OSCR

What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study.

Code ↔ Paper

11 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 11 matches
  1. [1] § Methods › Health › Physical health ↔ scripts/03_health.R, lines 190–279 · score 0.93 · sleep wake transition, excessive somnolence, sleep breathing, maintaining sleep, arousal, vision
  2. [2] § Results › Multimodal neural correlates of prospective loneliness ↔ scripts/06_rsfc.R, lines 156–212 · score 0.87 · fronto parietal, cingulo parietal, cingulo opercular, left pallidum, right caudate, mouth
  3. [3] § Results › Familial and broader environmental correlates of prospective loneliness ↔ scripts/02_exposome.R, lines 262–321 · score 0.82 · witnessing domestic violence, planned pregnancy, family conflict, family income, twin, Parental
  4. [4] § Methods › Health › Mental health ↔ scripts/03_health.R, lines 190–279 · score 0.81 · rule breaking, mental health, Prodromal, aggressive, anxious, somatic
  5. [5] § Methods › Environment ↔ scripts/02_exposome.R, lines 322–381 · score 0.76 · Neighborhood Socioeconomic Status, Neighborhood Safety, School Environment, Pollution, Family
  6. [6] § Methods › Brain MRI data acquisition and preprocessing ↔ scripts/14_gmv_sens_incident.R, lines 64–131 · score 0.76 · imgincl t1w, mrif_score, family ID, intracranial, winsorized, GMV
  7. [7] § Methods › Brain MRI data acquisition and preprocessing ↔ scripts/04_gmv.R, lines 66–140 · score 0.75 · imgincl t1w, mrif_score, family ID, intracranial, winsorized, GMV
  8. [8] § Results › Multimodal neural correlates of prospective loneliness ↔ scripts/06_rsfc.R, lines 156–212 · score 0.61 · cingulo opercular, left pallidum, right caudate, salience
  9. [9] § Methods › Associations of environment and health with prospective loneliness ↔ scripts/06_rsfc.R, lines 265–346 · score 0.59 · logistic regression, binomial, logit, mixed, models, ethnicity
  10. [10] § Methods › Associations of environment and health with prospective loneliness ↔ scripts/04_gmv.R, lines 191–272 · score 0.58 · logistic regression, binomial, logit, mixed, models, ethnicity
  11. [11] § Methods › Health › Mental health ↔ scripts/02_exposome.R, lines 382–443 · score 0.58 · mental health, anxious, somatic, thought, externalizing, depressed

