OSCR

Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex.

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] § Results › Patient demographics ↔ BOLDDiff_clinicaldata.R, lines 1–60 · score 0.92 · neonatal asphyxia, birth weight, birth height, head circumference, dMRI, fMRI
  2. [2] § Methods › Neonatal and demographic data statistical analysis ↔ BOLDDiff_clinicaldata.R, lines 1–60 · score 0.89 · birth weight, birth height, categorical variables, continuous variables, head circumference, SES
  3. [3] § Methods › MRI data statistical analysis ↔ BOLDDiff_Lasso_analysis.R, lines 62–188 · score 0.86 · pure LASSO, zero coefficients, glmnet, RMSE, elastic, selection
  4. [4] § Methods › Gene expression analysis ↔ Genetic_analysisANDgraphs.R, lines 91–133 · score 0.85 · mid fetal, early fetal, 8–13, 16–22, 35–37, late fetal
  5. [5] § Results › Genetic expression patterns during early brain development ↔ Genetic_analysisANDgraphs.R, lines 91–133 · score 0.79 · mid fetal, early fetal, 8–13, 16–22, 35–37, late fetal
  6. [6] § Results › Relationship between longitudinal cortical microstructural changes and BOLD variability delta changes ↔ BOLDDiff_Lasso_analysis.R, lines 230–273 · score 0.69 · pure LASSO, extraMD, elastic, diff, predicted, se
  7. [7] § Methods › Gene expression analysis ↔ Genetic_analysisANDgraphs.R, lines 1–39 · score 0.62 · A1C, DLPFC, IPC, M1, MFC, VLPFC
  8. [8] § Methods › Gene expression data analysis ↔ Genetic_analysisANDgraphs.R, lines 403–443 · score 0.62 · post hoc, HSD, Tukey, ANOVA, pairwise, interaction
  9. [9] § Methods › MRI data statistical analysis ↔ BOLDDiff_clustering_analysis.R, lines 54–98 · score 0.60 · ConsensusClusterPlus, Euclidean, elbow, distance, clustered, optimal
  10. [10] § Results › Longitudinal cortical BOLD variability and diffusion microstructural changes during early preterm brain development ↔ BOLDDiff_Longitudinal_graphs.R, lines 261–320 · score 0.58 · delta changes, BOLD variability longitudinal, asterisks, AUD, THAL, VIS
  11. [11] § Results › Longitudinal cortical BOLD variability and diffusion microstructural changes during early preterm brain development ↔ BOLDDiff_Longitudinal_analysis.R, lines 741–811 · score 0.57 · extraTrans, extraMD, diff, DTI, DKI, MK

