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Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months.

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  1. [1] § Materials and Methods › Participants ↔ materials/prenatal_vocab_volume_paper_script.R, lines 129–169 · score 0.54 · gestational week, female, scans, outlier, MRI, child
  2. [2] § Materials and Methods › Participants ↔ materials/prenatal_vocab_volume_paper_script.R, lines 129–169 · score 0.53 · gestation week, MRI scan, age

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The authors' code

R · 600 lines · 18 KB · CC-BY-4.0 · 2 matches

  1. #######CHILD: script for paper titled "Prenatal volume in the bilateral superior temporal gyrus predicts
  2. #######children’s expressive vocabulary at 24-36 months "
  3. #######Annika Werwach
  5. #set up
  6. rm(list=ls())
  7. setwd("/Users/werwach/Documents/CHILD/tables")
  8. options(scipen = 999)
  10. #load packages
  11. library(readxl)
  12. library(dplyr)
  13. library(tidyr)
  14. library(psych)
  15. library(lm.beta)
  17. #load tables
  18. data_volume_fet = read_excel("Fetal_Volumes(1).xlsx") #fetal brain volume
  19. names(data_volume_fet)[1] <- 'ID'
  21. data_general_and_cdi = read.csv("CHILD_main_VA_final.csv", header = TRUE, sep = ",")
  22. names(data_general_and_cdi)[2] <- 'ID'
  24. data_maternalage = read.csv("CHILD_VA_maternal_Age.csv", header = TRUE, sep = ",")
  25. names(data_maternalage)[1] <- 'ID'
  27. #rename total volume column
  28. names(data_volume_fet)[names(data_volume_fet) == "Total"] <- "total_volume_fet"
  30. #calculate IFG left & right and bilateral volume
  31. data_volume_fet$IFG_left_fet = (data_volume_fet$Frontal_Inf_Tri_L + data_volume_fet$Frontal_Inf_Oper_L + data_volume_fet$Frontal_Inf_Orb_L)
  32. data_volume_fet$IFG_right_fet = (data_volume_fet$Frontal_Inf_Tri_R + data_volume_fet$Frontal_Inf_Oper_R + data_volume_fet$Frontal_Inf_Orb_R)
  33. data_volume_fet$IFG_fet = (data_volume_fet$IFG_left_fet + data_volume_fet$IFG_right_fet)
  35. #create STG columns & calculate bilateral volume
  36. data_volume_fet$STG_left_fet = (data_volume_fet$Temporal_Sup_L)
  37. data_volume_fet$STG_right_fet = (data_volume_fet$Temporal_Sup_R)
  38. data_volume_fet$STG_fet = (data_volume_fet$Temporal_Sup_L + data_volume_fet$Temporal_Sup_R)
  40. #calculate residuals from regression of STG/IFG volume ~ total intracranial volume
  41. model_resIFG_left = IFG_left_fet ~ total_volume_fet
  42. fit_resIFG_left = lm(formula = model_resIFG_left, data=data_volume_fet)
  43. summary(fit_resIFG_left)
  44. data_volume_fet$IFG_left_fet_resid = fit_resIFG_left$residuals
  46. model_resIFG_right = IFG_right_fet ~ total_volume_fet
  47. fit_resIFG_right = lm(formula = model_resIFG_right, data=data_volume_fet)
  48. summary(fit_resIFG_right)
  49. data_volume_fet$IFG_right_fet_resid = fit_resIFG_right$residuals
  51. model_resIFG = IFG_fet ~ total_volume_fet
  52. fit_resIFG = lm(formula = model_resIFG, data=data_volume_fet)
  53. summary(fit_resIFG)
  54. data_volume_fet$IFG_fet_resid = fit_resIFG$residuals
  56. model_resSTG_left = STG_left_fet ~ total_volume_fet
  57. fit_resSTG_left = lm(formula = model_resSTG_left, data=data_volume_fet)
  58. summary(fit_resSTG_left)
  59. data_volume_fet$STG_left_fet_resid = fit_resSTG_left$residuals
  61. model_resSTG_right = STG_right_fet ~ total_volume_fet
  62. fit_resSTG_right = lm(formula = model_resSTG_right, data=data_volume_fet)
  63. summary(fit_resSTG_right)
  64. data_volume_fet$STG_right_fet_resid = fit_resSTG_right$residuals
  66. model_resSTG = STG_fet ~ total_volume_fet
