OSCR

Roles of beta synchronization for motor skill acquisition change after stroke.

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
  1. [1] § Materials and methods › Participants and setting ↔ scripts/main_script_beta_R.R, lines 41–83 · score 0.93 · Mini Mental, Fugl Meyer, Action Research Arm, Hole Peg, Upper Extremity, hand grip force
  2. [2] § Materials and methods › Data analysis and statistics › Dependence of the relationship between beta power and improvement on lesion location, beta frequency band and effects of fatigue ↔ scripts/main_script_beta_R.R, lines 318–400 · score 0.73 · 14–20 Hz, 21–29 Hz, cortical lesions, linear model, high beta, 21 Hz
  3. [3] § Results › Results do not depend on lesion location, beta sub-band or definition of improvement ↔ scripts/main_script_beta_R.R, lines 318–400 · score 0.62 · 14–20 Hz, 21–29 Hz, cortical lesions, 21 Hz, beta ERS, interaction
  4. [4] § Materials and methods › Participants and setting ↔ scripts/main_script_beta_R.R, lines 41–83 · score 0.59 · Hole Peg, hand grip force, NHP, Box, ARAT, UEFM
  5. [5] § Materials and methods › Data analysis › Clinical and movement data ↔ scripts/main_script_beta_R.R, lines 85–149 · score 0.59 · hand grip force, best block, movement rate, ARAT, UEFM, ratio
  6. [6] § Results › Clinical scores and structural imaging ↔ scripts/main_script_beta_R.R, lines 85–149 · score 0.56 · grip force ratio, hand grip force, corticospinal, tract, integrity, CST
  7. [7] § Results › Movement-related beta power in stroke survivors and control participants ↔ scripts/functions/cfc_figure_3C_3D.m, lines 25–86 · score 0.52 · 14–28 Hz, 0.6–1.4 s, 0.6 s, 0.3 s, 0.5 s, ERD
  8. [8] § Results › Movement-related beta power in stroke survivors and control participants ↔ scripts/functions/cfc_figure_3C_3D.m, lines 25–86 · score 0.51 · 14–28 Hz, 0.6–1.4 s, window, 0.6 s, 0.3 s, 0.5 s

