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Childhood white matter organization predicts adolescent internalizing problems among youth with and without ADHD.

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  1. ---
  2. title: "JHU_ADHD_analysis"
  3. author: "Mervyn Singh"
  4. date: "27/07/2026"
  5. output:
  6. word_document: default
  7. pdf_document: default
  8. ---
  10. ```{r setup, include=FALSE}
  11. knitr::opts_chunk$set(echo = TRUE)
  12. ```
  14. # DATA SET-UP
  16. ## Load required packages into your working environment
  17. ```{r, include=FALSE, echo=FALSE}
  18. rm(list = (ls()))
  19. packages <- c("tidyverse","ggplot2","psych","broom","lme4","lmerTest","gridExtra","ggpubr","emmeans","corrplot","cowplot","mediation","ggsignif","jmvReadWrite","sjstats")
  20. if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  21. install.packages(setdiff(packages, rownames(installed.packages())))
  22. }
  23. lapply(packages, library, character.only = TRUE)
  24. ```
  27. # AGE DISTRIBUTION PLOT
  29. ```{r}
  30. # load data
  31. agedat = read.csv("agedat.csv")
  32. str(agedat)
  33. ```
  35. ```{r}
  36. # reformat vars
  37. agedat = agedat %>% mutate_at(c(1,4:5), as.factor)
  38. ```
  40. ```{r}
  41. # convert data to long format
  42. long <- agedat %>% gather(wave,AGE,-ID1,-SEX,-GROUP) %>%
  43. mutate(wave = gsub("AGE_","",wave))
  45. # prepare data for plotting
  46. df_plot <- long %>% mutate(AGE = round(as.numeric(AGE),2)) %>% arrange(AGE) %>%
  47. mutate(ID1 = factor(ID1, unique(ID1)))
  48. ```
  50. ```{r}
  51. # plot age distribution
  53. plot_age <- ggplot(df_plot, aes(y=ID1, x=AGE, group=ID1, colour=GROUP,shape=SEX))+
  54. geom_line(size=.6,alpha=0.6) +
  55. ylab("Participants") + #Specify titles for y-axis...
  56. xlab("Age") + #x-axis...
  57. geom_point(size=2) +
  58. scale_color_manual(values=custom_colors) +
  59. theme_bw() +
  60. theme(axis.line = element_line(colour = "black"),
  61. axis.text.y = element_blank(),
  62. axis.ticks.y = element_blank(),
  63. legend.position="none",
  64. panel.grid.major = element_blank(),
  65. panel.grid.minor = element_blank(),
  66. panel.border = element_blank(),
  67. panel.background = element_blank()) + theme(legend.position="right") +
  68. theme(text = element_text(size = 20))
  69. plot_age
  70. ```
  72. ```{r}
  73. # save
  74. ggsave("age_distribution_JHU.png", plot_age)
  75. ```
  77. \nextpage
  79. # BRAIN-BEHAVIORAL ASSOCIATIONS
  82. ## Load datafile into working environment
  83. ```{r, include=FALSE, echo=FALSE}
  84. dat = read.csv("data_13_12.csv")
  85. ```
  87. ## View dataframe structure
  88. ```{r, include=FALSE, echo=FALSE}
  89. str(dat)
  90. ```
  92. ## format variables
  93. ```{r, include=FALSE, echo=FALSE}
  94. # categorical vars
  95. dat = dat %>% mutate_at(c(4,7),as.factor)
  97. # Recode factors
  98. levels(dat$GROUP) = list(TD_Controls = "0", ADHD = "1")
  99. levels(dat$SEX) = list(female = "0", male = "1")
  100. custom_colors <- c("ADHD" = "#F8766D", "TD_Controls" = "#00BFC4")
  102. # continuous vars
  103. dat = dat %>% mutate_at(c(1:3,5:6,8:55),as.numeric)
  105. # re-check data structure
  106. str(dat)
  107. ```
  109. \nextpage
  111. # BASC PARENT REPORT ANALYSIS
  113. ## Association between T1 residualised FBA METRICS and T2 residualised BASC PARENT-REPORT DEP scores (controlling for AGE & TCV)
  115. ```{r}
  117. # UNCINATE FASCICULUS
  119. # Scatterplot of Right UF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  120. p1 = dat %>%
  121. drop_na(GROUP) %>%
  122. ggplot(aes(x=UF_R_DEP_BASCP_R, y=T2_BASCP_DEP_R, color=GROUP)) +
  123. geom_point() +
  124. scale_colour_manual(values=custom_colors) +
  125. geom_smooth(method=lm, se=F) +
  126. xlab("Right UF FC") +
  127. ylab("BASC-P Depression (T2)") +
  128. theme_bw() +
  129. theme_minimal() +
  130. theme(axis.line = element_line(colour = "black"),
  131. legend.position="none",
  132. panel.grid.major = element_blank(),
  133. panel.grid.minor = element_blank(),
  134. panel.border = element_blank(),
  135. panel.background = element_blank()) +
