OSCR

Lower resting-state functional connectivity between frontoparietal and sensory networks is associated with recent pain intensity in a community sample of youth.

Code ↔ Paper

6 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 6 matches
  1. [1] § Methods › Resting-state fMRI ↔ ABCD_Pain_RSfMRI_Analysis/abcd_pain_rs_tables_figures_new.Rmd, lines 208–236 · score 0.88 · retrosplenial temporal, dorsal attention, ventral attention, cingulo opercular, salience, mouth
  2. [2] § Methods › Other analytic covariates ↔ ABCD_Pain_RSfMRI_Analysis/abcd_pain_rs_tables_figures_new.Rmd, lines 53–103 · score 0.62 · household income, sex assigned, birth, Answer, visit, Age
  3. [3] § Methods › Statistical analysis ↔ ABCD_Pain_RSfMRI_Analysis/abcd_pain_rs_run_models_new.Rmd, lines 117–227 · score 0.60 · lme4, intercepts, mixed, family, regression, models
  4. [4] § Results › Pain intensity ↔ ABCD_Pain_RSfMRI_Analysis/abcd_pain_rs_tables_figures_new.Rmd, lines 208–236 · score 0.58 · cingulo opercular, sensorimotor mouth, sensorimotor hand, auditory, pain
  5. [5] § Methods › Self-reported pain characteristics ↔ ABCD_Pain_RSfMRI_Analysis/abcd_pain_rs_tables_figures_new.Rmd, lines 349–378 · score 0.55 · pairwise complete, Spearman, daily, duration, body, correlation
  6. [6] § Methods › Final sample selection ↔ ABCD_Pain_RSfMRI_Analysis/1_abcd_pain_sample_setup_new.Rmd, lines 192–205 · score 0.51 · Y2 pain questionnaire, body map, reported pain, incomplete, discrepant, ABCD

