OSCR

Individual differences in adolescent cortical development are associated with neighborhood characteristics: Longitudinal findings from the ABCD study.

Code ↔ Paper

5 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 5 matches
  1. [1] § Materials and methods › Measures › Cortical development ↔ Analysis_Scripts/Adjusted_Latent_Growth_Models.R, lines 171–210 · score 0.90 · SIGNA UHP, Discovery MR750, SIGNA Premier, dStream, Prisma fit, Achieva
  2. [2] § Materials and methods › Statistical analyses › Adjusted analyses ↔ Analysis_Scripts/Adjusted_Latent_Growth_Models.R, lines 171–210 · score 0.86 · Discovery MR750, SIGNA Premier, dStream, Prisma fit, Achieva, Ingenia
  3. [3] § Materials and methods › Statistical analyses › Association between neighborhood characteristics and individual differences in cortical development ↔ Analysis_Scripts/Latent_Growth_Models.R, lines 308–347 · score 0.77 · maximum possible score, transformed surface area, model convergence, latent growth models, POMS, timepoints
  4. [4] § Materials and methods › Statistical analyses › Examining trajectories of cortical development: latent growth models ↔ Analysis_Scripts/Latent_Growth_Models.R, lines 44–91 · score 0.72 · latent basis growth, latent growth models, support model convergence, linear, variances, surface area
  5. [5] § Materials and methods › Statistical analyses › Assessing subgroups of cortical development: growth mixture models and k-means clustering ↔ Analysis_Scripts/Growth_Mixture_Models.R, lines 216–289 · score 0.54 · growth mixture model, cortical surface area, subgroups

