OSCR

Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life.

A correction to this paper has been published: the notice, 42337099, from Europe PMC.

Code ↔ Paper

4 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 4 matches
  1. [1] § Results › Heterogeneous aging of cerebellar tissue and associations with cognition ↔ Figures.R, lines 83–172 · score 0.74 · young adult, tissue loss, neocortical volume, eTIV, ROIs, sensory
  2. [2] § Methods › Task-level validation (HCP) ↔ Figures.R, lines 180–265 · score 0.61 · Johnson Neyman, score age, FDR, MoCA, slope, aging
  3. [3] § Methods › Microstructure analyses: T1W/T2W (HCP) ↔ Figures.R, lines 83–172 · score 0.55 · young adult, ROIs, T1W, T2W, tissue, predicted
  4. [4] § Regional cerebellar age-related volume changes and associations with cognition ↔ Figures.R, lines 180–265 · score 0.51 · visuospatial scores, signature, FDR, MoCA, maps, mm3

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 347 lines · 51 KB · no license · 4 matches

  1. ##############
  2. #### Figure 1
  4. library(sjPlot)
  5. library(MASS)
  6. library(stargazer)
  7. library(effectsize)
  8. library(ggplot2)
  9. library(ggeffects)
  11. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  12. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = CortexVol), color = "grey33", alpha = 0.2, size = 6, position=position_jitter(width=0), stroke=1) + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + labs(title = "", x = "Age [years]", y = "Neocortical\nVolume [mm³]") + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = -7), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = 2), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(310000, 610000)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100))
  15. ####
  16. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  17. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = eTIV), color = "goldenrod", alpha = 0.2, size = 6, position=position_jitter(width=0), stroke=1) + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T, se=TRUE) + labs(title = "", x = "Age [years]", y = "eTIV [mm³]") + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5),axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5),axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = -3), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = 2), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank() ) + scale_y_continuous(breaks = c(1000, 1800)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100))
  20. ####
  21. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  22. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = Total_Cerebel_Vol), color = "#428CD4", alpha = 0.2, size = 6, position=position_jitter(width=0), stroke=1) + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T, se=TRUE) + labs(title = "", x = "Age [years]", y = "Cerebellar\nVolume [mm³]") + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5),axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5),axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25),axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = -6), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = 2), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank() ) + scale_y_continuous(breaks = c(90000, 172000)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100))
  25. ####
  26. predictor_labels <- c("Cerebral [mm³]", "eTIV [mm³]","Cerebellar [mm³]")
  28. effect_sizes_df <- data.frame(
  29. Predictor = c("CortexVol", "eTIV", "Total_Cerebel_Vol"),
  30. Cohen_f = cohen_f_values,
  31. Label = predictor_labels
  32. )
  34. bar_chart_effect_size <- ggplot(data = effect_sizes_df, aes(x = Predictor, y = Cohen_f, fill = Predictor)) + geom_bar(stat = "identity") + scale_x_discrete(labels = c("CortexVol" = "Neocortical\n[mm³]", "eTIV"="eTIV\n[mm³]", "Total_Cerebel_Vol" = "Cerebellar\n[mm³]")) + scale_fill_manual(values = c("CortexVol" = "grey33", "eTIV"="goldenrod", "Total_Cerebel_Vol" = "#428CD4")) + labs(title = "", x = "Predictor", y = "Coefficient") + theme_classic() + theme(legend.position =("NA"), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.ticks.length = unit(0.25,"cm"), axis.line.x = element_blank(), axis.line.y = element_line(color = "black", size = 5), axis.text.y = element_text(color="black",face="plain",size = 52, vjust=0.3, margin=margin(4,4,4,4)), axis.text.x = element_text(color="black",face="plain",size = 63), axis.title.y = element_text(color="black",face="bold",size = 69, hjust = 0.5), axis.title.x = element_blank(), plot.margin=margin(1,3,1,1,"cm")) + labs(title = "", x = "", y = "\nSize of Age Effect") + theme(text = element_text(size = 69)) + theme(plot.title=element_text(hjust=0.5, face="bold"), axis.text.y.right=element_text(hjust=0.5, margin=margin(4,4,4,4)), axis.title.y.right=element_text(vjust=4.5)) + scale_y_continuous(position="right") + theme(plot.margin = margin(1,3,1,3, "cm"), panel.border = element_blank())
  37. ####
  38. library(ggplot2)
  39. library(gridExtra)
  40. library(patchwork)
  41. library(cowplot)
  43. models <- list()
  45. custom_palette <- c("#80D0C7", "#8CC7B3", "#99BF9F", "#A6B78B", "#B2AF77", "#BFA763", "#CC9F4F", "#D8973B", "#E58F27", "#F28713", "#FF7F00")
  47. lobules <- c("I.III", "IV", "V", "VI", "Crus.I", "Crus.II", "VIIB", "VIIIA", "VIIIB", "IX", "X")