Paper

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The authors' code

R Markdown · 607 lines · 24 KB · no license · 4 matches

  1. ---
  2. title: "ABCD Pain RS Tables & Figures"
  3. description: |
  4. author:
  5. - name: 'Sara Shao & Scott A. Jones'
  6. affiliation: Oregon Health & Science University
  7. date: "`r Sys.Date()`"
  8. #output:
  9. format:
  10. html:
  11. embed-resources: true
  12. page-layout: full
  13. ---
  15. ```{r setup, echo = FALSE, messsage = FALSE, warning = FALSE}
  16. # behind the scenes stuff for making a publishable markdown file
  17. knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
  19. # define packages to install
  20. packages_to_install <- c('tidyverse')
  22. # install all packages that are not already installed
  23. install.packages(setdiff(packages_to_install, rownames(installed.packages())),
  24. repos = "http://cran.us.r-project.org")
  25. ```
  27. # Setup
  29. ```{r load_packages}
  30. library(tidyverse)
  31. library(psych)
  32. library(ggcorrplot)
  33. library(patchwork)
  34. library(kableExtra)
  35. library(openxlsx)
  36. library(effects)
  37. library(lme4)
  38. library(conflicted)
  39. library(here)
  41. conflict_prefer("select", "dplyr")
  42. conflict_prefer("filter", "dplyr")
  43. conflict_prefer("count", "dplyr")
  44. ```
  46. # Data
  48. ```{r read_data}
  49. # this is the output of abcd_pain_rs_data_prep.Rmd
  50. load(here("data/abcd_pain_rs_cleaned_data.RData"))
  51. ```
  53. # Table 1 (Demographics)
  55. ```{r calculate-demographics}
  56. demographics_by_pain_status <- final_df %>%
  57. group_by(ph_y_pq_001) %>% # pain last month
  58. summarize(
  59. # Sample sizes
  60. total = n(),
  62. # Age statistics (mean ± SD)
  63. age = mean(ab_g_dyn__visit_age, na.rm = TRUE),
  64. age_sd = sd(ab_g_dyn__visit_age, na.rm = TRUE),
  66. # Sex assigned at birth (female = 2)
  67. sex = sum(ab_g_stc__cohort_sex == 2, na.rm=TRUE),
  68. sex_perc = sum(ab_g_stc__cohort_sex == 2, na.rm=TRUE)*100 / n(),
  70. # Race/Ethnicity categories
  71. white = sum(ab_g_stc__cohort_ethnrace__leg == 2, na.rm=TRUE),
  72. white_perc = sum(ab_g_stc__cohort_ethnrace__leg == 2, na.rm=TRUE)*100/n(),
  73. black = sum(ab_g_stc__cohort_ethnrace__leg == 3, na.rm=TRUE),
  74. black_perc = sum(ab_g_stc__cohort_ethnrace__leg == 3, na.rm=TRUE)*100/n(),
  75. asian = sum(ab_g_stc__cohort_ethnrace__leg == 4, na.rm=TRUE),
  76. asian_perc = sum(ab_g_stc__cohort_ethnrace__leg == 4, na.rm=TRUE)*100/n(),
  77. nat = sum(ab_g_stc__cohort_ethnrace__leg == 5, na.rm=TRUE),
  78. nat_perc = sum(ab_g_stc__cohort_ethnrace__leg == 5, na.rm=TRUE)*100/n(),
  79. pac = sum(ab_g_stc__cohort_ethnrace__leg == 6, na.rm=TRUE),
  80. pac_perc = sum(ab_g_stc__cohort_ethnrace__leg == 6, na.rm=TRUE)*100/n(),
  81. multi = sum(ab_g_stc__cohort_ethnrace__leg == 8, na.rm=TRUE),
  82. multi_perc = sum(ab_g_stc__cohort_ethnrace__leg == 8, na.rm=TRUE)*100/n(),
  83. other = sum(ab_g_stc__cohort_ethnrace__leg == 13, na.rm=TRUE),
  84. other_perc = sum(ab_g_stc__cohort_ethnrace__leg == 13, na.rm=TRUE)*100/n(),
  86. # Ethnicity
  87. hisp = sum(ab_g_stc__cohort_ethn == 1, na.rm=TRUE),
  88. hisp_perc = sum(ab_g_stc__cohort_ethn == 1, na.rm=TRUE)*100/n(),
  89. hisp = sum(ab_g_stc__cohort_ethnrace__leg == 1, na.rm=TRUE),
  90. hisp_perc = sum(ab_g_stc__cohort_ethnrace__leg == 1, na.rm=TRUE)*100/n(),
  92. # Household income (3-level categorization)
  93. less_inc = sum(income == "[<50K]", na.rm=TRUE),
  94. less_inc_perc = sum(income == "[<50K]", na.rm=TRUE)*100/n(),
  95. med_inc = sum(income == "[>=50K & <100K]", na.rm=TRUE),
  96. med_inc_perc = sum(income == "[>=50K & <100K]", na.rm=TRUE)*100/n(),
  97. more_inc = sum(income == "[>=100K]", na.rm=TRUE),
  98. more_inc_perc = sum(income == "[>=100K]", na.rm=TRUE)*100/n(),
  99. na_inc = sum(income == "DonotKnow/RefusetoAnswer", na.rm=TRUE),
  100. na_inc_perc = sum(income == "DonotKnow/RefusetoAnswer", na.rm=TRUE)*100/n()
  101. ) %>%
  102. ungroup()
  103. ```
  105. ```{r format-demo-table}
  106. demographics_table <- demographics_by_pain_status %>%
  107. mutate(ph_y_pq_001 = if_else(ph_y_pq_001 == 0, "No Pain", "Pain"),