  67. fit_resSTG = lm(formula = model_resSTG, data=data_volume_fet)
  68. summary(fit_resSTG)
  69. data_volume_fet$STG_fet_resid = fit_resSTG$residuals
  71. #filter only relevant columns from volume data
  72. data_volume_fet = data_volume_fet |>
  73. select(ID,
  74. IFG_left_fet, IFG_right_fet, IFG_fet,
  75. STG_left_fet, STG_right_fet, STG_fet,
  76. IFG_left_fet_resid, IFG_right_fet_resid, IFG_fet_resid,
  77. STG_left_fet_resid, STG_right_fet_resid, STG_fet_resid,
  78. total_volume_fet)
  80. #filter only relevant columns from general & language data & dummy-code sex variable
  81. data_general_and_cdi = data_general_and_cdi |>
  82. select(ID,sex,age_child, age_18, age_24,cdi1,cdi2)
  84. # Assuming your data frame is named 'data'
  85. data_general_and_cdi <- data_general_and_cdi %>%
  86. mutate(sex_dummy = ifelse(sex == "male", 1, 0))
  88. #filter only relevant columns from maternal age data
  89. data_maternalage = data_maternalage |>
  90. filter(redcap_event_name == "prenatal_arm_1") |>
  91. select(ID, age_mother)
  93. #combine datasets
  94. data = data_volume_fet |>
  95. inner_join(data_general_and_cdi, by = "ID") |>
  96. inner_join(data_maternalage, by = "ID")
  98. #filter for only the kids who have cdi1 or cdi2 or both (n = 30) -> for descriptive statistics
  99. data <- data |>
  100. filter(!is.na(cdi1) | !is.na(cdi2))
  102. #filter data for all kids who have the cdi1 (n = 25)
  103. data_cdi1_all <- data |>
  104. filter(!is.na(cdi1))
  106. #filter data for all kids who have the cdi2 (n = 24)
  107. data_cdi2_all <- data |>
  108. filter(!is.na(cdi2))
  110. #outlier exclusion
  111. describe(data_cdi1_all$cdi1)
  112. #M = 21.15, SD = 14.48 -> exclusion of all kids with a score > 50.11 -> ID 104 (score 51), ID 106 (score 52)
  114. data_cdi1 = data_cdi1_all |>
  115. filter(cdi1 <= 50.11)
  117. data_cdi1_outlier = data_cdi1_all |>
  118. filter(cdi1 > 50.11)
  121. describe(data_cdi2_all$cdi2)
  122. #M = 82.04, SD = 20.79 -> exclusion of all kids with a score < 40.46 (as 100 is the maximum anyway) -> ID 123 (score 19)
  123. data_cdi2 = data_cdi2_all |>
  124. filter(cdi2 >= 40.46)
  126. data_cdi2_outlier = data_cdi2_all |>
  127. filter(cdi2 < 40.46)
  129. ###Check sample size##
  130. #Calculate amount of overlapping IDs in the two datasets
  131. overlapping_ids <- intersect(data_cdi1$ID, data_cdi2$ID)
  132. length(overlapping_ids) #n = 19
  134. #Calculate amount of "unique" IDs in each dataset (sanity check)
  135. unique_ids_cdi1 <- setdiff(data_cdi1$ID, data_cdi2$ID)
  136. length(unique_ids_cdi1) #n = 6
  138. unique_ids_cdi2 <- setdiff(data_cdi2$ID, data_cdi1$ID)
  139. length(unique_ids_cdi2) #n = 5
  142. #######################################Descriptive statistics######################################
  143. #gestation week at MRI scan
  144. describe(data$age_child)
  145. data_cdi1_outlier$age_child # 30.3, 31.6
  146. data_cdi2_outlier$age_child # 30
  148. #age at cdi1 assessment
  149. describe(data_cdi1$age_18)
  150. data_cdi1_outlier$age_18 #81.0, 81.1
  151. data_cdi2_outlier$age_18 # 79.4
  153. #age at cdi2 assessment
  154. describe(data_cdi2$age_24)
  155. data_cdi1_outlier$age_24 # 154.6, 133.7
  156. data_cdi2_outlier$age_24 # 135
  158. #sex
  159. table(data$sex)
  160. #female(1): 14
  161. #male(0): 16
  163. table(data_cdi1$sex)
  164. #female: 11
  165. #male: 14
  167. table(data_cdi2$sex)
  168. #female: 13
  169. #male: 11
  171. ##foetal brain volume
  172. ##STG - left
  173. describe(data$STG_left_fet)
  174. data_cdi1_outlier$STG_left_fet # 1216, 1080
  175. data_cdi2_outlier$STG_left_fet# 1183
  177. ##STG - right
