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Probing the linguistic cerebellum: a qualitative review of the effects of cerebellar neurostimulation on language processing.

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2 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.

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  1. [1] § Results › General characteristics of NIBS studies ↔ Figure_code.R, lines 179–249 · score 0.71 · verbal fluency, language battery, speech production, sentence processing, domains, semantics
  2. [2] § Results › General characteristics of NIBS studies ↔ Figure_code.R, lines 179–249 · score 0.62 · verbal fluency, speech production, sentence processing, batteries, semantics, language

Paper

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The authors' code

R · 249 lines · 8.1 KB · no license · 2 matches

  1. # ==============================================================================
  2. # Figure code for: "Probing the linguistic cerebellum: a qualitative review of
  3. # the effects of cerebellar neurostimulation on language processing"
  4. # Turker & Hartwigsen (2026), Frontiers in Psychiatry
  5. #
  6. # This script reproduces Figures 2 and 3 from the review.
  7. #
  8. # Input data: NIBS_data_final.xlsx (deposited alongside this script and the
  9. # full study data set, Studies_upload_OSF.xlsx, on OSF).
  10. #
  11. # To run: place this script and NIBS_data_final.xlsx in the same folder,
  12. # open/set that folder as the working directory (e.g. in RStudio:
  13. # Session > Set Working Directory > To Source File Location), then run the
  14. # whole script.
  15. #
  16. # Package versions used to generate the original figures are listed at the
  17. # bottom of this script (see sessionInfo() call).
  18. # ==============================================================================
  20. library(stringr)
  21. library(readxl)
  22. library(tidyverse)
  23. library(patchwork)
  25. # -----------------------------
  26. # LOAD DATA
  27. # -----------------------------
  28. NIBS_data <- read_excel("NIBS_data_final.xlsx")
  30. NIBS_data <- NIBS_data %>%
  31. mutate(
  32. Sample = str_trim(Sample),
  33. NIBS = str_trim(NIBS),
  34. Timing = str_trim(Timing),
  35. Task = str_trim(Task)
  36. )
  38. # -----------------------------
  39. # DEFINE CONSISTENT COLORS FOR ALL PLOTS
  40. # -----------------------------
  41. nibs_colors <- c(
  42. "TMS" = "#4C78A8",
  43. "tDCS" = "#F58518",
  44. "HD-tDCS" = "#54A24B"
  45. )
  47. sample_colors <- c(
  48. "Healthy" = "#4C78A8",
  49. "Clinical" = "#E45756"
  50. )
  52. tdcs_colors <- c(
  53. "Anodal only" = "#FFB36B",
  54. "Cathodal only" = "darkorange1",
  55. "Both" = "darkorange3"
  56. )
  58. timing_colors <- c(
  59. "Offline" = "cadetblue2",
  60. "Online" = "cadetblue3"
  61. )
  63. # -----------------------------
  64. # Plot 1: NIBS METHODS
  65. # -----------------------------
  66. p1_counts <- NIBS_data %>% count(NIBS)
  68. p1 <- p1_counts %>%
  69. ggplot(aes(x = NIBS, y = n, fill = NIBS)) +
  70. geom_col(width = 0.6) +
  71. geom_text(aes(label = n), vjust = -0.3, size = 5, fontface = "bold") +
  72. expand_limits(y = max(p1_counts$n) + 2) +
  73. scale_fill_manual(values = nibs_colors) +
  74. labs(title = "Stimulation Method", x = NULL, y = "Number of studies") +
  75. theme_classic(base_size = 16) +
  76. theme(
  77. legend.position = "none",
  78. plot.title = element_text(face = "bold"),
  79. axis.text = element_text(color = "black")
  80. )
  82. # -----------------------------
  83. # Plot 2: tDCS POLARITY
  84. # -----------------------------
  85. tdcs_data <- NIBS_data %>%
  86. filter(NIBS == "tDCS") %>%
  87. mutate(
  88. polarity = case_when(
  89. str_detect(tDCS_type, "anodal") & str_detect(tDCS_type, "cathodal") ~ "Both",
  90. str_detect(tDCS_type, "anodal") ~ "Anodal only",
  91. str_detect(tDCS_type, "cathodal") ~ "Cathodal only"
  92. ),
  93. polarity = factor(polarity, levels = c("Anodal only", "Cathodal only", "Both"))
  94. )
  96. p2_counts <- tdcs_data %>% count(polarity)
  98. p2 <- p2_counts %>%
  99. ggplot(aes(x = polarity, y = n, fill = polarity)) +
  100. geom_col(width = 0.6) +
  101. geom_text(aes(label = n), vjust = -0.3, size = 5, fontface = "bold") +
  102. expand_limits(y = max(p2_counts$n) + 2) +
  103. scale_y_continuous(breaks = 0:(max(p2_counts$n) + 2)) +
  104. scale_fill_manual(values = tdcs_colors) +
  105. labs(title = "tDCS Polarity", x = NULL, y = "Number of studies") +
  106. theme_classic(base_size = 16) +
  107. theme(
  108. legend.position = "none",
  109. plot.title = element_text(face = "bold"),
  110. axis.text = element_text(color = "black")
  111. )
  113. # -----------------------------
  114. # Plot 3: SAMPLE
  115. # -----------------------------
  116. p3_counts <- NIBS_data %>% count(Sample)
  118. p3 <- p3_counts %>%
  119. ggplot(aes(x = Sample, y = n, fill = Sample)) +
  120. geom_col(width = 0.6) +