  108. total = as.character(total),
  109. age = paste0(round(age,2), " (", round(age_sd, 2), ")"),
  110. sex = paste0(round(sex,2), " (", round(sex_perc, 1), ")"),
  111. white = paste0(white, " (", round(white_perc, 2), ")"),
  112. black = paste0(black, " (", round(black_perc, 2), ")"),
  113. asian = paste0(asian, " (", round(asian_perc, 2), ")"),
  114. nat = paste0(nat, " (", round(nat_perc, 2), ")"),
  115. pac = paste0(pac, " (", round(pac_perc, 2), ")"),
  116. multi = paste0(multi, " (", round(multi_perc, 2), ")"),
  117. other = paste0(other, " (", round(other_perc, 2), ")"),
  118. hisp = paste0(hisp, " (", round(hisp_perc, 2), ")"),
  119. less_inc = paste0(less_inc, " (", round(less_inc_perc, 2), ")"),
  120. med_inc = paste0(med_inc, " (", round(med_inc_perc, 2), ")"),
  121. more_inc = paste0(more_inc, " (", round(more_inc_perc, 2), ")"),
  122. na_inc = paste0(na_inc, " (", round(na_inc_perc, 2), ")"),
  123. race = " ", ethn = " ", inc = " ") %>%
  124. select(ph_y_pq_001, total, age, sex, race, white, black, hisp, asian, other,
  125. inc, less_inc, med_inc, more_inc, na_inc) %>%
  126. pivot_longer(cols = -ph_y_pq_001, names_to = "Characteristic", values_to = "Value") %>%
  127. pivot_wider(names_from = ph_y_pq_001, values_from = Value) %>%
  128. mutate(Characteristic = case_match(Characteristic,
  129. "total" ~ "Total participants",
  130. "age" ~ "Age",
  131. "sex" ~ "Sex assigned at birth = female (%)",
  132. "race" ~ "Race / Ethnicity (%)",
  133. "white" ~ "White",
  134. "black" ~ "Black (African American)",
  135. "asian" ~ "Asian",
  136. "nat" ~ "American Indian/Alaska Native",
  137. "pac" ~ "Native Hawaiian or Other Pacific Islander",
  138. "multi" ~ "More than One Race",
  139. "other" ~ "Other",
  140. "ethn" ~ "Ethnicity (%)",
  141. "hisp" ~ "Hispanic",
  142. "par_ed" ~ "Parental Education (%)",
  143. "less" ~ 'Less than high school diploma',
  144. "hs" ~ 'High school diploma or GED',
  145. "college" ~ 'Some college',
  146. "bach" ~ "Bachelor's degree",
  147. "post" ~ "Post-graduate degree",
  148. "inc" ~ "Household income (%)",
  149. "less_inc" ~ "Less than $50,000",
  150. "med_inc" ~ "$50,000 to $100,000",
  151. "more_inc" ~ "Greater than $100,000",
  152. "na_inc" ~ "Don't know or decline to answer",
  153. .default = Characteristic))
  154. ```
  156. ```{r}
  157. demographics_table %>%
  158. knitr::kable(format = "html", booktabs = TRUE, linesep = "") %>%
  159. row_spec(c(4,10), bold=TRUE) %>%
  160. add_indent(setdiff(1:nrow(demographics_table), c(1:4,10)))
  161. ```
  163. ```{r, eval=FALSE}
  164. # Save to Excel file
  166. # Create workbook and sheet
  167. wb <- createWorkbook()
  168. addWorksheet(wb, "Demographics")
  170. # Write data to sheet
  171. writeData(wb, "Demographics", demographics_table)
  173. # Create a bold style
  174. bold_style <- createStyle(textDecoration = "bold")
  176. # Bold specific rows
  177. bold_rows <- c(1, 5, 11)
  178. for (row in bold_rows) {
  179. addStyle(wb, sheet = "Demographics", style = bold_style,
  180. rows = row, cols = 1:ncol(demographics_table), gridExpand = TRUE)
  181. }
  183. # Save the file
  184. saveWorkbook(wb, file = "clean_tables_figures/Table1_demographics.xlsx", overwrite = TRUE)
  185. ```
  187. # Table S1-S6 (Model Results)
  189. ```{r}
  190. pain_last_month <- read_csv("output/last_month.csv")
  191. painscale <- read_csv("output/average_pain.csv")
  192. pain_scale_worst <- read_csv("output/worst_pain.csv")
  193. pain_limit <- read_csv("output/limitations.csv")
  194. pain_how_long <- read_csv("output/how_long.csv")
  195. bm_count <- read_csv("output/bm_count.csv")
  196. datadict <- read_csv("/home/exacloud/gscratch/NagelLab/abcd/abcd-data-dictionary-6.0.csv")
  197. ```
  199. ```{r}
  200. # Get matching table for full ROI names from ABCD data dictionary
  201. roi_dict <- datadict %>%
  202. filter(name %in% paste0('mr_y_rsfmri__corr__gpnet__', pain_last_month$Region, '_mean')) %>%
  203. select(name, label) %>%
  204. mutate(label = str_remove(label, 'Average correlation between Gordon networks: '),
  205. name = str_remove_all(name, 'mr_y_rsfmri__corr__gpnet__|_mean'))