  49. plot_bilateral_average <- function(data, lobule_name, color) {
  50. data$bilateral_average <- rowMeans(data[, c(paste0("Left.", lobule_name), paste0("Right.", lobule_name))], na.rm = TRUE)
  51. plot <- ggplot(data = data, aes(x = interview_age_years, y = bilateral_average)) + geom_point(color = color, alpha=0.2, size=4) + geom_smooth(method = "lm", se = TRUE, color = color, fill=alpha(color,0.3)) + labs(title = paste("", lobule_name), x = "", y = "Bilateral Average Volume") + theme_classic() + theme(axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.line.x = element_line(color = "black", size = 2), axis.line.y = element_blank(), axis.text.x = element_text(color = "black", face = "plain", size = 35, hjust=0.8, vjust=-0.2), axis.text.y = element_blank(), axis.title.y = element_text(color = "black", face = "plain", size = 35, margin = margin(t = 12, b = 12)), axis.title.x = element_text()) + labs(title = "", x = "\n\n\n\n", y = "") + theme(text = element_text(size = 30)) +
  52. scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(0, 20000), limits = c(0, 22000))
  53. return(plot)
  54. }
  56. scatterplots <- lapply(1:length(lobules), function(i) {
  57. plot_bilateral_average(mydata, lobules[i], custom_palette[i])
  58. })
  60. master_plot <- grid.arrange(grobs = scatterplots, nrow = 1)
  62. master_plot <- ggdraw(master_plot) + draw_label("\n I-III IV V VI Crus I Crus II VIIb VIIIa VIIIb IX X ", fontface = 'bold', size = 36, x = 0.5, y = 0.210)
  63. master_plot <- ggdraw(master_plot) + draw_label("Age [years]", fontface = 'plain', size = 36, x = 0.5, y = 0.120)
  65. master_plot <- master_plot + geom_segment(aes(x = 0.0055, xend = 0.0055, y = 0.248, yend = 0.87), linetype = "solid", color = "black", size = 2) + annotate("text", x = 0.0087, y = 0.282, label = "0", vjust = 1, hjust = 0.001, size = 13) + annotate("text", x = 0.0087, y = 0.83, label = "20000", vjust = 0, hjust = 0.001, size = 13) + annotate("text", x = -0.008, y = 0.57, label = "Volume [mm³]", angle = 90, vjust = 0.6, hjust = 0.5, size = 15, fontface = 'bold') + theme(plot.margin = margin(0, 2, 0, 2, "cm"), panel.border = element_blank())
  67. print(master_plot)
  70. ####
  71. lobules <- c("I.III_t1t2","IV_t1t2","V_t1t2","VI_t1t2","Crus.I_t1t2","Crus.II_t1t2","VIIB_t1t2","VIIIA_t1t2","VIIIB_t1t2","IX_t1t2", "X_t1t2")
  73. models <- list()
  75. custom_palette <- c("#80D0C7", "#8CC7B3", "#99BF9F", "#A6B78B", "#B2AF77", "#BFA763", "#CC9F4F", "#D8973B", "#E58F27", "#F28713", "#FF7F00")
  77. plot_bilateral_average <- function(data, lobule_name, color) {
  78. data$bilateral_average <- rowMeans(data[, c(paste0("Left_", lobule_name), paste0("Right_", lobule_name))], na.rm = TRUE)
  79. plot <- ggplot(data = data, aes(x = interview_age_years, y = bilateral_average)) + geom_point(color = color, alpha=0.2, size=4) + geom_smooth(method = "lm", se = TRUE, color = color, fill=alpha(color,0.3)) + labs(title = paste("", lobule_name), x = "", y = "Bilateral Average T1/T2 Ratio") + theme_classic() + theme(axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.line.x = element_line(color = "black", size = 2), axis.line.y = element_blank(), axis.text.x = element_text(color = "black", face = "plain", size = 34.5, hjust=0.9, vjust=-0.1), axis.text.y = element_blank(), axis.title.y = element_text(color = "black", face = "plain", size = 35, margin = margin(t = 12, b = 12)), axis.title.x = element_text()) + labs(title = "", x = "\n\n\n\n", y = "") + theme(text = element_text(size = 30)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100))
  80. return(plot)
  81. }
  83. scatterplots <- lapply(1:length(lobules), function(i) {
  84. plot_bilateral_average(mydata, lobules[i], custom_palette[i])
  85. })
  87. master_plot <- grid.arrange(grobs = scatterplots, nrow = 1)
  89. master_plot <- ggdraw(master_plot) +
  90. draw_label("\n I-III IV V VI Crus I Crus II VIIb VIIIa VIIIb IX X ", fontface = 'bold', size = 36, x = 0.5, y = 0.190)
  92. master_plot <- ggdraw(master_plot) + draw_label("Age [years]", fontface = 'plain', size = 36, x = 0.5, y = 0.120)
  94. master_plot <- master_plot + geom_segment(aes(x = 0.0055, xend = 0.0055, y = 0.25, yend = 0.89), linetype = "solid", color = "black", size = 2) + annotate("text", x = 0.0087, y = 0.291, label = "1.5", vjust = 1, hjust = 0.001, size = 13) + annotate("text", x = 0.0087, y = 0.87, label = "3", vjust = 0, hjust = 0.001, size = 13) + annotate("text", x = -0.008, y = 0.56, label = "T1w/T2w ratio", angle = 90, vjust = 0.6, hjust = 0.5, size = 15, fontface = 'bold') + theme(plot.margin = margin(0, 2, 0, 2, "cm"), panel.border = element_blank())
  96. print(master_plot)
  99. ####
  100. young_data <- read.xlsx(file_path)
  102. young_data_filtered <- young_data %>%
  103. filter(dataset == "young adult")
  105. young_data_filtered <- young_data %>%
  106. dplyr::select(-starts_with("age"), -contains("dataset"), -contains("subject_id"))
  108. roi_data <- young_data_filtered[,-1]
  110. roi_data_cleaned <- data.frame(lapply(roi_data, function(x) as.numeric(as.character(x))))
  112. mean_t1t2 <- colMeans(roi_data_cleaned, na.rm = TRUE)
  114. mean_t1t2_df <- data.frame(ROI = names(mean_t1t2), Mean_T1T2 = mean_t1t2)
