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Effects of single-session transcranial direct current stimulation on response inhibition in stop-signal task performance: A meta-analysis and systematic review.

Code ↔ Paper

3 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 3 matches
  1. [1] § METHODS › Statistical analysis ↔ Code/analysis_script.R.R, lines 170–255 · score 0.78 · PET PEESE, meta outlier, meta analytic, Sensitivity, fill, funnel
  2. [2] § RESULTS › Predefined moderators and subgroup analysis in the anodal tDCS model ↔ Code/analysis_script.R.R, lines 128–168 · score 0.62 · electric density, stimulation montage, stop signal, intensity, electrode
  3. [3] § RESULTS › Characteristics and quality assessment of included studies ↔ Code/analysis_script.R.R, lines 170–255 · score 0.58 · rDLPFC, rIFC, duration, cortex, offline, online

Paper

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The authors' code

R · 257 lines · 8.2 KB · no license · 3 matches

  1. #The code of Meta-analysis of tDCS effects on inhibitory control
  6. ##################################################################
  7. #1.import data####
  8. setwd("C:/R")
  9. install.packages("metafor")#Version 5.0-1 (2026-04-26)
  10. library(metafor)
  11. data_1<-read.csv("tdcs.csv")
  12. data_m<- data_1[1:68, ]
  13. data_p<- data_1[69:71, ]
  15. #2.es calculation(Hedges g, SMD)####
  16. #(1)
  17. g<- escalc(n1i = data_m$active.group,
  18. n2i = data_m$control.group,
  19. m1i = data_m$M.active,
  20. m2i = data_m$M.control,
  21. sd1i = data_m$SD.active,
  22. sd2i = data_m$SD.control,
  23. measure = "SMD")
  25. data_m$yi <- g$yi
  26. data_m$vi <- g$vi
  28. #(2)
  29. install.packages("esc")
  30. library(esc)
  32. ga <- esc_t(p = data_p[1,12], grp1n = data_p[1,6], grp2n = data_p[1,7],es.type="g")
  33. data_p[1, 22] <- -ga$es
  34. data_p[1, 23] <- ga$se
  35. gb <- esc_t(p = data_p[2,12], grp1n = data_p[2,6], grp2n = data_p[2,7],es.type="g")
  36. data_p[2, 22] <- -gb$es
  37. data_p[2, 23] <- gb$se
  38. gc <- esc_t(p = data_p[3,12], grp1n = data_p[3,6], grp2n = data_p[3,7],es.type="g")
  39. data_p[3, 22] <- -gc$es
  40. data_p[3, 23] <- gc$se
  42. data_p$yi <-data_p[, 22]
  43. data_p$vi <-data_p[, 23]
  45. #(3)
  46. head(data_p)
  47. data_p <- subset(data_p, select = -c(active.group, control.group, M.active, SD.active,
  48. M.control, SD.control,p, V22, V23))
  49. head(data_p)
  51. head(data_m)
  52. data_m <- subset(data_m, select = -c(active.group, control.group, M.active, SD.active,
  53. M.control, SD.control, p))
  54. head(data_m)
  56. data_t<- rbind(data_p, data_m)
  58. #3.main model####
  59. main <- rma(yi, vi, slab = Experiment, data = data_t)
  60. summary(main)
  61. confint(main)
  64. #4.polarity####
  65. #(1)moderators_polarity####
  66. #factorization
  67. data_t$tDCS.polarity1<-factor(data_t$tDCS.polarity, levels = c("anodal","cathodal"))
  69. #moderators
  70. summary(rma(yi, vi, slab = Experiment,
  71. mods = ~ tDCS.polarity1,
  72. data = data_t,
  73. method = "REML" )) #yep
  74. confint(rma(yi, vi, slab = Experiment,
  75. mods = ~ tDCS.polarity1,
  76. data = data_t,
  77. method = "REML"))
  79. #(2)subgroup_polarity####
  80. data.a<-rma(yi, vi, slab = Experiment, subset = (tDCS.polarity == "anodal"), data = data_t )
  81. summary(data.a)
  82. confint(data.a)
  84. data.c<-rma(yi, vi, slab = Experiment, subset = (tDCS.polarity == "cathodal"), data = data_t )
  85. summary(data.c)
  87. #5.model of anodal polarity####
  88. data <- data_t[data_t$tDCS.polarity == "anodal", ]
  89. data <- data[order(data$yi), ]
  90. summary(data)
  92. main_a <- rma(yi, vi, slab = Experiment, data = data, method="REML" )
  93. summary(main_a)
  94. confint(main_a)
  96. datac <- data_t[data_t$tDCS.polarity == "cathodal", ]
  97. datac <- datac[order(datac$yi), ]
  98. summary(datac)
  100. main_c <- rma(yi, vi, slab = Experiment, data = datac, method="REML" )
  101. summary(main_c)
  102. confint(main_c)
  103. #(1)moderators of anodal polarity####
  104. #Blinding####
  105. summary(rma(yi, vi, slab = Experiment,
  106. mods = ~ Blinding,
  107. data = data,
  108. method = "REML" )) #drop
  110. #Design####
  111. summary(rma(yi, vi, slab = Experiment,
  112. mods = ~ Design,
  113. data = data,
  114. method = "REML" )) #drop
  116. #control.condition####
  117. summary(rma(yi, vi, slab = Experiment,
  118. mods = ~ Control,
  119. data = data,
  120. method = "REML" )) #drop
  122. #Population####
  123. summary(rma(yi, vi, slab = Experiment,
  124. mods = ~ Population,
  125. data = data,
  126. method = "REML" )) #drop
  128. #stop.signal.type####
  129. summary(rma(yi, vi, slab = Experiment,
  130. mods = ~ stop.signal.type,
  131. data = data,
  132. method = "REML" )) #drop
  134. #Stimulation.Montage####
  135. summary(rma(yi, vi, slab = Experiment,