  206. ```
  208. ```{r}
  209. # Define the order that the ROIs are to be listed in
  210. priority <- c(
  211. "sensorimotor hand", "sensorimotor mouth", "auditory", "visual",
  212. "fronto-parietal", "cingulo-opercular", "cingulo-parietal", "default",
  213. "dorsal attention", "retrosplenial temporal", "salience", "ventral attention"
  214. )
  216. # Helper function to reorder words based on priority
  217. reorder_words <- function(string, priority) {
  218. string <- str_trim(string)
  219. words <- str_split(string, " & ")[[1]] # Split into individual words
  220. sorted_words <- words[order(match(words, priority))] # Sort by priority
  221. paste(sorted_words, collapse = " & ") # Rejoin into a single string
  222. }
  224. # Function to rename ROIs and re-sort them based on priority
  225. clean_rois <- function(table, priority) {
  226. clean_table <- roi_dict %>%
  227. left_join(table, c('name' = 'Region')) %>%
  228. select(-name) %>%
  229. mutate(label = sapply(label, reorder_words, priority = priority)) %>%
  230. separate(label, into = c('roi1', 'roi2'), sep = ' & ') %>%
  231. mutate(order1 = match(roi1, priority), order2 = match(roi2, priority)) %>%
  232. arrange(order1, order2) %>%
  233. select(-order1, -order2)
  234. return(clean_table)
  235. }
  236. ```
  238. ```{r}
  239. # clean all tables except pain_how_long
  240. pain_last_month_clean <- clean_rois(pain_last_month, priority = priority)
  241. painscale_clean <- clean_rois(painscale, priority = priority)
  242. pain_scale_worst_clean <- clean_rois(pain_scale_worst, priority = priority)
  243. pain_limit_clean <- clean_rois(pain_limit, priority = priority)
  244. bm_count_clean <- clean_rois(bm_count, priority = priority)
  246. new_names <- c('Network 1', 'Network 2', 'Beta estimate', 'Standard error', 'T-statistic', 'P-value', 'Adj. p-value', 'Site variance', 'Family variance', 'Residual variance')
  247. colnames(pain_last_month_clean) <- new_names
  248. colnames(painscale_clean) <- new_names
  249. colnames(pain_scale_worst_clean) <- new_names
  250. colnames(pain_limit_clean) <- new_names
  251. colnames(bm_count_clean) <- new_names
  252. ```
  254. ```{r}
  255. # clean pain_how_long
  256. pain_how_long_clean <- clean_rois(pain_how_long, priority = priority)
  257. colnames(pain_how_long_clean) <- c('Network 1', 'Network 2', 'Term', 'F-statistic', 'P-value', 'Adj. p-value', 'Beta estimate', 'Standard error', 'T-statistic', 'T-test p-value', 'Site variance', 'Family variance', 'Residual variance')
  259. pain_how_long_clean <- pain_how_long_clean %>%
  260. # clean level names
  261. mutate(Term = case_when(Term == 'ph_y_pq_001__042' ~ 'few hours',
  262. Term == 'ph_y_pq_001__043' ~ 'half day',
  263. Term == 'ph_y_pq_001__044' ~ 'all day')) %>%
  264. # pivot wider
  265. pivot_wider(names_from = Term,
  266. values_from = c(`Beta estimate`, `Standard error`, `T-statistic`, `T-test p-value`),
  267. names_sep = ": ", names_vary = "slowest")
  268. ```
  270. ```{r, eval = FALSE}
  271. pain_last_month_clean
  272. painscale_clean
  273. pain_scale_worst_clean
  274. pain_limit_clean
  275. pain_how_long_clean
  276. bm_count_clean
  277. ```
  279. ```{r}
  280. write_csv(pain_last_month_clean, "clean_tables_figures/TableS1_pain_last_month.csv")
  281. write_csv(painscale_clean, "clean_tables_figures/TableS2_painscale.csv")
  282. write_csv(pain_scale_worst_clean, "clean_tables_figures/TableS3_pain_scale_worst.csv")
  283. write_csv(pain_limit_clean, "clean_tables_figures/TableS4_pain_limit.csv")
  284. write_csv(pain_how_long_clean, "clean_tables_figures/TableS5_pain_how_long.csv")
  285. write_csv(bm_count_clean, "clean_tables_figures/TableS6_bm_count.csv")
  286. ```
  288. # Table S7-S12 (Sex Interaction Model Results)
  290. ```{r}
  291. pain_last_month_sx <- read_csv("output/last_month_sx.csv")
  292. painscale_sx <- read_csv("output/average_pain_sx.csv")
  293. pain_scale_worst_sx <- read_csv("output/worst_pain_sx.csv")
  294. pain_limit_sx <- read_csv("output/limitations_sx.csv")
  295. pain_how_long_sx <- read_csv("output/how_long_sx.csv")
  296. bm_count_sx <- read_csv("output/bm_count_sx.csv")