Paper

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The authors' code

R · 901 lines · 32 KB · CC-BY-4.0 · 4 matches

  1. library(dplyr)
  2. library(factoextra)
  3. library(tidyr)
  4. library(cluster)
  5. library(ggrepel)
  6. library(ggplot2)
  7. library(tidyverse)
  8. library(nlme)
  9. library(mgcv)
  10. require(lme4)
  11. library(purrr)
  12. require("emmeans")
  13. library(broom.mixed)
  14. library(lmerTest)
  15. library(easystats)
  17. setwd("")
  19. data <- read.csv("PsychENCODE-W5-bulk-RPKM-data-scRNA-filtered-log2_revised.csv", header = TRUE)
  22. # Remove ITC and add PC to data
  23. data <- data %>%
  24. filter(region != 'ITC') %>% # Correct filtering
  25. mutate(PC = case_when(
  26. region %in% c('DLPFC', 'VLPFC') ~ 'PC2', # PFC
  27. region == 'OFC' ~ 'PC2', # Paralimbic belt
  28. region == 'MFC' ~ 'PC2', # Limbic
  29. region %in% c('M1', 'S1') ~ 'PC1', # SSM
  30. region == 'V1' ~ 'PC1', # Visual
  31. region %in% c('A1C', 'STC') ~ 'PC1', # Auditory
  32. region == 'IPC' ~ 'PC1' # Precuneus
  33. ))
  37. # Add data in Gestational weeks
  38. data <- data %>%
  39. mutate(age_weeks = round(age/7, 0))
  41. # Create sample_counts
  42. sample_counts <- data %>%
  43. group_by(age_weeks) %>%
  44. dplyr::summarize(observations = n_distinct(sample), .groups = 'drop')
  46. ggplot(sample_counts, aes(x = age_weeks, y = observations)) +
  47. geom_bar(stat = "identity", fill = "skyblue") + # Bar plot for number of observations
  48. scale_x_continuous(breaks = sample_counts$age_weeks, labels = sample_counts$age_weeks) + # Set x-axis breaks and labels
  49. theme_minimal() +
  50. labs(title = "Number of Observations per Age",
  51. x = "Age (weeks)",
  52. y = "Number of Observations") +
  53. theme(legend.position = "none") # No legend needed
  56. data <- data %>%
  57. filter(!age_weeks %in% 55)
  59. mean_age_weeks <- mean(data$age_weeks)
  60. sd_age_weeks <- sd(data$age_weeks)
  61. num_males <- nrow(data[data$sex == "M", ])
  62. total_subjects <- nrow(data)
  63. percentage_males <- (num_males / total_subjects) * 100
  64. mean_RIN <- mean(data$RIN)
  65. sd_RIN <- sd(data$RIN)
  66. mean_PMI <- mean(data$PMI)
  67. sd_PMI <- sd(data$PMI)
  69. region_count <- data %>%
  70. filter(symbol == "A2M") %>%
  71. group_by(sample) %>%
  72. summarise(region_count = n_distinct(region))
  74. all_region_count <- region_count %>%
  75. summarise(total = sum(region_count))
  77. write.csv(region_count, "region_count_per_sample.csv", row.names = FALSE)
  79. # Normality tests
  80. normality_results <- data %>%
  81. group_by(symbol, PC, Time) %>%
  82. summarise(p_value = map_dbl(list(log2_rpkm), ~ shapiro.test(.)$p.value), .groups = 'drop')
  84. non_normal_regions <- normality_results %>%
  85. filter(p_value < 0.05)
  87. write.csv(non_normal_regions, "non_normal_regions.csv",row.names = FALSE)
  91. ############### Fit model per gene ####################
  92. # OPTION 1 (T0 = mid fetal: 16-22; T1 = late fetal = 35-37 )
  93. data <- data %>%
  94. filter(!age_weeks %in% c(8, 9, 12, 13, 55)) %>% # Correct filtering
  95. mutate(Time = case_when(
  96. age_weeks %in% c('16', '17', '19', '21', '22') ~ 'T0',
  97. age_weeks %in% c('35', '37') ~ 'T1',
  98. ))
  100. # OPTION 2 (T0 = early fetal: 8-13; T1 = mid fetal = 16-22 )
  101. data <- data %>%
  102. filter(!age_weeks %in% c(35, 37, 55)) %>% # Correct filtering
  103. mutate(Time = case_when(
  104. age_weeks %in% c('8', '9', '12', '13') ~ 'T0',
  105. age_weeks %in% c('16', '17', '19', '21', '22' ) ~ 'T1',
  106. ))
  109. # OPTION 4 (T0 = early fetal: 8-13; T1 = mid fetal: 16-22; T2 = late fetal = 35-37 )
  110. data <- data %>%
  111. filter(!age_weeks %in% 55) %>% # Correct filtering
  112. mutate(Time = case_when(
  113. age_weeks %in% c('8', '9', '12', '13') ~ 'T0',
  114. age_weeks %in% c('16', '17', '19', '21', '22') ~ 'T1',
  115. age_weeks %in% c('35', '37') ~ 'T2',
  116. ))
  120. # Make data factorial
  121. data <- within(data, {
  122. PC <- factor(PC)
  123. Time <- factor(Time)
  124. sample <- factor(sample)
  125. symbol <- factor(symbol)
  126. region <- factor(region)
  127. age <- as.numeric(age)
  128. sex <- factor(sex)
  129. })
  131. genes <- unique(data$symbol)
  132. genes_df <- data.frame(gene = genes)
  133. #write.csv(genes_df, "genes.csv", row.names = FALSE)
  136. ######## MODEL LMER - Loop through each gene
  137. model_results <- list()
  139. for (gene in genes) {
  140. # Filter data for the current gene
  141. gene_data <- filter(data, symbol == gene)
  143. # Fit the model
  144. model <- tryCatch({
  145. lmer(log2_rpkm ~ Time * PC + RIN + sex + (1 | sample) + (1 | region),
  146. data = gene_data)
  147. }, error = function(e) {
  148. message("Error fitting model for gene: ", gene)
  149. return(NA)
  150. })
  152. # Store the results
  153. model_results[[gene]] <- model
  154. }
  156. #### Initialize a list to store all p-values
  157. all_pvalues_TimeT1 <- list()
  158. all_pvalues_PCPC2 <- list()
  159. all_pvalues_TimeT1PCPC2 <- list()
  161. for (gene in names(model_results)) {
  162. model <- model_results[[gene]]
  163. if (!is.na(model)) {
  164. # Check if the model is fitted
  165. model_summary <- summary(model)
  166. coefficients <- model_summary$coefficients
  167. # Extract p-values and estimates for TimeT1, PCPC2, and their interaction
  168. if ("TimeT1" %in% rownames(coefficients)) {
  169. estimate <- coefficients["TimeT1", "Estimate"]
  170. p_value <- coefficients["TimeT1", "Pr(>|t|)"]
  171. all_pvalues_TimeT1[[gene]] <- list(estimate = estimate, p_value = p_value)
  172. }
  173. if ("PCPC2" %in% rownames(coefficients)) {
  174. estimate <- coefficients["PCPC2", "Estimate"]