  121. geom_text(aes(label = n), vjust = -0.3, size = 5, fontface = "bold") +
  122. expand_limits(y = max(p3_counts$n) + 2) +
  123. scale_fill_manual(values = sample_colors) +
  124. labs(title = "Study Population", x = NULL, y = "Number of studies") +
  125. theme_classic(base_size = 16) +
  126. theme(
  127. legend.position = "none",
  128. plot.title = element_text(face = "bold"),
  129. axis.text = element_text(color = "black")
  130. )
  132. # -----------------------------
  133. # Plot 4: TIMING
  134. # -----------------------------
  135. p4_counts <- NIBS_data %>% count(Timing)
  137. p4 <- p4_counts %>%
  138. ggplot(aes(x = Timing, y = n, fill = Timing)) +
  139. geom_col(width = 0.6) +
  140. geom_text(aes(label = n), vjust = -0.3, size = 5, fontface = "bold") +
  141. expand_limits(y = max(p4_counts$n) + 2) +
  142. scale_fill_manual(values = timing_colors) +
  143. labs(title = "Stimulation Timing", x = NULL, y = "Number of studies") +
  144. theme_classic(base_size = 16) +
  145. theme(
  146. legend.position = "none",
  147. plot.title = element_text(face = "bold"),
  148. axis.text = element_text(color = "black")
  149. )
  151. # -----------------------------
  152. # Plot 5: INTERACTION (Method x Population)
  153. # -----------------------------
  154. p5_counts <- NIBS_data %>% count(NIBS, Sample)
  156. p5 <- p5_counts %>%
  157. ggplot(aes(x = NIBS, y = n, fill = Sample)) +
  158. geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  159. geom_text(
  160. aes(label = n),
  161. position = position_dodge(width = 0.7),
  162. vjust = -0.3,
  163. size = 5,
  164. fontface = "bold"
  165. ) +
  166. expand_limits(y = max(p5_counts$n) + 2) +
  167. scale_fill_manual(values = sample_colors) +
  168. labs(
  169. title = "Stimulation Method by Population",
  170. x = "Stimulation method",
  171. y = "Number of studies"
  172. ) +
  173. theme_classic(base_size = 16) +
  174. theme(
  175. plot.title = element_text(face = "bold"),
  176. axis.text = element_text(color = "black")
  177. )
  179. # -----------------------------
  180. # COMBINE (Figure 2)
  181. # -----------------------------
  182. final_plot <- (p1 | p2) / (p3 | p4) / p5 +
  183. plot_layout(heights = c(1.2, 1, 1.5)) &
  184. theme(plot.title = element_text(face = "bold", hjust = 0))
  186. # -----------------------------
  187. # SAVE FIGURE 2
  188. # -----------------------------
  189. ggsave("Figure2.png", final_plot, width = 12, height = 12, dpi = 300)
  191. # ==============================================================================
  192. # FIGURE 3: TASK DISTRIBUTION
  193. # ==============================================================================
  194. task_data <- NIBS_data %>%
  195. separate_rows(Task, sep = ",") %>%
  196. mutate(
  197. Task_original = str_trim(Task)
  198. )
  200. task_data <- task_data %>%
  201. mutate(Task = case_when(
  202. str_detect(Task_original, regex("semantic", TRUE)) ~ "Semantics",
  203. str_detect(Task_original, regex("sentence", TRUE)) ~ "Sentence processing",
  204. str_detect(Task_original, regex("speech", TRUE)) ~ "Speech production",
  205. str_detect(Task_original, regex("naming", TRUE)) ~ "Naming",
  206. str_detect(Task_original, regex("language", TRUE)) ~ "Language battery",
  207. str_detect(Task_original, regex("reading", TRUE)) ~ "Reading",
  208. str_detect(Task_original, regex("fluency", TRUE)) ~ "Verbal fluency",
  209. str_detect(Task_original, regex("word generation", TRUE)) ~ "Verbal fluency",
  210. TRUE ~ NA_character_
  211. ))
  213. task_data <- task_data %>%
  214. filter(!is.na(Task))
  216. task_counts <- task_data %>%
  217. count(Sample, Task, NIBS)
  219. p_task <- task_counts %>%
  220. ggplot(aes(x = NIBS, y = Task, fill = n)) +
  221. geom_tile(color = "white") +
  222. geom_text(aes(label = n), size = 3.8, fontface = "bold") +
  223. scale_fill_gradient(low = "grey90", high = "#4C78A8") +
  224. facet_wrap(~Sample) +
  225. labs(
  226. title = "Task Distribution",
  227. x = "Stimulation method",
  228. y = "Task domain",
  229. fill = "Count"
  230. ) +
  231. theme_classic(base_size = 10) +
  232. theme(
  233. plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
  234. axis.title = element_text(size = 16),
  235. axis.text = element_text(size = 12, color = "black"),
  236. legend.title = element_text(size = 14),
  237. legend.text = element_text(size = 10),
  238. strip.text = element_text(size = 14, face = "bold")
  239. )
  241. # -----------------------------
  242. # SAVE FIGURE 3
  243. # -----------------------------
  244. ggsave("Figure3.png", p_task, width = 7, height = 4.5, dpi = 300)
  246. # ==============================================================================
  247. # SESSION INFO (for reproducibility; written alongside the figures)
  248. # ==============================================================================
  249. writeLines(capture.output(sessionInfo()), "sessionInfo.txt")