Paper

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The authors' code

R · 880 lines · 23 KB · no license · 2 matches

  1. ######################################################
  3. # Project: Association between Neighbourhood Characteristics and Cortical Development
  5. # Author: Chloe Carrick
  7. # Date: September 2025
  9. ######################################################
  10. # Load libraries
  12. library(dplyr)
  13. library(psych)
  14. library(ggplot2)
  15. library(cowplot)
  16. library(pacman)
  17. library(lavaan)
  18. library(tidyverse)
  19. library(lcmm)
  20. library(gridExtra)
  21. library(semPlot)
  22. library(corrplot)
  23. library(kml)
  25. # Read in data
  27. brain = read.csv("brain.csv")
  29. # Set variables as factor/numeric
  31. brain[,c(1:3, 11, 12)] <- lapply(brain[,c(1:3, 11, 12)], as.factor)
  32. brain[,c(4:10, 13)] <- lapply(brain[,c(4:10, 13)], as.numeric)
  34. sapply(brain, class)
  37. # Set up data
  39. ######################################################
  41. # Neighbourhood Disadvantage
  43. ######################################################
  45. # Thickness
  46. t2brain <- brain[,c(1, 3, 4, 10, 11, 12, 13)]
  47. # Surface area
  48. s2brain <- brain[,c(1, 3, 5, 10, 11, 12, 13)]
  50. # The following code selects scanner type for the first neuroimaging timepoint
  51. # If there is no neuroimaging timepoint 1, the scanner type at second neuroimaging timepoint is used
  52. # If there is no neuroimaging timepoint 1 or 2, the scanner type at the third neuroimaging timepoint is used
  54. t2brain <- t2brain %>% group_by(src_subject_id) %>% mutate(
  55. scanner = case_when(
  56. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  57. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  58. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]), TRUE ~ NA_character_ )) %>%
  59. ungroup()
  62. s2brain <- s2brain %>% group_by(src_subject_id) %>% mutate(
  63. scanner = case_when(
  64. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  65. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  66. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]))) %>%
  67. ungroup()
  69. # Convert to wide format
  71. wt2brain <- t2brain %>% pivot_wider(names_from = timepoint, values_from = thickness)
  72. ws2brain <- s2brain %>% pivot_wider(names_from = timepoint, values_from = SA)
  75. # Rename columns to T1, T2, T3
  77. wt2brain <- wt2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  78. ws2brain <- ws2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  81. # Make thickness values larger
  83. wt2brain$T1 <- wt2brain$T1*10
  84. wt2brain$T2 <- wt2brain$T2*10
  85. wt2brain$T3 <- wt2brain$T3*10
  87. ######################################################
  89. # Educational Opportunity
  91. ######################################################
  93. # Thickness
  95. education_t2brain <- brain[,c(1, 3, 4, 8, 11, 12, 13)]
  97. # Surface area
  99. education_s2brain <- brain[,c(1, 3, 5, 8, 11, 12, 13)]
  102. # The following code selects scanner type for the first neuroimaging timepoint
  103. # If there is no neuroimaging timepoint 1, the scanner type at second neuroimaging timepoint is used
  104. # If there is no neuroimaging timepoint 1 or 2, the scanner type at the third neuroimaging timepoint is used
  106. education_t2brain <- education_t2brain %>% group_by(src_subject_id) %>% mutate(
  107. scanner = case_when(
  108. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  109. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  110. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]))) %>%
  111. ungroup()
  114. education_s2brain <- education_s2brain %>% group_by(src_subject_id) %>% mutate(
  115. scanner = case_when(
  116. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  117. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  118. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]))) %>%
  119. ungroup()
  121. # Convert to wide format
  123. education_wt2brain <- education_t2brain %>% pivot_wider(names_from = timepoint, values_from = thickness)
  124. education_ws2brain <- education_s2brain %>% pivot_wider(names_from = timepoint, values_from = SA)
  127. # Rename columns to T1, T2, T3
  129. education_wt2brain <- education_wt2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  130. education_ws2brain <- education_ws2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  133. # Make thickness values larger
  135. education_wt2brain$T1 <- education_wt2brain$T1*10
  136. education_wt2brain$T2 <- education_wt2brain$T2*10
  137. education_wt2brain$T3 <- education_wt2brain$T3*10
  139. ######################################################
  141. # Health/Environmental Opportunity
  143. ######################################################
  145. # Thickness
  146. health_t2brain <- brain[,c(1, 3, 4, 9, 11, 12, 13)]
  148. # Surface area
  149. health_s2brain <- brain[,c(1, 3, 5, 9, 11, 12, 13)]
  151. # The following code selects scanner type for the first neuroimaging timepoint