  175. p_value <- coefficients["PCPC2", "Pr(>|t|)"]
  176. all_pvalues_PCPC2[[gene]] <- list(estimate = estimate, p_value = p_value)
  177. }
  178. if ("TimeT1:PCPC2" %in% rownames(coefficients)) {
  179. estimate <- coefficients["TimeT1:PCPC2", "Estimate"]
  180. p_value <- coefficients["TimeT1:PCPC2", "Pr(>|t|)"]
  181. all_pvalues_TimeT1PCPC2[[gene]] <- list(estimate = estimate, p_value = p_value)
  182. }
  183. }
  184. }
  186. ###### Convert lists to data frames
  187. create_df <- function(pvalue_list, effect_name) {
  188. df <- tibble(
  189. gene = names(pvalue_list),
  190. estimate = sapply(pvalue_list, function(x) x$estimate),
  191. p_value = sapply(pvalue_list, function(x) x$p_value)
  192. ) %>%
  193. mutate(
  194. adjusted_p_value_fdr = p.adjust(p_value, method = "fdr"),
  195. effect = effect_name
  196. )
  197. return(df)
  198. }
  200. TimeT1_df <- create_df(all_pvalues_TimeT1, "TimeT1")
  201. PCPC2_df <- create_df(all_pvalues_PCPC2, "PCPC2")
  202. TimeT1PCPC2_df <- create_df(all_pvalues_TimeT1PCPC2, "TimeT1PCPC2")
  204. # Function to rename columns
  205. rename_columns <- function(df, prefix) {
  206. df %>%
  207. rename_with(~paste0(prefix, "_", .), -gene)
  208. }
  210. # Rename columns in each dataframe
  211. TimeT1_df <- rename_columns(TimeT1_df, "TimeT1")
  212. TimeT1_df <- TimeT1_df %>%
  213. select(-TimeT1_effect)
  214. PCPC2_df <- rename_columns(PCPC2_df, "PCPC2")
  215. PCPC2_df <- PCPC2_df %>%
  216. select(-PCPC2_effect)
  218. TimeT1PCPC2_df <- rename_columns(TimeT1PCPC2_df, "TimeT1_PCPC2")
  219. TimeT1PCPC2_df <- TimeT1PCPC2_df %>%
  220. select(-TimeT1_PCPC2_effect)
  223. # Join the dataframes
  224. all_effects_wide_df <- TimeT1_df %>%
  225. full_join(PCPC2_df, by = "gene") %>%
  226. full_join(TimeT1PCPC2_df, by = "gene")
  228. # Create separate dataframes for significant genes for each effect
  229. significant_TimeT1 <- TimeT1_df %>%
  230. filter(TimeT1_adjusted_p_value_fdr < 0.05)
  231. significant_PCPC2 <- PCPC2_df %>%
  232. filter(PCPC2_adjusted_p_value_fdr < 0.05)
  233. significant_TimeT1PCPC2 <- TimeT1PCPC2_df %>%
  234. filter(TimeT1_PCPC2_adjusted_p_value_fdr < 0.05)
  237. # Write results to CSV files
  238. write.csv(all_effects_wide_df, "all_effects_df.csv", row.names = FALSE)
  239. write.csv(significant_TimeT1, "Sign_TimeT1_df.csv", row.names = FALSE)
  240. write.csv(significant_PCPC2, "Sign_PCPC2_df.csv", row.names = FALSE)
  241. write.csv(significant_TimeT1PCPC2, "Sign_TimeT1PCPC2.csv", row.names = FALSE)
  244. ######## Initialize a list to store emmeans results for significant genes
  245. emmeans_results <- list()
  246. pairwise_Time <- list()
  247. pairwise_Cluster <- list()
  248. p_value_threshold <- 0.05
  250. all_significant_interaction_genes <- unique(significant_TimeT1PCPC2$gene)
  251. significant_interaction_genes_df <- data.frame(gene = all_significant_interaction_genes)
  252. write.csv(significant_interaction_genes_df, "significant_interaction_genes.csv", row.names = FALSE)
  255. # Perform emmeans analysis for significant genes in the interaction only
  256. for (gene in all_significant_interaction_genes) {
  257. model <- model_results[[gene]]
  259. if (!is.na(model)) {
  260. # Compute estimated marginal means
  261. emmCluster <- emmeans(model, ~ Time * PC)
  262. emmTime <- emmeans(model, ~ PC * Time)
  264. # Perform pairwise comparisons between T0 and T1 within each PC
  265. pairwise_comparisonsCluster <- contrast(emmCluster, "pairwise", by = "PC")
  266. # Perform pairwise comparisons between PC1 and PC2 within each Time point
  267. pairwise_comparisonsTime <- contrast(emmTime, "pairwise", by = "Time")
  269. # Store summary of significant differences, not adjusted for multiple comparisons
  270. pairwise_summaryCluster <- summary(pairwise_comparisonsCluster) %>%
  271. as.data.frame() %>%
  272. mutate(comparison_type = "Cluster"
  273. )
  275. pairwise_summaryTime <- summary(pairwise_comparisonsTime) %>%
  276. as.data.frame() %>%
  277. mutate(comparison_type = "Time"
  278. )
  280. #Apply FDR correction separately for Cluster and Time comparisons
  281. pairwise_summaryCluster <- pairwise_summaryCluster %>%
  282. mutate(p.adj = p.adjust(p.value, method = "fdr"),
  283. is_significant = ifelse(p.adj < p_value_threshold, TRUE, FALSE)) # Add significance based on FDR-adjusted p-value
  285. pairwise_summaryTime <- pairwise_summaryTime %>%
  286. mutate(p.adj = p.adjust(p.value, method = "fdr"),
  287. is_significant = ifelse(p.adj < p_value_threshold, TRUE, FALSE)) # Add significance based on FDR-adjusted p-value
  289. # Combine the results
  290. combined_summary <- bind_rows(pairwise_summaryCluster, pairwise_summaryTime) %>%
  291. mutate(gene = gene)
  293. # Store the combined results in the list
  294. emmeans_results[[gene]] <- combined_summary
  295. pairwise_Time[[gene]] <- pairwise_summaryTime
  296. pairwise_Cluster[[gene]] <- pairwise_summaryCluster
  297. }
  298. }
  300. # Convert the list of emmeans results to a data frame
  301. emmeans_results_df <- bind_rows(emmeans_results, .id = "gene") %>%
  302. select(gene, comparison_type, contrast, PC, Time, estimate, SE, df, t.ratio, p.value, p.adj, is_significant) # Reorder columns
  303. significant_emmeans_results_df <- emmeans_results_df %>%
  304. filter(is_significant == TRUE)
  306. pairwise_Time_df <- bind_rows(pairwise_Time, .id = "gene") %>%
  307. select(gene, contrast, Time, estimate, SE, df, t.ratio, p.value, p.adj, is_significant) # Reorder columns
  308. significant_pairwise_Time_df <- pairwise_Time_df %>%
  309. filter(is_significant == TRUE)
  311. pairwise_Cluster_df <- bind_rows(pairwise_Cluster, .id = "gene") %>%