  116. roi_columns <- grep("_t1t2$", names(mydata), value = TRUE)
  118. slopes <- numeric(length(roi_columns))
  120. for (i in seq_along(roi_columns)) {
  121. roi <- roi_columns[i]
  122. model <- rlm(as.formula(paste(roi, "~ interview_age_years")), data = mydata)
  123. slopes[i] <- coef(model)["interview_age_years"]
  124. }
  126. mean_t1t2_df$ROI_clean <- sub("_t1t2$", "", mean_t1t2_df$ROI)
  127. results <- data.frame(ROI = roi_columns, Slope = slopes)
  128. results$ROI_clean <- sub("_t1t2$", "", results$ROI)
  130. results$Mean_T1T2 <- mean_t1t2_df$Mean_T1T2[match(results$ROI_clean, mean_t1t2_df$ROI_clean)]
  132. results_clean <- results %>%
  133. filter(!ROI_clean %in% exclude_rois)
  135. results_clean <- results_clean %>%
  136. filter(!is.na(Mean_T1T2) & !is.infinite(Mean_T1T2) & !is.na(Slope) & !is.infinite(Slope))
  138. results_clean$Z_Slope <- scale(results_clean$Slope)
  140. library(ggplot2)
  141. library(grid)
  143. ggplot(results_clean, aes(x = Mean_T1T2, y = Z_Slope, color = Slope)) + geom_point(size = 3) + scale_color_gradient2(low = "blue", mid = "turquoise", high = "red", midpoint = 0 ) + theme_classic() + geom_smooth(aes(x = Mean_T1T2, y = Z_Slope), method = "lm", color = "darkgray", se = TRUE) + labs(x = "T1w/T2w (young adult ratios)", y = "Aging Effect [z-score slopes]", color = "Aging Slope") + theme(plot.margin = margin(t = 10, r = 5, b = 10, l = 5)) + annotate("segment", x = 1.45, xend = 1.45, y = -1.3, yend = -2.5, arrow = arrow(length = unit(0.25, "cm"), type = "closed"), color = "black") + annotate("text", x = 1.45, y = 0.25, label = "Tissue Loss", angle = 90, vjust = 0.5, hjust = 1, color = "black" ) + annotate("text",x = 1.74, y =-4.5, label = "Association", angle = 0, vjust = 0.5, hjust = 1, color = "black") + annotate("text", x = 2.3, y =-4.5, label = "Sensory", angle = 0, vjust = 0.5, hjust = 1, color = "black") + annotate("segment", x = 1.75, xend = 2.1, y = -4.5, yend = -4.5, arrow = arrow( length = unit(0.25, "cm"),type = "closed", ends="both"), color = "black") + theme(axis.text.x=element_text(color="black", face="plain", size=10), axis.text.y=element_text(color="black", face="plain", size=10)) + theme(axis.title.y=element_text(color="black", face="bold")) + theme(legend.key.size=unit(0.4,"cm"), legend.text=element_text(size=7), legend.title=element_text(size=9), legend.position = c(0.02, 1.02), legend.justification=c(0,1))
  146. ####
  147. predicted_values <- ggpredict(modela, terms = "CortexVol")
  148. plot(predicted_values) + geom_point(data = mydata, aes(x = CortexVol, y = moca01_moca_total), color = "grey33", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Neocortical Volume [mm³]", y = "Total MoCA Score") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=F) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(21, 30)) + scale_x_continuous(breaks = c(310000, 610000))
  151. ####
  152. predicted_values <- ggpredict(modela, terms = "eTIV")
  153. plot(predicted_values) + geom_point(data = mydata, aes(x = eTIV, y = moca01_moca_total), color = "goldenrod", alpha = 0.2, size = 6, stroke=1) + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=F) + labs(title = "", x = "eTIV [mm³]", y = "Total MoCA Score") + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5),axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25),axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(21, 30)) + scale_x_continuous(breaks = c(1000, 1800))
  156. ####
  157. predicted_values <- ggpredict(modela, terms = "Total_Cerebel_Vol")
  158. plot(predicted_values) + geom_point(data = mydata, aes(x = Total_Cerebel_Vol, y = moca01_moca_total), color = "#428CD4", alpha = 0.2, size = 6, stroke=1) + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=F) + labs(title = "", x = "Total Cerebellar Volume [mm³]", y = "Total MoCA Score") + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5),axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25),axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1), plot.margin = margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border = element_blank() ) + scale_y_continuous(breaks = c(21, 30)) + scale_x_continuous(breaks = c(90000, 167000), labels=c("90000","172000"))
  161. ##############
  162. #### Figure 2
  164. library(ggplot2)
  165. library(gridExtra)
  166. library(patchwork)
  168. models <- list()
  170. custom_palette <- c("#80D0C7", "#8CC7B3", "#99BF9F", "#A6B78B", "#B2AF77", "#BFA763", "#CC9F4F", "#D8973B", "#E58F27", "#F28713", "#FF7F00")
  172. library(cowplot)
  174. plot_bilateral_average <- function(data, lobule_name, color) {
  175. data$bilateral_average <- rowMeans(data[, c(paste0("Left.", lobule_name), paste0("Right.", lobule_name))], na.rm = TRUE)
  176. plot <- ggplot(data = data, aes(x = moca01_moca_total, y = bilateral_average)) + geom_point(color = color, alpha=0.2, size=4) + geom_smooth(method = "lm", se = TRUE, color = color, fill=alpha(color,0.3)) + labs(title = paste("", lobule_name), x = "", y = "Bilateral Average Volume") + theme_classic() + theme(axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.line.x = element_line(color = "black", size = 2), axis.line.y = element_blank(), axis.text.x = element_text(color = "black", face = "plain", size = 35, hjust=0.7, vjust=-0.2), axis.text.y = element_blank(), axis.title.y = element_text(color = "black", face = "plain", size = 35, margin = margin(t = 12, b = 12)), axis.title.x = element_text()) + labs(title = "", x = "\n\n\n\n", y = "") + theme(text = element_text(size = 30)) + scale_x_continuous(breaks = c(18, 33), labels=c("18", "33"), limits = c(18, 33)) + scale_y_continuous(breaks = c(0, 20000), limits = c(0, 22000))