  152. # If there is no neuroimaging timepoint 1, the scanner type at second neuroimaging timepoint is used
  153. # If there is no neuroimaging timepoint 1 or 2, the scanner type at the third neuroimaging timepoint is used
  155. health_t2brain <- health_t2brain %>% group_by(src_subject_id) %>% mutate(
  156. scanner = case_when(
  157. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  158. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  159. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]))) %>%
  160. ungroup()
  163. health_s2brain <- health_s2brain %>% group_by(src_subject_id) %>% mutate(
  164. scanner = case_when(
  165. !all(is.na(scanner[timepoint == "1"])) ~ first(scanner[timepoint == "1"]),
  166. !all(is.na(scanner[timepoint == "2"])) ~ first(scanner[timepoint == "2"]),
  167. !all(is.na(scanner[timepoint == "3"])) ~ first(scanner[timepoint == "3"]))) %>%
  168. ungroup()
  171. # Convert to wide format
  172. health_wt2brain <- health_t2brain %>% pivot_wider(names_from = timepoint, values_from = thickness)
  173. health_ws2brain <- health_s2brain %>% pivot_wider(names_from = timepoint, values_from = SA)
  176. # Rename columns to T1, T2, T3
  178. health_wt2brain <- health_wt2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  179. health_ws2brain <- health_ws2brain %>% rename(T1 = 6, T2 = 7, T3 = 8)
  182. # Make thickness values larger
  184. health_wt2brain$T1 <- health_wt2brain$T1*10
  185. health_wt2brain$T2 <- health_wt2brain$T2*10
  186. health_wt2brain$T3 <- health_wt2brain$T3*10
  189. ######################################################
  191. # Adjusted models including sex and scanner type as covariates
  193. ######################################################
  195. # 1. Neighbourhood Disadvantage
  197. ######################################################
  200. # Thickness
  202. # Dummy code sex
  204. wt2brain$sex <- ifelse(wt2brain$sex == "2", 1, 0)
  207. # Dummy code scanner
  208. wt2brain$scanner <- factor(wt2brain$scanner, levels = c("Prisma", "Prisma_fit", "DISCOVERY MR750",
  209. "Achieva dStream", "Ingenia",
  210. "SIGNA Premier", "SIGNA UHP"))
  212. # Dummy-code 7 columns
  213. dums <- dummy.code(wt2brain$scanner)
  215. colnames(dums) <- paste0("scanner", seq_len(ncol(dums)))
  217. wt2brain = cbind(wt2brain, dums)
  220. # Fit model
  222. sex_ND_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  223. s =~ 0*T1 + T2 + 1*T3
  225. s ~ ND + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  226. int ~ ND + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  228. T1 ~~ variance*T1
  229. T2 ~~ variance*T2
  230. T3 ~~ variance*T3'
  233. sex_ND_Tfitnl <- growth(sex_ND_nonlinearmodel, data = wt2brain, estimator ='mlr', missing='fiml.x')
  236. summary(sex_ND_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  240. # Surface area
  243. # Dummy code sex
  245. ws2brain$sex <- ifelse(ws2brain$sex == "2", 1, 0)
  248. # Covert to POMS
  251. # Sample min SA
  253. min_SA <- min(ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  254. max_SA <- max(ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  257. # Convert
  259. ws2brain$T1_POMS <- (ws2brain$T1 - min_SA) / (max_SA - min_SA)
  260. ws2brain$T2_POMS <- (ws2brain$T2 - min_SA) / (max_SA - min_SA)
  261. ws2brain$T3_POMS <- (ws2brain$T3 - min_SA) / (max_SA - min_SA)
  263. ws2brainPOMS = ws2brain[,c(1:5, 9:11)]
  264. ws2brain <- ws2brain[, 1:8]
  267. # Add dummy-coded scanner
  269. ws2brainPOMS = cbind(ws2brainPOMS, dums)
  271. # Fit model
  273. S_sex_ND_nonlinearmodel <-
  275. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  276. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  278. s~ND + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  279. int~ND + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  283. T1_POMS ~~ variance*T1_POMS
  284. T2_POMS ~~ variance*T2_POMS
  285. T3_POMS ~~ variance*T3_POMS'
  289. S_sex_ND_fitnl <- growth(S_sex_ND_nonlinearmodel, data = ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  292. summary(S_sex_ND_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  295. ######################################################
  297. #2. Educational Opportunity
  299. ######################################################
  302. # Thickness
  304. # Dummy code sex
  306. education_wt2brain$sex <- ifelse(education_wt2brain$sex == "2", 1, 0)
  308. # Add dummy coded scanner
  310. education_wt2brain = cbind(education_wt2brain, dums)
  312. # Fit model
  314. sex_education_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  315. s =~ 0*T1 + T2 + 1*T3
  317. s ~ education + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  318. int ~ education + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  320. T1 ~~ variance*T1
  321. T2 ~~ variance*T2
  322. T3 ~~ variance*T3'
  326. sex_education_Tfitnl <- growth(sex_education_nonlinearmodel, data = education_wt2brain, estimator ='mlr', missing='fiml.x')
  329. summary(sex_education_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  332. # Extract parameter estimates for small values