  312. select(gene, contrast, PC, estimate, SE, df, t.ratio, p.value, p.adj, is_significant) # Reorder columns
  313. significant_pairwise_Cluster_df <- pairwise_Cluster_df %>%
  314. filter(is_significant == TRUE)
  316. write.csv(emmeans_results_df, "combined_pairwise_comparisons.csv", row.names = FALSE)
  317. write.csv(pairwise_Time_df, "Time_pairwise_comparisons.csv", row.names = FALSE)
  318. write.csv(pairwise_Cluster_df, "Cluster_pairwise_comparisons.csv", row.names = FALSE)
  320. write.csv(significant_emmeans_results_df, "significant_combined_pairwise_comparisons.csv", row.names = FALSE)
  321. write.csv(significant_pairwise_Time_df, "significant_Time_pairwise_comparisons.csv", row.names = FALSE)
  322. write.csv(significant_pairwise_Cluster_df, "significant_Cluster_pairwise_comparisons.csv", row.names = FALSE)
  326. ### Find Gene expression differences bewteen cluster between T0 and T1
  327. library(tidyr)
  328. pivot_Cluster <- pivot_wider(
  329. pairwise_Cluster_df,
  330. id_cols = gene,
  331. names_from = PC,
  332. values_from = c(estimate, is_significant),
  333. values_fill = NA
  334. )
  336. # Filter for genes that significantly increase/decrease in Cluster 1 (PC1) compared to PC2
  337. sig_differential_increase_morePC1 <- pivot_Cluster %>%
  338. filter(
  339. estimate_PC1 < 0 & # PC1 shows a negative estimate (indicating increase)
  340. is_significant_PC1 == TRUE & # PC1 change is significant
  341. estimate_PC1 < estimate_PC2 # PC1 increase is greater than PC2
  342. )
  344. sig_differential_decrease_morePC1 <- pivot_Cluster %>%
  345. filter(
  346. estimate_PC1 > 0 & # PC1 shows a positive estimate (indicating decrease)
  347. is_significant_PC1 == TRUE & # PC1 change is significant
  348. estimate_PC1 > estimate_PC2 # PC1 decrease is greater than PC2
  349. )
  351. # Filter for genes that significantly increase in Cluster 2 (PC1) compared to PC1
  352. sig_differential_increase_morePC2 <- pivot_Cluster %>%
  353. filter(
  354. estimate_PC2 < 0 & # PC1 shows a negative estimate (indicating increase)
  355. is_significant_PC2 == TRUE & # PC1 change is significant
  356. estimate_PC2 < estimate_PC1 # PC1 increase is greater than PC2
  357. )
  358. sig_differential_decrease_morePC2 <- pivot_Cluster %>%
  359. filter(
  360. estimate_PC2 > 0 & # PC1 shows a positive estimate (indicating decrease)
  361. is_significant_PC2 == TRUE & # PC1 change is significant
  362. estimate_PC2 > estimate_PC1 # PC1 decrease is greater than PC2
  363. )
  365. write.csv(sig_differential_increase_morePC1, "sig_differential_increase_morePC1.csv", row.names = FALSE)
  366. write.csv(sig_differential_increase_morePC2, "sig_differential_increase_morePC2.csv", row.names = FALSE)
  367. write.csv(sig_differential_decrease_morePC1, "sig_differential_decrease_morePC1.csv", row.names = FALSE)
  368. write.csv(sig_differential_decrease_morePC2, "sig_differential_decrease_morePC2.csv", row.names = FALSE)
  371. ###### Pairwise Time : evaluate changes between Clusters in each time point
  373. pairwise_Time_df <- read.csv("Time_pairwise_comparisons.csv", header = TRUE)
  375. library(tidyr)
  376. pivot_Time <- pivot_wider(
  377. pairwise_Time_df,
  378. id_cols = c(gene, contrast),
  379. names_from = Time,
  380. values_from = c(estimate, is_significant),
  381. values_fill = NA
  382. )
  384. # Classify genes based on the direction of their estimates
  386. # For T0
  387. T0_PC1supPC2 <- subset(pivot_Time, estimate_T0 > 0 & is_significant_T0 == TRUE)
  388. T0_PC2supPC1 <- subset(pivot_Time, estimate_T0 < 0 & is_significant_T0 == TRUE)
  390. # For T1
  391. T1_PC1supPC2 <- subset(pivot_Time, estimate_T1 > 0 & is_significant_T1 == TRUE)
  392. T1_PC2supPC1 <- subset(pivot_Time, estimate_T1 < 0 & is_significant_T1 == TRUE)
  394. write.csv(T0_PC1supPC2, "T0_PC1_sup_PC2_sig.csv", row.names = FALSE)
  395. write.csv(T0_PC2supPC1, "T0_PC2_sup_PC1_sig.csv", row.names = FALSE)
  396. write.csv(T1_PC1supPC2, "T1_PC1_sup_PC2_sig.csv", row.names = FALSE)
  397. write.csv(T1_PC2supPC1, "T1_PC2_sup_PC1_sig.csv", row.names = FALSE)
  403. ####### Perform t-test for each gene on emmeans
  404. wide_Cluster_df <- pairwise_Cluster_df %>%
  405. select(gene, PC, estimate) %>%
  406. pivot_wider(names_from = PC, values_from = estimate)
  408. Ttest_results <- wide_Cluster_df %>%
  409. rowwise() %>%
  410. mutate(
  411. p.value = t.test(c(PC1, PC2), alternative = "two.sided", na.rm = TRUE)$p.value,
  412. is_significant = p.value < p_value_threshold # Add significance column
  413. ) %>%
  414. select(gene, p.value, is_significant) # Keep desired columns
  416. sign_Ttest_results <- Ttest_results %>%
  417. filter(is_significant)
  419. write.csv(sign_Ttest_results, "significant_Ttest_Cluster.csv", row.names = FALSE)
  424. ####### Initialize a list to store ANOVA results
  425. anova_results_list <- list()
  427. # Loop through each significant gene
  428. # Loop through each significant gene
  429. for (gene in all_significant_interaction_genes) {
  431. # Subset the data for the current gene
  432. gene_data <- subset(pairwise_Cluster_df, gene == pairwise_Cluster_df$gene) # Ensure you're checking the correct column
  434. # Conduct ANOVA for the current gene
  435. anova_model <- aov(estimate ~ PC, data = gene_data)
  437. # Store the summary of ANOVA
  438. anova_results <- summary(anova_model)
  440. # Check if ANOVA is significant
  441. if (anova_results[[1]][["Pr(>F)"]][1] < 0.05) {
  442. # Perform post-hoc test
  443. post_hoc <- TukeyHSD(anova_model)
  445. # Extract contrasts from post-hoc results
  446. post_hoc_df <- as.data.frame(post_hoc$PC)
  448. # Add the gene name to the post-hoc results
  449. post_hoc_df$Gene <- gene