Paper

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The authors' code

R · 346 lines · 10 KB · MIT · 3 matches

  1. # Environment ------------------------------------------------------------------
  2. library(tidyverse)
  3. library(lme4)
  4. library(ggseg)
  5. library(patchwork)
  6. library(here)
  7. library(glue)
  8. library(ggraph)
  9. library(igraph)
  11. source(here("src", "R", "plot_ggseg_brain.R"))
  12. source(here("src", "R", "run_lda_workflow.R"))
  15. # Data I/O ---------------------------------------------------------------------
  16. master_preprocessed_df <- here(
  17. "data", "processed", "master_preprocessed_df.rds"
  18. ) %>%
  19. read_rds()
  21. # using data release 5.1
  22. # please change the path to ABCD study folder
  23. abcd_folder_path <- "/Users/tywong/OneDrive/opendata/abcd" %>%
  24. here("abcd-data-release-5.1/core")
  26. # general
  27. mri_y_qc_clfind <- here(abcd_folder_path, "imaging", "mri_y_qc_clfind.csv") %>%
  28. read_csv(show_col_types = FALSE)
  29. mri_y_qc_incl <- here(abcd_folder_path, "imaging", "mri_y_qc_incl.csv") %>%
  30. read_csv(show_col_types = FALSE)
  31. mri_y_adm_info <- here(abcd_folder_path, "imaging", "mri_y_adm_info.csv") %>%
  32. read_csv(show_col_types = FALSE) %>%
  33. select(src_subject_id, eventname, mri_info_deviceserialnumber)
  34. mri_y_qc_motion <- here(abcd_folder_path, "imaging", "mri_y_qc_motion.csv") %>%
  35. read_csv(show_col_types = FALSE)
  37. # RSFC variables
  38. rsfc_variables_df <- here("data", "raw", "included_variables.xlsx") %>%
  39. readxl::read_excel(sheet = "RSFC")
  40. selected_filenames <- c(unique(pull(rsfc_variables_df, table_name)))
  41. selected_variables <- c(pull(rsfc_variables_df, variable_name)) # 416
  43. selected_files <- list()
  44. for (ith in seq_along(selected_filenames)) {
  45. selected_files[[ith]] <- list.files(
  46. path = abcd_folder_path,
  47. full.names = TRUE,
  48. pattern = glue("{selected_filenames[ith]}.csv"),
  49. recursive = TRUE
  50. ) %>%
  51. read_csv(show_col_types = FALSE) %>%
  52. select(src_subject_id, eventname, any_of(selected_variables))
  53. }
  56. master_rsfc_df <- selected_files %>%
  57. reduce(left_join, by = c("src_subject_id", "eventname")) %>%
  58. left_join(mri_y_qc_clfind, by = c("src_subject_id", "eventname")) %>%
  59. left_join(mri_y_qc_incl, by = c("src_subject_id", "eventname")) %>%
  60. left_join(mri_y_adm_info, by = c("src_subject_id", "eventname")) %>%
  61. left_join(mri_y_qc_motion, by = c("src_subject_id", "eventname")) %>%
  62. filter(eventname == "baseline_year_1_arm_1") %>%
  63. mutate(
  64. across(where(is.numeric), ~na_if(., 777)),
  65. across(where(is.numeric), ~na_if(., 999))
  66. ) %>%
  67. filter(
  68. # https://docs.abcdstudy.org/latest/documentation/imaging/type_qc.html
  69. mrif_score %in% c(1, 2) & imgincl_t1w_include == 1,
  70. imgincl_rsfmri_include == 1
  71. ) %>%
  72. select(src_subject_id:rsfmri_cor_ngd_vta_scs_vtdcrh, rsfmri_meanmotion,
  73. mri_info_deviceserialnumber) %>%
  74. right_join(master_preprocessed_df, by = "src_subject_id") %>%
  75. drop_na() # only complete data
  77. find_identical_columns <- function(df) {
  78. col_names <- colnames(df)
  79. n <- length(col_names)
  80. identical_pairs <- list()
  82. for (i in 1:(n - 1)) {
  83. for (j in (i + 1):n) {
  84. if (identical(df[[i]], df[[j]])) {
  85. identical_pairs[[length(identical_pairs) + 1]] <-
  86. c(col1 = col_names[i], col2 = col_names[j])
  87. }
  88. }
  89. }
  91. if (length(identical_pairs) == 0) {
  92. message("No identical columns found.")
  93. return(invisible(NULL))
  94. }
