OSCR

Neighborhood opportunity, white matter, and cognition in a large pediatric neuroimaging study.

Code ↔ Paper

8 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 8 matches
  1. [1] § Methods › Neurocognitive assessment ↔ 5. Visualization.Rmd, lines 150–223 · score 0.98 · Dimensional Change Card, Picture Sequence Memory, Fluid Cognition Composite, Crystallized Cognition Composite, Flanker Inhibitory Control, Sorting Working Memory
  2. [2] § Methods › Statistical analysis › Modeling approach ↔ 2. Association_Fibers_GAM_Analysis.Rmd, lines 75–160 · score 0.73 · model diagnostics, Deviance Explained, fitted GAMs, smooth term, R2, COI
  3. [3] § Methods › Statistical analysis › Covariate control ↔ 1_Data_Preprocessing.ipynb, lines 373–415 · score 0.70 · correlation matrices, Family ID, Site ID, BMI, ethnicity, Race
  4. [4] § Results › Neighborhood opportunity and neurocognitive performance ↔ 5. Visualization.Rmd, lines 150–223 · score 0.62 · Picture Vocabulary, Fluid Cognition, Crystallized Cognition, R2, COI
  5. [5] § Methods › Participants ↔ 2. Association_Fibers_GAM_Analysis.Rmd, lines 8–73 · score 0.56 · association fiber, AF, CB, III, ILF, IOFF
  6. [6] § Methods › Statistical analysis › Mediation analyses ↔ 4. Mediation_Analysis.Rmd, lines 150–212 · score 0.55 · confidence intervals, Mediation proportions, bootstrapping, indirect, mediator, COI
  7. [7] § Results › Neighborhood opportunity and white matter microstructure ↔ 5. Visualization.Rmd, lines 225–289 · score 0.54 · MdLF, AF, CB, III, ILF, IOFF
  8. [8] § Methods › Participants ↔ 5. Visualization.Rmd, lines 225–289 · score 0.53 · MdLF, AF, CB, III, ILF, IOFF