  450. post_hoc_df$Post_Hoc_Comparison <- rownames(post_hoc_df)
  452. # Add a significance column based on adjusted p-values
  453. post_hoc_df$Significant <- ifelse(post_hoc_df$`p adj` < 0.05, "Yes", "No")
  455. # Store significant results in the list
  456. anova_results_list[[gene]] <- post_hoc_df
  457. }
  458. }
  460. significant_anova_results_df <- do.call(rbind, anova_results_list)
  461. significant_anova_results_df <- significant_anova_results_df[, c("Gene", "Post_Hoc_Comparison", "diff", "lwr", "upr", "p adj", "Significant")]
  463. write.csv(significant_anova_results_df, "significant_anova_results.csv", row.names = FALSE)
  466. # First, let's verify that we have the same genes in both dataframes
  467. genes1 <- unique(significant_anova_results_df$Gene)
  468. genes2 <- unique(pairwise_Cluster_df$gene)
  471. # Merging the data frames
  472. combined_results <- cbind( pairwise_Cluster_df, significant_anova_results_df)
  474. write.csv(combined_results, "combined_results_pairwise_anova_results.csv", row.names = FALSE)
  478. ############### PLOTS
  479. ############### Genes increasing from T0 to T1 - PC1
  480. setwd("")
  482. ensheathment_genes <- read.csv("ensheathmentneurons.csv", header = TRUE) # Use header = TRUE if the first row contains column names
  483. gliogenesis_genes <- read.csv("gliogenesis.csv", header = TRUE) # Use header = TRUE if the first row contains column names
  484. oligo_genes <- read.csv("oligodendrocyte.csv", header = TRUE)
  486. all_genes <- Reduce(union, list(ensheathment_genes$gene, gliogenesis_genes$gene, oligo_genes$gene))
  487. all_genes_df <- as.data.frame(all_genes)
  488. common_genes <- Reduce(intersect, list(ensheathment_genes$gene, gliogenesis_genes$gene, oligo_genes$gene))
  489. common_genes_df <- as.data.frame(common_genes)
  491. oligo_ensheat_genes <- Reduce(union, list(ensheathment_genes$gene, oligo_genes$gene))
  492. oligo_ensheat_genes_df <- as.data.frame(oligo_ensheat_genes)
  493. gliogenesis_genes <- gliogenesis_genes$gene
  494. gliogenesis_genes_df <- as.data.frame(gliogenesis_genes)
  496. write.csv(all_genes_df, "combined_oligo_ensheatement_gliogenesis_genes.csv", row.names = FALSE)
  497. write.csv(common_genes_df, "common_oligo_ensheatement_gliogenesis_genes.csv", row.names = FALSE)
  498. write.csv(oligo_ensheat_genes_df, "oligo_ensheatement_genes.csv", row.names = FALSE)
  499. write.csv(gliogenesis_genes_df, "gliogenesis_genes.csv", row.names = FALSE)
  502. ############### Genes increased at T1 - PC1 vs PC2
  503. setwd("")
  505. ensheathment_genes_T1 <- read.csv("ensheathment_neurons_T1.csv", header = TRUE) # Use header = TRUE if the first row contains column names
  506. gliogenesis_genes_T1 <- read.csv("gliogenesis_T1.csv", header = TRUE) # Use header = TRUE if the first row contains column names
  507. T1_PC1supPC2_genes <- read.csv("T1_PC1_sup_PC2_sig.csv", header = TRUE) # Use header = TRUE if the first row contains column names
  510. allT1C1_genes <- Reduce(union, list(ensheathment_genes_T1$gene, gliogenesis_genes_T1$gene))
  511. allT1C1_genes_df <- as.data.frame(allT1C1_genes)
  512. allT1C1common_genes <- Reduce(intersect, list(ensheathment_genes_T1$gene, gliogenesis_genes_T1$gene))
  513. allT1C1common_genes_df <- as.data.frame(allT1C1common_genes)
  515. ## Genes that are increasing significantly from T0 to T1 more in PC1 vs PC2 and that are increased signficiantly at T1 in PC1 vs PC2
  516. long_allgenes_atT1 <- Reduce(intersect, list(all_genes_df$all_genes, T1_PC1supPC2_genes$gene))
  517. long_allgenes_atT1_df <- as.data.frame(long_allgenes_atT1)
  518. ## From the ones in the the neuro ensheatment, oligo and gliogenesis, increasing longitudinally more in PC1vsPC2
  519. common_long_T1_C1genes <- Reduce(intersect, list(allT1C1_genes_df$allT1C1_genes, all_genes_df$all_genes))
  520. common_long_T1_C1genes_df <- as.data.frame(common_long_T1_C1genes)
  522. write.csv(allT1C1_genes_df, "combinedT1C1_ensheatement_gliogenesis_genes.csv", row.names = FALSE)
  523. write.csv(allT1C1common_genes_df, "commonT1C1_ensheatement_gliogenesis_genes.csv", row.names = FALSE)
  524. write.csv(common_long_T1_C1genes_df, "common_LongT1_C1_genes.csv", row.names = FALSE)
  525. write.csv(long_allgenes_atT1_df, "common_LongT1_atC1_genes.csv", row.names = FALSE)
  528. #oligo <- oligo$Gene
  529. # Filter the dataset for genes of interest
  530. merged_data <- data %>%
  531. filter(symbol %in% all_genes)
  532. common_data <- data %>%
  533. filter(symbol %in% common_genes)
  534. oligo_ensheat_data <- data %>%
  535. filter(symbol %in% oligo_ensheat_genes)
  536. gliogenesis_data <- data %>%
  537. filter(symbol %in% gliogenesis_genes)
  539. write.csv(merged_data, "sigGenes_violinplots.csv", row.names = FALSE)
  543. custom_colors <- c("PC1" = "#A50000", # Example for PC1 (red)
  544. "PC2" = "#275D90") # Example for PC2 (blue)
  546. # Violin PLOT
  547. violin_plot <- ggplot(common_data, aes(x = Time, y = log2_rpkm, fill = PC)) +
  548. geom_violin(alpha = 0.7) +
  549. facet_wrap(~symbol, scales = "free_y", ncol = 4) +
  550. theme_bw() +
  551. labs(
  552. title = "Ensheathment, Gliogenesis and Oligodendrocyte Gene Expression Changes Over Time by Cluster",
  553. x = "Time Point",
  554. y = "Log2 RPKM",
  555. fill = "Cluster"
  556. ) +
  557. scale_fill_manual(values = custom_colors) +
  558. theme(
  559. axis.text.x = element_text(angle = 45, hjust = 1),
  560. strip.background = element_rect(fill = "white"),
  561. strip.text = element_text(face = "bold")
  562. )
  564. ggplot(merged_data, aes(x = Time, y = log2_rpkm, fill = PC, color = PC)) +
  565. geom_violin(alpha = 0.6) +
  566. # Remove width parameter and adjust position_dodge