  96. do.call(rbind, identical_pairs) |> as.data.frame()
  97. }
  99. identical_labels <- master_rsfc_df %>%
  100. find_identical_columns() %>%
  101. pull(col2)
  103. rsfc_labels <- master_rsfc_df %>%
  104. select(rsfmri_c_ngd_ad_ngd_ad:rsfmri_cor_ngd_vta_scs_vtdcrh) %>%
  105. select(-all_of(identical_labels)) %>%
  106. colnames() # 33
  110. master_rsfc_resid_df <- master_rsfc_df
  111. for (ith in seq_along(rsfc_labels)) {
  113. cat(sprintf("\rExtract residuals: %d / %d", ith, length(rsfc_labels)))
  115. label <- rsfc_labels[ith]
  117. # perform Fisher's z transformation
  118. master_rsfc_resid_df[[label]] <- atanh(master_rsfc_resid_df[[label]])
  120. formula_ <- glue::glue(
  121. "{label} ~ loneliness_bl + interview_age + demo_sex_v2 + \
  122. race_ethnicity_3 + rsfmri_meanmotion + \
  123. (1 | mri_info_deviceserialnumber) + (1 | rel_family_id)"
  124. ) %>%
  125. as.formula()
  127. master_rsfc_resid_df[[label]] <- master_rsfc_df %>%
  128. mutate(
  129. !!label := as.numeric(scale(.data[[label]]))
  130. ) %>%
  131. lmer(
  132. formula = formula_,
  133. control = lmerControl(
  134. optimizer = "bobyqa", optCtrl = list(maxfun = 2e5)
  135. )
  136. ) %>%
  137. resid() %>%
  138. scale() %>%
  139. as.numeric()
  140. }
  142. rsfc_lda_result <- run_lda_workflow(
  143. df = master_rsfc_resid_df,
  144. target = "loneliness_fu_any",
  145. predictors = rsfc_labels,
  146. n_bootstraps = 1000,
  147. alpha = 0.05,
  148. replace = TRUE
  149. )
  151. rsfc_lda_result$bal_acc_mean
  152. rsfc_lda_result$bal_acc_sd
  153. rsfc_lda_result$roc_auc_mean
  154. rsfc_lda_result$roc_auc_sd
  156. rsfc_lda_result %>%
  157. left_join(rsfc_variables_df, by = "variable_name") %>%
  158. select(variable_name, from, to, mean_weight, ci_low, ci_high, is_sig) %>%
  159. mutate_if(is.numeric, round, digits = 3) %>%
  160. rename(
  161. "LDA weight" = "mean_weight",
  162. "95% CI (Lower)" = "ci_low",
  163. "95% CI (Upper)" = "ci_high",
  164. "Significant?" = "is_sig"
  165. ) %>%
  166. write_csv(here("outputs", "tables", "lda_results_rsfc.csv"))
  168. rsfc_lda_sig_result <- rsfc_lda_result %>%
  169. left_join(rsfc_variables_df, by = "variable_name") %>%
  170. select(variable_name, from, to, mean_weight, ci_low, ci_high, is_sig) %>%
  171. filter(is_sig) %>%
  172. select(from, to, mean_weight) %>%
  173. mutate(from = case_when(
  174. from == "cingulo-parietal" ~ "CPT",
  175. from == "fronto-parietal" ~ "FPN",
  176. from == "salience" ~ "SAL",
  177. from == "sensorimotor mouth" ~ "SML",
  178. from == "cingulo-opercular" ~ "COP"
  179. )) %>%
  180. mutate(to = case_when(
  181. to == "visual" ~ "VIS",
  182. to == "none" ~ "NON",
  183. to == "salience" ~ "SAL",
  184. to == "left-pallidum" ~ "L Pallidum",
  185. to == "right-caudate" ~ "R Caudate"
  186. )) %>%
  187. mutate(col = ifelse(mean_weight > 0, "#CE204E", "#395D9C"))
  189. library(circlize)
  191. node_colors <- c(
  192. CPT = "#006dfe",
  193. NON = "#c1c0be",
  194. FPN = "#f3e601",
  195. SAL = "#0a0a08",
  196. SML = "#ff7f00",
  197. VIS = "#3a469a",
  198. COP = "#810080",
  199. `L Pallidum` = "#666666",
  200. `R Caudate` = "#666666"
  201. )
  203. circos.clear()
  204. pdf(here("outputs", "figures", "chord_diagram_plot.pdf"), width = 5, height = 5)
  205. chordDiagram(
  206. x = rsfc_res,
  207. grid.col = node_colors,
  208. col = rsfc_res$col,
  209. transparency = 0.3,
  210. annotationTrack = "grid",
  211. preAllocateTracks = 1
  212. )
  214. # Add inner labels
  215. circos.trackPlotRegion(
  216. track.index = 1,
  217. panel.fun = function(x, y) {
  218. circos.text(
  219. x = CELL_META$xcenter,