Paper

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The authors' code

R Markdown · 291 lines · 10 KB · CC-BY-4.0 · 4 matches

  1. ---
  2. title: "GAM Model partial effect graphs + heatmaps"
  3. author: "Neslihan Yildiz Ozhan"
  4. date: "2024-01-16"
  5. output: html_document
  6. ---
  8. ```{r}
  9. #NIH Toolbox visualization
  10. library(ggplot2)
  11. library(mgcv)
  12. library(dplyr)
  13. library(gratia)
  14. data <- read.csv("~/Desktop/ABCD/abcd_coi.csv")
  15. # z-score scale
  17. data <- data %>%
  18. mutate(
  19. across(ends_with("_Ten1_FA"), scale),
  20. across(starts_with("nih"), scale),
  21. across(starts_with("coi"), scale)
  22. )
  25. optimize_k_by_aic <- function(data, y_column, coi_column = "coi_total_raw") {
  26. k_values <- 3:12
  27. best_k <- NULL
  28. best_aic <- Inf
  30. model_data <- data %>%
  31. filter(!is.na(y_column))
  33. for (k in k_values) {
  34. model <- gam(as.formula(paste(y_column, "~ s(", coi_column, ", k = ", k, ") +
  35. s(interview_age, k = ", k, ") + factor(sex) + factor(race) +
  36. factor(ethnicity) + factor(totalincome) + s(parenteducation, k = ", k, ") +
  37. s(bmi, k =", k, ") + factor(puberty_stage) + factor(sleep_category) +
  38. factor(fam_hx) + s(rel_family_id, k = ", k, ") + factor(site_id_l)"
  39. ))
  40. ,
  41. data = model_data)
  43. aic_score <- AIC(model)
  44. if (aic_score < best_aic) {
  45. best_aic <- aic_score
  46. best_k <- k
  47. }
  48. }
  50. final_model <- gam(as.formula(paste(y_column, "~ s(", coi_column, ", k = ", best_k, ") +
  51. s(interview_age, k = ", best_k, ") + factor(sex) + factor(race) +
  52. factor(ethnicity) + factor(totalincome) + s(parenteducation, k = ", best_k, ") +
  53. s(bmi, k =", best_k, ") + factor(puberty_stage) + factor(sleep_category) +
  54. factor(fam_hx) + s(rel_family_id, k =", best_k, ") +
  55. + factor(site_id_l)")),
  56. data = model_data)
  58. return(list(model = final_model, best_k = best_k))
  59. }
  61. y_columns <- grep("^nih", names(data), value = TRUE)
  62. plots <- list()
  64. for (y_col in y_columns) {
  65. result <- optimize_k_by_aic(data, y_col)
  66. model <- result$model
  67. best_k <- result$best_k
  69. plot <- draw(model, select = "s(coi_total_raw)", ci_col = "steelblue",ci_alpha = 0.3, rug = FALSE) +
  70. ggtitle(paste(" ")) +
  71. ylab(paste("Partial effect (", y_col, ")", sep=""))+
  72. xlab("COI Overall")+
  73. geom_line(linewidth=1.5, col="darkblue")
  75. plots[[y_col]] <- plot
  76. }
  78. plots$nihtbx_totalcomp_uncorrected
  79. plots$nihtbx_cryst_uncorrected
  80. plots$nihtbx_fluidcomp_uncorrected
  81. plots$nihtbx_picvocab_uncorrected
  82. plots$nihtbx_reading_uncorrected
  83. plots$nihtbx_flanker_uncorrected
  84. plots$nihtbx_list_uncorrected
  85. plots$nihtbx_cardsort_uncorrected
  86. plots$nihtbx_pattern_uncorrected
  87. plots$nihtbx_picture_uncorrected
  88. ```
  90. ```{r}
  93. optimize_k_by_aic <- function(data, y_column, coi_column = "coi_total_raw") {
  94. k_values <- 3:11
  95. best_k <- NULL
  96. best_aic <- Inf
  97. model_data <- data %>%
  98. filter(!is.na(y_column))
  100. for (k in k_values) {
  101. model <- gam(as.formula(paste(y_column, "~ s(", coi_column, ", k = ", k, ") +
  102. s(interview_age, k = ", k, ") + factor(sex) + factor(race) +
  103. factor(ethnicity) + factor(totalincome) + s(parenteducation, k = ", k, ") +
  104. s(bmi, k =", k, ") + factor(puberty_stage) + factor(sleep_category) +
  105. factor(fam_hx) + s(rel_family_id, k =", k, ") +
  106. s(dmri_meanmotion, k=", k, ")")),