  178. describe(data$STG_right_fet)
  179. data_cdi1_outlier$STG_right_fet # 1457, 1352
  180. data_cdi2_outlier$STG_right_fet # 1204
  182. ##IFG - left
  183. describe(data$IFG_left_fet)
  184. data_cdi1_outlier$IFG_left_fet # 2049, 1637
  185. data_cdi2_outlier$IFG_left_fet# 1540
  187. ##IFG - right
  188. describe(data$IFG_right_fet)
  189. data_cdi1_outlier$IFG_right_fet # 2437, 1832
  190. data_cdi2_outlier$IFG_right_fet # 1176
  192. #STG - by sex
  193. describeBy(data$STG_fet_resid,data$sex)
  194. #left
  195. describeBy(data$STG_left_fet_resid,data$sex)
  196. #right
  197. describeBy(data$STG_right_fet_resid,data$sex)
  198. #IFG
  199. describeBy(data$IFG_fet_resid,data$sex)
  200. #left
  201. describeBy(data$IFG_left_fet_resid,data$sex)
  202. #right
  203. describeBy(data$IFG_right_fet_resid,data$sex)
  206. #vocabulary scores
  207. #cdi1
  208. describe(data_cdi1$cdi1)
  209. describe(data_cdi1$cdi1_per)
  211. #cdi2
  212. describe(data_cdi2$cdi2)
  213. describe(data_cdi2$cdi2_per)
  215. #test cdi scores of bi-/multilingual vs. monolingual kids
  216. data_cdi1_mono = data_cdi1[-c(3,4,6,7,9,19,21,22,23),]
  217. data_cdi1_multi = data_cdi1[c(3,4,6,7,9,19,21,22,23),]
  218. t.test(data_cdi1_mono$cdi1,data_cdi1_multi$cdi1) #no significant difference (actually multilingual kids higher average)
  220. data_cdi2_mono = data_cdi2[-c(2,4,7,8,12,18,21,22),]
  221. data_cdi2_multi = data_cdi2[c(2,4,7,8,12,18,21,22),]
  222. t.test(data_cdi2_mono$cdi2,data_cdi2_multi$cdi2) #no significant difference (actually multilingual kids higher average)
  225. #break-up descriptive statistics by sex
  226. t.test(data$STG_left_fet_resid~data$sex)
  227. t.test(data$STG_right_fet_resid~data$sex)
  229. t.test(data$IFG_left_fet_resid~data$sex)
  230. t.test(data$IFG_right_fet_resid~data$sex)
  233. ##test if the two hemispheres are significantly different from each other
  234. t.test(data$IFG_left_fet_resid,data$IFG_right_fet_resid)
  235. t.test(data$STG_left_fet_resid,data$STG_right_fet_resid)
  237. t.test(data$IFG_left_fet,data$IFG_right_fet)
  238. t.test(data$STG_left_fet,data$STG_right_fet) # right one is bigger
  240. ##correlation of IFG and STG volume with potential covariates
  241. cor.test(data$IFG_right_fet_resid, data$age_child)
  242. #cor.test(data$IFG_right_fet_resid, data$total_volume_fet)
  243. cor.test(data$IFG_right_fet_resid, data$age_mother)
  245. cor.test(data$IFG_left_fet_resid, data$age_child)
  246. #cor.test(data$IFG_left_fet_resid, data$total_volume_fet)
  247. cor.test(data$IFG_left_fet_resid, data$age_mother)
  249. cor.test(data$IFG_fet_resid, data$age_child)
  250. #cor.test(data$IFG_fet, data$total_volume_fet)
  251. cor.test(data$IFG_fet_resid, data$age_mother)
  253. cor.test(data$STG_right_fet_resid, data$age_child)
  254. #cor.test(data$STG_right_fet, data$total_volume_fet)
  255. cor.test(data$STG_right_fet_resid, data$age_mother)
  257. cor.test(data$STG_left_fet_resid, data$age_child)
  258. #cor.test(data$STG_left_fet_resid, data$total_volume_fet)
  259. cor.test(data$STG_left_fet_resid, data$age_mother)
  261. cor.test(data$STG_fet_resid, data$age_child)
  262. #cor.test(data$STG_fet, data$total_volume_fet)
  263. cor.test(data$STG_fet_resid, data$age_mother)
  265. ##first-order correlations
  266. #split by hemisphere
  267. cor.test(data_cdi1$STG_left_fet_resid, data_cdi1$cdi1)
  268. cor.test(data_cdi1$STG_right_fet_resid, data_cdi1$cdi1)
  269. cor.test(data_cdi1$IFG_left_fet_resid, data_cdi1$cdi1)
  270. cor.test(data_cdi1$IFG_right_fet_resid, data_cdi1$cdi1)
  272. cor.test(data_cdi2$STG_left_fet_resid, data_cdi2$cdi2)