  136. theme(legend.position="right") +
  137. theme(legend.position="right") +
  138. theme(text = element_text(size = 20)) +
  139. stat_cor(
  140. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  141. method = "pearson",
  142. r.accuracy = 0.001,
  143. p.accuracy = 0.001,
  144. label.x.npc = "left",
  145. label.y.npc = "top",
  146. size = 6
  147. )
  148. p1
  149. ```
  151. ```{r}
  152. # Scatterplot of Left UF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  153. p2 = dat %>%
  154. drop_na(GROUP) %>%
  155. ggplot(aes(x=UF_L_DEP_BASCP_R, y=T2_BASCP_DEP_R, color=GROUP)) +
  156. geom_point() +
  157. scale_colour_manual(values=custom_colors) +
  158. geom_smooth(method=lm, se=F) +
  159. xlab("Left UF FC") +
  160. ylab("BASC-P Depression (T2)") +
  161. theme_bw() +
  162. theme_minimal() +
  163. theme(axis.line = element_line(colour = "black"),
  164. legend.position="none",
  165. panel.grid.major = element_blank(),
  166. panel.grid.minor = element_blank(),
  167. panel.border = element_blank(),
  168. panel.background = element_blank()) +
  169. theme(legend.position="right") +
  170. theme(legend.position="right") +
  171. theme(text = element_text(size = 20)) +
  172. stat_cor(
  173. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  174. method = "pearson",
  175. r.accuracy = 0.001,
  176. p.accuracy = 0.001,
  177. label.x.npc = "left",
  178. label.y.npc = "top",
  179. size = 6
  180. )
  181. p2
  182. ```
  183. ```{r}
  184. # join both plots
  185. jp1 = ggpubr::ggarrange(p1,p2,common.legend = TRUE, legend = "bottom")
  186. jp1
  187. # save image
  188. ggsave("UF_FBA_parent_report_scatterplot.png", jp1,width = 10, height = 5, dpi = 300, units = "in", device='png')
  189. ```
  192. ```{r}
  194. # INFERIOR LONGITUIDNAL FASCICULUS
  196. # Scatterplot of Right ILF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  197. p3 = dat %>%
  198. drop_na(GROUP) %>%
  199. ggplot(aes(x=ILF_R_DEP_BASCP_R, y=T2_BASCP_DEP_R, color=GROUP)) +
  200. geom_point() +
  201. scale_colour_manual(values=custom_colors) +
  202. geom_smooth(method=lm, se=F) +
  203. xlab("Right ILF FC") +
  204. ylab("BASC-P Depression (T2)") +
  205. theme_bw() +
  206. theme_minimal() +
  207. theme(axis.line = element_line(colour = "black"),
  208. legend.position="none",
  209. panel.grid.major = element_blank(),
  210. panel.grid.minor = element_blank(),
  211. panel.border = element_blank(),
  212. panel.background = element_blank()) +
  213. theme(legend.position="right") +
  214. theme(legend.position="right") +
  215. theme(text = element_text(size = 20)) +
  216. stat_cor(
  217. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  218. method = "pearson",
  219. r.accuracy = 0.001,
  220. p.accuracy = 0.001,
  221. label.x.npc = "left",
  222. label.y.npc = "top",
  223. size = 6
  224. )
  225. p3
  226. ```
  228. ```{r}
  229. # Scatterplot of Left ILF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  230. p4 = dat %>%
  231. drop_na(GROUP) %>%
  232. ggplot(aes(x=ILF_L_DEP_BASCP_R, y=T2_BASCP_DEP_R, color=GROUP)) +
  233. geom_point() +
  234. scale_colour_manual(values=custom_colors) +
  235. geom_smooth(method=lm, se=F) +
  236. xlab("Left ILF FC") +
  237. ylab("BASC-P Depression (T2)") +
  238. theme_bw() +
  239. theme_minimal() +
  240. theme(axis.line = element_line(colour = "black"),
  241. legend.position="none",
  242. panel.grid.major = element_blank(),
  243. panel.grid.minor = element_blank(),
  244. panel.border = element_blank(),
  245. panel.background = element_blank()) +
  246. theme(legend.position="right") +
  247. theme(legend.position="right") +
  248. theme(text = element_text(size = 20)) +
  249. stat_cor(
  250. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  251. method = "pearson",
  252. r.accuracy = 0.001,
  253. p.accuracy = 0.001,
  254. label.x.npc = "left",
  255. label.y.npc = "top",
  256. size = 6
  257. )
  258. p4
  259. ```
  261. ```{r}
  262. # join both plots
  263. jp2 = ggpubr::ggarrange(p3,p4,common.legend = TRUE, legend = "bottom")
  264. jp2
  265. # save image