  334. parameterestimates(sex_education_Tfitnl)$se[7]
  335. parameterestimates(sex_education_Tfitnl)$se[15]
  339. # Surface area
  342. # Dummy code sex
  344. education_ws2brain$sex <- ifelse(education_ws2brain$sex == "2", 1, 0)
  347. # Covert to POMS
  350. # Sample min SA
  352. min_SA <- min(education_ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  353. max_SA <- max(education_ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  356. # Convert
  358. education_ws2brain$T1_POMS <- (education_ws2brain$T1 - min_SA) / (max_SA - min_SA)
  359. education_ws2brain$T2_POMS <- (education_ws2brain$T2 - min_SA) / (max_SA - min_SA)
  360. education_ws2brain$T3_POMS <- (education_ws2brain$T3 - min_SA) / (max_SA - min_SA)
  362. education_ws2brainPOMS = education_ws2brain[,c(1:5, 9:11)]
  363. education_ws2brain <- education_ws2brain[, 1:8]
  367. # Add dummy coded scanner
  369. education_ws2brainPOMS = cbind(education_ws2brainPOMS, dums)
  372. # Fit model
  374. S_sex_education_nonlinearmodel <-
  376. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  377. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  379. s~education + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  380. int~education + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  384. T1_POMS ~~ variance*T1_POMS
  385. T2_POMS ~~ variance*T2_POMS
  386. T3_POMS ~~ variance*T3_POMS'
  390. S_sex_education_fitnl <- growth(S_sex_education_nonlinearmodel, data = education_ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  393. summary(S_sex_education_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  395. # Extract parameter estimates for small values
  397. parameterestimates(S_sex_education_fitnl)$est[7]
  398. parameterestimates(S_sex_education_fitnl)$se[7]
  399. parameterestimates(S_sex_education_fitnl)$est[15]
  400. parameterestimates(S_sex_education_fitnl)$se[15]
  403. ######################################################
  405. #3. Health/Environmental Opportunity
  407. ######################################################
  409. # Thickness
  411. # Dummy code sex
  413. health_wt2brain$sex <- ifelse(health_wt2brain$sex == "2", 1, 0)
  416. # Add dummy coded scanner
  418. health_wt2brain = cbind(health_wt2brain, dums)
  421. # Fit model
  424. sex_health_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  425. s =~ 0*T1 + T2 + 1*T3
  427. s ~ health + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  428. int ~ health + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  430. T1 ~~ variance*T1
  431. T2 ~~ variance*T2
  432. T3 ~~ variance*T3'
  437. sex_health_Tfitnl <- growth(sex_health_nonlinearmodel, data = health_wt2brain, estimator ='mlr', missing='fiml.x')
  440. summary(sex_health_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  443. # Extract parameter estimates for small values
  444. parameterestimates(sex_health_Tfitnl)$se[7]
  445. parameterestimates(sex_health_Tfitnl)$se[15]
  449. # Surface area
  452. # Dummy code sex
  454. health_ws2brain$sex <- ifelse(health_ws2brain$sex == "2", 1, 0)
  457. # Covert to POMS
  460. # Sample min SA
  462. min_SA <- min(health_ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  463. max_SA <- max(health_ws2brain[, c("T1", "T2", "T3")], na.rm = TRUE)
  466. # Convert
  468. health_ws2brain$T1_POMS <- (health_ws2brain$T1 - min_SA) / (max_SA - min_SA)
  469. health_ws2brain$T2_POMS <- (health_ws2brain$T2 - min_SA) / (max_SA - min_SA)
  470. health_ws2brain$T3_POMS <- (health_ws2brain$T3 - min_SA) / (max_SA - min_SA)
  472. health_ws2brainPOMS = health_ws2brain[,c(1:5, 9:11)]
  473. health_ws2brain <- health_ws2brain[, 1:8]
  478. # Add dummy coded scanner
  480. health_ws2brainPOMS = cbind(health_ws2brainPOMS, dums)
  483. # Fit model
  485. S_sex_health_nonlinearmodel <-
  487. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  488. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  490. s~ health + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  491. int~ health + sex + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  495. T1_POMS ~~ variance*T1_POMS
  496. T2_POMS ~~ variance*T2_POMS
  497. T3_POMS ~~ variance*T3_POMS'
  502. S_sex_health_fitnl <- growth(S_sex_health_nonlinearmodel, data = health_ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  505. summary(S_sex_health_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  508. # Extract parameter estimates for small values
  510. parameterestimates(S_sex_health_fitnl)$est[7]
  511. parameterestimates(S_sex_health_fitnl)$est[15]
  512. parameterestimates(S_sex_health_fitnl)$se[7]
  513. parameterestimates(S_sex_health_fitnl)$se[15]
  515. ######################################################
  517. # Adjusted models including sex, scanner-type, and InR as covariates
  519. ######################################################
  521. # 1. Neighbourhood Disadvantage
  523. ######################################################
  525. # Thickness
  527. # Fit model
  529. I_sex_ND_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  530. s =~ 0*T1 + T2 + 1*T3