  220. y = CELL_META$ylim[1] - 0.25,
  221. labels = CELL_META$sector.index,
  222. facing = "bending.inside",
  223. col = "white",
  224. niceFacing = TRUE,
  225. adj = c(0.5, 0.5),
  226. cex = 0.8
  227. )
  228. },
  229. bg.border = NA,
  230. track.height = 0.1
  231. )
  232. dev.off()
  234. highlight_df <- tibble::tibble(
  235. label = c("Left-Pallidum", "Right-Caudate"),
  236. highlight = TRUE
  237. )
  239. # Join with aseg and plot
  240. aseg_highlighted_vis <- highlight_df %>%
  241. brain_join(aseg) %>%
  242. reposition_brain(position = "coronal") %>%
  243. ggplot() +
  244. geom_sf(
  245. aes(fill = highlight),
  246. color = "black"
  247. ) +
  248. scale_fill_manual(
  249. values = c("TRUE" = "#666666", "FALSE" = "gray85"),
  250. na.value = "gray85"
  251. ) +
  252. theme_void() +
  253. theme(legend.position = "none")
  256. ggsave(
  257. plot = aseg_highlighted_vis,
  258. filename = here("outputs", "figures", "aseg_highlighted.pdf"),
  259. device = cairo_pdf,
  260. width = 4,
  261. height = 4
  262. )
  265. # Logistic Regression ----------------------------------------------------------
  266. rsfc_lr_res_list <- list()
  268. for (ith in 1:(length(rsfc_labels))) {
  270. cat(sprintf("\rRSFC Running: %d / %d", ith, length(rsfc_labels)))
  272. rsfc_var <- rsfc_labels[ith]
  274. f1 <- glue::glue(
  275. "loneliness_fu_any ~ {rsfc_var} + loneliness_bl + demo_sex_v2 + \
  276. interview_age + race_ethnicity_3 + (1 | site_id_l) + \
  277. (1 | rel_family_id)"
  278. ) |>
  279. as.formula()
  281. res <- master_rsfc_df |>
  282. mutate(!!rsfc_var := as.numeric(scale(.data[[rsfc_var]]))) |>
  283. glmer(
  284. formula = f1,
  285. family = binomial(link = "logit"),
  286. control = glmerControl(tolPwrss = 1e-10)
  287. ) |>
  288. parameters::model_parameters(
  289. effect = "fixed",
  290. verbose = FALSE
  291. ) |>
  292. as_tibble() |>
  293. dplyr::slice(2) |>
  294. mutate(
  295. cohend = effectsize::logoddsratio_to_d(Coefficient, log = TRUE),
  296. cohend_low = effectsize::logoddsratio_to_d(CI_low, log = TRUE),
  297. cohend_high = effectsize::logoddsratio_to_d(CI_high, log = TRUE),
  298. variable_name = rsfc_var,
  299. .before = "p"
  300. )
  302. rsfc_lr_res_list[[ith]] <- res
  303. }
  306. rsfc_res_df <- tibble(
  307. variable_name = rsfc_lda_result$weights$variable_name,
  308. lr_weights = rsfc_lr_res_list |> reduce(bind_rows) |> pull(cohend),
  309. lda_weights = rsfc_lda_result$weights$mean_weight
  310. )
  312. cor_results <- correlation::correlation(rsfc_res_df)
  313. r_val <- cor_results$r[1]
  314. p_val <- cor_results$p[1]
  315. label_text <- sprintf("italic(r) == %.2f*','~~italic(p) == %.2g", r_val, p_val)
  317. rsfc_comp_fig <- rsfc_res_df |>
  318. ggplot(aes(x = lda_weights, y = lr_weights)) +
  319. geom_point(size = 4, color = "gray30", alpha = 0.75, shape = 16) +
  320. geom_smooth(method = "lm", color = "tomato3", fill = "tomato2") +
  321. annotate(
  322. "text",
  323. x = -Inf, y = Inf,
  324. label = label_text,
  325. parse = TRUE,
  326. hjust = -0.1, vjust = 1.5,
  327. size = 4.5
  328. ) +
  329. labs(
  330. title = "Resting-state Functional Connectivity",
  331. x = "Mean LDA Weights (Bootstrapped, n = 1,000)",
  332. y = "Cohen's d from Mixed-effects Logistic Regression"
  333. ) +
  334. ggthemes::theme_pander() +
  335. theme(
  336. plot.margin = margin(5, 5, 5, 5, "mm"),
  337. plot.title.position = "plot"
  338. )
  339. rsfc_comp_fig
  341. ggsave(
  342. plot = rsfc_comp_fig,
  343. filename = here("outputs", "figures", "rsfc_comp_fig.pdf"),
  344. width = 6,
  345. height = 5
  346. )