  107. data = model_data)
  109. aic_score <- AIC(model)
  111. if (aic_score < best_aic) {
  112. best_aic <- aic_score
  113. best_k <- k
  114. }
  115. }
  117. final_model <- gam(as.formula(paste(y_column, "~ s(", coi_column, ", k = ", best_k, ") +
  118. s(interview_age, k = ", best_k, ") + factor(sex) + factor(race) +
  119. factor(ethnicity) + factor(totalincome) + s(parenteducation, k = ", best_k, ") +
  120. s(bmi, k =", best_k, ") + factor(puberty_stage) + factor(sleep_category) +
  121. factor(fam_hx) + s(rel_family_id, k =", best_k, ") +
  122. s(dmri_meanmotion, k=", best_k, ")")),
  123. data = model_data)
  125. return(list(model = final_model, best_k = best_k))
  126. }
  128. y_columns <- c("SLF_I_left_Ten1_FA", "SLF_I_right_Ten1_FA")
  129. plots <- list()
  131. for (y_col in y_columns) {
  132. result <- optimize_k_by_aic(data, y_col)
  133. model <- result$model
  134. best_k <- result$best_k
  136. plot <- draw(model, select = "s(coi_total_raw)", ci_col = "steelblue",ci_alpha = 0.3, rug = FALSE) +
  137. ggtitle(paste(" ")) +
  138. ylab(paste("Partial effect (", y_col, ")", sep=""))+
  139. xlab("COI Overall")+
  140. geom_line(size=1.5, col="darkblue")
  142. plots[[y_col]] <- plot
  143. }
  145. plots$SLF_I_left_Ten1_FA
  146. plots$SLF_I_right_Ten1_FA
  147. ```
  150. ```{r}
  152. # Cognition heatmap
  154. library(ggplot2)
  155. library(reshape2)
  156. library(openxlsx)
  158. combined_results = read.csv("~/Desktop/ABCD/combined_results_coi.csv")
  160. heatmap_data <- combined_results %>%
  161. select(Column, AdjR2, DevExp, FStatistic, PValue, CorrectedPValue, Type) %>%
  162. mutate(Significance = case_when(
  163. CorrectedPValue < 0.001 ~ "***",
  164. CorrectedPValue < 0.01 ~ "**",
  165. CorrectedPValue < 0.05 ~ "*",
  166. TRUE ~ ""
  167. ))
  169. column_mapping <- c(
  170. "nihtbx_totalcomp_uncorrected" = "Total Cognition Composite",
  171. "nihtbx_fluidcomp_uncorrected" = "Fluid Cognition Composite",
  172. "nihtbx_cryst_uncorrected" = "Crystallized Cognition Composite",
  173. "nihtbx_picvocab_uncorrected" = "Picture Vocabulary",
  174. "nihtbx_flanker_uncorrected" = "Flanker Inhibitory Control and Attention",
  175. "nihtbx_list_uncorrected" = "List Sorting Working Memory",
  176. "nihtbx_cardsort_uncorrected" = "Dimensional Change Card Sort",
  177. "nihtbx_pattern_uncorrected" = "Pattern Comparison Processing Speed",
  178. "nihtbx_picture_uncorrected" = "Picture Sequence Memory",
  179. "nihtbx_reading_uncorrected" = "Oral Reading Recognition"
  180. )
  182. heatmap_data$Column <- recode(heatmap_data$Column, !!!column_mapping)
  184. heatmap_data$Column <- factor(heatmap_data$Column, levels = c(
  185. "Oral Reading Recognition",
  186. "Picture Sequence Memory",
  187. "Pattern Comparison Processing Speed",
  188. "Dimensional Change Card Sort",
  189. "List Sorting Working Memory",
  190. "Flanker Inhibitory Control and Attention",
  191. "Picture Vocabulary",
  192. "Crystallized Cognition Composite",
  193. "Fluid Cognition Composite",
  194. "Total Cognition Composite"
  195. ))
  197. heatmap_matrix <- dcast(heatmap_data, Column ~ Type, value.var = "FStatistic", fill = 0)
  199. long_data <- melt(heatmap_matrix, id.vars = "Column") %>%
  200. rename(Type = variable) %>%
  201. left_join(heatmap_data %>% select(Column, Type, Significance, FStatistic),
  202. by = c("Column", "Type", "value" = "FStatistic")) %>%
  203. mutate(Type = factor(Type, levels = c("Overall", "ED", "SE", "HE")))