  567. geom_jitter(position = position_jitterdodge(dodge.width = 0.8, jitter.width = 0.2),
  568. size = 1, alpha = 0.6) +
  569. facet_wrap(~symbol, scales = "free_y", ncol = 7) +
  570. scale_fill_manual(values = custom_colors) +
  571. scale_color_manual(values = custom_colors) +
  572. theme_minimal() +
  573. labs(
  574. title = "Ensheathment, Gliogenesis and Oligodendrocyte Gene Expression Changes Over Time by Cluster",
  575. x = "Time Point",
  576. y = "Log2 RPKM",
  577. fill = "Cluster"
  578. ) +
  579. theme(legend.position = "none",
  580. strip.text = element_text(face = "bold", size = 14),
  581. plot.title = element_text(face = "bold", size = 18, hjust = 0.5)
  582. ) + # Adjust main title size
  583. labs(x = "Time Point", y = "Log2 RPKM")
  585. # BOXPLOTs
  586. box_plot <- ggplot(gliogenesis_data, aes(x = Time, y = log2_rpkm, fill = region)) +
  587. geom_boxplot(outlier.shape = NA) + # Removing outliers for cleaner visualization
  588. facet_wrap(~symbol, scales = "free_y", ncol = 7) +
  589. theme_bw() +
  590. labs(
  591. title = "Gliogenesis Gene Expression Changes Over Time by Cluster",
  592. x = "Time Point",
  593. y = "Log2 RPKM",
  594. fill = "Cluster"
  595. ) +
  596. theme(
  597. axis.text.x = element_text(angle = 45, hjust = 1),
  598. strip.background = element_rect(fill = "white"),
  599. strip.text = element_text(face = "bold")
  600. )
  603. # Summaries - mean and std dev
  604. # MERGED DATA
  605. gene_merged_data <- merged_data %>%
  606. group_by(symbol, Time, PC) %>%
  607. summarise(
  608. gene_mean_expression = mean(log2_rpkm),
  609. gene_se_expression = sd(log2_rpkm) / sqrt(n()),
  610. .groups = 'drop'
  611. )
  612. cluster_merged_data <- merged_data %>%
  613. group_by(Time, PC) %>%
  614. summarise(
  615. cluster_mean_expression = mean(log2_rpkm),
  616. cluster_se_expression = sd(log2_rpkm) / sqrt(n()),
  617. .groups = 'drop'
  618. )
  620. write.csv(cluster_merged_data, "sigGenes_lineGraphs.csv", row.names = FALSE)
  623. # COMMON DATA
  624. gene_common_data <- PC1_common_data %>%
  625. group_by(symbol, Time, PC) %>%
  626. summarise(
  627. gene_mean_expression = mean(log2_rpkm),
  628. gene_se_expression = sd(log2_rpkm) / sqrt(n()),
  629. .groups = 'drop'
  630. )
  631. cluster_common_data <- PC1_common_data %>%
  632. group_by(Time, PC) %>%
  633. summarise(
  634. cluster_mean_expression = mean(log2_rpkm),
  635. cluster_se_expression = sd(log2_rpkm) / sqrt(n()),
  636. .groups = 'drop'
  637. )
  641. common_distinct_shapes <- c(16, 17, 15, 18, 8, 23, 25, 0)
  642. distinct_shapes <- common_distinct_shapes[1:8] # Use only the first 8 shapes for 8 ge
  644. merged_distinct_shapes <- c(16, 17, 15, 18, 8, 23, 25, 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 24, 26)
  645. distinct_shapes <- merged_distinct_shapes[1:27]
  647. # Sample data for plotting
  648. shapes <- data.frame(
  649. x_var = rnorm(100),
  650. y_var = rnorm(100),
  651. shape_var = sample(c(0:24, 21, 22, "★", "▲"), 100, replace = TRUE) # Numeric and character shapes
  652. )
  653. # Ensure shapes are either all numeric or all characters
  654. # Convert numeric shapes to factor
  655. shapes$shape_var <- as.factor(shapes$shape_var)
  658. # Create the plot with average values
  659. avg_dot_line_plot <- ggplot(gene_merged_data,
  660. aes(x = Time, y = gene_mean_expression,
  661. color = PC, shape = symbol,
  662. group = interaction(symbol, PC))) +
  663. geom_point(size = 4) +
  664. geom_line(linewidth = 1, alpha = 0.7) +
  665. geom_errorbar(aes(ymin = gene_mean_expression - gene_se_expression,
  666. ymax = gene_mean_expression + gene_se_expression),
  667. width = 0.2) +
  668. scale_shape_manual(values = c(0:24, 21, 22)) + # Using only numeric shapes
  669. scale_color_brewer(palette = "Set1") +
  670. theme_bw() +
  671. labs(
  672. title = "Average Gene Expression Changes Over Time by Cluster",
  673. x = "Time Point",
  674. y = "Average Log2 RPKM",
  675. color = "Cluster",
  676. shape = "Gene"
  677. ) +
  678. theme(
  679. axis.text.x = element_text(angle = 45, hjust = 1),
  680. legend.position = "right",
  681. panel.grid.minor = element_blank(),
  682. legend.key.size = unit(1.2, "cm"),
  683. plot.title = element_text(size = 14, face = "bold"),
  684. axis.title = element_text(size = 12),
  685. legend.title = element_text(size = 11),
  686. legend.text = element_text(size = 10)
  687. )
  690. # Plot 1: Line plot of cluster averages
  691. cluster_line_plot <- ggplot(cluster_merged_data,
  692. aes(x = Time, y = cluster_mean_expression, color = PC, group = PC)) +
  693. geom_line(linewidth = 1) +
  694. geom_point(size = 4) +
  695. geom_errorbar(aes(ymin = cluster_mean_expression - cluster_se_expression,
  696. ymax = cluster_mean_expression + cluster_se_expression),
  697. width = 0.2) +
  698. scale_color_brewer(palette = "Set1") +
  699. theme_bw() +
  700. labs(
  701. title = "Average Neuronal Ensheathment, Gliogenesis, Oligdendrocyte \nGene Expression Changes Over Time by Cluster",
  702. x = "Time Point",
  703. y = "Average Log2 RPKM",
  704. color = "Cluster"
  705. ) +
  706. theme(
  707. axis.text.x = element_text(angle = 45, hjust = 1),
  708. panel.grid.minor = element_blank(),
  709. plot.title = element_text(size = 14, face = "bold"),
  710. axis.title = element_text(size = 12)
  711. )
  714. gene_merged_data <- gene_merged_data %>%
  715. mutate(x_position = case_when(
  716. Time == "T0" & PC == "PC1" ~ 0.95,
  717. Time == "T0" & PC == "PC2" ~ 1.05,
  718. Time == "T1" & PC == "PC1" ~ 1.95,
  719. Time == "T1" & PC == "PC2" ~ 2.05
  720. ))
  722. gene_common_data <- gene_common_data %>%
  723. mutate(x_position = case_when(