  532. s ~ ND + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  533. int ~ ND + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  535. T1 ~~ variance*T1
  536. T2 ~~ variance*T2
  537. T3 ~~ variance*T3'
  540. I_sex_ND_Tfitnl <- growth(I_sex_ND_nonlinearmodel, data = wt2brain, estimator ='mlr', missing='fiml.x')
  543. summary(I_sex_ND_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  547. # Surface area
  549. # Fit model
  551. I_S_sex_ND_nonlinearmodel <-
  553. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  554. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  556. s~ND + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  557. int~ND + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  561. T1_POMS ~~ variance*T1_POMS
  562. T2_POMS ~~ variance*T2_POMS
  563. T3_POMS ~~ variance*T3_POMS'
  567. I_S_sex_ND_fitnl <- growth(I_S_sex_ND_nonlinearmodel, data = ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  570. summary(I_S_sex_ND_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  573. parameterestimates(I_S_sex_ND_fitnl)$se[18]
  575. ######################################################
  577. # 1. Educational Opportunity
  579. ######################################################
  581. # Thickness
  583. # Fit model
  585. I_sex_education_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  586. s =~ 0*T1 + T2 + 1*T3
  588. s ~ education + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  589. int ~ education + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  591. T1 ~~ variance*T1
  592. T2 ~~ variance*T2
  593. T3 ~~ variance*T3'
  597. I_sex_education_Tfitnl <- growth(I_sex_education_nonlinearmodel, data = education_wt2brain, estimator ='mlr', missing='fiml.x')
  600. summary(I_sex_education_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  603. # Extract parameter estimates for smaller values
  605. parameterestimates(I_sex_education_Tfitnl)$se[7]
  606. parameterestimates(I_sex_education_Tfitnl)$se[16]
  609. # Surface area
  611. # Fit model
  613. I_S_sex_education_nonlinearmodel <-
  615. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  616. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  618. s~education + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  619. int~education + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  623. T1_POMS ~~ variance*T1_POMS
  624. T2_POMS ~~ variance*T2_POMS
  625. T3_POMS ~~ variance*T3_POMS'
  628. I_S_sex_education_fitnl <- growth(I_S_sex_education_nonlinearmodel, data = education_ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  631. summary(I_S_sex_education_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  634. # Extract parameter estimates for smaller values
  636. parameterestimates((I_S_sex_education_fitnl))$est[7]
  637. parameterestimates((I_S_sex_education_fitnl))$est[16]
  638. parameterestimates((I_S_sex_education_fitnl))$se[7]
  639. parameterestimates((I_S_sex_education_fitnl))$se[16]
  642. ######################################################
  644. # 1. Health/Environmental Opportunity
  646. ######################################################
  648. # Thickness
  651. # Fit model
  654. I_sex_health_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  655. s =~ 0*T1 + T2 + 1*T3
  657. s ~ health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  658. int ~ health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  660. T1 ~~ variance*T1
  661. T2 ~~ variance*T2
  662. T3 ~~ variance*T3'
  665. I_sex_health_Tfitnl <- growth(I_sex_health_nonlinearmodel, data = health_wt2brain, estimator ='mlr', missing='fiml.x')
  668. summary(I_sex_health_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  672. # Surface area
  674. # Fit model
  676. I_S_sex_health_nonlinearmodel <-
  678. 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  679. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  681. s~ health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  682. int~ health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  686. T1_POMS ~~ variance*T1_POMS
  687. T2_POMS ~~ variance*T2_POMS
  688. T3_POMS ~~ variance*T3_POMS'
  692. I_S_sex_health_fitnl <- growth(I_S_sex_health_nonlinearmodel, data = health_ws2brainPOMS, estimator ='mlr', missing='fiml.x')
  695. summary(I_S_sex_health_fitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  698. # Extract parameter estimates for small values
  700. parameterestimates(I_S_sex_health_fitnl)$est[7]
  701. parameterestimates(I_S_sex_health_fitnl)$est[16]
  702. parameterestimates(I_S_sex_health_fitnl)$se[7]
  703. parameterestimates(I_S_sex_health_fitnl)$se[16]
  707. ######################################################
  709. # Specificity analysis: including 3 neighbourhood factors in one model
  711. ######################################################
  713. # Thickness - new data frame
  714. all_neighbourhood_wtbrain <- wt2brain
  715. all_neighbourhood_wtbrain$education<- education_wt2brain$education