  297. ```
  299. ```{r}
  300. # clean all tables except pain_how_long
  301. pain_last_month_sx_clean <- clean_rois(pain_last_month_sx, priority = priority)
  302. painscale_sx_clean <- clean_rois(painscale_sx, priority = priority)
  303. pain_scale_worst_sx_clean <- clean_rois(pain_scale_worst_sx, priority = priority)
  304. pain_limit_sx_clean <- clean_rois(pain_limit_sx, priority = priority)
  305. bm_count_sx_clean <- clean_rois(bm_count_sx, priority = priority)
  307. new_names <- c('Network 1', 'Network 2', 'Beta estimate', 'Standard error', 'T-statistic', 'P-value', 'Adj. p-value', 'Site variance', 'Family variance', 'Residual variance')
  308. colnames(pain_last_month_sx_clean) <- new_names
  309. colnames(painscale_sx_clean) <- new_names
  310. colnames(pain_scale_worst_sx_clean) <- new_names
  311. colnames(pain_limit_sx_clean) <- new_names
  312. colnames(bm_count_sx_clean) <- new_names
  313. ```
  315. ```{r}
  316. # clean pain_how_long
  317. pain_how_long_sx_clean <- clean_rois(pain_how_long_sx, priority = priority)
  318. colnames(pain_how_long_sx_clean) <- c('Network 1', 'Network 2', 'Term', 'F-statistic', 'P-value', 'Adj. p-value', 'Beta estimate', 'Standard error', 'T-statistic', 'T-test p-value', 'Site variance', 'Family variance', 'Residual variance')
  320. pain_how_long_sx_clean <- pain_how_long_sx_clean %>%
  321. # clean level names
  322. mutate(Term = case_when(grepl('ph_y_pq_001__042', Term) ~ 'few hours',
  323. grepl('ph_y_pq_001__043', Term) ~ 'half day',
  324. grepl('ph_y_pq_001__044', Term) ~ 'all day')) %>%
  325. # pivot wider
  326. pivot_wider(names_from = Term,
  327. values_from = c(`Beta estimate`, `Standard error`, `T-statistic`, `T-test p-value`),
  328. names_sep = ": ", names_vary = "slowest")
  329. ```
  331. ```{r, eval = FALSE}
  332. pain_last_month_sx_clean
  333. painscale_sx_clean
  334. pain_scale_worst_sx_clean
  335. pain_limit_sx_clean
  336. pain_how_long_sx_clean
  337. bm_count_sx_clean
  338. ```
  340. ```{r}
  341. write_csv(pain_last_month_sx_clean, "clean_tables_figures/TableS7_pain_last_month_sx.csv")
  342. write_csv(painscale_sx_clean, "clean_tables_figures/TableS8_painscale_sx.csv")
  343. write_csv(pain_scale_worst_sx_clean, "clean_tables_figures/TableS9_pain_scale_worst_sx.csv")
  344. write_csv(pain_limit_sx_clean, "clean_tables_figures/TableS10_pain_limit_sx.csv")
  345. write_csv(pain_how_long_sx_clean, "clean_tables_figures/TableS11_pain_how_long_sx.csv")
  346. write_csv(bm_count_sx_clean, "clean_tables_figures/TableS12_bm_count_sx.csv")
  347. ```
  349. # Figure S13 (Pain Correlation Plot)
  351. ```{r}
  352. library(dplyr)
  353. library(corrplot)
  355. vars_of_interest <- final_df %>%
  356. select(
  357. `Average pain intensity` = ph_y_pq_001__02,
  358. `Worst pain intensity` = ph_y_pq_001__03,
  359. `Pain Limitations` = ph_y_pq_001__05,
  360. `Number of body sites` = bm_count,
  361. `Daily pain duration` = ph_y_pq_001__04
  362. )
  364. cor_matrix <- cor(vars_of_interest,
  365. use = "pairwise.complete.obs",
  366. method = "spearman")
  368. # Save plot
  369. pdf("clean_tables_figures/FigureS13_correlation_plot.pdf", width = 7, height = 10)
  371. corrplot(cor_matrix,
  372. method = "circle",
  373. type = "upper",
  374. tl.col = "black",
  375. addCoef.col = "black",
  376. col = colorRampPalette(c("blue", "white", "red"))(200))
  377. dev.off()
  378. ```
  380. # Figure 1 (Sig. Connectivity Matrices)
  382. ```{r}
  383. # note: orientation (TL or BL) indicates how it will look in the plot, not in the table
  384. # `pmat = TRUE` will turn values into indicators of significance (default alpha = 0.05)
  385. results_mat <- function(table, column, pmat = FALSE, pthresh = 0.05, orientation = 'TL') {
  386. mat <- table %>%
  387. select(all_of(c('Network 1', 'Network 2')), !!sym(column)) %>%
  388. pivot_wider(names_from = 'Network 2', values_from = column) %>%
  389. column_to_rownames('Network 1')
  391. if (orientation == 'BL') { # if bottom-left
  392. mat <- mat %>%
  393. select(all_of(rev(names(.))))
  394. }
  396. if (pmat == TRUE) {
  397. mat <- mat %>%
  398. mutate(across(everything(), ~if_else(. < pthresh, 1, 0)))