  724. Time == "T0" & PC == "PC1" ~ 0.95,
  725. Time == "T0" & PC == "PC3" ~ 1.05,
  726. Time == "T1" & PC == "PC1" ~ 1.95,
  727. Time == "T1" & PC == "PC3" ~ 2.05
  728. ))
  732. # Scatter plot per gene in COMMON GENES
  733. refined_scatter_plot <- ggplot() +
  734. # Add individual gene points with different symbols
  735. geom_point(data = gene_merged_data,
  736. aes(x = x_position, y = gene_mean_expression,
  737. color = PC, shape = symbol),
  738. size = 3, alpha = 0.7) +
  740. # Add average lines
  741. geom_line(data = cluster_merged_data,
  742. aes(x = case_when(
  743. Time == "T0" ~ 1.025,
  744. Time == "T1" ~ 2.025
  745. ),
  746. y = cluster_mean_expression,
  747. color = PC,
  748. group = PC),
  749. linewidth = 1) +
  751. # Add error bars for cluster averages
  752. geom_errorbar(data = cluster_merged_data,
  753. aes(x = case_when(
  754. Time == "T0" ~ 1.025,
  755. Time == "T1" ~ 2.025
  756. ),
  757. ymin = cluster_mean_expression - cluster_se_expression,
  758. ymax = cluster_mean_expression + cluster_se_expression,
  759. color = PC),
  760. width = 0.05) +
  762. # Use custom colors
  763. scale_color_manual(values = custom_colors) +
  765. scale_shape_manual(values = c(0:24, 21, 22)) + # Using only numeric shapes +
  766. scale_x_continuous(breaks = c(1, 2), labels = c("T0", "T1")) +
  768. theme_bw() +
  769. labs(
  770. title = "Average Neuronal Ensheathment, Gliogenesis, Oligodendrocyte \nGene Expression Changes Over Time by Cluster",
  771. x = "Time Point",
  772. y = "Average Log2 RPKM",
  773. color = "Cluster",
  774. shape = "Gene"
  775. ) +
  777. theme(
  778. panel.grid.minor = element_blank(),
  779. plot.title = element_text(size = 14, face = "bold"),
  780. axis.title = element_text(size = 12),
  781. legend.position = "right"
  782. )
  785. # Scatter plot per gene in MERGED GENES
  786. library(ggplot2)
  787. library(dplyr)
  788. library(ggrepel)
  790. unique_genes <- gene_merged_data %>%
  791. filter(Time == "T1") %>%
  792. distinct(symbol, PC, .keep_all = TRUE) # Keep unique combinations of gene names and clusters
  794. unique_genes <- gene_common_data %>%
  795. filter(Time == "T1") %>%
  796. distinct(symbol, PC, .keep_all = TRUE) # Keep unique combinations of gene names and clusters
  799. ggplot() +
  800. # Add individual gene points without shape, just use color
  801. geom_point(data = gene_merged_data,
  802. aes(x = x_position, y = gene_mean_expression, color = PC),
  803. size = 3, alpha = 0.7) +
  805. # Add lighter lines for each gene within the same cluster
  806. geom_line(data = gene_merged_data,
  807. aes(x = x_position, y = gene_mean_expression, group = interaction(symbol, PC), color = PC),
  808. linewidth = 0.5, alpha = 0.3) +
  810. # Label unique genes for each cluster at Time Point 1
  811. geom_text_repel(data = unique_genes,
  812. aes(x = x_position, # Use the original x_position for labeling
  813. y = gene_mean_expression + 0.1,
  814. label = symbol,
  815. color = PC),
  816. size = 3,
  817. box.padding = 0.1, # Reduced padding around labels
  818. point.padding = 0.1, # Reduced padding around points
  819. segment.color = 'grey50') + # Color for connecting lines
  821. # Add average lines for each cluster
  822. geom_line(data = cluster_merged_data,
  823. aes(x = case_when(
  824. Time == "T0" ~ 1.025,
  825. Time == "T1" ~ 2.025
  826. ),
  827. y = cluster_mean_expression,
  828. color = PC,
  829. group = PC),
  830. linewidth = 1) +
  832. # Add error bars for cluster averages
  833. geom_errorbar(data = cluster_merged_data,
  834. aes(x = case_when(
  835. Time == "T0" ~ 1.025,
  836. Time == "T1" ~ 2.025
  837. ),
  838. ymin = cluster_mean_expression - cluster_se_expression,
  839. ymax = cluster_mean_expression + cluster_se_expression,
  840. color = PC),
  841. width = 0.05) +
  843. scale_color_brewer(palette = "Set1") +
  845. # Add extra space on both sides for the gene labels
  846. scale_x_continuous(breaks = c(1, 2), labels = c("T0", "T1"), expand = expansion(mult = c(0.1, 0.1))) +
  848. theme_bw() +
  849. labs(
  850. title = "Average Neuronal Ensheathment, Gliogenesis, Oligodendrocyte \nGene Expression Changes Over Time by Cluster",
  851. x = "Time Point",
  852. y = "Average Log2 RPKM",
  853. color = "Cluster"
  854. ) +
  856. theme(
  857. panel.grid.minor = element_blank(),
  858. plot.title = element_text(size = 14, face = "bold"),
  859. axis.title = element_text(size = 12),
  860. legend.position = "right"
  861. )
  864. cluster_common_data <- cluster_common_data %>%
  865. mutate(ymin = cluster_mean_expression - cluster_se_expression,
  866. ymax = cluster_mean_expression + cluster_se_expression)
  868. gene_scatter_plot <- ggplot(gene_common_data,
  869. aes(x = Time, y = gene_mean_expression, color = PC)) +
  870. # Jittered points for individual observations
  871. geom_jitter(width = 0.2, size = 3, alpha = 0.7) +
  873. # Use a color palette for clusters
  874. scale_color_brewer(palette = "Set1") +
  875. theme_bw() +
  876. # Add lines for cluster averages
  877. geom_line(data = cluster_common_data,
  878. aes(x = Time,
  879. y = cluster_mean_expression,
  880. color = PC,
  881. group = PC),
  882. linewidth = 1) +
  883. # Labels and theme adjustments
  884. labs(
  885. title = "Gene Expression Distribution by Time and Cluster",
  886. x = "Time Point",
  887. y = "Average Log2 RPKM",
  888. color = "Cluster"
  889. ) +
  890. # Formatting for plot appearance
  891. theme(
  892. axis.text.x = element_text(angle = 45, hjust = 1),
  893. panel.grid.minor = element_blank(),
  894. plot.title = element_text(size = 14, face = "bold"),
  895. axis.title = element_text(size = 12)
  896. )