Paper

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The authors' code

R · 821 lines · 36 KB · no license · 6 matches

  1. # beta ERS and motor skill acquisition
  3. # prepare environment
  5. install.packages('rstatix')
  6. install.packages('emmeans')
  7. install.packages('tidyplots')
  8. install.packages('dplyr')
  9. install.packages('patchwork')
  10. install.packages('ggplot2')
  11. install.packages('ggeffects')
  12. install.packages('RColorBrewer')
  13. install.packages('extrafont')
  15. library(rstatix)
  16. library(emmeans)
  17. library(tidyplots)
  18. library(dplyr)
  19. library(patchwork)
  20. library(ggplot2)
  21. library(ggeffects)
  22. library(RColorBrewer)
  23. library(extrafont)
  24. loadfonts(device = "all")
  26. # load data
  28. setwd("scripts_for_data_availability") # your own path to correct working directory
  29. data <- read.csv("data/subjectlevel_alldata.csv")
  32. ################################################################################
  33. #### PART 1: STATISTICS AND MODELLING ####
  34. ################################################################################
  37. ################################################################################
  38. ### mean and SD of age, month after stroke, clinical scores, integrity of the cortico-spinal-tract, task behavior ###
  39. ################################################################################
  41. # age
  42. mean(data$age[data$group=='stroke'])
  43. sd(data$age[data$group=='stroke'])
  44. mean(data$age[data$group=='control'])
  45. sd(data$age[data$group=='control'])
  47. # month after stroke
  48. mean(data$mas[data$group=='stroke'])
  49. sd(data$mas[data$group=='stroke'])
  51. # Mini Mental Status Exam (MMST)
  52. mean(data$mmst[data$group=='stroke'])
  53. mean(data$mmst[data$group=='control'])
  55. # Upper Extremity Fugl-Meyer Test (UEFM)
  56. mean(data$fmue[data$group=='stroke'])
  57. sd(data$fmue[data$group=='stroke'])
  58. mean(data$fmue[data$group=='control'])
  59. sd(data$fmue[data$group=='control'])
  61. # Action Research Arm Test of performing/affected hand (ARAT)
  62. mean(data$arat_a[data$group=='stroke'])
  63. sd(data$arat_a[data$group=='stroke'])
  64. mean(data$arat_a[data$group=='control'])
  65. sd(data$arat_a[data$group=='control'])
  67. # Box and Blot Test of affected/performing hand (BBT)
  68. mean(data$bbt_a[data$group=='stroke'])
  69. sd(data$bbt_a[data$group=='stroke'])
  70. mean(data$bbt_a[data$group=='control'])
  71. sd(data$bbt_a[data$group=='control'])
  73. # Nine Hole Peg Test of affected/performing hand (NHP)
  74. mean(data$nhp_a[data$group=='stroke'])
  75. sd(data$nhp_a[data$group=='stroke'])
  76. mean(data$nhp_a[data$group=='control'])
  77. sd(data$nhp_a[data$group=='control'])
  79. # Whole hand grip force ratio, i.e. affected(performing)/unaffected(not performing) hand
  80. mean(data$grip_ratio[data$group=='stroke'])
  81. sd(data$grip_ratio[data$group=='stroke'])
  82. mean(data$grip_ratio[data$group=='control'])
  83. sd(data$grip_ratio[data$group=='control'])
  85. # Key grip force ratio, i.e. affected(performing)/unaffected(not performing) hand
  86. mean(data$key_ratio[data$group=='stroke'])
  87. sd(data$key_ratio[data$group=='stroke'])
  88. mean(data$key_ratio[data$group=='control'])
  89. sd(data$key_ratio[data$group=='control'])
  91. # Integrity of the cortico-spinal-tract (CST) ratio, i.e. affected(contralateral to performing)/unaffected(ipsilateral to performing) hemisphere
  92. mean(data$mesencephalon_ratio[data$group=='stroke'], na.rm=TRUE)
  93. sd(data$mesencephalon_ratio[data$group=='stroke'], na.rm=TRUE)
  94. mean(data$mesencephalon_ratio[data$group=='control'])
  95. sd(data$mesencephalon_ratio[data$group=='control'])
  97. # behavior task: movement rate block 1
  98. mean(data$performance_b1[data$group=='stroke'])
  99. sd(data$performance_b1[data$group=='stroke'])
  100. mean(data$performance_b1[data$group=='control'])
  101. sd(data$performance_b1[data$group=='control'])
  103. # behavior task: improvement (first to best block)
  104. mean(data$improvement_perf[data$group=='stroke'])
  105. sd(data$improvement_perf[data$group=='stroke'])
  106. mean(data$improvement_perf[data$group=='control'])
  107. sd(data$improvement_perf[data$group=='control'])
  110. ################################################################################
  111. ### clinical scores, CST, behavior, beta ERD and ERS in pMV: differences between stroke survivors and control participants ###
  112. ################################################################################
  114. ## test for normal distribution with Shapiro-Wilk-Test ##
  115. shapiro.test(data$fmue[data$group=='stroke']) # UEFM
  116. shapiro.test(data$fmue[data$group=='control'])
  118. shapiro.test(data$arat_a[data$group=='stroke']) # ARAT
  119. shapiro.test(data$arat_a[data$group=='control'])
  121. shapiro.test(data$bbt_a[data$group=='stroke']) # BBT
  122. shapiro.test(data$bbt_a[data$group=='control'])
  124. shapiro.test(data$nhp_a[data$group=='stroke']) # NHP
  125. shapiro.test(data$nhp_a[data$group=='control'])
  127. shapiro.test(data$grip_ratio[data$group=='stroke']) # whole hand grip force
  128. shapiro.test(data$grip_ratio[data$group=='control'])
  130. shapiro.test(data$key_ratio[data$group=='stroke']) # key grip force
  131. shapiro.test(data$key_ratio[data$group=='control'])
  133. shapiro.test(data$mesencephalon_ratio[data$group=='stroke']) # CST
  134. shapiro.test(data$mesencephalon_ratio[data$group=='control'])
  136. shapiro.test(data$performance_b1[data$group=='stroke']) # movement rate block 1
  137. shapiro.test(data$performance_b1[data$group=='control'])
  139. shapiro.test(data$performance_b6[data$group=='stroke']) # movement rate block 6
  140. shapiro.test(data$performance_b6[data$group=='control'])
  142. shapiro.test(data$improvement_perf[data$group=='stroke']) # improvement
  143. shapiro.test(data$improvement_perf[data$group=='control'])
  145. shapiro.test(data$pmva_erd_brainnetome[data$group=='stroke']) # beta ERD pMV
  146. shapiro.test(data$pmva_erd_brainnetome[data$group=='control'])
  148. shapiro.test(data$pmva_ers_brainnetome[data$group=='stroke']) # beta ERS pMV
  149. shapiro.test(data$pmva_ers_brainnetome[data$group=='control'])