  273. cor.test(data_cdi2$STG_right_fet_resid, data_cdi2$cdi2)
  274. cor.test(data_cdi2$IFG_left_fet_resid, data_cdi2$cdi2)
  275. cor.test(data_cdi2$IFG_right_fet_resid, data_cdi2$cdi2)
  278. ###############################Regression analysis#######################################
  279. #select relevant columns for regression
  280. data_cdi1_reg = data_cdi1 |>
  281. select(ID, cdi1,
  282. IFG_left_fet, IFG_right_fet,
  283. STG_left_fet, STG_right_fet,
  284. IFG_left_fet_resid, IFG_right_fet_resid, IFG_fet_resid,
  285. STG_left_fet_resid, STG_right_fet_resid, STG_fet_resid,
  286. total_volume_fet, age_child, age_18, sex, sex_dummy)
  288. data_cdi2_reg = data_cdi2 |>
  289. select(ID, cdi2,
  290. IFG_left_fet, IFG_right_fet,
  291. STG_left_fet, STG_right_fet,
  292. IFG_left_fet_resid, IFG_right_fet_resid, IFG_fet_resid,
  293. STG_left_fet_resid, STG_right_fet_resid, STG_fet_resid,
  294. total_volume_fet, age_child, age_24, sex, sex_dummy)
  296. #transform from wide to long format
  297. data_cdi1_reg_long <- data_cdi1_reg %>%
  298. pivot_longer(
  299. cols = starts_with(c("IFG_left_fet_resid", "IFG_right_fet_resid", "STG_left_fet_resid", "STG_right_fet_resid")),
  300. names_to = c(".value", "hem"),
  301. names_pattern = "(.+)_(left|right)")
  303. data_cdi2_reg_long <- data_cdi2_reg %>%
  304. pivot_longer(
  305. cols = starts_with(c("IFG_left_fet_resid", "IFG_right_fet_resid", "STG_left_fet_resid", "STG_right_fet_resid")),
  306. names_to = c(".value", "hem"),
  307. names_pattern = "(.+)_(left|right)")
  310. ##regressions
  311. model1 = cdi1 ~ STG * hem + IFG * hem + age_18 + sex_dummy
  312. fit1 = lm(formula = model1, data=data_cdi1_reg_long)
  313. summary(fit1)
  314. lm.beta(fit1)
  316. residuals1 <- resid(fit1)
  317. fitted1 <- fitted(fit1)
  318. #linearity check
  319. qqnorm(residuals1)
  320. qqline(residuals1)
  321. # Homoscedasticity check
  322. plot(fitted1, residuals1,
  323. main = "Residuals vs Fitted Values",
  324. xlab = "Fitted values", ylab = "Residuals")
  325. abline(h = 0, col = "red") # horizontal line at 0
  326. # Linearity check: Residuals vs each predictor
  327. predictors <- model.matrix(fit2)[,-1] # remove intercept
  328. par(mfrow=c(ceiling(ncol(predictors)/2),2))
  329. for (i in 1:ncol(predictors)) {
  330. plot(predictors[,i], residuals2,
  331. main = paste("Residuals vs", colnames(predictors)[i]),
  332. xlab = colnames(predictors)[i], ylab = "Residuals")
  333. abline(h = 0, col = "red")
  334. }
  335. par(mfrow=c(1,1))
  337. model2 = cdi2 ~ STG * hem + IFG * hem + age_24 + sex_dummy
  338. fit2 = lm(formula = model2, data=data_cdi2_reg_long)
  339. summary(fit2)
  340. lm.beta(fit2)
  342. residuals2 <- resid(fit2)
  343. fitted2 <- fitted(fit2)
  344. #normality check
  345. qqnorm(residuals2)
  346. qqline(residuals2)
  347. # Homoscedasticity check
  348. plot(fitted2, residuals2,
  349. main = "Residuals vs Fitted Values",
  350. xlab = "Fitted values", ylab = "Residuals")
  351. abline(h = 0, col = "red") # horizontal line at 0
  352. # Linearity check: Residuals vs each predictor
  353. predictors <- model.matrix(fit2)[,-1] # remove intercept
  354. par(mfrow=c(ceiling(ncol(predictors)/2),2))
  355. for (i in 1:ncol(predictors)) {
  356. plot(predictors[,i], residuals2,
  357. main = paste("Residuals vs", colnames(predictors)[i]),
  358. xlab = colnames(predictors)[i], ylab = "Residuals")
  359. abline(h = 0, col = "red")
  360. }
  361. par(mfrow=c(1,1))
  363. #####adjust for multiple comparisons#######
  364. #IFG
  365. p11 = c(0.68515,0.21567)
  366. p.adjust(p11, method = "holm", n = length(p11))