  205. p <- ggplot(long_data, aes(x = Type, y = Column, fill = value)) +
  206. geom_tile(color = "white") +
  207. geom_text(aes(label = paste0(Significance)), size = 14, color="white", vjust=0.8) +
  208. scale_fill_gradient(low = "#E8F5E9", high = "#1B5E20") +
  209. theme_minimal() +
  210. labs(title = " ",
  211. x = " ",
  212. y = " ",
  213. fill = "F-statistics") +
  214. theme(axis.text.x = element_text(hjust = 0.5, size = 18, face="bold"),
  215. axis.text.y=element_text(size =20, face= "bold"),
  216. legend.key.height = unit(1.2, "cm"), # Increase height
  217. legend.key.width = unit(0.7, "cm"),
  218. legend.title = element_text(size = 20), # Increase legend title size
  219. legend.text = element_text(size = 16))
  222. ggsave("~/Desktop/coi_combined_nih_heatmap.png", p, width = 12, height = 8)
  223. ```
  225. ```{r}
  227. combined_results = read.csv("~/Desktop/ABCD/combined_results_dmri.csv")
  229. heatmap_data_dmri <- combined_results_dmri %>%
  230. select(Column, AdjR2, DevExp, FStatistic, PValue, CorrectedPValue, Type) %>%
  231. mutate(Significance = case_when(
  232. CorrectedPValue < 0.001 ~ "***",
  233. CorrectedPValue < 0.01 ~ "**",
  234. CorrectedPValue < 0.05 ~ "*",
  235. TRUE ~ ""
  236. ))
  238. dmri_column_mapping <- c(
  239. "UF -R" = "UF_right_Ten1_FA",
  240. "UF -L" = "UF_left_Ten1_FA",
  241. "SLF -III -R" = "SLF_III_right_Ten1_FA",
  242. "SLF -III -L" = "SLF_III_left_Ten1_FA",
  243. "SLF -II -R" = "SLF_II_right_Ten1_FA",
  244. "SLF -II -L" = "SLF_II_left_Ten1_FA",
  245. "SLF -I -R" = "SLF_I_right_Ten1_FA",
  246. "SLF -I -L" = "SLF_I_left_Ten1_FA",
  247. "MdLF -R" = "MdLF_right_Ten1_FA",
  248. "MdLF -L" = "MdLF_left_Ten1_FA",
  249. "IOFF -R" = "IOFF_right_Ten1_FA",
  250. "IOFF -L" = "IOFF_left_Ten1_FA",
  251. "ILF -R" = "ILF_right_Ten1_FA",
  252. "ILF -L" = "ILF_left_Ten1_FA",
  253. "EmC -R" = "EmC_right_Ten1_FA",
  254. "EmC -L" = "EmC_left_Ten1_FA",
  255. "CB -R" = "CB_right_Ten1_FA",
  256. "CB -L" = "CB_left_Ten1_FA",
  257. "AF -R" = "AF_right_Ten1_FA",
  258. "AF -L" = "AF_left_Ten1_FA"
  259. )
  261. heatmap_data_dmri$Column <- recode(heatmap_data_dmri$Column, !!!setNames(names(dmri_column_mapping), dmri_column_mapping))
  263. heatmap_data_dmri$Column <- factor(heatmap_data_dmri$Column, levels = names(dmri_column_mapping))
  265. heatmap_matrix_dmri <- dcast(heatmap_data_dmri, Column ~ Type, value.var = "FStatistic", fill = 0)
  267. long_data_dmri <- melt(heatmap_matrix_dmri, id.vars = "Column") %>%
  268. rename(Type = variable) %>%
  269. left_join(heatmap_data_dmri %>% select(Column, Type, Significance, FStatistic),
  270. by = c("Column", "Type", "value" = "FStatistic")) %>%
  271. mutate(Type = factor(Type, levels = c("Overall", "ED", "SE", "HE")))
  273. p_dmri <- ggplot(long_data_dmri, aes(x = Type, y = Column, fill = value)) +
  274. geom_tile(color = "white") +
  275. geom_text(aes(label = paste0(Significance)), size = 12, color="white", vjust=0.7) +
  276. scale_fill_gradient(low = "lavenderblush1", high = "darkorchid4") +
  277. theme_minimal() +
  278. labs(title = " ",
  279. x = " ",
  280. y = " ",
  281. fill = "F-statistics") +
  282. theme(axis.text.x = element_text(vjust = 1, size = 18, face = "bold"), axis.text.y = element_text(size = 20,face = "bold"),
  283. legend.key.height = unit(1.2, "cm"),
  284. legend.key.width = unit(0.7, "cm"),
  285. legend.title = element_text(size = 20),
  286. legend.text = element_text(size = 20))
  288. ggsave("~/Desktop/dmri_heatmap.png", p_dmri, width = 8, height = 8)
  289. ```