  151. ## based on Shapiro-Wilk-Test: choose adequate test (T-test or Wilcoxon test) to test group differences ##
  153. # select comparisons
  154. data$group <- factor(data$group,levels = c('stroke','control'), order=TRUE)
  155. my_comparisons=list(c('stroke', 'control'))
  157. # apply tests
  158. wilcox_test(data, fmue ~ group, comparisons=my_comparisons) # UEFM
  159. wilcox_test(data, arat_a ~ group, comparisons=my_comparisons) # ARAT
  160. t_test(data, nhp_a ~ group, comparisons=my_comparisons) # NHP
  161. t_test(data, bbt_a ~ group, comparisons=my_comparisons) # BBT
  162. wilcox_test(data, grip_ratio ~ group, comparisons=my_comparisons) # grip force
  163. t_test(data, key_ratio ~ group, comparisons=my_comparisons) # key grip force
  164. t_test(data, mesencephalon_ratio ~ group, comparisons=my_comparisons) # CST
  165. t_test(data, performance_b1 ~ group, comparisons=my_comparisons) # movement rate block 1
  166. t_test(data, improvement_perf ~ group, comparisons=my_comparisons) # improvement
  167. t_test(data, pmva_erd_brainnetome ~ group, comparisons=my_comparisons) # beta ERD pMV
  168. p=(t_test(data, pmva_erd_brainnetome ~ group, comparisons=my_comparisons))$p # get p value
  169. wilcox_test(data, pmva_ers_brainnetome ~ group, comparisons=my_comparisons) # beta ERS pMV
  170. p[2]=(wilcox_test(data, pmva_ers_brainnetome ~ group, comparisons=my_comparisons))$p # get p value
  171. p.adjust(p, method='bonferroni') # correct for two comparisons
  173. # also compare movement rate between block 1 and block 6 for both groups
  174. t.test(data$performance_b1[data$group=='stroke'], data$performance_b6[data$group=='stroke'], paired=TRUE) # stroke survivors
  175. p=(t.test(data$performance_b1[data$group=='stroke'], data$performance_b6[data$group=='stroke'], paired=TRUE))$p.value
  176. t.test(data$performance_b1[data$group=='control'], data$performance_b6[data$group=='control'], paired=TRUE) # control participants
  177. p[2]=(t.test(data$performance_b1[data$group=='control'], data$performance_b6[data$group=='control'], paired=TRUE))$p.value
  178. p.adjust(p, method='bonferroni') # correct for two comparisons
  181. ################################################################################
  182. ### main model: modeling relation between improvement and maximum cluster beta ERS ###
  183. ################################################################################
  185. #load data
  186. datatmp <- data
  188. # build main model and get summary
  189. model<- lm(cluster_ers_max ~ improvement_perf*group+performance_b1+performed_hand,data=datatmp)
  190. summary(model)
  192. # check general assumptions for linear models
  193. par(mfrow = c(2, 2)) # Set up the layout of plots
  194. plot(model) # Plot diagnostics for the model
  196. # post-hoc analysis: interaction improvement x group
  197. emm_group <- emmeans(model, ~ improvement_perf * group)
  198. test(emm_group, by = "group")
  200. # clear environment
  201. rm(list=setdiff(ls(), "data")) # rm(list = ls())
  204. ################################################################################
  205. ### testing robustness of the main model ###
  206. ################################################################################
  208. ## organised into calculations (1) - (5) ##
  211. ### (1) outlier testing: rebuilding model after removal of outlier ###
  213. # load data
  214. datatmp <- data
  216. # main model
  217. model<- lm(cluster_ers_max ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  219. # detect outliers with cook's distance
  220. cooksd <- cooks.distance(model) # calculate cook's distance
  221. plot(cooksd, pch="*", cex=2, main="Influential Obs by Cooks distance") # plot cook's distance
  222. abline(h = 4*mean(cooksd, na.rm=T), col="red") # add cutoff line
  223. text(x=1:length(cooksd)+1, y=cooksd, labels=ifelse(cooksd>4*mean(cooksd, na.rm=T),names(cooksd),""), col="red") # add labels
  225. # remove detected outliers
  226. datatmp <- data %>%
  227. filter(!index==28) %>%
  228. filter(!index==9)
  230. # calculate reduced model without outliers and get summary
  231. modelred<- lm(cluster_ers_max ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  232. summary(modelred)
  234. # post-hoc analysis of reduced model
  235. emm_group <- emmeans(modelred, ~ improvement_perf * group)
  236. test(emm_group, by = "group")
  238. # clear environment
  239. rm(list=setdiff(ls(), "data"))
  242. ### (2) outlier testing: Bonferroni Outlier Test ###
  244. #load data
  245. datatmp <- data
  247. # build main model
  248. model<- lm(cluster_ers_max ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  250. # calculate Bonferroni Outlier Test
  251. car::outlierTest(model)
  253. # clear environment
  254. rm(list=setdiff(ls(), "data"))
  257. ### (3) leave-one-out analysis ###
  259. #load data
  260. datatmp <- data
  262. # build main model
  263. model<- lm(cluster_ers_max ~ performance_b1 + performed_hand + improvement_perf*group, data=datatmp)
  265. # define model formula
  266. model_formula <- cluster_ers_max ~ performance_b1 + performed_hand + improvement_perf*group
  268. # initialize a vector to store p-values for the interaction term
  269. loo_pvalues <- numeric(nrow(datatmp))
  271. ## perform leave-one-out analysis ##
  272. for (i in 1:nrow(datatmp)) {
  273. # Exclude the i-th observation
  274. train_data <- datatmp[-i, ]
  276. # fit the model on the remaining data
  277. loo_model <- lm(model_formula, data = train_data)
  279. # extract the p-value of the interaction term if it exists
  280. interaction_term <- "improvement_perf:groupstroke"
  282. if (interaction_term %in% rownames(summary(loo_model)$coefficients)) {
  283. loo_pvalues[i] <- summary(loo_model)$coefficients[interaction_term, "Pr(>|t|)"]
  284. } else {
  285. loo_pvalues[i] <- NA # Assign NA if the term is not present
  286. }
  287. }
  289. # output the p-values for the interaction term
  290. loo_pvalues
  292. # clear environment
  293. rm(list=setdiff(ls(), "data"))
  296. ### (4) recalculating the model with only stroke survivors with (a) subcortical and (b) cortical lesions ###
  298. ## (4a) only stroke survivors with subcortical lesions ##
  300. # load data and select only stroke survivors with subcortical lesions
  301. datatmp <-data %>%