  177. return(plot)
  178. }
  180. scatterplots <- lapply(1:length(lobules), function(i) {
  181. plot_bilateral_average(mydata, lobules[i], custom_palette[i])
  182. })
  184. master_plot <- grid.arrange(grobs = scatterplots, nrow = 1)
  186. master_plot <- ggdraw(master_plot) + draw_label("\n I-III IV V VI Crus I Crus II VIIb VIIIa VIIIb IX X ", fontface = 'bold', size = 36, x = 0.5, y = 0.200)
  187. master_plot <- ggdraw(master_plot) + draw_label("MoCA Total Score", fontface = 'plain', size = 36, x = 0.5, y = 0.120)
  189. master_plot <- master_plot + geom_segment(aes(x = 0.0055, xend = 0.0055, y = 0.248, yend = 0.87), linetype = "solid", color = "black", size = 2) + annotate("text", x = 0.0087, y = 0.282, label = "0", vjust = 1, hjust = 0.001, size = 13) + annotate("text", x = 0.0087, y = 0.83, label = "20000", vjust = 0, hjust = 0.001, size = 13) + annotate("text", x = -0.008, y = 0.57, label = "Volume [mm³]", angle = 90, vjust = 0.2, hjust = 0.5, size = 15, fontface = 'bold') + theme(plot.margin = margin(0, 2, 0, 2, "cm"), panel.border = element_blank())
  191. print(master_plot)
  194. ####
  195. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  196. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = moca01_moca_total), color = "gray33", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Age [years]\n", y = "\nTotal MoCA Score") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(21, 30), labels=c("21", "30")) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  199. ####
  200. plot_model(modela, type="pred", terms=c("interview_age_years", "MOCA_FX"), partial.residuals=TRUE, colors=c("red2", "green4", "purple"), show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aging\nMoCA\nCerebellar\nVolume") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.99), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nTotal MoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD")) + scale_y_continuous(breaks = c(23, 29), labels=c("23", "29"), limits = c(23, 29))
  203. ####
  204. jn <- johnson_neyman(modela, pred= MOCA_FX, modx= interview_age_years, mod.range=c(36,100), control.fdr=TRUE, title="Johnson-Neyman Plot", line.thickness=1)
  205. png(filename="JohnsonNeyman - MOCA Total Scores ~ Age x CerebMOCA.png", width=22, height=15, units="cm", bg="white", res=280)
  206. jn$plot + theme_classic() + theme(axis.line.x=element_line(color="black", size=.5), axis.line.y=element_line(color="black", size=.5), legend.title=element_blank(), legend.position=c(0.10, 0.90), legend.key.size=unit(0.5, "cm"), legend.text=element_text(size=20.5), axis.ticks=element_blank(), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30,vjust=-0.9), axis.title.x=element_text(color="black", face="plain", size=30)) + labs(title="", x="Age [years]\n", y="MoCA Total Scores\nSlope") + geom_text(aes(x=51, y=0.25, label="48", fontface="italic"), color="black", size=8, vjust=-0.5, hjust=0.5) + scale_y_continuous(breaks = c(-5, 30), labels=c("0", "30"), limits=c(-5,30)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits=c(36, 100)) + theme(plot.margin = margin(1,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  209. ####
  210. predicted_values <- ggpredict(modela, terms = "Visuospatial_FX")
  211. plot(predicted_values) + geom_point(data = mydata, aes(x = Visuospatial_FX, y = moca01_visuospatial_executive), color = "gray33", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Aging x Visuospatial MoCa Score\nCerebellar Tissue Volume [mm³]", y = "Visuospatial\nMoCA Score") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_x_continuous(breaks = c(0.3, 0.7), labels=c("0.3", "0.7")) + scale_y_continuous(breaks = c(0, 5), labels=c("0", "5")) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  214. ####
  215. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  216. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = moca01_visuospatial_executive), color = "gray33", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Age [years]\n", y = "Visuospatial\nMoCA Score") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(0, 5), labels=c("0", "5"), limits = c(0, 6)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  219. ####
  220. plot_model(modela, type="pred", terms=c("interview_age_years", "Visuospatial_FX"), partial.residuals=TRUE, show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aging\nVisuospatial\nCerebellar\nSignature") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.5), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nVisuospatial Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(2.75, 5), labels=c("3", "5"), limits = c(2.75, 5)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD"))
  223. ####
  224. jn <- johnson_neyman(modela, pred= Visuospatial_FX, modx= interview_age_years, mod.range=c(36, 100), control.fdr=TRUE, title="Johnson-Neyman Plot", line.thickness=1)