  367. #0.68515 0.43134
  369. #hemisphere
  370. p12 = c(0.98599,0.84460)
  371. p.adjust(p12, method = "holm", n = length(p12))
  372. # 1 1
  374. #STG
  375. p13 = c(0.93243,0.02176)
  376. p.adjust(p13, method = "holm", n = length(p13))
  377. #0.93243 0.04352
  380. #age_18
  381. p14 = c(0.12892, 0.00324)
  382. p.adjust(p14, method = "holm", n = length(p14))
  383. #0.12892 0.00648
  385. #sex
  386. p114 = c(0.00322,0.59043)
  387. p.adjust(p114, method = "holm", n = length(p114))
  388. #0.00644 0.59043
  390. #interaction IFG x hemisphere
  391. p15 = c(0.92402,0.90627)
  392. p.adjust(p15, method = "holm", n = length(p15))
  393. # 1 1
  395. #interaction STG x hemisphere
  396. p16 = c(0.66845,0.94847)
  397. p.adjust(p16, method = "holm", n = length(p16))
  398. # 1 1
  400. #whole model
  401. p17 = c(0.08805, 0.0009793)
  402. p.adjust(p17, method = "holm", n = length(p17))
  403. #0.0880500 0.0019586
  407. #############################################Analysis without high-likelihood children###############################################
  408. data_cdi1_norisk = data_cdi1[-c(23:25),]
  409. data_cdi1_reg_long_norisk = data_cdi1_reg_long[-c(45:50),]
  410. data_cdi2_norisk = data_cdi2[-c(22:24),]
  411. data_cdi2_reg_long_norisk = data_cdi2_reg_long[-c(43:48),]
  413. ##regressions
  414. model11 = cdi1 ~ STG * hem + IFG * hem + age_18 + sex
  415. fit11 = lm(formula = model11, data=data_cdi1_reg_long_norisk)
  416. summary(fit11)
  417. lm.beta(fit11)
  419. model12 = cdi2 ~ STG * hem + IFG * hem + age_24 + sex
  420. fit12 = lm(formula = model12, data=data_cdi2_reg_long_norisk)
  421. summary(fit12)
  422. lm.beta(fit12)
  424. #####adjust for multiple comparisons#######
  425. #IFG
  426. p11 = c(0.2351,0.5825)
  427. p.adjust(p11, method = "holm", n = length(p11))
  428. #0.4702 0.5825
  430. #hemisphere
  431. p12 = c(0.7901,0.8339)
  432. p.adjust(p12, method = "holm", n = length(p12))
  433. # 1 1
  435. #STG
  436. p13 = c(0.2684,0.0167)
  437. p.adjust(p13, method = "holm", n = length(p13))
  438. #0.2684 0.0334
  441. #age
  442. p14 = c(0.0337,0.0538)
  443. p.adjust(p14, method = "holm", n = length(p14))
  444. #0.0674 0.0674
  446. #sex
  447. p114 = c(0.0000401,0.6311)
  448. p.adjust(p114, method = "holm", n = length(p114))
  449. #0.0000802 0.6311000
  451. #interaction IFG x hemisphere
  452. p15 = c(0.7551,0.7202)
  453. p.adjust(p15, method = "holm", n = length(p15))
  454. # 1 1
  456. #interaction STG x hemisphere
  457. p16 = c(0.3606,0.8499)
  458. p.adjust(p16, method = "holm", n = length(p16))
  459. # 0.7212 0.8499
  461. #whole model
  462. p17 = c(0.001543, 0.0009227)
  463. p.adjust(p17, method = "holm", n = length(p17))
  464. #0.0018454 0.0018454
  470. ######plot_ with whole sample (including ASD risk children)########
  471. library(ggplot2)
  472. library(gridExtra)
  473. library(cowplot)
  475. plot1 = ggplot(data_cdi1, aes(x=IFG_fet_resid, y=cdi1)) +
  476. geom_point() +
  477. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  478. theme_classic() +
  479. theme(axis.title.y = element_text(margin=margin(r=8), size = 10),
  480. axis.title.x = element_text(size = 10),
  481. plot.title = element_text(size=14)) +
  482. xlim(-1000,1000) +
  483. xlab("Foetal IFG volume")+
  484. ylim(0,50) +
  485. ylab("CDI score at 18 months")+
  486. ggtitle("A")
  487. #plot1
  488. plot2 = ggplot(data_cdi1, aes(x=STG_fet_resid, y=cdi1)) +
  489. geom_point() +
  490. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  491. theme_classic() +
  492. theme(axis.title.y = element_text(margin=margin(r=8), size = 10),
  493. axis.title.x = element_text(size = 10),