  399. }
  401. return(mat)
  402. }
  403. ```
  405. ### Average Pain
  407. ```{r, warning=FALSE}
  408. beta_mat <- results_mat(painscale_clean, 'Beta estimate', orientation = 'BL')
  409. sig_mat <- results_mat(painscale_clean, 'Adj. p-value', pmat = TRUE, orientation = 'BL', pthresh = 0.01)
  410. ```
  412. ```{r}
  413. avg_pain_plot <- ggcorrplot(beta_mat,
  414. p.mat = as.matrix(sig_mat),
  415. ggtheme = theme_classic,
  416. #title = "",
  417. outline.color = 'black', # box outline
  418. pch = "*", # symbol for significance
  419. pch.cex = 5,
  420. tl.cex = 10, # size of axis labels
  421. digits = 5) +
  422. theme(
  423. #plot.title = element_text(size = 14, hjust = 0.5),
  424. #legend.title = element_text(size = 12)
  425. ) +
  426. scale_fill_gradient2(lim = c(-0.1,0.1), low = '#2f4c9e', mid = '#e2e2e2', high = '#bb1b2c') +
  427. labs(title = "Average Pain Intensity", fill = expression(beta))
  428. ```
  430. ### Worst Pain
  432. ```{r, warning=FALSE}
  433. beta_mat <- results_mat(pain_scale_worst_clean, 'Beta estimate', orientation = 'BL')
  434. sig_mat <- results_mat(pain_scale_worst_clean, 'Adj. p-value', pmat = TRUE, orientation = 'BL', pthresh = 0.01)
  435. ```
  437. ```{r}
  438. worst_pain_plot <- ggcorrplot(beta_mat,
  439. p.mat = as.matrix(sig_mat),
  440. ggtheme = theme_classic,
  441. #title = "",
  442. outline.color = 'black', # box outline
  443. pch = "*", # symbol for significance
  444. pch.cex = 5,
  445. tl.cex = 10, # size of axis labels
  446. digits = 5) +
  447. theme(
  448. #plot.title = element_text(size = 14, hjust = 0.5),
  449. #legend.title = element_text(size = 12)
  450. ) +
  451. scale_fill_gradient2(lim = c(-0.1,0.1), low = '#2f4c9e', mid = '#e2e2e2', high = '#bb1b2c') +
  452. labs(title = "Worst Pain Intensity", fill = expression(beta))
  453. ```
  455. ### Combine Plots
  457. ```{r, fig.width = 9, fig.height = 4}
  458. avg_pain_plot + worst_pain_plot +
  459. plot_annotation(tag_levels = "A", tag_suffix = ")")
  460. ```
  462. ```{r}
  463. ggsave("clean_tables_figures/Figure1.png", width = 14, height = 4, units = "in", dpi = 350)
  464. ```
  466. # Regression Figures
  468. ```{r}
  469. painscale %>%
  470. filter(`Corrected p-value` < 0.01)
  471. pain_scale_worst %>%
  472. filter(`Corrected p-value` < 0.01)
  473. ```
  475. ### Average Pain
  477. ```{r}
  478. # aud__frp
  479. mod1 <- lmer(mr_y_rsfmri__corr__gpnet__aud__frp_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__02 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  480. effects <- effect(term = "ph_y_pq_001__02", mod = mod1)
  481. effects_df <- as.data.frame(effects)
  483. p1 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__02, y = fit)) +
  484. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__02, y = mr_y_rsfmri__corr__gpnet__aud__frp_mean),
  485. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  486. geom_line(color = 'blue', size = 1.1) +
  487. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  488. theme_classic() +
  489. labs(x = "Average pain intensity", y = "Auditory to fronto-parietal")
  491. p1
  492. ```
  494. ```{r}
  495. # aud__vis
  496. mod2 <- lmer(mr_y_rsfmri__corr__gpnet__aud__vis_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__02 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  497. effects <- effect(term = "ph_y_pq_001__02", mod = mod2)
  498. effects_df <- as.data.frame(effects)
  500. p2 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__02, y = fit)) +
  501. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__02, y = mr_y_rsfmri__corr__gpnet__aud__vis_mean),
  502. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  503. geom_line(color = 'blue', size = 1.1) +
  504. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  505. theme_classic() +
  506. labs(x = "Average pain intensity", y = "Auditory to visual")
  508. p2
  509. ```
  511. ```{r}
  512. # cip__frp
  513. mod3 <- lmer(mr_y_rsfmri__corr__gpnet__cip__frp_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__02 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  514. effects <- effect(term = "ph_y_pq_001__02", mod = mod3)