  136. mods = ~ Stimulation.Montage,
  137. data = data,
  138. method = "REML" )) #yep
  140. #return.Montage####
  141. summary(rma(yi, vi, slab = Experiment,
  142. mods = ~ return.Montage,
  143. data = data,
  144. method = "REML" )) #drop
  146. #electrode.size####
  147. summary(rma(yi, vi, slab = Experiment,
  148. mods = ~ electrode.size,
  149. data = data,
  150. method = "REML" )) #drop
  152. #Intensity####
  153. summary(rma(yi, vi, slab = Experiment,
  154. mods = ~ Intensity,
  155. data = data,
  156. method = "REML" )) #drop
  158. #electric.density####
  159. summary(rma(yi, vi, slab = Experiment,
  160. mods = ~ electric.density,
  161. data = data,
  162. method = "REML" )) #drop
  164. #Timing####
  165. summary(rma(yi, vi, slab = Experiment,
  166. mods = ~ Timing,
  167. data = data,
  168. method = "REML" ))#yep
  170. #Duration####
  171. summary(rma(yi, vi, slab = Experiment,
  172. mods = ~ Duration,
  173. data = data,
  174. method = "REML" )) #drop
  176. #(2)model with significant moderators - reduced heterogeneity####
  177. result <- rma(yi, vi, slab = Experiment,
  178. mods = ~ Stimulation.Montage + Timing ,
  179. data = data,
  180. method = "REML" )
  181. summary(result)
  182. confint(result)
  184. #(3)subset of anodal polarity####
  185. #stimulation site
  186. TDCS.lDLPFC<-rma(yi, vi, slab = Experiment, subset = (Stimulation.Montage == "lDLPFC"), data = data )
  187. summary(TDCS.lDLPFC)
  188. TDCS.M<-rma(yi, vi, slab = Experiment, subset = (Stimulation.Montage == "M"), data = data )
  189. summary(TDCS.M)
  190. TDCS.other.cortex<-rma(yi, vi, slab = Experiment, subset = (Stimulation.Montage == "other cortex"), data = data )
  191. summary(TDCS.other.cortex)
  192. TDCS.rDLPFC<-rma(yi, vi, slab = Experiment, subset = (Stimulation.Montage == "rDLPFC"), data = data )
  193. summary(TDCS.rDLPFC)
  194. TDCS.rIFC<-rma(yi, vi, slab = Experiment, subset = (Stimulation.Montage == "rIFC"), data = data )
  195. summary(TDCS.rIFC)
  197. #timing
  198. TDCS.on<-rma(yi, vi, slab = Experiment, subset = (Timing == "online"), data = data )
  199. summary(TDCS.on)
  200. TDCS.off<-rma(yi, vi, slab = Experiment, subset = (Timing == "offline"), data = data )
  201. summary(TDCS.off)
  203. #6.sensitivity analyses####
  204. #(1)trim-and-fill####
  205. taf <- trimfill(main_a, estimator = "L0")
  206. funnel(taf, legend=TRUE)
  207. taf
  208. regtest(main_a)
  210. #(2)PET-PEESE####
  211. data$sei <- sqrt(data$vi)
  213. # PET PEESE code below is adapted from Joe Hilgard: https://github.com/Joe-Hilgard/PETPEESE/blob/master/PETPEESE_functions.R
  214. # PET
  215. PET <- rma(yi = yi, sei = sei, mods = ~sei, data = data, method = "REML")
  216. PET
  218. # PEESE
  219. PEESE <- rma(yi = yi, sei = sei, mods = ~I(sei^2), data = data, method = "REML")
  220. PEESE
  222. # PET-PEESE Funnel Plot
  223. funnel(result, level = 0.95, refline = 0, main = paste("REML d =", round(result$b[1],2),"PET d =", round(PET$b[1],2),"PEESE d =", round(PEESE$b[1],2)))
  224. abline(v = result$b[1], lty = "dashed") #draw vertical line at meta-analytic effect size estimate
  225. points(x = result$b[1], y = 0, cex = 1.5, pch = 17) #draw point at meta-analytic effect size estimate
  226. # PET PEESE code below is adapted from Joe Hilgard: https://github.com/Joe-Hilgard/PETPEESE/blob/master/PETPEESE_functions.R
  227. # PEESE line and point
  228. sei <- (seq(0, max(sqrt(result$vi)), .001))
  229. vi <- sei^2
  230. yi <- PEESE$b[1] + PEESE$b[2]*vi
  231. grid <- data.frame(yi, vi, sei)
  232. lines(x = grid$yi, y = grid$sei, typ = 'l') # add line for PEESE
  233. points(x = (PEESE$b[1]), y = 0, cex = 1.5, pch = 5) # add point estimate for PEESE
  234. # PET line and point
  235. abline(a = -PET$b[1]/PET$b[2], b = 1/PET$b[2]) # add line for PET
  236. points(x = PET$b[1], y = 0, cex = 1.5) # add point estimate for PET
  237. segments(x0 = PET$ci.lb[1], y0 = 0, x1 = PET$ci.ub[1], y1 = 0, lty = "dashed") #Add 95% CI around PET
  239. #(3)meta outliers####
  240. ###############detection of outliers according to Viechtbauer & Cheung (2010), using altmeta package
  241. Sys.setenv(JAGS_HOME="C:/Program Files/JAGS/JAGS-4.3.1")
  243. install.packages("altmeta")
  244. library(altmeta)
  245. metaoutliers(data$yi, data$vi, model = "RE")
  247. #7.forest####
  248. #main
  249. forest(main_a, addpred=, xlim=c(-4,3.5), at=seq(-2.5,1.5,by=0.5),
  250. xlab = 'Summary effect sizes for anodal tDCS effect on response inhibition in SST',
  251. mlab = 'Summary Effect Size Total (REML)', psize = 1, header = 'Author(s) and Year')
  253. forest(main_c, addpred=, xlim=c(-4,3.5), at=seq(-2.5,1.5,by=0.5),
  254. xlab = 'Summary effect sizes for cathodal tDCS effect on response inhibition in SST',
  255. mlab = 'Summary Effect Size Total (REML)', psize = 1, header = 'Author(s) and Year')