Figure_code.R, no license · at the source

Overview

Authors: Sabrina Turker1,2, Gesa Hartwigsen2,3
  1. Brain and Language Lab, Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria
  2. Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Leipzig, Germany
  3. Cognitive and Biological Psychology, Wilhelm Wundt Institute for Psychology, Leipzig, Leipzig University, Leipzig, Germany
Institutions: University of Vienna (Austria); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Leipzig University (Germany)
Journal: Frontiers in psychiatry, volume 17, article 1850241
Dates: received 8 April 2026; accepted 15 June 2026; published online 14 July 2026
Type: Systematic review · Language: English
License: CC BY
Identifiers: DOI 10.3389/fpsyt.2026.1850241 · PMID 42523961 · PMCID PMC13408518 · OpenAlex W7168257596
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Statistics, Physiology & signal measures
Keywords: cerebellum, language, neurostimulation, non-invasive brain stimulation (NIBS), reading, review, tDCS, TMS
Topic: Vestibular and auditory disorders (Neurology, Neuroscience), according to OpenAlex
Funding: European Research Council (101043747)
Citations: not cited yet (Europe PMC); 90 references in the paper

Abstract

The cerebellum has historically been underrepresented in language research. However, converging evidence from lesion and neuroimaging studies supports its crucial involvement in phonological and semantic aspects of language processing, highlighting the relevance of cerebro-cerebellar circuits in language-related functions. Likewise, cerebellar neurostimulation has emerged as a promising tool for modulating sensorimotor and higher-order cognitive processes. A systematic search was conducted in PubMed and Web of Science following PRISMA guidelines and the present review synthesizes findings from 30 neurostimulation studies probing the causal role of the cerebellum for language processing. It summarizes transcranial electric and magnetic stimulation studies in neurotypical speakers and readers, and individuals with higher-order language disorders. Special emphasis is placed on studies combining cerebellar stimulation with behavioral interventions to assess the potential of improving language-related outcomes in clinical populations. Collectively, these studies support the role of the right posterolateral cerebellum as a key modulator of language functions, especially meaning-related operations.

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 2 matches between paragraphs and lines of code.

OSF 9c4ps

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: patchwork (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source:

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

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

Data availability statement

The original contributions presented in the study are included in the article. The comprehensive study details, the R code to reproduce the figures and the data sheet for using the R code can be found on this OSF project page: 10.17605/OSF.IO/9C4PS. Further inquiries can be directed to the corresponding author.

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 8 keywords, 1 funder, 90 references.

Cite

This paper

Turker, S., & Hartwigsen, G. (2026). Probing the linguistic cerebellum: a qualitative review of the effects of cerebellar neurostimulation on language processing. Frontiers in psychiatry, 17, 1850241. https://doi.org/10.3389/fpsyt.2026.1850241

BibTeX

@article{turker2026probing,
author = {Turker, Sabrina and Hartwigsen, Gesa},
title = {{Probing the linguistic cerebellum: a qualitative review of the effects of cerebellar neurostimulation on language processing}},
journal = {Frontiers in psychiatry},
year = {2026},
month = jul,
volume = {17},
pages = {1850241},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/fpsyt.2026.1850241},
url = {https://doi.org/10.3389/fpsyt.2026.1850241},
pmid = {42523961},
pmcid = {PMC13408518}
}

RIS

TY - JOUR
AU - Turker, Sabrina
AU - Hartwigsen, Gesa
TI - Probing the linguistic cerebellum: a qualitative review of the effects of cerebellar neurostimulation on language processing
T2 - Frontiers in psychiatry
J2 - Front Psychiatry
PY - 2026
DA - 2026/07/14
VL - 17
SP - 1850241
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/fpsyt.2026.1850241
UR - https://doi.org/10.3389/fpsyt.2026.1850241
LA - en
ER -

CSL-JSON

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"title": "Probing the linguistic cerebellum: a qualitative review of the effects of cerebellar neurostimulation on language processing",
"container-title": "Frontiers in psychiatry",
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"family": "Turker",
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"container-title-short": "Front Psychiatry",
"volume": "17",
"page": "1850241",
"DOI": "10.3389/fpsyt.2026.1850241",
"PMID": "42523961",
"PMCID": "PMC13408518",
"ISSN": "1664-0640",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fpsyt.2026.1850241",
"language": "en",
"issued": {
"date-parts": [
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2026,
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]
}
}

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