06_rsfc.R at commit bc774c6, under MIT · at the source

Overview

Authors: Ting Yat Wong1,2,3,4, Ting Sam Wong5, Wai Kai Hou1,2,3, Angel Nga Man Leung1,2,6, Jie Xiao1,2, Kenneth S L Yuen7,8, Simon S Y Lui9, Anqi Qiu10,11,12, Tyler M Moore13,14, Ruben C Gur13,14
ORCID iDs: Ting Yat Wong, Wai Kai Hou, Angel Nga Man Leung, Simon S Y Lui
14 affiliations
  1. Department of Psychology, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
  2. Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
  3. AI, Brain and Child Research Centre (ABC‐RC), Academy of Educational Development and Innovation, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
  4. SWU‐EdUHK Joint Lab for Research on Brain, Education, and Intelligence (BEI Lab), Hong Kong Special Administrative Region, China
  5. Independent Data Analyst, Nagoya City, Japan
  6. Analytics\Assessment Research Centre, The Education University of Hong Kong, Hong Kong Special Administrative Region, China
  7. Focus Program Translational Neuroscience, Neuroimaging Center, Johannes Gutenberg University Medical Center, Mainz, Germany
  8. Leibniz Institute for Resilience Research, Mainz, Germany
  9. Department of Psychiatry, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
  10. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
  11. Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
  12. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
  13. Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  14. Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
Institutions: Education University of Hong Kong (Hong Kong SAR China); Johannes Gutenberg University Mainz (Germany); Leibniz Institute for Resilience Research (Germany); University Medical Center of the Johannes Gutenberg University Mainz (Germany); University of Hong Kong (Hong Kong SAR China); Hong Kong Polytechnic University (Hong Kong SAR China); Johns Hopkins University (United States); National University of Singapore (Singapore); Children's Hospital of Philadelphia (United States); University of Pennsylvania Health System (United States); University of Pennsylvania (United States)
Journal: Journal of child psychology and psychiatry, and allied disciplines, volume 67, issue 10, pages 1714-1728
Dates: accepted 12 May 2026; published online 25 May 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/jcpp.70178 · PMID 42186177 · PMCID PMC13535708 · OpenAlex W7162461818
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Machine learning, fMRI & imaging
Keywords: Prospective loneliness, late childhood, environmental and health factors, neuroimaging correlates
MeSH: Loneliness*, Adolescent, Child, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Neuroimaging, Prospective Studies (* major topic)
Topic: Health disparities and outcomes (Health, Social Sciences), according to OpenAlex
Funding: Education University of Hong Kong (RG 14/2023‐2024R, RG 14/2023-2024R); NIDA NIH HHS (U01 DA041106, U01 DA041148, U01 DA041093, U01 DA041120, U24 DA041147, U01 DA041089, U01 DA041174, U01 DA041048, U01 DA041028, U01 DA041117, U01 DA041022, U01 DA041025, U01 DA041134, U01 DA041156, U24 DA041123)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Background: Loneliness in childhood is a growing public health concern, yet early multilevel candidate risk and protective factors remain insufficiently mapped. Systematic investigation is essential to guide prevention and intervention during sensitive developmental periods. This study identifies environmental, health, and neurobiological factors associated with prospective loneliness in children.

Methods: A population‐based longitudinal cohort study used data from children aged 9–10 years and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2022. A total of 9,602 children with complete baseline and follow‐up loneliness and demographic data were included after exclusions for exposure completeness and quality assurance. Baseline measures included 347 environmental exposures, 61 health indicators, and 558 MRI features capturing gray matter volume, white matter microstructure, and resting‐state functional connectivity. The primary outcome was prospective loneliness reported from ages 10–14. Linear mixed‐effects models assessed associations with environmental and health variables. Linear discriminant analysis was applied to neuroimaging features to distinguish children with and without prospective loneliness.

Results: Among 9,602 children (mean [SD] age, 119.01 [7.52] months; 48% girls; 55% White, 14% Black, 31% other races/ethnicities), 12% (n = 1,158) reported loneliness at baseline, and 71.6% (n = 829) of those re‐experienced loneliness over 3 years. Prospective loneliness was significantly associated with 40 environmental variables (|d| = 0.069–0.388), most strongly parental psychopathology, developmental history, and family income. Twenty‐six health indicators were also associated (|d| = 0.061–0.390), with general mental health showing the largest effect. Neuroimaging features associated with prospective loneliness converged in brain systems involved in socioemotional processing.

Conclusions: Prospective loneliness was associated with modifiable environmental and health factors, as well as neurobiological differences. Early identification and targeted interventions that support socioemotional development, particularly within family, neighborhood, and school contexts, may help mitigate loneliness and its long‐term impact.