  716. all_neighbourhood_wtbrain$health <- health_wt2brain$health
  719. # Surface area - new data frame
  721. all_neighbourhood_wsbrain <- ws2brainPOMS
  722. all_neighbourhood_wsbrain$education<- education_ws2brainPOMS$education
  723. all_neighbourhood_wsbrain$health <- health_ws2brainPOMS$health
  726. # Check correlations
  728. cor.test(all_neighbourhood_wtbrain$ND, all_neighbourhood_wtbrain$health) #-0.73
  729. cor.test(all_neighbourhood_wtbrain$ND, all_neighbourhood_wtbrain$education) #-0.71
  730. cor.test(all_neighbourhood_wtbrain$education, all_neighbourhood_wtbrain$health) # 0.66
  733. ######################################################
  736. # Thickness
  738. # Fit model
  740. all_neighb_nonlinearmodel <- 'int =~ 1*T1 + 1*T2 + 1*T3
  741. s =~ 0*T1 + T2 + 1*T3
  743. s ~ ND + education + health
  744. int ~ ND + education + health
  747. T1 ~~ variance*T1
  748. T2 ~~ variance*T2
  749. T3 ~~ variance*T3'
  753. all_neighb_Tfitnl <- growth(all_neighb_nonlinearmodel, data = all_neighbourhood_wtbrain, estimator ='mlr', missing='fiml.x')
  755. summary(all_neighb_Tfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  757. parameterestimates(all_neighb_Tfitnl)$est[8]
  758. parameterestimates(all_neighb_Tfitnl)$se[8]
  759. parameterestimates(all_neighb_Tfitnl)$se[9]
  760. parameterestimates(all_neighb_Tfitnl)$se[11]
  761. parameterestimates(all_neighb_Tfitnl)$se[12]
  763. ######################################################
  766. # Adjusting for sex, scanner, income to needs
  769. ######################################################
  771. # Fit model
  773. all_neighb_nonlinearmodel_2 <- 'int =~ 1*T1 + 1*T2 + 1*T3
  774. s =~ 0*T1 + T2 + 1*T3
  776. s ~ ND + education + health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  777. int ~ ND + education + health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  779. T1 ~~ variance*T1
  780. T2 ~~ variance*T2
  781. T3 ~~ variance*T3'
  785. all_neighb_Tfitnl_2 <- growth(all_neighb_nonlinearmodel_2, data = all_neighbourhood_wtbrain, estimator ='mlr', missing='fiml.x')
  787. summary(all_neighb_Tfitnl_2, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  790. # Extract parameter estimates for small values
  792. parameterestimates(all_neighb_Tfitnl_2)$est[8]
  793. parameterestimates(all_neighb_Tfitnl_2)$se[8]
  794. parameterestimates(all_neighb_Tfitnl_2)$se[9]
  796. parameterestimates(all_neighb_Tfitnl_2)$se[19]
  797. parameterestimates(all_neighb_Tfitnl_2)$se[20]
  799. ##############################################################
  802. # Surface area
  804. # Fit model
  807. SA_all_neighb_nonlinearmodel <- 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  808. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  810. s ~ ND + education + health
  811. int ~ ND + education + health
  813. T1_POMS ~~ variance*T1_POMS
  814. T2_POMS ~~ variance*T2_POMS
  815. T3_POMS ~~ variance*T3_POMS'
  820. all_neighb_Sfitnl <- growth(SA_all_neighb_nonlinearmodel, data = all_neighbourhood_wsbrain, estimator ='mlr', missing='fiml.x')
  822. summary(all_neighb_Sfitnl, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  825. # Extract parameter estimates for small values
  827. parameterestimates(all_neighb_Sfitnl)$est[7]
  828. parameterestimates(all_neighb_Sfitnl)$est[8]
  829. parameterestimates(all_neighb_Sfitnl)$est[9]
  830. parameterestimates(all_neighb_Sfitnl)$est[11]
  831. parameterestimates(all_neighb_Sfitnl)$est[12]
  832. parameterestimates(all_neighb_Sfitnl)$se[8]
  833. parameterestimates(all_neighb_Sfitnl)$se[9]
  834. parameterestimates(all_neighb_Sfitnl)$se[11]
  835. parameterestimates(all_neighb_Sfitnl)$se[12]
  837. ######################################################
  840. # Adjusting for sex, scanner, InR
  843. ######################################################
  845. SA_all_neighb_nonlinearmodel_2 <- 'int =~ 1*T1_POMS + 1*T2_POMS + 1*T3_POMS
  846. s =~ 0*T1_POMS + T2_POMS + 1*T3_POMS
  848. s ~ ND + education + health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  849. int ~ ND + education + health + sex + income_to_needs + scanner1 + scanner2 + scanner3 + scanner4 + scanner5 + scanner6
  851. T1_POMS ~~ variance*T1_POMS
  852. T2_POMS ~~ variance*T2_POMS
  853. T3_POMS ~~ variance*T3_POMS'
  857. # Using nonlinear model
  860. all_neighb_Sfitnl_2 <- growth(SA_all_neighb_nonlinearmodel_2, data = all_neighbourhood_wsbrain, estimator ='mlr', missing='fiml.x')
  862. summary(all_neighb_Sfitnl_2, fit.measures = TRUE, rsquare = TRUE, standardized = TRUE)
  864. # Extract parameter estimates for small values
  867. parameterestimates(all_neighb_Sfitnl_2)$est[8]
  868. parameterestimates(all_neighb_Sfitnl_2)$est[9]
  869. parameterestimates(all_neighb_Sfitnl_2)$est[19]
  870. parameterestimates(all_neighb_Sfitnl_2)$est[20]
  872. parameterestimates(all_neighb_Sfitnl_2)$se[8]
  873. parameterestimates(all_neighb_Sfitnl_2)$se[9]
  874. parameterestimates(all_neighb_Sfitnl_2)$se[19]
  875. parameterestimates(all_neighb_Sfitnl_2)$se[20]
  880. ######################################################