  515. effects_df <- as.data.frame(effects)
  517. p3 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__02, y = fit)) +
  518. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__02, y = mr_y_rsfmri__corr__gpnet__cip__frp_mean),
  519. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  520. geom_line(color = 'blue', size = 1.1) +
  521. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  522. theme_classic() +
  523. labs(x = "Average pain intensity", y = "Fronto-parietal to cingulo-parietal")
  525. p3
  526. ```
  528. ```{r}
  529. # frp__smh
  530. mod4 <- lmer(mr_y_rsfmri__corr__gpnet__frp__smh_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__02 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  531. effects <- effect(term = "ph_y_pq_001__02", mod = mod4)
  532. effects_df <- as.data.frame(effects)
  534. p4 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__02, y = fit)) +
  535. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__02, y = mr_y_rsfmri__corr__gpnet__frp__smh_mean),
  536. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  537. geom_line(color = 'blue', size = 1.1) +
  538. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  539. theme_classic() +
  540. labs(x = "Average pain intensity", y = "Sensorimotor hand to fronto-parietal")
  542. p4
  543. ```
  545. ```{r}
  546. # frp__smm
  547. mod5 <- lmer(mr_y_rsfmri__corr__gpnet__frp__smm_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__02 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  548. effects <- effect(term = "ph_y_pq_001__02", mod = mod5)
  549. effects_df <- as.data.frame(effects)
  551. p5 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__02, y = fit)) +
  552. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__02, y = mr_y_rsfmri__corr__gpnet__frp__smm_mean),
  553. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  554. geom_line(color = 'blue', size = 1.1) +
  555. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  556. theme_classic() +
  557. labs(x = "Average pain intensity", y = "Sensorimotor mouth to fronto-parietal")
  559. p5
  560. ```
  562. ### Worst Pain
  564. ```{r}
  565. # aud__frp
  566. mod6 <- lmer(mr_y_rsfmri__corr__gpnet__aud__frp_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__03 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  567. effects <- effect(term = "ph_y_pq_001__03", mod = mod6)
  568. effects_df <- as.data.frame(effects)
  570. p6 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__03, y = fit)) +
  571. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__03, y = mr_y_rsfmri__corr__gpnet__aud__frp_mean),
  572. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  573. geom_line(color = 'blue', size = 1.1) +
  574. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  575. theme_classic() +
  576. labs(x = "Worst pain intensity", y = "Auditory to fronto-parietal")
  578. p6
  579. ```
  581. ```{r}
  582. # aud__vis
  583. mod7 <- lmer(mr_y_rsfmri__corr__gpnet__aud__vis_mean ~ ab_g_dyn__visit_age + ab_g_stc__cohort_sex + mr_y_qc__mot__rsfmri__mot_mean + income + ph_y_pq_001__03 + (1 | ab_g_dyn__design_site) + (1|ab_g_stc__design_id__fam), data = final_df)
  584. effects <- effect(term = "ph_y_pq_001__03", mod = mod7)
  585. effects_df <- as.data.frame(effects)
  587. p7 <- ggplot(data = effects_df, mapping = aes(x = ph_y_pq_001__03, y = fit)) +
  588. geom_point(data = final_df, mapping = aes(x = ph_y_pq_001__03, y = mr_y_rsfmri__corr__gpnet__aud__vis_mean),
  589. alpha = 0.3, color = 'darkgrey', size = 0.8) +
  590. geom_line(color = 'blue', size = 1.1) +
  591. geom_ribbon(aes(ymin = lower, ymax = upper), data = effects_df, alpha = 0.3, fill = 'blue') +
  592. theme_classic() +
  593. labs(x = "Worst pain intensity", y = "Auditory to visual")
  595. p7
  596. ```
  598. ### Combine Plots
  600. ```{r, fig.height = 10, fig.width = 7}
  601. (p1 + p2) / (p3 + p4) / (p5 + p6) / (p7 + plot_spacer()) +
  602. plot_annotation(tag_levels = "A", tag_suffix = ")")
  603. ```
  605. ```{r, eval=FALSE}
  606. ggsave("clean_tables_figures/Figure2_reg_plots.png", width = 7, height = 10, dpi = 350)
  607. ```