  266. ggsave("ILF_FBA_parent_report_scatterplot.png", jp2,width = 10, height = 5, dpi = 300, units = "in", device='png')
  267. ```
  270. ## Covarying for CBCL Depression scores at T1
  271. ```{r}
  272. # UNCINATE FASCICULUS
  274. # Scatterplot of Right UF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  275. p5 = dat %>%
  276. drop_na(GROUP) %>%
  277. ggplot(aes(x=UF_R_DEP_BASCP_CBCL_R, y=T2_BASCP_DEP_CBCL_R, color=GROUP)) +
  278. geom_point() +
  279. scale_colour_manual(values=custom_colors) +
  280. geom_smooth(method=lm, se=F) +
  281. xlab(" Right UF FC") +
  282. ylab("BASC-P Depression (T2)") +
  283. labs(caption = "covarying for CBCL scores at T1") +
  284. theme_bw() +
  285. theme_minimal() +
  286. theme(axis.line = element_line(colour = "black"),
  287. legend.position="none",
  288. panel.grid.major = element_blank(),
  289. panel.grid.minor = element_blank(),
  290. panel.border = element_blank(),
  291. panel.background = element_blank()) +
  292. theme(legend.position="bottom") + theme(text = element_text(size = 20))+
  293. stat_cor(
  294. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  295. method = "pearson",
  296. r.accuracy = 0.001,
  297. p.accuracy = 0.001,
  298. label.x.npc = "left",
  299. label.y.npc = "top",
  300. size = 6
  301. )
  302. p5
  303. ```
  305. ```{r}
  307. # Scatterplot of Left UF FC (controlling for Age and TCV) with BASC Depression scores : Parent report (split into groups)
  308. p6 = dat %>%
  309. drop_na(GROUP) %>%
  310. ggplot(aes(x=UF_L_DEP_BASCP_CBCL_R, y=T2_BASCP_DEP_CBCL_R, color=GROUP)) +
  311. geom_point() +
  312. scale_colour_manual(values=custom_colors) +
  313. geom_smooth(method=lm, se=F) +
  314. xlab("Left UF FC") +
  315. ylab("BASC-P Depression (T2)") +
  316. labs(caption = "covarying for CBCL scores at T1") +
  317. theme_bw() +
  318. theme_minimal() +
  319. theme(axis.line = element_line(colour = "black"),
  320. legend.position="none",
  321. panel.grid.major = element_blank(),
  322. panel.grid.minor = element_blank(),
  323. panel.border = element_blank(),
  324. panel.background = element_blank()) +
  325. theme(legend.position="right") +
  326. theme(legend.position="right") +
  327. theme(legend.position="bottom") + theme(text = element_text(size = 20))+
  328. stat_cor(
  329. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  330. method = "pearson",
  331. r.accuracy = 0.001,
  332. p.accuracy = 0.001,
  333. label.x.npc = "left",
  334. label.y.npc = "top",
  335. size = 6
  336. )
  337. p6
  338. ```
  340. ```{r}
  341. # join both plots
  342. jp3 = ggpubr::ggarrange(p5,p6,labels = c("A", "B"),common.legend = TRUE, legend = "bottom")
  343. jp3
  344. # save image
  345. ggsave("UF_FBA_parent_report_CBCL_scatterplot.png",height = 5, dpi = 300, units = "in", device='png')
  346. ```
  349. \nextpage
  352. # BASC CHILD REPORT ANALYSIS
  354. ## Association between T1 residualised FBA METRICS and T2 residualised BASC CHILD-REPORT DEP scores (controlling for AGE & TCV)
  355. ```{r}
  356. # UNCINATE FASCICULUS
  358. # Scatterplot of Right UF FC (controlling for Age and TCV) with BASC Depression scores : Child report (split into groups)
  359. c1 = dat %>%
  360. drop_na(GROUP) %>%
  361. ggplot(aes(x=UF_R_DEP_BASSC_R, y=T2_BASCC_DEP_R, color=GROUP)) +
  362. geom_point() +
  363. scale_colour_manual(values=custom_colors) +
  364. geom_smooth(method=lm, se=F) +
  365. xlab("Right UF FC") +
  366. ylab("BASC-C Depression (T2)") +
  367. theme_bw() +
  368. theme_minimal() +
  369. theme(axis.line = element_line(colour = "black"),
  370. legend.position="none",
  371. panel.grid.major = element_blank(),
  372. panel.grid.minor = element_blank(),
  373. panel.border = element_blank(),
  374. panel.background = element_blank()) +
  375. theme(legend.position="right") +
  376. theme(legend.position="right") + theme(text = element_text(size = 20))+
  377. stat_cor(
  378. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  379. method = "pearson",
  380. r.accuracy = 0.001,
  381. p.accuracy = 0.001,
  382. label.x.npc = "left",
  383. label.y.npc = "top",