analysis_script.R.R, no license · at the source

Overview

Authors: Qiuxuan Yu1, Mingyue Zhang2, Ximei Zhu3, Sanwang Wang2, Tingting Wu1, Yinjiao Li2, Wenrong Wen2, Yanping Bao4, Jie Shi4, Lin Lu1,2,4, Jiahui Deng2
ORCID iDs: Qiuxuan Yu, Mingyue Zhang, Ximei Zhu, Tingting Wu, Yinjiao Li, Wenrong Wen, Jiahui Deng
  1. Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder Chinese Academy of Medical Sciences (No. 2018RU006) Institute of Basic Medical Sciences Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China
  2. Peking University Sixth Hospital Peking University Institute of Mental Health NHC Key Laboratory of Mental Health (Peking University) National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) Beijing China
  3. Department of Psychiatry The Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou Zhejiang China
  4. National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Peking University Beijing China
Institutions: Chinese Academy of Medical Sciences & Peking Union Medical College (China); Institute of Basic Medical Sciences of the Chinese Academy of Medical Sciences; Peking University (China); Peking University Sixth Hospital (China); Second Affiliated Hospital of Zhejiang University (China); Zhejiang University (China)
Journal: General psychiatry, volume 39, issue 4, article e70042
Dates: received 6 August 2025; accepted 27 May 2026; published online 13 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/gps3.70042 · PMID 42602099 · PMCID PMC13473178 · OpenAlex W7202362387
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), cognitive (subfield)
Methods: Statistics
Keywords: inhibitory control, response inhibition, stop‐signal task, tDCS, transcranial direct current stimulation
Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (82271528, 82288101); STI2030-Major Projects (2021ZD0204300)
Citations: not cited yet (Europe PMC); 94 references in the paper

Abstract

Background: Response inhibition is a fundamental component of executive control and a transdiagnostic mechanism underpinning numerous mental disorders. Transcranial direct current stimulation (tDCS) has been investigated as a potential neuromodulatory intervention to enhance response inhibition; however, findings remain inconsistent due to methodological heterogeneity in stimulation parameters and outcome measures.