  225. jn$plot + theme_classic() + theme(axis.line.x=element_line(color="black", size=.5), axis.line.y=element_line(color="black", size=.5), legend.title=element_blank(), legend.position=c(0.10, 0.90), legend.key.size=unit(0.5, "cm"), legend.text=element_text(size=20.5), axis.ticks=element_blank(), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30,vjust=0.5), axis.title.x=element_text(color="black", face="plain", size=30)) + labs(title="", x="Age [years]\n", y="Visuospatial Scores\nSlope") + geom_text(aes(x=47, y=0.25, label="44", fontface="italic"), color="black", size=8, vjust=-0.5, hjust=0.5) + scale_y_continuous(breaks = c(-2, 16), labels=c("0", "5"), limits=c(-2,16 )) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits=c(36, 100)) + theme(plot.margin = margin(1,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  228. ####
  229. library(ggseg)
  230. library(ggseg3d)
  232. Data = data.frame(
  233. region = c("precuneus","postcingulate"),
  234. p = sample(seq(0,.5,.05,.0001), 3),
  235. stringsAsFactors = FALSE)
  236. ggseg(.data= Data, atlas=dk, colour="gray33", mapping=aes(fill=p))
  239. ####
  240. predicted_values <- ggpredict(modela, terms = "MOCA_FX")
  241. plot(predicted_values) + geom_point(data = mydata, aes(x = MOCA_FX, y = bh_precuneus_PCC_volume), color = "gray33", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Aging-MoCA\nCerebellar Tissue Volume [mm³]", y = "Precuneus-Postcingulate\nTissue Volume [mm³]") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_x_continuous(breaks = c(0.2, 0.8), labels=c("0.2", "0.8"), limits = c(0.2, 0.8)) + scale_y_continuous(breaks = c(3500, 10000), labels=c("3500", "10000"), limits = c(3500, 10000)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  244. ####
  245. predicted_values <- ggpredict(modela, terms = "interview_age_years")
  246. plot(predicted_values) + geom_point(data = mydata, aes(x = interview_age_years, y = bh_precuneus_PCC_volume), color = "dodgerblue3", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Age [years]\n", y = "Precuneus-Postcingulate\nTissue Volume [mm³]") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=T) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(16000, 40000), labels=c("16000", "40000"), limits = c(16000, 40000))
  249. ####
  250. predicted_values <- ggpredict(modela, terms = "bh_precuneus_PCC_volume")
  251. plot(predicted_values) + geom_point(data = mydata, aes(x = bh_precuneus_PCC_volume, y = moca01_moca_total), color = "dodgerblue3", alpha = 0.2, size = 6, stroke=1) + labs(title = "", x = "Precuneus-Postcingulate\nTissue Volume [mm³]", y = "Total MoCA Score") + geom_smooth(method = "lm", formula = y ~ x, color = "black", fullrange=F) + theme_classic() + theme(axis.ticks.x = element_line(color = "black", size = 0.5), axis.ticks.y = element_line(color = "black", size = 0.5), axis.line.x = element_line(color = "black", size = 0.5), axis.line.y = element_line(color = "black", size = 0.5), axis.text.x = element_text(color = "black", face = "plain", size = 25), axis.text.y = element_text(color = "black", face = "plain", size = 25), axis.title.y = element_text(color = "black", face = "plain", size = 30, hjust = 0.5, vjust = 2), axis.title.x = element_text(color = "black", face = "plain", size = 30, vjust = -0.1)) + scale_y_continuous(breaks = c(19, 30), labels=c("19", "30"), limits = c(19, 31)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_x_continuous(breaks = c(3500, 10000), labels=c("3500", "10000"), limits = c(3500, 10000))
  254. ####
  255. plot_model(modela, type="pred", terms=c("interview_age_years", "bh_precuneus_PCC_volume"), partial.residuals=TRUE, colors=c("red2", "green4", "purple"), show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Precuneus\nPostcingulate\nVolume") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.99), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nTotal MoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(23, 30), labels=c("23", "30"), limits = c(23, 30)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD"))
  258. ####
  259. plot_model(modela, type="pred", terms=c("interview_age_years", "MOCA_FX"), partial.residuals=TRUE, colors=c("red2", "green4", "purple"), show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aging\nMoCA\nCerebellar\nVolume") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.99), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nTotal MoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(36, 100), labels=c("36", "100"), limits = c(36, 100)) + scale_y_continuous(breaks = c(23, 30), labels=c("23", "30"), limits = c(23, 30)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD"))
  262. ##############
  263. #### Figure 3
  265. models <- list()
  267. fit_model_and_extract_age_coef <- function(data, lobule_name) {
  268. bilateral_avg <- rowMeans(data[, c(paste0("lh_", lobule_name), paste0("rh_", lobule_name))], na.rm = TRUE)
  269. model <- lm(bilateral_avg ~ data$Sex + data$SES_eduLvl_releveled + data$Vol + data$Age.at.scan)
  270. age_coef <- coef(model)["data$Age.at.scan"]
  271. return(age_coef)
  272. }
  274. custom_palette <- c("#B2AF77", "#BFA763","#F28713")
  276. library(cowplot)
  277. library(gridExtra)
  279. plot_bilateral_average <- function(data, lobule_name, color) {