  384. size = 6
  385. )
  386. c1
  387. ```
  389. ```{r}
  390. # Scatterplot of Left UF FC (controlling for Age and TCV) with BASC Depression scores : Child report (split into groups)
  391. c2 = dat %>%
  392. drop_na(GROUP) %>%
  393. ggplot(aes(x=UF_L_DEP_BASCC_R, y=T2_BASCC_DEP_R, color=GROUP)) +
  394. geom_point() +
  395. scale_colour_manual(values=custom_colors) +
  396. geom_smooth(method=lm, se=F) +
  397. xlab("Left UF FC") +
  398. ylab("BASC-C Depression (T2)") +
  399. theme_bw() +
  400. theme_minimal() +
  401. theme(axis.line = element_line(colour = "black"),
  402. legend.position="none",
  403. panel.grid.major = element_blank(),
  404. panel.grid.minor = element_blank(),
  405. panel.border = element_blank(),
  406. panel.background = element_blank()) +
  407. theme(legend.position="right") +
  408. theme(legend.position="right") + theme(text = element_text(size = 20))+
  409. stat_cor(
  410. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  411. method = "pearson",
  412. r.accuracy = 0.001,
  413. p.accuracy = 0.001,
  414. label.x.npc = "left",
  415. label.y.npc = "top",
  416. size = 6
  417. )
  418. c2
  419. ```
  421. ```{r}
  422. # join both plots
  423. jc1 = ggpubr::ggarrange(c1,c2, common.legend = TRUE, legend = "bottom", nrow = 1)
  424. jc1
  425. # save image
  426. ggsave("UF_FBA_child_report_DEP_scatterplot.png", jc1,width = 10, height = 5, dpi = 300, units = "in", device='png')
  427. ```
  431. ```{r}
  432. # Scatterplot of Left UF FC (controlling for Age and TCV) with BASC Anxiety scores : Child report (split into groups)
  433. c3 = dat %>%
  434. drop_na(GROUP) %>%
  435. ggplot(aes(x=UF_L_ANX_BASSC_R, y=T2_BASCC_ANX_R, color=GROUP)) +
  436. geom_point() +
  437. scale_colour_manual(values=custom_colors) +
  438. geom_smooth(method=lm, se=F) +
  439. xlab("Left UF FC") +
  440. ylab("BASC-C Anxiety (T2)") +
  441. theme_bw() +
  442. theme_minimal() +
  443. theme(axis.line = element_line(colour = "black"),
  444. legend.position="none",
  445. panel.grid.major = element_blank(),
  446. panel.grid.minor = element_blank(),
  447. panel.border = element_blank(),
  448. panel.background = element_blank()) +
  449. theme(legend.position="bottom") +
  450. theme(legend.position="bottom") + theme(text = element_text(size = 20))+
  451. stat_cor(
  452. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  453. method = "pearson",
  454. r.accuracy = 0.001,
  455. p.accuracy = 0.001,
  456. label.x.npc = "left",
  457. label.y.npc = "top",
  458. size = 6
  459. )
  460. c3
  461. # save image
  462. ggsave("UF_FBA_child_report_ANX_scatterplot.png", c3,width = 10, height = 5, dpi = 300, units = "in", device='png')
  463. ```
  466. ## Covarying for CBCL Depression scores at T1
  467. ```{r}
  468. # UNCINATE FASCICULUS
  471. # Scatterplot of Left UF FC (controlling for Age and TCV) with BASC Depression scores : Child report (split into groups)
  472. c4 = dat %>%
  473. drop_na(GROUP) %>%
  474. ggplot(aes(x=UF_L_DEP_BASCC_CBCL_R, y=T2_BASCC_DEP_CBCL_R, color=GROUP)) +
  475. geom_point() +
  476. scale_colour_manual(values=custom_colors) +
  477. geom_smooth(method=lm, se=F) +
  478. xlab("Left UF FC") +
  479. ylab("BASC-C Depression (T2)") +
  480. labs(caption = "covarying for CBCL scores at T1") +
  481. theme_bw() +
  482. theme_minimal() +
  483. theme(axis.line = element_line(colour = "black"),
  484. legend.position="none",
  485. panel.grid.major = element_blank(),
  486. panel.grid.minor = element_blank(),
  487. panel.border = element_blank(),
  488. panel.background = element_blank()) +
  489. theme(legend.position="right") +
  490. theme(legend.position="right")+
  491. theme(legend.position="bottom") + theme(text = element_text(size = 20))+
  492. stat_cor(
  493. aes(label = paste(..r.label.., ..p.label.., sep = "~`,`~")),
  494. method = "pearson",
  495. r.accuracy = 0.001,
  496. p.accuracy = 0.001,
  497. label.x.npc = "left",
  498. label.y.npc = "top",
  499. size = 6
  500. )
  501. c4
  502. # save image
  503. ggsave("UF_FBA_child_report_CBCL_scatterplot.png", c4)
  504. ```