Aims: To quantitatively evaluate the effects of tDCS on response inhibition as measured by the stop‐signal task (SST) and to explore potential moderators influencing tDCS efficacy.

Methods: This meta‐analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. We systematically searched PubMed/MEDLINE, Web of Science, PsycINFO, Embase and Scopus up to 1 April 2026. Quality assessment was conducted utilising the Cochrane risk of bias 2 tool, with random‐effects meta‐analysis, moderator and subgroup analyses and sensitivity analysis for statistical evaluation.

Results: The search yielded 2982 articles, of which 35 were considered eligible for inclusion, including 1768 participants. The risk of bias 2 assessment indicated acceptable methodological quality, with the majority of studies rated as low risk of bias or as having some concerns. The overall effect of tDCS on response inhibition was modest yet statistically significant (Hedges' g = −0.22, 95% confidence interval (CI) −0.34 to −0.11, p < 0.001). Subgroup analysis indicated a small‐to‐moderate effect of anodal tDCS (Hedges' g = −0.38, 95% CI −0.51 to −0.26, p < 0.001), especially when targeting the right inferior frontal gyrus, right dorsolateral prefrontal cortex or motor‐related cortex; additionally, online tDCS demonstrated greater efficacy than offline tDCS. Cathodal tDCS demonstrated a small but statistically significant detrimental effect on response inhibition (Hedges' g = 0.19, 95% CI 0.03–0.35, p = 0.023).

Conclusions: Our findings support the potential of anodal tDCS as a neuromodulatory approach for enhancing response inhibition. The polarity‐, target‐ and timing‐specific effects highlight key methodological considerations for optimising tDCS protocols in research on response inhibition impairments.

PROSPERO Registration Number: CRD42024565038.

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

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OSF bftrc

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 2 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: metafor (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: osf.io/bftrc/?view_only=84202adc19314111a0a4615cc9e2199c

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 1 script, each with its path and the digest of its content;
  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Data availability statement

Extracted data and code used in the analysis can be found on OSF: https://osf.io/bftrc/?view_only=84202adc19314111a0a4615cc9e2199c.

Reproduced under the paper's license (CC BY-NC), 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, 11 authors, 5 keywords, 2 funders, 91 references.

Cite

This paper

Yu, Q., Zhang, M., Zhu, X., Wang, S., Wu, T., Li, Y., Wen, W., Bao, Y., Shi, J., Lu, L., & Deng, J. (2026). Effects of single-session transcranial direct current stimulation on response inhibition in stop-signal task performance: A meta-analysis and systematic review. General psychiatry, 39(4), e70042. https://doi.org/10.1002/gps3.70042

BibTeX

@article{yu2026effects,
author = {Yu, Qiuxuan and Zhang, Mingyue and Zhu, Ximei and Wang, Sanwang and Wu, Tingting and Li, Yinjiao and Wen, Wenrong and Bao, Yanping and Shi, Jie and Lu, Lin and Deng, Jiahui},
title = {{Effects of single-session transcranial direct current stimulation on response inhibition in stop-signal task performance: A meta-analysis and systematic review}},
journal = {General psychiatry},
year = {2026},
month = aug,
volume = {39},
number = {4},
pages = {e70042},
publisher = {Shanghai Mental Health Center},
issn = {2517-729X},
doi = {10.1002/gps3.70042},
url = {https://doi.org/10.1002/gps3.70042},
pmid = {42602099},
pmcid = {PMC13473178}
}

RIS

TY - JOUR
AU - Yu, Qiuxuan
AU - Zhang, Mingyue
AU - Zhu, Ximei
AU - Wang, Sanwang
AU - Wu, Tingting
AU - Li, Yinjiao
AU - Wen, Wenrong
AU - Bao, Yanping
AU - Shi, Jie
AU - Lu, Lin
AU - Deng, Jiahui
TI - Effects of single-session transcranial direct current stimulation on response inhibition in stop-signal task performance: A meta-analysis and systematic review
T2 - General psychiatry
J2 - Gen Psychiatr
PY - 2026
DA - 2026/08/13
VL - 39
IS - 4
SP - e70042
SN - 2517-729X
PB - Shanghai Mental Health Center
DO - 10.1002/gps3.70042
UR - https://doi.org/10.1002/gps3.70042
LA - en
ER -

CSL-JSON

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"page": "e70042",
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"PMID": "42602099",
"PMCID": "PMC13473178",
"ISSN": "2517-729X",
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