  302. filter(index!=7) %>%
  303. filter(index!=11) %>%
  304. filter(index!=14)
  306. # build model with only stroke survivors with subcortical lesions and get summary
  307. model<- lm(cluster_ers_max ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  308. summary(model)
  310. # check general assumptions for linear models of model with only stroke survivors with subcortical lesions
  311. par(mfrow = c(2, 2)) # Set up the layout of plots
  312. plot(model) # Plot diagnostics for the model
  314. # post-hoc analysis for model with only stroke survivors with subcortical lesions: interaction improvement x group
  315. emm_group <- emmeans(model, ~ improvement_perf * group)
  316. test(emm_group, by = "group")
  318. # clear environment
  319. rm(list=setdiff(ls(), "data"))
  321. ## (4b) only stroke survivors with cortical lesions ##
  323. # load data and select only stroke survivors with cortical lesions
  324. datatmp <-data %>%
  325. filter(index==7|index==11|index==14|group=="control")
  327. # build model with only stroke survivors with cortical lesions and get summary
  328. model<- lm(cluster_ers_max ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  329. summary(model)
  331. # check general assumptions for linear models of model with only stroke survivors with cortical lesions
  332. par(mfrow = c(2, 2)) # Set up the layout of plots
  333. plot(model) # Plot diagnostics for the model
  335. # post-hoc analysis for model with only stroke survivors with cortical lesions: interaction improvement x group
  336. emm_group <- emmeans(model, ~ improvement_perf * group)
  337. test(emm_group, by = "group")
  339. # clear environment
  340. rm(list=setdiff(ls(), "data"))
  343. ### (5) recalculating the model with only stroke survivors with (a) low beta ERS and (b) high beta ERS ###
  345. ## (5a) low beta ERS (14 - 20 Hz) ##
  347. # load data
  348. datatmp <- data
  350. # build model with low beta ERS and get summary
  351. model<- lm(cluster_ers_0_01_max_low ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  352. summary(model)
  354. # check general assumptions for linear models of model with low beta ERS
  355. par(mfrow = c(2, 2)) # Set up the layout of plots
  356. plot(model) # Plot diagnostics for the model
  358. # post-hoc analysis for model with low beta ERS: interaction improvement x group
  359. emm_group <- emmeans(model, ~ improvement_perf * group)
  360. test(emm_group, by = "group")
  362. # clear environment
  363. rm(list=setdiff(ls(), "data"))
  365. ## (5b) high beta ERS (21 - 29 Hz) ##
  367. # load data
  368. datatmp <- data
  370. # build model with high beta ERS and get summary
  371. model<- lm(cluster_ers_0_01_max_high ~ improvement_perf*group + performance_b1 + performed_hand, data=datatmp)
  372. summary(model)
  374. # check general assumptions for linear models of model with high beta ERS
  375. par(mfrow = c(2, 2)) # Set up the layout of plots
  376. plot(model) # Plot diagnostics for the model
  378. # post-hoc analysis for model with high beta ERS: interaction improvement x group
  379. emm_group <- emmeans(model, ~ improvement_perf * group)
  380. test(emm_group, by = "group")
  382. # clear environment
  383. rm(list=setdiff(ls(), "data"))
  386. ################################################################################
  387. ### exploratory analysis ###
  388. ################################################################################
  390. ## prepare data and environment ##
  392. # load data
  393. datatmp <- data
  395. # select parameters to include in model
  396. vars <- list("fmue", "arat_a", "bbt_a", "nhp_a", "grip_ratio", "key_ratio", "mesencephalon_ratio", "age")
  397. names <- list("UEFM", "ARAT", "BBT", "NHP", "grip force", "key grip force", "CST", "Age") # list of names of parameters
  399. # build exploratory model with improvement x group x parameter interaction
  400. allModelsList <- lapply(paste("cluster_ers_max ~ performed_hand + performance_b1 + improvement_perf*group*", vars), as.formula)
  402. # function to extract p value of the whole model
  403. overall_p <- function(my_model) {
  404. f <- summary(my_model)$fstatistic
  405. p <- pf(f[1],f[2],f[3],lower.tail=F)
  406. attributes(p) <- NULL
  407. return(p)}
  409. ## calculate exploratory model for all selected parameters and extract results##
  411. # get coefficients: p-values of each term in model
  412. allModelsResults_1 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[1,4], error=function(e) NaN)) # coefficients model p-value: intercept
  413. allModelsResults_2 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[2,4], error=function(e) NaN)) # coefficients model p-value: performed hand r
  414. allModelsResults_3 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[3,4], error=function(e) NaN)) # coefficients model p-value: movement rate block 1
  415. allModelsResults_4 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[4,4], error=function(e) NaN)) # coefficients model p-value: improvement
  416. allModelsResults_5 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[5,4], error=function(e) NaN)) # coefficients model p-value: group stroke survivors
  417. allModelsResults_6 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[6,4], error=function(e) NaN)) # coefficients model p-value: parameter
  418. allModelsResults_7 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[7,4], error=function(e) NaN)) # coefficients model p-value: improvement * group stroke
  419. allModelsResults_8 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[8,4], error=function(e) NaN)) # coefficients p-value: improvement * parameter
  420. allModelsResults_9 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[9,4], error=function(e) NaN)) # coefficients p-value: group stroke * parameter
  421. allModelsResults_10 <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = datatmp))$coefficients[10,4], error=function(e) NaN)) # coefficients p-value: improvement * group stroke * parameter
  423. # get coefficients: SE of each term in model
  424. allModelsResults_1SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[1,2], error=function(e) NaN)) # coefficients model SE: intercept
  425. allModelsResults_2SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[2,2], error=function(e) NaN)) # coefficients model SE: performed hand r