Adjusted_Latent_Growth_Models.R, no license · at the source

Overview

Authors: Chloe Carrick1, Divyangana Rakesh2, Lea Michel3, Kathryn Bates1, Delia Fuhrmann1
ORCID iDs: Chloe Carrick, Kathryn Bates, Delia Fuhrmann
  1. Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, Guy's Campus, Great Maze Pond, London SE1 1UL, United Kingdom
  2. Neuroimaging Department, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, Centre for Neuroimaging Sciences, De Crespigny Park, Camberwell, London SE5 8AF, United Kingdom
  3. Cognitive Neuroscience Department, Radboud University Medical Center, Nijmegen, The Netherlands
Institutions: King's College London (United Kingdom); Radboud University Nijmegen (Netherlands); Radboud University Medical Center (Netherlands)
Journal: Cerebral cortex (New York, N.Y. : 1991), volume 36, issue 4, article bhag034
Dates: received 21 November 2025; accepted 3 March 2026; published online 7 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/cercor/bhag034 · PMID 41955291 · PMCID PMC13064848 · OpenAlex W7152680646
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), developmental (subfield)
Methods: Statistics, Machine learning, Preprocessing
Keywords: ABCD study, adolescence, cortical development, individual differences, neighborhood characteristics
MeSH: Adolescent Development*, Cerebral Cortex*, Individuality*, Neighborhood Characteristics*, Residence Characteristics*, Adolescent, Child, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Neurodevelopment (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: UKRI Medical Research Council (MR/Z506667/1); Medical Research Council (MR/W006820/1); Brain and Behavior Research Foundation (32908)
Citations: not cited yet (Europe PMC); 104 references in the paper