  494. plot.title = element_text(size=14)) +
  495. xlim(-500,500) +
  496. xlab("Foetal STG volume")+
  497. ylim(0,50) +
  498. ylab("CDI score at 18 months")+
  499. ggtitle("B")
  500. #plot2
  501. plot3 = ggplot(data_cdi2, aes(x=IFG_fet_resid, y=cdi2)) +
  502. geom_point() +
  503. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  504. theme_classic()+
  505. theme(axis.title.x = element_text(size = 10),
  506. axis.title.y = element_text(size = 10),
  507. plot.title = element_text(size=14))+
  508. xlim(-1000,1000) +
  509. xlab("Foetal IFG volume")+
  510. ylim(50,100) +
  511. ylab("CDI score at 24-36 months")+
  512. ggtitle("C")
  513. #plot3
  514. plot4 = ggplot(data_cdi2, aes(x=STG_fet_resid, y=cdi2)) +
  515. geom_point() +
  516. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  517. theme_classic() +
  518. theme(axis.title.x = element_text(size = 10),
  519. axis.title.y = element_text(size = 10),
  520. plot.title = element_text(size=14))+
  521. xlim(-500,500) +
  522. xlab("Foetal STG volume")+
  523. ylim(50,100) +
  524. ylab("CDI score at 24-36 months")+
  525. ggtitle("D")
  526. plot4
  528. p1 = grid.arrange(plot1, plot2, plot3, plot4, nrow = 2)
  531. ggsave(p1, file="/Users/werwach/Documents/CHILD/paper_prenatal_volume_vocab/figures/regression_plots_withrisk.eps", device="eps")
  537. ######plot_ without ASD risk children########
  538. library(ggplot2)
  539. library(gridExtra)
  540. library(cowplot)
  542. plot1 = ggplot(data_cdi1_norisk, aes(x=IFG_fet, y=cdi1_per)) +
  543. geom_point() +
  544. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  545. theme_classic() +
  546. theme(axis.title.y = element_text(margin=margin(r=8), size = 10),
  547. axis.title.x = element_text(size = 10),
  548. plot.title = element_text(size=14)) +
  549. xlim(2000,5300) +
  550. xlab("Foetal IFG volume")+
  551. ylim(0,100) +
  552. ylab("CDI score at 18 months")+
  553. ggtitle("A")
  554. #plot1
  555. plot2 = ggplot(data_cdi1_norisk, aes(x=STG_fet, y=cdi1_per)) +
  556. geom_point() +
  557. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  558. theme_classic() +
  559. theme(axis.title.y = element_text(margin=margin(r=8), size = 10),
  560. axis.title.x = element_text(size = 10),
  561. plot.title = element_text(size=14)) +
  562. xlim(1500,3300) +
  563. xlab("Foetal STG volume")+
  564. ylim(0,100) +
  565. ylab("CDI score at 18 months")+
  566. ggtitle("B")
  567. #plot2
  568. plot3 = ggplot(data_cdi2_norisk, aes(x=IFG_fet, y=cdi2_per)) +
  569. geom_point() +
  570. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  571. theme_classic()+
  572. theme(axis.title.x = element_text(size = 10),
  573. axis.title.y = element_text(size = 10),
  574. plot.title = element_text(size=14))+
  575. xlim(2000,5300) +
  576. xlab("Foetal IFG volume")+
  577. ylim(0,100) +
  578. ylab("CDI score at 24-36 months")+
  579. ggtitle("C")
  580. #plot3
  581. plot4 = ggplot(data_cdi2_norisk, aes(x=STG_fet, y=cdi2_per)) +
  582. geom_point() +
  583. geom_smooth(fullrange = TRUE,method='lm', se = FALSE, color = "black") +
  584. theme_classic() +
  585. theme(axis.title.x = element_text(size = 10),
  586. axis.title.y = element_text(size = 10),
  587. plot.title = element_text(size=14))+
  588. xlim(1500,3300) +
  589. xlab("Foetal STG volume")+
  590. ylim(0,100) +
  591. ylab("CDI score at 24-36 months")+
  592. ggtitle("D")
  593. #plot4
  595. p2 = grid.arrange(plot1, plot2, plot3, plot4, nrow = 2)
  598. ggsave(p2, file="/Users/werwach/Documents/CHILD/paper_prenatal_volume_vocab/figures/regression_plots_norisk.eps", device="eps")