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 11 matches between paragraphs and lines of code.

lonilab/abcd_prospective_loneliness

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: bc774c6b877603767f4d888222c3a02db2942c1e, 8 April 2026
Languages: R (26)
Size: 29 files, 26 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (24 files), lme4 (20 files), ggseg (13 files), patchwork (12 files), easystats (11 files), caret (9 files), ggplot2 (8 files), ggpubr (4 files), igraph (4 files), circlize (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
28 files
At the source: github.com/lonilab/abcd_prospective_loneliness

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;
  • 26 scripts, each with its path and the digest of its content;
  • 11 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 data that support the findings of this study are openly available in the NIMH Data Archive (NDA) at https://abcdstudy.org, reference number https://doi.org/10.15154/z563‐zd24 (https://doi.org/10.15154/z563-zd24).

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

Data Availability Statement

The data utilized in this study were sourced from the ABCD Study (https://abcdstudy.org), available through the NIMH Data Archive (NDA). The ABCD data repository is subject to growth and evolution over time. The current study utilized the data release 5.1 (https://doi.org/10.15154/z563‐zd24 (https://doi.org/10.15154/z563-zd24)). All preprocessing and analysis scripts for the current study can be found in the corresponding GitHub repository: https://github.com/lonilab/abcd_prospective_loneliness.

The data that support the findings of this study are openly available in the NIMH Data Archive (NDA) at https://abcdstudy.org, reference number https://doi.org/10.15154/z563‐zd24 (https://doi.org/10.15154/z563-zd24).

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, 10 authors, 4 keywords, 10 MeSH terms, 2 funders, 87 references.

Cite

This paper

Wong, T. Y., Wong, T. S., Hou, W. K., Leung, A. N. M., Xiao, J., Yuen, K. S. L., Lui, S. S. Y., Qiu, A., Moore, T. M., & Gur, R. C. (2026). What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study. Journal of child psychology and psychiatry, and allied disciplines, 67(10), 1714-1728. https://doi.org/10.1111/jcpp.70178

BibTeX

@article{wong2026what,
author = {Wong, Ting Yat and Wong, Ting Sam and Hou, Wai Kai and Leung, Angel Nga Man and Xiao, Jie and Yuen, Kenneth S L and Lui, Simon S Y and Qiu, Anqi and Moore, Tyler M and Gur, Ruben C},
title = {{What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study}},
journal = {Journal of child psychology and psychiatry, and allied disciplines},
year = {2026},
month = may,
volume = {67},
number = {10},
pages = {1714--1728},
publisher = {Wiley},
issn = {0021-9630},
doi = {10.1111/jcpp.70178},
url = {https://doi.org/10.1111/jcpp.70178},
pmid = {42186177},
pmcid = {PMC13535708}
}

RIS

TY - JOUR
AU - Wong, Ting Yat
AU - Wong, Ting Sam
AU - Hou, Wai Kai
AU - Leung, Angel Nga Man
AU - Xiao, Jie
AU - Yuen, Kenneth S L
AU - Lui, Simon S Y
AU - Qiu, Anqi
AU - Moore, Tyler M
AU - Gur, Ruben C
TI - What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study
T2 - Journal of child psychology and psychiatry, and allied disciplines
J2 - J Child Psychol Psychiatry
PY - 2026
DA - 2026/05/25
VL - 67
IS - 10
SP - 1714
EP - 1728
SN - 0021-9630
PB - Wiley
DO - 10.1111/jcpp.70178
UR - https://doi.org/10.1111/jcpp.70178
LA - en
ER -

CSL-JSON

{
"id": "10.1111/jcpp.70178",
"type": "article-journal",
"title": "What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study",
"container-title": "Journal of child psychology and psychiatry, and allied disciplines",
"author": [
{
"family": "Wong",
"given": "Ting Yat"
},
{
"family": "Wong",
"given": "Ting Sam"
},
{
"family": "Hou",
"given": "Wai Kai"
},
{
"family": "Leung",
"given": "Angel Nga Man"
},
{
"family": "Xiao",
"given": "Jie"
},
{
"family": "Yuen",
"given": "Kenneth S L"
},
{
"family": "Lui",
"given": "Simon S Y"
},
{
"family": "Qiu",
"given": "Anqi"
},
{
"family": "Moore",
"given": "Tyler M"
},
{
"family": "Gur",
"given": "Ruben C"
}
],
"container-title-short": "J Child Psychol Psychiatry",
"volume": "67",
"issue": "10",
"page": "1714-1728",
"DOI": "10.1111/jcpp.70178",
"PMID": "42186177",
"PMCID": "PMC13535708",
"ISSN": "0021-9630",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/jcpp.70178",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
25
]
]
}
}

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