5. Visualization.Rmd at commit 61bdb2e, under CC-BY-4.0 · at the source

Overview

Authors: Neslihan Yildiz-Ozhan1, Benson S Ku2, Leo R Zekelman3,4, Ryan Zurrin1, Fan Zhang3, Lauren J O’Donnell3, Yogesh Rathi1,3, Johanna Seitz-Holland1, Suheyla Cetin-Karayumak1
ORCID iDs: Neslihan Yildiz-Ozhan, Benson S Ku, Fan Zhang, Lauren J O’Donnell, Johanna Seitz-Holland, Suheyla Cetin-Karayumak
  1. Department of Psychiatry, Mass General Brigham, Harvard Medical School, Boston, USA
  2. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
  3. Department of Radiology, Mass General Brigham, Harvard Medical School, Boston, USA
  4. Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, Massachusetts USA
Institutions: Harvard University (United States); Mass General Brigham (United States); Emory University (United States)
Journal: Translational psychiatry, volume 16, issue 1, article 404
Dates: received 12 August 2025; accepted 22 May 2026; published online 5 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04143-x · PMID 42248835 · PMCID PMC13454431 · OpenAlex W7163684757
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Preprocessing, fMRI & imaging
Keywords: Neuroscience, Learning and memory
MeSH: Brain*, Child Development*, Cognition*, Neighborhood Characteristics*, Residence Characteristics*, White Matter*, Child, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Male, Neuroimaging, Socioeconomic Factors (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: NIDA NIH HHS (U01 DA041022, U01 DA041025, U01 DA041089, U01 DA051016, U01 DA051038, U01 DA041048, U01 DA050988, U24 DA041123, U01 DA041117, U01 DA041120, U01 DA041148, U01 DA051039, U24 DA041147, U01 DA041028, U01 DA051018, U01 DA051037, U01 DA041134, U01 DA041156, U01 DA050987, U01 DA050989, U01 DA041093, U01 DA041106, U01 DA041174); U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (K23 MH129684, K99 MH131850, R01 MH119222); NIMH NIH HHS (K23 MH129684, R01 MH119222, K99 MH131850); U.S. Department of Health &amp; Human Services | NIH | National Institute of Mental Health (K23 MH129684, K99 MH131850, R01 MH119222); Women’s Brain Initiative Pilot Award Program at Brigham and Women’s Hospital; Lawrence and Tina Rand and the Brain and Behavior Research Foundation NARSAD Young Investigator Award (PI: Dr. Suheyla Cetin-Karayumak),
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Neighborhood opportunities are key influences on child development, yet existing research has largely emphasized socioeconomic indicators as proxies for environmental context. However, it remains unclear which specific components of multidimensional neighborhood features are most strongly associated with tract-level white matter microstructure and cognition in children. Using data from 6141 children (ages 8–10 years) in the Adolescent Brain Cognitive Development (ABCD) Study and well-validated harmonized diffusion MRI, we examined multilevel associations between neighborhood opportunity, white matter microstructure, and cognitive performance. Neighborhood opportunity was measured using the Child Opportunity Index 2.0 (COI), which includes an overall score, three domain-level scores capturing Education, Social-Economic, and Health-Environment conditions, and 29 individual neighborhood indicators. Generalized additive models were used to characterize potentially non-linear associations while accounting for demographic and familial factors. Higher neighborhood opportunity was associated with greater fractional anisotropy (FA) in the Superior Longitudinal Fasciculus I (SLF-I), a fronto-parietal association tract implicated in higher-order cognitive functions. Higher neighborhood opportunity was also associated with better cognitive performance, with the strongest associations observed for Crystallized Cognition, reflecting accumulated knowledge and experience-based abilities. Mediation analyses indicated that SLF-I FA partially accounted for the association between neighborhood opportunity and cognitive performance. At the indicator level, access to high-quality education, higher neighborhood employment rates, and greater health insurance coverage emerged as the strongest correlates of neurocognitive outcomes. Together, these findings emphasize the value of multidimensional neighborhood measures and highlight specific, modifiable contextual factors that may represent targets for community-level interventions aimed at supporting neurocognitive development during childhood.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

neslihanyildizozhan/ABCD-COI

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 61bdb2e23d75417c953b5872cd330fb6925e6a6f, 22 July 2025
Languages: R (4), Jupyter (1)
Size: 7 files, 5 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 5 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: mgcv (4 files), tidyverse (4 files), ggplot2 (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), reshape2 (1 file), SciPy (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/neslihanyildizozhan/ABCD-COI.git