  280. data$bilateral_average <- rowMeans(data[, c(paste0("lh_", lobule_name), paste0("rh_", lobule_name))], na.rm = TRUE)
  281. plot <- ggplot(data = data, aes(x = Age.at.scan, y = bilateral_average)) + geom_point(color = color, alpha=0.3, size=6) + geom_smooth(method = "lm", se = TRUE, color = color, fill=alpha(color,0.3), size=4) + labs(title = paste("", lobule_name), x = "\n\n\n", y = "Bilateral Average Volume") + theme_classic() + theme(axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.line.x = element_line(color = "black", size = 3), axis.line.y = element_blank(), axis.text.x = element_text(color = "black", face = "plain", size = 44, hjust=0.7, vjust=-0.2), axis.text.y = element_blank(), axis.title.y = element_text(), axis.title.x = element_text()) + labs(title = "", x = "\n\n\n", y = "") + theme(text = element_text(size = 30)) + scale_x_continuous(breaks = c(44, 81), labels=c("44", "81"), limits = c(44, 81)) + scale_y_continuous(breaks = c(0, 25000), limits = c(0, 25000))
  282. return(plot)
  283. }
  285. scatterplots <- lapply(1:length(lobules), function(i) {
  286. plot_bilateral_average(myUKdata, lobules[i], custom_palette[i])
  287. })
  289. master_plot <- grid.arrange(grobs = scatterplots, nrow = 1)
  291. master_plot <- ggdraw(master_plot) + draw_label("\n Crus I Crus II IX ", fontface = 'bold', size = 55, x = 0.5, y = 0.185)
  292. master_plot <- ggdraw(master_plot) + draw_label("Age [years]", fontface = 'plain', size = 55, x = 0.5, y = 0.059)
  294. master_plot <- master_plot + geom_segment(aes(x = 0.0055, xend = 0.0055, y = 0.225, yend = 0.90), linetype = "solid", color = "black", size = 2) + annotate("text", x = 0.0088, y = 0.270, label = "0", vjust = 1, hjust = -0.4, size = 15) + annotate("text", x = 0.0088, y = 0.86, label = "25000", vjust = 0, hjust = -0.1, size = 15) + annotate("text", x = -0.008, y = 0.56, label = "Volume [mm³]", angle = 90, vjust = -0.2, hjust = 0.5, size = 20, fontface = 'bold') + theme(plot.margin = margin(0,2.1,0,3, "cm"), panel.border = element_blank())
  296. print(master_plot)
  299. ####
  300. library(ggpointdensity)
  301. library(dplyr)
  302. library(viridis)
  303. library(ggplot2)
  305. ggplot(myUKdata, aes(x = Age.at.scan, y = correct_numberofDSSTmatches)) + geom_jitter(size=1, width = 0, height = 0, color = "black", alpha = 0.01) + geom_pointdensity(size=4, alpha=0.2) + geom_smooth(method = "lm", se = T, color = "darkgray", fill = "lightgray", size=2) + theme_classic() + scale_color_viridis() + theme(axis.ticks.x=element_line(color="black", size=0.5), axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=17), axis.text.y=element_text(color="black", face="plain", size=17), axis.title.y=element_text(color="black", face="plain", size=23, hjust=0.5,vjust=3), axis.title.x=element_text(color="black", face="plain", size=23, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=17), legend.position="right", legend.text=element_text(size=14, hjust=1), legend.key=element_rect(colour="white"), legend.key.size=unit(0.6, "cm"), legend.spacing.y=unit(0.3,'cm')) + labs(title="", x="Age [years]",y="DSST [score]", color="") + theme(text=element_text(size=22)) + ylim(0,40) + theme(plot.margin = margin(0,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  308. ####
  309. ggplot(myUKdata, aes(x = Age.at.scan, y = durationtocomplete_Trails2)) + geom_jitter(size=1, width = 0, height = 0, color = "black", alpha = 0.01) + geom_pointdensity(size=4, alpha=0.2) + geom_smooth(method = "lm", se = T, color = "darkgray", fill = "lightgray", size=2) + theme_classic() + scale_color_viridis() + theme(axis.ticks.x=element_line(color="black", size=0.5), axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=17), axis.text.y=element_text(color="black", face="plain", size=17), axis.title.y=element_text(color="black", face="plain", size=23, hjust=0.5,vjust=3), axis.title.x=element_text(color="black", face="plain", size=23, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=17), legend.position="right", legend.text=element_text(size=14, hjust=1), legend.key=element_rect(colour="white"), legend.key.size=unit(0.6, "cm"), legend.spacing.y=unit(0.3,'cm')) + labs(title="", x="Age [years]",y="TMB [seconds]", color="") + theme(text=element_text(size=22)) + theme(plot.margin = margin(0,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  312. ####
  313. plot_model(modela, type="int", terms=c("Age.at.scan", "bh_Cerebellum2Lobules_FAST_vol_category"), partial.residuals=TRUE, colors=c("purple","darkorange2"), show.legend=F, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Cerebellar\nVolume\nGroup") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=17), axis.text.y=element_text(color="black", face="plain", size=17), axis.title.y=element_text(color="black", face="plain", size=23, hjust=0.5,vjust=2), axis.title.x=element_text(color="black", face="plain", size=23, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=17), legend.position=c(0.9,0.8), legend.text=element_text(size=14, hjust=1), legend.key=element_rect(colour="white"), legend.key.size=unit(0.6, "cm"), legend.spacing.y=unit(0.3,'cm')) + labs(title="", x="Age [years]",y="DSST [score]") + theme(text=element_text(size=22)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank())
  316. ####