  426. allModelsResults_3SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[3,2], error=function(e) NaN)) # coefficients model SE: movement rate block 1
  427. allModelsResults_4SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[4,2], error=function(e) NaN)) # coefficients model SE: improvement
  428. allModelsResults_5SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[5,2], error=function(e) NaN)) # coefficients model SE: group stroke survivors
  429. allModelsResults_6SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[6,2], error=function(e) NaN)) # coefficients model SE: parameter
  430. allModelsResults_7SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[7,2], error=function(e) NaN)) # coefficients model SE: improvement * group stroke
  431. allModelsResults_8SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[8,2], error=function(e) NaN)) # coefficients SE: improvement * parameter
  432. allModelsResults_9SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[9,2], error=function(e) NaN)) # coefficients SE: group stroke * parameter
  433. allModelsResults_10SE <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$coefficients[10,2], error=function(e) NaN)) # coefficients SE: improvement * group stroke * parameter
  435. # get overall p-value, F-statistics and R-squared of each model
  436. allModelsResultsR <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$r.squared, error=function(e) NaN)) # R-squared
  437. allModelsResultsF <- lapply(allModelsList, function(x) tryCatch(summary(lm(x, data = data))$fstatistic[1], error=function(e) NaN)) # F-statistics
  438. allModelsResults <- lapply(allModelsList, function(x) tryCatch(overall_p(lm(x, data = data)), error=function(e) NaN)) # overall p-value of model
  440. ## results of exploratory analysis ##
  442. # summaries results in one data frame
  443. df <- data.frame(Parameters=(unlist(vars)),
  444. Intercept_p=unlist(allModelsResults_1),
  445. performed_handr_p=unlist(allModelsResults_2),
  446. performance_b1_p=unlist(allModelsResults_3),
  447. improvement_perf_p=unlist(allModelsResults_4),
  448. groupstroke_p=unlist(allModelsResults_5),
  449. varaiable_p=unlist(allModelsResults_6),
  450. improvement_perf_groupstroke_p=unlist(allModelsResults_7),
  451. improvement_perf_variable_p=unlist(allModelsResults_8),
  452. groupstroke_variable_p=unlist(allModelsResults_9),
  453. improvement_perf_groupstroke_variabl_p=unlist(allModelsResults_10),
  454. Rsquared=unlist(allModelsResultsR),
  455. Fstat=unlist(allModelsResultsF),
  456. model_p=unlist(allModelsResults),
  457. Intercept_SE=unlist(allModelsResults_1SE),
  458. performed_handr_SE=unlist(allModelsResults_2SE),
  459. performance_b1_SE=unlist(allModelsResults_3SE),
  460. improvement_SE=unlist(allModelsResults_4SE),
  461. groupstroke_SE=unlist(allModelsResults_5SE),
  462. varaiable_SE=unlist(allModelsResults_6SE),
  463. improvement_perf_groupstroke_SE=unlist(allModelsResults_7SE),
  464. improvement_perf_variable_SE=unlist(allModelsResults_8SE),
  465. groupstroke_variable_SE=unlist(allModelsResults_9SE),
  466. improvement_perf_groupstroke_variabl_SE=unlist(allModelsResults_10SE))
  467. df = t(df)
  469. # output results
  470. df
  472. # clear environment
  473. rm(list=setdiff(ls(), "data"))
  476. ################################################################################
  477. ### further analysis of results of exploratory analysis: improvement x group x BBT interaction ###
  478. ################################################################################
  480. # load data
  481. datatmp <- data
  483. # build model with improvement x group x BBT interaction an get summary
  484. model <- lm(cluster_ers_max ~ performed_hand + performance_b1 + improvement_perf*group*bbt_a, data=datatmp)
  485. summary(model)
  487. # check general assumptions for linear models
  488. par(mfrow = c(2, 2)) # Set up the layout of plots
  489. plot(model) # Plot diagnostics for the model
  491. # post-hoc test to check for group differences
  492. emmeans_result <- emmeans(model, pairwise ~ group|improvement_perf*bbt_a)
  493. summary(emmeans_result, adjust = "turkey")
  495. ### as groups differ significantly in post-hoc test: recalculate improvement x BBT interaction model for each group separately ###
  497. ## control participants ##
  499. # select group
  500. datatmp <-data %>%
  501. filter(group=="control")
  503. # calculate model and get summary
  504. model <- lm(cluster_ers_max ~ performed_hand + performance_b1 + improvement_perf*bbt_a, data=datatmp)
  505. summary(model)
  507. # check general assumptions for linear models
  508. par(mfrow = c(2, 2)) # Set up the layout of plots
  509. plot(model) # Plot diagnostics for the model
  511. ## stroke survivors ##
  513. # select group
  514. datatmp <-data %>%
  515. filter(group=="stroke")
  517. # calculate model and get summary
  518. model <- lm(cluster_ers_max ~ performed_hand + performance_b1 + improvement_perf*bbt_a, data=datatmp)
  519. summary(model)
  521. # check general assumptions for linear models
  522. par(mfrow = c(2, 2)) # Set up the layout of plots
  523. plot(model) # Plot diagnostics for the model
  525. # clear environment
  526. rm(list=setdiff(ls(), "data"))
  530. ################################################################################
  531. #### PART 2: PLOTTING ####
  532. ################################################################################
  534. ################################################################################
  535. ### figure 1B, 1C, 1D and supplementary figure 1B ###
  536. ################################################################################
  538. ### create function for plotting ###
  539. myboxplot <- function(data, yparameter, stat_test, title, ytitle, limits=NULL) {
  541. group_colors <- c("stroke" = "#0072c7", "control" = "#f39f18") # select group colors
  543. data <- data %>%
  544. mutate(group = factor(group, levels = c("stroke", "control"))) # swap the order of the groups
  546. plot <- data |>
  547. tidyplot(x=group, y=!!sym(yparameter), color=group) |>
  548. add_data_points_jitter(data = filter_rows(group=="stroke"), shape = 16,
  549. alpha=0.8, size=0.3, jitter_width =1) |>