prenatal_vocab_volume_paper_script.R at commit 3a1120d, under CC-BY-4.0 · at the source

Overview

Authors: Annika Werwach1,2, Alex Tsompanidis3, Luca Villa3,4, Roger Tait5, John Suckling3, Topun Austin6, Sarah Hampton3,7,8, Carrie Allison3, Rosemary Holt3, Simon Baron‐Cohen3, Gesa Schaadt9,10
ORCID iDs: Annika Werwach, Alex Tsompanidis, Topun Austin, Sarah Hampton, Gesa Schaadt
  1. Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
  2. Max Planck School of Cognition, Leipzig, Germany
  3. Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
  4. Qynapse, Paris, France
  5. Cambridge Open Zettascale Lab, University of Cambridge, Cambridge, UK
  6. The Rosie Hospital, Cambride University Hospitals Foundation Trust, Cambridge, UK
  7. Department of Health Sciences, University of York, York, UK
  8. University College London, London, UK
  9. Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
  10. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Institutions: Max Planck Institute for Human Development (Germany); University of Cambridge (United Kingdom); Rosie Hospital (United Kingdom); University College London (United Kingdom); University of York (United Kingdom); Max Planck Institute for Human Cognitive and Brain Sciences (Germany); Freie Universität Berlin (Germany)
Journal: Developmental science, volume 29, issue 3, article e70187
Dates: received 18 June 2025; accepted 24 March 2026; published online 11 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/desc.70187 · PMID 41964581 · PMCID PMC13069930 · OpenAlex W7153625413
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: expressive vocabulary, language acquisition, prenatal brain development, structural MRI
MeSH: Language Development*, Temporal Lobe*, Vocabulary*, Child, Preschool, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Pregnancy (* major topic)
Topic: Neurobiology of Language and Bilingualism (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014, BRC‐1215‐20014); UKRI | Medical Research Council (MRC); Wellcome Trust (214322, 214322∖Z∖18∖Z); NIHR HealthTech Research Centre for Brain Injury; Innovative Medicines Initiative 2 Joint Undertaking (777394); NIHR Applied Research Collaboration East of England; Templeton World Charitable Fund; Autism Centre of Excellence; Simons Foundation Autism Research Initiative
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