  317. p <- plot_model(modela, type="int", terms=c("Age.at.scan", "bh_Cerebellum2Lobules_FAST_vol_category"), partial.residuals=TRUE, colors=c("purple", "darkorange2"), show.legend=FALSE, show.data=F, jitter=0.5, dot.size=1, sort.est=TRUE, show.values=TRUE, legend.title="") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=17), axis.text.y=element_text(color="black", face="plain", size=17), axis.title.y=element_text(color="black", face="plain", size=23, hjust=0.5,vjust=2), axis.title.x=element_text(color="black", face="plain", size=23, vjust=-0.3), legend.title=element_text(color="black", face="plain", size=17), legend.position=c(0.13, 0.80), legend.text=element_text(size=17, hjust=0), legend.spacing.y=unit(0.2,'cm'), legend.spacing.x=unit(0.3,'cm'),legend.key.width=unit(0.65,'cm'),legend.key.height=unit(0.65,'cm'), legend.box.margin=margin(0.5, 0.5, 0.5, 0.5, "cm")) + labs(title="", x="Age [years]", y="TMB [seconds]") + theme(text=element_text(size=22, face="plain")) + theme(plot.margin=margin(0.5, 0.5, 0.5, 0.5, "cm"), panel.border=element_blank())+ guides(fill=guide_legend(byrow=TRUE, override.aes=list(alpha=0.6)))
  319. draw_plot <- ggdraw(p) + draw_text("Cerebellar Volume", x = 0.17, y = 0.83, size = 20, color = "black", hjust = 0)
  320. print(draw_plot)
  323. ##############
  324. #### Figure 4
  326. plot_model(modela, type="pred", terms=c("Age", "Total_Cerebel_Vol"), partial.residuals=TRUE, colors=c("red2", "green4", "purple"), show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aβ-\nTotal\nCerebellar\nVolume") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.99), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nMoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(55, 100), labels=c("56", "100"), limits = c(55, 100)) + scale_y_continuous(breaks = c(18, 26), labels=c("20", "30"), limits = c(18, 26)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD"))
  329. ####
  330. plot_model(modela, type="pred", terms=c("Age", "Total_Cerebel_Vol"), partial.residuals=TRUE, colors=c("red2", "green4", "purple"), show.legend=T, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aβ+\nTotal\nCerebellar\nVolume") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=25), axis.text.y=element_text(color="black", face="plain", size=25), axis.title.y=element_text(color="black", face="plain", size=30, vjust=1.75), axis.title.x=element_text(color="black", face="plain", size=30, vjust=0.99), legend.title=element_text(color="black", face="plain", size=25), legend.position="right", legend.text=element_text(size=25), legend.key=element_rect(colour="white"), legend.key.size=unit(0.5, "cm"), legend.spacing.y=unit(0.2,'cm')) + labs(title="", x="Age [years]\n",y="\nMoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(55, 100), labels=c("56", "100"), limits = c(55, 100)) + scale_y_continuous(breaks = c(18, 26), labels=c("20", "30"), limits = c(18, 26)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_discrete(labels=c("-1 SD","mean","+1 SD"))
  333. ####
  334. plot_model(modela, type="pred", terms=c("Total_Cerebel_Vol", "DxABstatus_new"), partial.residuals=TRUE, colors=c("firebrick2","deepskyblue4","forestgreen","darkorchid","darkorange","blue2"), show.legend=F, show.data=F, jitter=0.5, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=28), axis.text.y=element_text(color="black", face="plain", size=28), axis.title.y=element_text(color="black", face="plain", size=33, vjust=-1.5), axis.title.x=element_text(color="black", face="plain", size=33, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=28), legend.position="right", legend.text=element_text(size=23, margin=margin(t=7), vjust=1.2, hjust=0), legend.spacing.y = unit(0.1, 'cm'), legend.key=element_rect(colour="white"), legend.key.size=unit(0.8, "cm"), legend.key.width=unit(1,'cm')) + labs(title="", x="Cerebellar Volume [mm³]\n",y="\nMoCA Score") + theme(text=element_text(size=22)) + scale_x_continuous(breaks = c(100000, 176000), labels=c("100000", "176000"), limits = c(100000, 180000)) + scale_y_continuous(breaks = c(10, 30), labels=c("10", "30"), limits = c(10, 30)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_color_manual(values = c("firebrick2","deepskyblue4","forestgreen","darkorchid","darkorange","blue2"), labels = c(" CN Aβ-"," CN Aβ+"," MCI Aβ-"," MCI Aβ+"," AD Aβ-"," AD Aβ+")) + scale_fill_manual(values = c("firebrick2","deepskyblue4","forestgreen","darkorchid","darkorange","blue2"), labels = c(" CN Aβ-"," CN Aβ+"," MCI Aβ-"," MCI Aβ+"," AD Aβ-"," AD Aβ+")) + guides(fill=guide_legend(byrow=TRUE, override.aes=list(alpha=0.4)))
  337. ####
  338. p <- plot_model(modela, type="pred", terms=c("Total_Cerebel_Vol", "APOE4"), partial.residuals=TRUE, show.legend=F, show.data=F, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aβ-\nAPOE") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=20), axis.text.y=element_text(color="black", face="plain", size=20), axis.title.y=element_text(color="black", face="plain", size=25, hjust=0.45,vjust=.25), axis.title.x=element_text(color="black", face="plain", size=25, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=20), legend.position=c("right"), legend.text=element_text(size=20, hjust=1, margin=margin(t=7), vjust=1.3), legend.key=element_rect(colour="white"), legend.key.size=unit(0.6, "cm"), legend.key.width=unit(0.8,'cm'), legend.spacing.y=unit(0.1,'cm')) + labs(title="", x="Cerebellar Volume [mm³]",y="MoCA Score") + theme(text=element_text(size=22)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(10,30), labels=c("10","30"), limits = c(10, 30.5)) + scale_y_continuous(breaks = c(10,30), labels=c("10","30"), limits = c(10, 30.5)) + scale_color_manual(values = c("firebrick2", "goldenrod", "darkblue"), labels = c(" 33"," 34"," 44")) + scale_fill_manual(values = c("firebrick2", "goldenrod", "darkblue"), labels = c(" 33"," 34"," 44")) + scale_x_continuous(breaks = c(100000,176000), labels=c("100000","176000"), limits = c(95000, 180000)) + guides(fill=guide_legend(byrow=TRUE, override.aes=list(alpha=0.4)))