pnlbwh/dMRIharmonization

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d4f6854d1e3edb400b04eb96dcc6907dec15bbfd, 14 May 2025
Languages: Python (30), Shell (3), MATLAB (1)
Size: 48 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, CITATION.cff, environment (environment.yml, requirements.txt, Singularity), tests, documentation
Not found: continuous integration
Tools: NumPy (10 files), DIPY (4 files), NiBabel (4 files), SciPy (3 files), pandas (2 files), scikit-image (2 files), ANTs (1 file), Matplotlib (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
36 files
At the source: github.com/pnlbwh/dMRIharmonization

Zenodo 2584275

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (10 files), DIPY (4 files), NiBabel (4 files), SciPy (3 files), pandas (2 files), scikit-image (2 files), ANTs (1 file), Matplotlib (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
36 files
At the source:

Code availability

The data processing and statistical analysis code for this study can be found at https://github.com/neslihanyildizozhan/ABCD-COI.git. The multi-site diffusion MRI harmonization code is available at https://github.com/pnlbwh/dMRIharmonization.

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

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 73 scripts, each with its path and the digest of its content;
  • 8 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

Researchers interested in accessing the data can visit the National Institutes of Health ABCD study site (https://nda.nih.gov/abcd/). Further details on the harmonization method are available in our previous study (https://www.nature.com/articles/s41597-024-03058-w).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 keywords, 14 MeSH terms, 6 funders, 74 references.

Cite

This paper

Yildiz-Ozhan, N., Ku, B. S., Zekelman, L. R., Zurrin, R., Zhang, F., O’Donnell, L. J., Rathi, Y., Seitz-Holland, J., & Cetin-Karayumak, S. (2026). Neighborhood opportunity, white matter, and cognition in a large pediatric neuroimaging study. Translational psychiatry, 16(1), 404. https://doi.org/10.1038/s41398-026-04143-x

BibTeX

@article{yildizozhan2026neighborhood,
author = {Yildiz-Ozhan, Neslihan and Ku, Benson S and Zekelman, Leo R and Zurrin, Ryan and Zhang, Fan and O’Donnell, Lauren J and Rathi, Yogesh and Seitz-Holland, Johanna and Cetin-Karayumak, Suheyla},
title = {{Neighborhood opportunity, white matter, and cognition in a large pediatric neuroimaging study}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {404},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04143-x},
url = {https://doi.org/10.1038/s41398-026-04143-x},
pmid = {42248835},
pmcid = {PMC13454431}
}

RIS

TY - JOUR
AU - Yildiz-Ozhan, Neslihan
AU - Ku, Benson S
AU - Zekelman, Leo R
AU - Zurrin, Ryan
AU - Zhang, Fan
AU - O’Donnell, Lauren J
AU - Rathi, Yogesh
AU - Seitz-Holland, Johanna
AU - Cetin-Karayumak, Suheyla
TI - Neighborhood opportunity, white matter, and cognition in a large pediatric neuroimaging study
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/05
VL - 16
IS - 1
SP - 404
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04143-x
UR - https://doi.org/10.1038/s41398-026-04143-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41398-026-04143-x",
"type": "article-journal",
"title": "Neighborhood opportunity, white matter, and cognition in a large pediatric neuroimaging study",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Yildiz-Ozhan",
"given": "Neslihan"
},
{
"family": "Ku",
"given": "Benson S"
},
{
"family": "Zekelman",
"given": "Leo R"
},
{
"family": "Zurrin",
"given": "Ryan"
},
{
"family": "Zhang",
"given": "Fan"
},
{
"family": "O’Donnell",
"given": "Lauren J"
},
{
"family": "Rathi",
"given": "Yogesh"
},
{
"family": "Seitz-Holland",
"given": "Johanna"
},
{
"family": "Cetin-Karayumak",
"given": "Suheyla"
}
],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "404",
"DOI": "10.1038/s41398-026-04143-x",
"PMID": "42248835",
"PMCID": "PMC13454431",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04143-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
5
]
]
}
}

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