JHU_analysis_FBA_Associations_27July2026.Rmd at commit c1bdb5a, no license · at the source

Overview

Authors: C. Hyde1, I. Fuelscher1, K.S. Rosch2,3,4, K.E. Seymour5, D. Crocetti2, T. Silk1,6, M. Singh1, S.H. Mostofsky2,4,7
  1. School of Psychology, Deakin University, Burwood, Australia
  2. Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
  3. Department of Neuropsychology, Kennedy Krieger Institute, Baltimore, MD, USA
  4. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
  5. National Institute on Drug Abuse, Department of Health and Human Services, Rockville, MD, USA
  6. Murdoch Children's Research Institute, Melbourne, Australia
  7. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Institutions: Deakin University (Australia); Kennedy Krieger Institute (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); National Institute on Drug Abuse (United States); Murdoch Children's Research Institute (Australia)
Journal: NeuroImage. Clinical, volume 51, article 104053
Dates: received 26 February 2026; accepted 27 August 2026; published online 1 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.nicl.2026.104053 · PMID 42691943 · PMCID PMC13571588 · OpenAlex W7204585495
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), depression (population), ADHD (population), developmental (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: ADHD, Internalizing problems, Motor, Longitudinal, White matter, Fixel based analysis
MeSH: Attention Deficit Disorder with Hyperactivity*, Brain*, White Matter*, Adolescent, Child, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Longitudinal Studies, Male, Motor Skills (* major topic)
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: NIH (K23-MH101322, P50HD103538, R01-MH078160, R01-MH085328, R03-MH119457, K23-MH107734); Waterloo Foundation (2013–4545); Brain and Behavior Foundation NARSAD Young Investigator's Award; Eunice Kennedy Shriver National Institute of Child Health and Human Development (1S10OD021648, P50HD103538)
Citations: not cited yet (Europe PMC); 50 references in the paper