Children's language development starts in utero, with language‐relevant brain areas starting to develop and differentiate during the second trimester of pregnancy. Postnatal development in language‐relevant brain areas such as the inferior frontal gyrus (IFG) and superior temporal gyrus (STG) has been shown to be related to language skills. In this study, as part of the Cambridge Human Imaging and Longitudinal Development (CHILD) project, prenatal structural characteristics of the IFG and STG (30th – 33rd GW) and their association with English children's language skills, obtained longitudinally at two postnatal assessment points (n = 24 and n = 25) was examined. Prenatal bilateral STG volume was found to be associated with expressive vocabulary 2–3 years after birth (M = 139.1 weeks), as measured by the Communicative Development Inventory (CDI). These results highlight the relevance of prenatal brain development for language acquisition after birth.

Summary: Postnatal structural characteristics of neural language network, including IFG and STG, are known to be related to language skills in children and adults

Structural characteristics of IFG and STG were assessed prenatally in this study and related to language outcomes in early childhood

Bilateral STG volume at birth predicts vocabulary scores 2–3 years later

Findings support the importance of prenatal brain development for postnatal language acquisition

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.

annikawerwach/child-volume-vocab

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3a1120de86cf66f1d721cf36d90bb958c5ec4513, 19 March 2026
Languages: R (1)
Size: 9 files, 1 script
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, CITATION.cff, documentation
Not found: environment file, tests, continuous integration
Tools: cowplot (1 file), ggplot2 (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/annikawerwach/CHILD-volume-vocab/tree/main/materials

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.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 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.

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

Data

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

Data Availability Statement

The analysis script can be found under https://github.com/annikawerwach/CHILD‐volume‐vocab/tree/main/materials (https://github.com/annikawerwach/CHILD-volume-vocab/tree/main/materials). Access to raw data is restricted by ethics regulations and participant consent.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 4 keywords, 10 MeSH terms, 9 funders, 67 references.

Cite

This paper

Werwach, A., Tsompanidis, A., Villa, L., Tait, R., Suckling, J., Austin, T., Hampton, S., Allison, C., Holt, R., Baron‐Cohen, S., & Schaadt, G. (2026). Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months. Developmental science, 29(3), e70187. https://doi.org/10.1111/desc.70187

BibTeX

@article{werwach2026prenatal,
author = {Werwach, Annika and Tsompanidis, Alex and Villa, Luca and Tait, Roger and Suckling, John and Austin, Topun and Hampton, Sarah and Allison, Carrie and Holt, Rosemary and Baron‐Cohen, Simon and Schaadt, Gesa},
title = {{Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months}},
journal = {Developmental science},
year = {2026},
month = may,
volume = {29},
number = {3},
pages = {e70187},
publisher = {Wiley},
issn = {1363-755X},
doi = {10.1111/desc.70187},
url = {https://doi.org/10.1111/desc.70187},
pmid = {41964581},
pmcid = {PMC13069930}
}

RIS

TY - JOUR
AU - Werwach, Annika
AU - Tsompanidis, Alex
AU - Villa, Luca
AU - Tait, Roger
AU - Suckling, John
AU - Austin, Topun
AU - Hampton, Sarah
AU - Allison, Carrie
AU - Holt, Rosemary
AU - Baron‐Cohen, Simon
AU - Schaadt, Gesa
TI - Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months
T2 - Developmental science
J2 - Dev Sci
PY - 2026
DA - 2026/05/01
VL - 29
IS - 3
SP - e70187
SN - 1363-755X
PB - Wiley
DO - 10.1111/desc.70187
UR - https://doi.org/10.1111/desc.70187
LA - en
ER -

CSL-JSON

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"id": "10.1111/desc.70187",
"type": "article-journal",
"title": "Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months",
"container-title": "Developmental science",
"author": [
{
"family": "Werwach",
"given": "Annika"
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{
"family": "Tsompanidis",
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{
"family": "Villa",
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{
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],
"container-title-short": "Dev Sci",
"volume": "29",
"issue": "3",
"page": "e70187",
"DOI": "10.1111/desc.70187",
"PMID": "41964581",
"PMCID": "PMC13069930",
"ISSN": "1363-755X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/desc.70187",
"language": "en",
"issued": {
"date-parts": [
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2026,
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}
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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