Abstract

Developmental trajectories of adolescent cortical structure differ between individuals. Neighborhood environments are increasingly recognized as influencing this variability. Few studies have examined how multifaceted neighborhood contexts relate to individual changes in cortical maturation patterns. Using 3 waves of neuroimaging data from the ABCD study (n = 11,639 with at least one scan), and latent growth models, the present investigation examined associations between exposure to neighborhood disadvantage and educational, health, and environmental opportunities at ages 9 to 10, and interindividual variability in trajectories of cortical thickness and surface area development between ages 9 and 15. Individuals exposed to disadvantaged neighborhoods showed lower cortical thickness and surface area, and accelerated rates of change in these metrics across adolescence, whereas greater neighborhood opportunities were associated with higher cortical thickness and surface area and a slower pace of change. Our findings indicate interindividual variability in cortical maturational trajectories and provide evidence for the role of neighborhood environments, including positive and negative features, in shaping this variability. This emphasizes the need for future studies examining multiple facets of neighborhood ecologies when examining their influence on adolescent cortical development.

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 5 matches between paragraphs and lines of code.

OSF avu29

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (5)
Size: 7 files, 5 scripts
Software Heritage: not checked
Found in: the text, “Statistical analyses”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (5 files), ggplot2 (5 files), lavaan (5 files), psych (5 files), tidyverse (5 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/avu29/

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;
  • 5 scripts, each with its path and the digest of its content;
  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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

This study harnessed data from the ABCD study (https://abcdstudy.org/), hosted on the National Institute of Mental Health Data Archive (https://nda.nih.gov/).

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 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 13 MeSH terms, 3 funders, 101 references.

Cite

This paper

Carrick, C., Rakesh, D., Michel, L., Bates, K., & Fuhrmann, D. (2026). Individual differences in adolescent cortical development are associated with neighborhood characteristics: Longitudinal findings from the ABCD study. Cerebral cortex (New York, N.Y. : 1991), 36(4), bhag034. https://doi.org/10.1093/cercor/bhag034

BibTeX

@article{carrick2026individual,
author = {Carrick, Chloe and Rakesh, Divyangana and Michel, Lea and Bates, Kathryn and Fuhrmann, Delia},
title = {{Individual differences in adolescent cortical development are associated with neighborhood characteristics: Longitudinal findings from the ABCD study}},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
year = {2026},
month = apr,
volume = {36},
number = {4},
pages = {bhag034},
publisher = {Oxford University Press},
issn = {1047-3211},
doi = {10.1093/cercor/bhag034},
url = {https://doi.org/10.1093/cercor/bhag034},
pmid = {41955291},
pmcid = {PMC13064848}
}

RIS

TY - JOUR
AU - Carrick, Chloe
AU - Rakesh, Divyangana
AU - Michel, Lea
AU - Bates, Kathryn
AU - Fuhrmann, Delia
TI - Individual differences in adolescent cortical development are associated with neighborhood characteristics: Longitudinal findings from the ABCD study
T2 - Cerebral cortex (New York, N.Y. : 1991)
J2 - Cereb Cortex
PY - 2026
DA - 2026/04/01
VL - 36
IS - 4
SP - bhag034
SN - 1047-3211
PB - Oxford University Press
DO - 10.1093/cercor/bhag034
UR - https://doi.org/10.1093/cercor/bhag034
LA - en
ER -

CSL-JSON

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