Abstract

This study investigated whether childhood white matter organization in those with and without ADHD predicted the emergence of internalizing problems in adolescence. Further, we aimed to determine whether (i) this longitudinal effect was mediated by motor skill level, and/or (ii) white matter organization in children with ADHD mediated the expected relationship between motor skill level and internalizing problems in adolescence. Participants were 40 children with ADHD and 36 typically developing (TD) controls aged between 8 and 13 years, who were followed-up during adolescence (12–18 years). All underwent diffusion MRI at Time 1, and assessment of motor competence using the Movement ABC-2 (MABC-2). At Time 2, internalizing problems (i.e., ‘depression’, ‘anxiety’, and ‘somatization’) were measured using the parent- and self-report Behavior Assessment System for Children (BASC). Following pre-processing of diffusion MRI scans, fixel based analysis was conducted and fibre cross-section (FC) extracted. TractSeg was then used to delineate the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF) and the uncinate fasciculus (UC). Results showed that childhood FC within the UF was a significant predictor of parent and child-rated depression scores in adolescence. Further, childhood FC of the bilaterial ILF was a significant predictor of parent report depression scores in adolescence. These effects were stable for those with and without ADHD, and no group differences in FC were observed in those white matter regions found to be associated with adolescent depressive symptoms. We failed to find evidence that motor ability mediated the relationship between white matter organization of the UF of ILF and depressive symptoms. When childhood motor ability was considered as a predictor of adolescent depressive scores (an effect that was significant for parent reported effects), this effect was mediated by childhood FC of the UF. This work suggests that low motor skill in children with and without ADHD may provide a developmental marker for emotional and behavioral dysregulation in adolescence, with this effect partly explained by childhood white matter organization within fronto-limbic circuitry.

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chydeDeakin/Hyde_et_al_2026

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: c1bdb5a7bce37b4503537c1d94a46bf9a854ea46, 27 July 2026
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: the text, “Design and analysis”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), cowplot (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
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1 file
At the source: github.com/chydeDeakin/Hyde_et_al_2026.git

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 12 MeSH terms, 3 funders, 42 references.

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This paper

Hyde, C., Fuelscher, I., Rosch, K., Seymour, K., Crocetti, D., Silk, T., Singh, M., & Mostofsky, S. (2026). Childhood white matter organization predicts adolescent internalizing problems among youth with and without ADHD. NeuroImage. Clinical, 51, 104053. https://doi.org/10.1016/j.nicl.2026.104053

BibTeX

@article{hyde2026childhood,
author = {Hyde, C. and Fuelscher, I. and Rosch, K.S. and Seymour, K.E. and Crocetti, D. and Silk, T. and Singh, M. and Mostofsky, S.H.},
title = {{Childhood white matter organization predicts adolescent internalizing problems among youth with and without ADHD}},
journal = {NeuroImage. Clinical},
year = {2026},
month = sep,
volume = {51},
pages = {104053},
publisher = {Elsevier},
issn = {2213-1582},
doi = {10.1016/j.nicl.2026.104053},
url = {https://doi.org/10.1016/j.nicl.2026.104053},
pmid = {42691943},
pmcid = {PMC13571588}
}

RIS

TY - JOUR
AU - Hyde, C.
AU - Fuelscher, I.
AU - Rosch, K.S.
AU - Seymour, K.E.
AU - Crocetti, D.
AU - Silk, T.
AU - Singh, M.
AU - Mostofsky, S.H.
TI - Childhood white matter organization predicts adolescent internalizing problems among youth with and without ADHD
T2 - NeuroImage. Clinical
J2 - Neuroimage Clin
PY - 2026
DA - 2026/09/01
VL - 51
SP - 104053
SN - 2213-1582
PB - Elsevier
DO - 10.1016/j.nicl.2026.104053
UR - https://doi.org/10.1016/j.nicl.2026.104053
LA - en
ER -

CSL-JSON

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"author": [
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