  550. add_data_points_jitter(data = filter_rows(group=="control"), shape = 17,
  551. alpha=0.8, size=0.3, jitter_width =1) |>
  552. add_boxplot(show_outliers = FALSE) |>
  553. add_test_asterisks(method = stat_test,hide_info = TRUE,
  554. family = "Arial", label.size = 6/ggplot2::.pt) |>
  555. adjust_font(family = "Arial", fontsize=6) |>
  556. adjust_colors(new_colors = group_colors) |>
  557. adjust_title(title) |>
  558. remove_x_axis_ticks() |>
  559. remove_x_axis_labels() |>
  560. remove_x_axis_title() |>
  561. adjust_y_axis_title(ytitle) |>
  562. adjust_y_axis(limits=limits) |>
  563. remove_legend() |>
  564. adjust_size(width = 11, height = 20, unit = "mm")
  566. return(plot)
  567. }
  569. ### plot figure 1 ###
  571. # load data
  572. datatmp <- data
  573. # create plots
  574. uefm <- myboxplot(datatmp, "fmue", "wilcoxon", "UEFM", "score")
  575. arat <- myboxplot(datatmp, "arat_a", "wilcoxon", "ARAT", "score")
  576. grip <- myboxplot(datatmp, "grip_ratio", "t_test", "grip force", "ratio")
  577. keygrip <- myboxplot(datatmp, "key_ratio", "t_test", "key grip force", "ratio", c(0.53, 1.42))
  578. nhp <- myboxplot(datatmp, "nhp_a", "t_test", "NHP", "pegs/s")
  579. bbt <- myboxplot(datatmp, "bbt_a", "t_test", "BBT", "blocks/min")
  580. cst <- myboxplot(datatmp, "mesencephalon_ratio", "t_test", "CST", "ratio")
  582. # combine figure
  583. figure_1_BCD <- (uefm | arat | bbt | nhp) / (grip | keygrip | cst )
  584. # output figure
  585. figure_1_BCD
  587. # eventually save figure
  588. #ggsave("figures/fig_1B_1C_1D.svg",
  589. # plot = plot, width = 180, height = 80, units = "mm", device = "svg", dpi = 300)
  591. ### plot supplementary figure 1B ###
  593. erd_pmv <- myboxplot(datatmp, "pmva_erd_brainnetome", "t_test", "PMv beta ERD", "relative power")
  594. ers_pmv <- myboxplot(datatmp, "pmva_ers_brainnetome", "wilcoxon", "PMv beta ERS", "relative power")
  596. # combine figure
  597. suppl_figure_1B <- erd_pmv | ers_pmv
  598. # output figure
  599. suppl_figure_1B
  601. # clear environment
  602. rm(list=setdiff(ls(), "data"))
  605. ################################################################################
  606. ### figure 4D & figure 5 ###
  607. ################################################################################
  609. ### create function for plotting ###
  611. mypredictionplot <- function(data, model, prediction_terms, yparameter, ytitle, title=NULL) {
  613. pred_data <- ggpredict(model, terms = prediction_terms) # calculate prediction
  615. plot <- ggplot() +
  616. geom_point(data = data, aes(x = improvement_perf, y=!!sym(yparameter), shape = group, color = group), size = 1, alpha = 0.8) +
  617. scale_shape_manual(values = c("stroke" = 16, "control" = 17)) +
  618. geom_line(data = pred_data, aes(x = x, y = predicted, color = group), linewidth = 0.236) +
  619. geom_ribbon(data = pred_data, aes(x = x, ymin = conf.low, ymax = conf.high, fill = group), alpha = 0.15) +
  620. scale_color_manual(values = c("stroke" = "#0072c7", "control" = "#f39f18")) +
  621. scale_fill_manual(values = c("stroke" = "#0072c7", "control" = "#f39f18"),name="fill") +
  622. labs(title = title,
  623. x = "improvement (%)",
  624. y = ytitle,
  625. shape = "group",
  626. color = "group") +
  627. theme_classic()+
  628. theme(legend.position = "none", text = element_text(family = "Arial", size = 6),
  629. axis.text=element_text(colour="black", size=6),
  630. axis.line = element_line(linewidth = 0.236),
  631. axis.ticks = element_line(size = 0.236),
  632. plot.title = element_text(size = 6, family = "Arial"))
  634. return(plot)
  635. }
  637. ### plot figure 4D (main model) ###
  639. # load data
  640. datatmp <- data
  641. # create model and plot
  642. model <- lm(cluster_ers_max ~ improvement_perf*group+performance_b1+performed_hand, data=datatmp)
  643. figure_4D <- mypredictionplot(datatmp, model, c("improvement_perf", "group"), "cluster_ers_max", "cluster ERS")
  644. # output figure 4D
  645. figure_4D
  646. # eventually save figure
  647. #ggsave("figures/fig_4D.svg",
  648. # plot = figure_4D, width = 45, height = 55, units = "mm", device = "svg", dpi = 300)
  651. ### plot figure 5 ###
  653. ## plot model with only stroke survivors with subcortical lesions ##
  654. # load data
  655. datatmp <- data %>%
  656. filter(index!=7) %>%
  657. filter(index!=11) %>%
  658. filter(index!=14)
  659. # create model and plot
  660. model <- lm(cluster_ers_max ~ improvement_perf*group+performance_b1+performed_hand,data=datatmp)
  661. subcortical <- mypredictionplot(datatmp, model, c("improvement_perf", "group"), "cluster_ers_max", "cluster ERS", "subgroup stroke survivors:\nsubcortical lesions")
  663. ## plot model with only stroke survivors with cortical lesions ##
  664. # load data
  665. datatmp <- data %>%
  666. filter(index==7|index==11|index==14|group=="control")
  667. # create model and plot
  668. model<- lm(cluster_ers_max ~ improvement_perf*group+performance_b1+performed_hand,data=datatmp)
  669. cortical <- mypredictionplot(datatmp, model, c("improvement_perf", "group"), "cluster_ers_max", "cluster ERS", "subgroup stroke survivors:\ncortical lesions")
  671. ## plot model with low beta ERS ##
  672. # load data
  673. datatmp <- data
  674. # create model and plot
  675. model <- lm(cluster_ers_0_01_max_low ~ improvement_perf*group+performance_b1+performed_hand,data=datatmp)
  676. low_beta <- mypredictionplot(datatmp, model, c("improvement_perf", "group"), "cluster_ers_0_01_max_low", "cluster ERS", "low frequency beta")
  678. ## plot model with high beta ERS ##
  679. # load data
  680. datatmp <- data
  681. # create model and plot
  682. model<- lm(cluster_ers_0_01_max_high ~ improvement_perf*group+performance_b1+performed_hand,data=datatmp)
  683. high_beta <- mypredictionplot(datatmp, model, c("improvement_perf", "group"), "cluster_ers_0_01_max_high", "cluster ERS", "high frequency beta")
  685. ## combine plots for figure 5 ##
  686. figure_5 <- subcortical + cortical + low_beta + high_beta + plot_layout(ncol = 2)
  687. # output figure 5
  688. figure_5
  689. #eventually save figure 5
  690. #ggsave("figures/fig_5_R.svg",
  691. # plot = figure_5, width = 80, height = 80, units = "mm", device = "svg", dpi = 300)
  693. # clear environment
  694. rm(list=setdiff(ls(), "data"))