Genetic_analysisANDgraphs.R, under CC-BY-4.0 · at the source

Overview

Authors: Joana Sa de Almeida1,2,3, Andrew Boehringer2, Serafeim Loukas2, Elda Fischi-Gomez4,5,6, Annemijn Van Der Veek2, Lara Lordier1, Sebastien Courvoisier7,8, François Lazeyras7,8, Dimitri Van De Ville7,8,9, Gareth Ball3,10, Petra S Hüppi1,2
ORCID iDs: Joana Sa de Almeida, Elda Fischi-Gomez, Annemijn Van Der Veek, Sebastien Courvoisier, Dimitri Van De Ville, Gareth Ball, Petra S Hüppi
  1. Division of Development and Growth, Department of Women’s, Children’s, and Adolescent Health, University Hospitals of Geneva, Geneva, Switzerland
  2. Department of Pediatrics, Gynecology and Obstetrics, University of Geneva, Geneva, Switzerland
  3. Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, VIC Australia
  4. CIBM Center for Biomedical Imaging, University of Lausanne, Lausanne, Switzerland
  5. Department of Radiology, University Hospital and University of Lausanne, Lausanne, Switzerland
  6. Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
  7. CIBM Center for Biomedical Imaging, University of Geneva, Geneva, Switzerland
  8. Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
  9. Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
  10. Department of Paediatrics, University of Melbourne, Melbourne, VIC Australia
Institutions: University of Geneva (Switzerland); University Hospital of Geneva (Switzerland); Murdoch Children's Research Institute (Australia); Centre Hospitalier Universitaire Vaudois (Switzerland); Centre d'Imagerie BioMedicale (Switzerland); École Polytechnique Fédérale de Lausanne (Switzerland); University of Lausanne (Switzerland); The University of Melbourne (Australia)
Journal: Nature communications, volume 17, issue 1, article 4849
Dates: received 2 August 2025; accepted 20 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71415-x · PMID 41957008 · PMCID PMC13222875 · OpenAlex W4412840896
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.30.667792
Status: code verified
Categories: fMRI (modality), human (organism), other condition (population), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Glial development, Neuro-vascular interactions, Gene expression
MeSH: Cerebral Cortex*, Infant, Premature*, Neurons*, Oxygen*, Female, Gestational Age, Humans, Infant, Newborn, Magnetic Resonance Imaging, Male, Neurodevelopment (* major topic)
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Swiss National Science Foundation (135817, 324730-163084); Schweizerische Akademie der Medizinischen Wissenschaften (Swiss Academy of Medical Sciences) (YTCR 49/19)
Citations: not cited yet (Europe PMC); 123 references in the paper

Abstract

Blood Oxygen Level Dependent (BOLD) variability reflects meaningful brain activity, yet its structural and biological correlates during early development remain unknown. Using longitudinal resting-state fMRI and multi-shell diffusion imaging acquired longitudinally in 54 very preterm infants (at 33-weeks’ gestational age and term-equivalent-age) and 24 full-term newborns, we investigated how BOLD variability evolves in very preterm infants, its relationship with cortical microstructure and gene expression, using the BrainSpan dataset, and how it differs from full-term newborns at term-equivalent age. During preterm development, BOLD variability increased in primary sensory-sensorimotor and proto-Default-Mode-Network regions, accompanied by decreases in cortical diffusivity. Gene expression analysis revealed concurrent upregulation of genes mediating gliogenesis and neuronal ensheathment. At term-equivalent age, very preterm infants showed decreased BOLD variability and increased cortical diffusivity, compared to full-term newborns. In this work, we show that BOLD variability reflects cortical microstructural maturation, mediated by upregulation of gliogenesis and neuronal ensheathment. Interruption of these processes by preterm birth identifies putative mechanisms of preterm brain injury.

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

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Code availability

Software and code used in this study for MRI analysis are publicly available as part of FSL v5.0.10 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), MRtrix3 (Tournier et al.110), SMT (https://github.com/ekaden/smt) and DIPY (https://docs.dipy.org/stable/interfaces/reconstruction_flow.html?utm_source=chatgpt.com) software packages. dMRI data were pre-processed using the EDDY command adapted for neonatal motion, from the neonatal dMRI automated pipeline from the developing Human Connectome Project (dHCP, http://www.developingconnectome.org), and can be found at: https://git.fmrib.ox.ac.uk/matteob/dHCP_neo_dMRI_pipeline_release (Bastiani et al.116). Supporting code for this manuscript, used to generate the results and figures is available on Zenodo (10.5281/zenodo.18875986).

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:

  • 5 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 49 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

All neuroimaging data were acquired in the context of a research project approved by the ethical committee in 2016. The raw data are protected and are not available due to data privacy laws. Developmental RNA-seq data used in this study were downloaded from: http://development.psychencode.org/. Source data are provided with this paper.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 3 keywords, 11 MeSH terms, 2 funders, 121 references.

Cite

This paper

Sa de Almeida, J., Boehringer, A., Loukas, S., Fischi-Gomez, E., Van Der Veek, A., Lordier, L., Courvoisier, S., Lazeyras, F., Van De Ville, D., Ball, G., & Hüppi, P. S. (2026). Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex. Nature communications, 17(1), 4849. https://doi.org/10.1038/s41467-026-71415-x

BibTeX

@article{sadealmeida2026regional,
author = {Sa de Almeida, Joana and Boehringer, Andrew and Loukas, Serafeim and Fischi-Gomez, Elda and Van Der Veek, Annemijn and Lordier, Lara and Courvoisier, Sebastien and Lazeyras, François and Van De Ville, Dimitri and Ball, Gareth and Hüppi, Petra S},
title = {{Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4849},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71415-x},
url = {https://doi.org/10.1038/s41467-026-71415-x},
pmid = {41957008},
pmcid = {PMC13222875}
}

RIS

TY - JOUR
AU - Sa de Almeida, Joana
AU - Boehringer, Andrew
AU - Loukas, Serafeim
AU - Fischi-Gomez, Elda
AU - Van Der Veek, Annemijn
AU - Lordier, Lara
AU - Courvoisier, Sebastien
AU - Lazeyras, François
AU - Van De Ville, Dimitri
AU - Ball, Gareth
AU - Hüppi, Petra S
TI - Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/09
VL - 17
IS - 1
SP - 4849
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71415-x
UR - https://doi.org/10.1038/s41467-026-71415-x
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-71415-x",
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"title": "Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex",
"container-title": "Nature communications",
"author": [
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"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4849",
"DOI": "10.1038/s41467-026-71415-x",
"PMID": "41957008",
"PMCID": "PMC13222875",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-71415-x",
"language": "en",
"issued": {
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}
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