.28.715039 · OpenAlex W7147393433
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), developmental (subfield)
Methods: Statistics, fMRI & imaging
Keywords: Developmental language impairment, nonword repetition, nonword learning, vocabulary, paediatric, neurodevelopment
Topic: Language Development and Disorders (Developmental and Educational Psychology, Psychology), according to OpenAlex
Funding: Wellcome Trust (203139/A/16/Z, 203139/Z/16/Z); National Institute for Health Research (NIHR) (NIHR203316)
Citations: not cited yet (Europe PMC); 0 references in the paper

Abstract

Children with developmental language disorder (DLD) have persistent language learning difficulties and often perform poorly on pseudoword repetition, a task that probes phonological, memory, and speech-motor processes that support vocabulary acquisition. Research on the neural basis of pseudoword repetition in DLD is limited. We used whole-brain functional MRI (fMRI) to examine pseudoword repetition and repetition-based learning in 46 children with DLD (ages 10–15 years) and 71 age-matched children with typical language development. During scanning, children heard and repeated pseudowords paired with visual referents, allowing us to track learning-related changes in neural activity across repetitions. Repeated pseudoword production yielded comparable behavioural learning across groups, with faster productions by later repetitions. Post-scan, form–referent recognition was comparable across groups, whereas pseudoword repetition accuracy was lower in DLD. Pseudoword repetition engaged a distributed neural network, including inferior frontal cortex bilaterally (greater on the left), premotor and sensorimotor cortex, and posterior temporal and occipital regions. Group differences emerged primarily in regions where activity was task negative (i.e., below baseline or deactivated): lateral occipito-parietal cortex (posterior angular gyrus), medial parieto-occipital cortex (retrosplenial), and right posterior cingulate cortex. Learning-related decreases in activity were similar across groups, but region-of-interest analyses showed reduced leftward lateralisation of activity in inferior frontal gyrus in DLD. These findings suggest weaker disengagement of the default mode network during a linguistically demanding task in DLD. Although repetition-based pseudoword learning recruited similar neural mechanisms in both groups, these mechanisms may operate less efficiently in DLD, alongside reduced hemispheric specialisation in inferior frontal cortex.

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OSF n76sz

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
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Languages: R (3)
Size: 12 files, 3 scripts
Software Heritage: not checked
Found in: “Data and code accessibility”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lme4 (3 files), tidyverse (3 files), emmeans (2 files), ggplot2 (2 files), lmerTest (2 files), multcomp (2 files), patchwork (2 files), easystats (1 file), psych (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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At the source: osf.io/n76sz/overview?view_only=6f329bf021ab46409e9e62c34775b5a5

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

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The anonymised neuropsychological and performance on pseudoword task and scripts supporting the findings presented are openly available on the Open Science Framework (https://osf.io/n76sz/overview?view_only=6f329bf021ab46409e9e62c34775b5a5).

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Cite

This paper

Bahar, N., Cler, G. J., Asaridou, S. S., Smith, H. J., Willis, H. E., Healy, M. P., Chughtai, S., Haile, M., Krishnan, S., & Watkins, K. E. (2026). Neural correlates of novel word-form learning in developmental language disorder. bioRxiv (preprint). https://doi.org/10.64898/2026.03.28.715039

BibTeX

@article{bahar2026neural,
author = {Bahar, Nilgoun and Cler, Gabriel J. and Asaridou, Salomi S. and Smith, Harriet J. and Willis, Hanna E. and Healy, Máiréad P. and Chughtai, Sana and Haile, Meron and Krishnan, Saloni and Watkins, Kate E.},
title = {{Neural correlates of novel word-form learning in developmental language disorder}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.28.715039},
url = {https://doi.org/10.64898/2026.03.28.715039}
}

RIS

TY - JOUR
AU - Bahar, Nilgoun
AU - Cler, Gabriel J.
AU - Asaridou, Salomi S.
AU - Smith, Harriet J.
AU - Willis, Hanna E.
AU - Healy, Máiréad P.
AU - Chughtai, Sana
AU - Haile, Meron
AU - Krishnan, Saloni
AU - Watkins, Kate E.
TI - Neural correlates of novel word-form learning in developmental language disorder
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/31
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.28.715039
UR - https://doi.org/10.64898/2026.03.28.715039
ER -

CSL-JSON

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