abcd_pain_rs_tables_figures_new.Rmd, no license · at the source

Overview

Authors: Carmen I Bango1,2, Scott A Jones1,3, Angelica M Morales1,3, Sara Shao1,3, Dani Y Del Rubin1,3, Arturo Lopez Flores1, Amy L Holley2, Bonnie J Nagel1,3,4, Anna C Wilson2
ORCID iDs: Carmen I Bango, Scott A Jones, Angelica M Morales, Sara Shao, Dani Y Del Rubin, Anna C Wilson
  1. Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
  2. Department of Pediatrics, Institute on Development and Disability, Oregon Health & Science University, Portland, OR, USA
  3. Steven J. Sharp Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, USA
  4. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
Institutions: Oregon Health & Science University (United States)
Journal: Neuroimage. Reports, volume 6, issue 3, article 100366
Dates: received 16 January 2026; accepted 5 June 2026; published online 12 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.ynirp.2026.100366 · PMID 42327571 · PMCID PMC13279023 · OpenAlex W7164546387
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), pain (population)
Methods: Spectral & time-frequency, Statistics, Connectivity, fMRI & imaging
Keywords: ABCD study, Resting-state functional connectivity, Adolescent pain, Pain intensity, Functional magnetic resonance imaging, Brain networks
Topic: Pain Mechanisms and Treatments (Physiology, Medicine), according to OpenAlex
Funding: National Institutes of Health (S10OD016356, S10OD034224, S10OD021701); Oregon Health and Science University; National Institute of Child Health and Human Development (R21 HD112210); Office of Research Infrastructure Programs
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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OSF w482t

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (4)
Size: 4 files, 4 scripts
Software Heritage: not checked
Found in: the text, “Statistical analysis”
Holds: 4 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), data.table (3 files), lme4 (2 files), broom (1 file), lmerTest (1 file), patchwork (1 file), psych (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/w482t/overview?view_only=0c361d0ecd174976948f51c710469ab9

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 6 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

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Code and data availability statement

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Read it in the paper: doi.org/10.1016/j.ynirp.2026.100366.

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Version 2, 28 September 2026

  • Authors: added Carmen I Bango (0009-0006-8866-0725); Scott A Jones (0000-0003-1051-4200); Sara Shao (0009-0005-4403-6296); Dani Y Del Rubin (0000-0002-6292-7392); Anna C Wilson (0000-0001-8672-068X); removed Carmen I Bango; Scott A Jones; Sara Shao; Dani Y Del Rubin; Anna C Wilson

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 4 funders, 96 references.

Cite

This paper

Bango, C. I., Jones, S. A., Morales, A. M., Shao, S., Del Rubin, D. Y., Lopez Flores, A., Holley, A. L., Nagel, B. J., & Wilson, A. C. (2026). Lower resting-state functional connectivity between frontoparietal and sensory networks is associated with recent pain intensity in a community sample of youth. Neuroimage. Reports, 6(3), 100366. https://doi.org/10.1016/j.ynirp.2026.100366

BibTeX

@article{bango2026lower,
author = {Bango, Carmen I and Jones, Scott A and Morales, Angelica M and Shao, Sara and Del Rubin, Dani Y and Lopez Flores, Arturo and Holley, Amy L and Nagel, Bonnie J and Wilson, Anna C},
title = {{Lower resting-state functional connectivity between frontoparietal and sensory networks is associated with recent pain intensity in a community sample of youth}},
journal = {Neuroimage. Reports},
year = {2026},
month = jun,
volume = {6},
number = {3},
pages = {100366},
publisher = {Elsevier},
issn = {2666-9560},
doi = {10.1016/j.ynirp.2026.100366},
url = {https://doi.org/10.1016/j.ynirp.2026.100366},
pmid = {42327571},
pmcid = {PMC13279023}
}

RIS

TY - JOUR
AU - Bango, Carmen I
AU - Jones, Scott A
AU - Morales, Angelica M
AU - Shao, Sara
AU - Del Rubin, Dani Y
AU - Lopez Flores, Arturo
AU - Holley, Amy L
AU - Nagel, Bonnie J
AU - Wilson, Anna C
TI - Lower resting-state functional connectivity between frontoparietal and sensory networks is associated with recent pain intensity in a community sample of youth
T2 - Neuroimage. Reports
J2 - Neuroimage Rep
PY - 2026
DA - 2026/06/12
VL - 6
IS - 3
SP - 100366
SN - 2666-9560
PB - Elsevier
DO - 10.1016/j.ynirp.2026.100366
UR - https://doi.org/10.1016/j.ynirp.2026.100366
LA - en
ER -

CSL-JSON

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