  697. ################################################################################
  698. ### figure 6 ###
  699. ################################################################################
  701. # load data
  702. datatmp <- data
  704. ## define color palettes (outside loop) ##
  705. # line/ribbon colors
  706. control_line_colors <- brewer.pal(9, "Oranges")[c(5, 8)]
  707. stroke_line_colors <- brewer.pal(9, "Blues")[c(5, 8)]
  708. # point colors (lighter/darker shades for below/above median)
  709. control_point_colors <- setNames(brewer.pal(9, "Oranges")[c(4, 7)], c("below median", "above median"))
  710. stroke_point_colors <- setNames(brewer.pal(9, "Blues")[c(4, 7)], c("below median", "above median"))
  712. ## create list to store plots ##
  713. plot_list <- list()
  715. ### loop through specified groups ###
  716. groups_to_process <- c("control", "stroke")
  718. for (g in groups_to_process) {
  720. # subset data for the current group
  721. group_data <- subset(datatmp, group == g)
  722. # print(paste("--- Processing Group:", g, "---")) # Optional progress indicator
  724. # calculate median split values & labels *within* the group
  725. group_median_bbt_a <- median(group_data$bbt_a, na.rm = TRUE)
  726. mean_bbt_a_low <- mean(group_data$bbt_a[group_data$bbt_a < group_median_bbt_a], na.rm = TRUE)
  727. mean_bbt_a_high <- mean(group_data$bbt_a[group_data$bbt_a >= group_median_bbt_a], na.rm = TRUE)
  729. # define the split values and labels based on group calculations
  730. split_values <- c(mean_bbt_a_low, mean_bbt_a_high)
  731. split_values_legend_labels <- c(paste("below med (avg:", round(split_values[1], 1), ")"),
  732. paste("above med (avg:", round(split_values[2], 1), ")"))
  734. # add BBT split group column based on GROUP median
  735. group_data <- group_data %>%
  736. mutate(bbt_a = as.numeric(bbt_a)) %>%
  737. mutate(bbt_a_split_group = factor(ifelse(bbt_a < group_median_bbt_a, "below median", "above median"),
  738. levels = c("below median", "above median"))) %>%
  739. filter(!is.na(bbt_a_split_group)) # keep only rows with valid split group
  741. ## model fitting and prediction ##
  742. # fit the model on the original subset for the group
  743. model_fit_data <- subset(datatmp, group == g)
  744. group_model <- lm(cluster_ers_max ~ performed_hand + performance_b1 + improvement_perf * bbt_a,
  745. data = model_fit_data)
  746. summary(group_model)
  748. # use ggpredict with the group-specific split_values
  749. pred_data <- ggpredict(group_model, terms = c("improvement_perf", paste0("bbt_a [", paste(split_values, collapse=","), "]")))
  751. # map prediction levels to the group-specific legend labels
  752. label_map <- setNames(split_values_legend_labels, as.character(round(split_values, 6)))
  753. if (is.factor(pred_data$group)) {
  754. numeric_group_vals <- as.numeric(levels(pred_data$group))[pred_data$group]
  755. } else {
  756. numeric_group_vals <- as.numeric(pred_data$group)
  757. }
  758. group_vals_for_lookup <- as.character(round(numeric_group_vals, 6))
  759. pred_data$bbt_a_level <- factor(label_map[group_vals_for_lookup], levels = split_values_legend_labels)
  761. ## plotting ##
  762. # assign colors (based on group 'g')
  763. current_line_colors <- if (tolower(g) == "control") control_line_colors else stroke_line_colors
  764. current_fill_colors <- current_line_colors
  765. current_point_palette <- if (tolower(g) == "control") control_point_colors else stroke_point_colors
  766. namegroup <- if (tolower(g) == "control") c("control participants") else c("stroke survivors")
  768. # build the ggplot80
  769. p <- ggplot() +
  770. # layers for lines/ribbons
  771. geom_ribbon(data = pred_data, aes(x = x, ymin = conf.low, ymax = conf.high, fill = bbt_a_level), alpha = 0.15) +
  772. geom_line(data = pred_data, aes(x = x, y = predicted, color = bbt_a_level), linewidth = 0.236) +
  773. scale_color_manual(values = current_line_colors, name = "prediction (BBT)", labels = levels(pred_data$bbt_a_level)) +
  774. scale_fill_manual(values = current_fill_colors, name = "prediction (BBT)", labels = levels(pred_data$bbt_a_level)) +
  776. # new color scale for points
  777. ggnewscale::new_scale_color() +
  779. # layers for points (using group_data with the group-specific split)
  780. geom_point(data = group_data, aes(x = improvement_perf, y = cluster_ers_max, color = bbt_a_split_group), size = 1, shape = 16) +
  781. # hide point legend using guide = "none"
  782. scale_color_manual(values = current_point_palette, name = "Data Points (BBT)", labels = levels(group_data$bbt_a_split_group), na.translate = FALSE,
  783. guide = "none") +
  785. # labels, title, theme
  786. labs(x = "improvement (%)", y = "cluster ERS", title = paste(namegroup)) +
  787. theme_bw() +
  788. theme(aspect.ratio = 1, # Square plot panel
  789. legend.position = "bottom") # Legend at bottom
  791. plot_list[[as.character(g)]] <- p
  793. } # end of loop
  795. ### combine the plots ###
  796. # directly attempt combination assuming both plots were generated
  797. combined_plot <- plot_list[["control"]] + plot_list[["stroke"]] +
  798. plot_layout(ncol = 2, guides = 'collect') & # collect the single active legend
  799. theme_classic()+
  800. theme(legend.position = 'bottom', legend.title.position = "top", legend.text = element_text(size=6),legend.justification = c("left"),legend.direction="vertical",
  801. legend.key.size = unit(0.3, 'cm'),
  802. text = element_text(family = "Arial", size = 6),
  803. axis.text=element_text(colour="black"),
  804. axis.line = element_line(linewidth = 0.236),
  805. axis.ticks = element_line(linewidth = 0.236),
  806. plot.title = element_text(size = 6, family = "Arial"))
  808. combined_plot <- combined_plot +
  809. plot_annotation(title = "Interaction (group-specific median split) between improvement and BBT",
  810. theme = theme(plot.title = element_text(family= "Arial", size= 6, face = "bold", hjust = 0.5),
  811. plot.margin = margin(t = 20, r = 5, b = 5, l = 5)))
  813. # output final plot
  814. print(combined_plot)
  816. # eventually save plot
  817. #ggsave("figures/fig_6_R.svg",
  818. # plot = combined_plot, width = 80, height = 70, units = "mm", device = "svg", dpi = 300)
  820. # clear environment
  821. rm(list=setdiff(ls(), "data"))

main_script_beta_R.R at commit 5e8450d, no license · at the source

Overview

Authors: Lena S Timmsen1,2, Benjamin Haverland1,2, Silke Wolf1, Charlotte J Stagg3,4, Jan Feldheim1, The Vinh Luu1, Robert Schulz1, Till R Schneider2, Fanny Quandt1, Bettina C Schwab1,5
ORCID iDs: Silke Wolf, Till R Schneider, Fanny Quandt, Bettina C Schwab
  1. Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
  2. Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
  3. Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
  4. UKRI Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
  5. Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Enschede 7522 NB, The Netherlands
Institutions: University Medical Center Hamburg-Eppendorf (Germany); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); MRC Brain Network Dynamics Unit (United Kingdom); University of Twente (Netherlands)
Journal: Brain communications, volume 8, issue 2, article fcag077
Dates: received 1 October 2025; accepted 10 March 2026; published online 11 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag077 · PMID 41884601 · PMCID PMC13010071 · OpenAlex W7135088789
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), stroke (population)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Source localization, Physiology & signal measures, fMRI & imaging
Keywords: stroke, MEG, beta ERS, motor skill acquisition, source reconstruction
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Medical Faculty of the University Medical Center Hamburg-Eppendorf; Deutsche Forschungsgemeinschaft (DFG); EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council); Else Kröner-Fresenius-Stiftung; Gemeinnützige Hertie-Stiftung (Hertie Network of Excellence in Clinical Neuroscience; Wellcome Trust
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Beta event-related synchronization (ERS) following movement has been associated with motor skill acquisition in healthy individuals, yet its role in stroke recovery remains unclear. Given the prevalence of motor impairments after stroke, understanding how beta ERS relates to motor skill acquisition in this population is of significant clinical relevance, especially in view of emerging opportunities for neuromodulation. In this cohort study, we investigated whole-brain beta ERS during a feedback-guided motor skill acquisition task using magnetoencephalography (MEG) in 14 well-recovered stroke survivors in the chronic phase and 15 age-eligible healthy control participants. Motor ability was assessed with standardized clinical scales, and structural brain metrics were derived from magnetic resonance imaging. MEG data were projected into source space to enable comprehensive cluster-based analyses across the cortex. While stroke survivors exhibited significantly lower overall task performance, their capacity for motor skill acquisition did not significantly differ from that of control participants. In healthy participants, motor skill acquisition was strongly and positively associated with beta ERS in a cluster encompassing bilateral sensorimotor areas, which was absent in stroke survivors. Instead, stroke survivors showed a trend towards a negative association of motor skill acquisition with beta ERS. An exploratory analysis revealed that among various clinical and structural measures, only the Box and Block Test, a measure of gross manual dexterity, significantly moderated the association between beta ERS and motor skill acquisition. These findings suggest that although stroke survivors may retain the ability to acquire motor skills, the underlying neural mechanisms can be altered. In healthy adults with high manual dexterity, beta ERS appears to support short-term motor skill acquisition, whereas at lower dexterity levels, alternative mechanisms may compensate to sustain skill acquisition.

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

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gitlab.rrz.uni-hamburg.de/xeni/beta-synchronisation-after-stroke

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Evidence: files inventoried
Commit: 5e8450d9d127143fbca330a31743ebd7d12eec5c, 29 September 2025
Languages: MATLAB (9), R (1)
Size: 63 files, 10 scripts
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Found in: “Data availability”
Holds: README
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Tools: FieldTrip (7 files), Statistics and Machine Learning Toolbox (2 files), car (1 file), emmeans (1 file), ggplot2 (1 file), patchwork (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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At the source: gitlab.rrz.uni-hamburg.de/xeni/beta-synchronisation-after-stroke

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

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Data

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Data availability

Data and code for this study are available via GitLab (https://gitlab.rrz.uni-hamburg.de/xeni/beta-synchronisation-after-stroke).

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

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Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 6 funders, 53 references.

Cite

This paper

Timmsen, L. S., Haverland, B., Wolf, S., Stagg, C. J., Feldheim, J., Luu, T. V., Schulz, R., Schneider, T. R., Quandt, F., & Schwab, B. C. (2026). Roles of beta synchronization for motor skill acquisition change after stroke. Brain communications, 8(2), fcag077. https://doi.org/10.1093/braincomms/fcag077

BibTeX

@article{timmsen2026roles,
author = {Timmsen, Lena S and Haverland, Benjamin and Wolf, Silke and Stagg, Charlotte J and Feldheim, Jan and Luu, The Vinh and Schulz, Robert and Schneider, Till R and Quandt, Fanny and Schwab, Bettina C},
title = {{Roles of beta synchronization for motor skill acquisition change after stroke}},
journal = {Brain communications},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {fcag077},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag077},
url = {https://doi.org/10.1093/braincomms/fcag077},
pmid = {41884601},
pmcid = {PMC13010071}
}

RIS

TY - JOUR
AU - Timmsen, Lena S
AU - Haverland, Benjamin
AU - Wolf, Silke
AU - Stagg, Charlotte J
AU - Feldheim, Jan
AU - Luu, The Vinh
AU - Schulz, Robert
AU - Schneider, Till R
AU - Quandt, Fanny
AU - Schwab, Bettina C
TI - Roles of beta synchronization for motor skill acquisition change after stroke
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/03/11
VL - 8
IS - 2
SP - fcag077
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag077
UR - https://doi.org/10.1093/braincomms/fcag077
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag077",
"type": "article-journal",
"title": "Roles of beta synchronization for motor skill acquisition change after stroke",
"container-title": "Brain communications",
"author": [
{
"family": "Timmsen",
"given": "Lena S"
},
{
"family": "Haverland",
"given": "Benjamin"
},
{
"family": "Wolf",
"given": "Silke"
},
{
"family": "Stagg",
"given": "Charlotte J"
},
{
"family": "Feldheim",
"given": "Jan"
},
{
"family": "Luu",
"given": "The Vinh"
},
{
"family": "Schulz",
"given": "Robert"
},
{
"family": "Schneider",
"given": "Till R"
},
{
"family": "Quandt",
"given": "Fanny"
},
{
"family": "Schwab",
"given": "Bettina C"
}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "2",
"page": "fcag077",
"DOI": "10.1093/braincomms/fcag077",
"PMID": "41884601",
"PMCID": "PMC13010071",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag077",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
11
]
]
}
}

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