  339. draw_plot <- ggdraw(p) + draw_text("Aβ-\nAPOE", x = 0.87, y = 0.68, size = 20, color = "black", hjust = 0, lineheight=0.95)
  340. print(draw_plot)
  343. ####
  344. p <- plot_model(modela, type="pred", terms=c("Total_Cerebel_Vol", "APOE4"), partial.residuals=TRUE, show.legend=F, show.data=F, dot.size=7, sort.est=TRUE, show.values=T, int.width=0.95, legend.title="Aβ+\nAPOE") + theme_classic() + theme(axis.ticks.x=element_line(color="black", size=0.5),axis.ticks.y=element_line(color="black", size=0.5), axis.line.x=element_line(color="black", size=0.5), axis.line.y=element_line(color="black", size=0.5), axis.text.x=element_text(color="black", face="plain", size=20), axis.text.y=element_text(color="black", face="plain", size=20), axis.title.y=element_text(color="black", face="plain", size=25, hjust=0.45,vjust=.25), axis.title.x=element_text(color="black", face="plain", size=25, vjust=-0.1), legend.title=element_text(color="black", face="plain", size=20), legend.position=c("right"), legend.text=element_text(size=20, hjust=1, margin=margin(t=7), vjust=1.3), legend.key=element_rect(colour="white"), legend.key.size=unit(0.6, "cm"), legend.key.width=unit(0.8,'cm'), legend.spacing.y=unit(0.1,'cm')) + labs(title="", x="Cerebellar Volume [mm³]",y="MoCA Score") + theme(text=element_text(size=22)) + theme(plot.margin = margin(0.5,0.5,0.5,0.5, "cm"), panel.border = element_blank()) + scale_y_continuous(breaks = c(10,30), labels=c("10","30"), limits = c(10, 30.5)) + scale_color_manual(values = c("firebrick2", "goldenrod", "darkblue"), labels = c(" 33"," 34"," 44")) + scale_fill_manual(values = c("firebrick2", "goldenrod", "darkblue"), labels = c(" 33"," 34"," 44")) + scale_x_continuous(breaks = c(100000,176000), labels=c("100000","176000"), limits = c(95000, 180000)) + guides(fill=guide_legend(byrow=TRUE, override.aes=list(alpha=0.4)))
  345. draw_plot <- ggdraw(p) + draw_text("Aβ+\nAPOE", x = 0.87, y = 0.68, size = 20, color = "black", hjust = 0, lineheight=0.95)
  346. print(draw_plot)

Figures.R at commit 346d622, no license · at the source

Overview

Authors: Federico d’Oleire Uquillas1, Esra Sefik1, Jakob Seidlitz2,3,4, Edan Daniel Hertz1, Rafael Romero-Garcia5, Varun Warrier6,7, Richard A. I. Bethlehem6, Aaron F. Alexander-Bloch2,3,4, Jonathan D. Cohen1, Samuel S.-H. Wang1, Jorge Sepulcre8,9,10, Patrizia Vannini9,11, Jesse Gomez1
ORCID iDs: Federico d’Oleire Uquillas, Jakob Seidlitz, Edan Daniel Hertz, Varun Warrier, Richard A. I. Bethlehem, Aaron F. Alexander-Bloch, Jonathan D. Cohen, Samuel S.-H. Wang, Jorge Sepulcre, Patrizia Vannini, Jesse Gomez
  1. Princeton Neuroscience Institute, Princeton University,Princeton, NJ USA
  2. Lifespan Brain Institute, Children’s Hospital of Philadelphia, University of Pennsylvania,Philadelphia, PA USA
  3. Department of Psychiatry, University of Pennsylvania,Philadelphia, PA USA
  4. Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia,Philadelphia, PA USA
  5. Department of Medical Physiology and Biophysics, Institute of Biomedicine of Seville (IBiS) HUVR/CSIC/University of Seville/CIBERSAM, ISCIII,Seville, Spain
  6. Department of Psychology, University of Cambridge,Cambridge, UK
  7. Department of Psychiatry, University of Cambridge,Cambridge, UK
  8. Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School,Boston, MA USA
  9. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School,Boston, MA USA
  10. Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University,New Haven, CT USA
  11. Brigham and Women’s Hospital, Harvard Medical School,Boston, MA USA
Institutions: Princeton University (United States); University of Pennsylvania (United States); Children's Hospital of Philadelphia (United States); Universidad de Sevilla (Spain); University of Cambridge (United Kingdom); Massachusetts General Hospital (United States); Yale University (United States); Brigham and Women's Hospital (United States)
Journal: Nature neuroscience, volume 29, issue 7, pages 1699-1710
Dates: received 16 December 2024; accepted 31 March 2026; published online 10 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41593-026-02289-x · PMID 42271047 · PMCID PMC13337503 · OpenAlex W7164120690
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Cognitive ageing, Cognitive neuroscience
MeSH: Aging*, Cerebellum*, Cognition*, Cognitive Reserve*, Adult, Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Vestibular and auditory disorders (Neurology, Neuroscience), according to OpenAlex
Funding: NSF (CAREER 2337373, GRFP 2021314805); Ford Foundation (20210323); ONR (N00014-22-12002)
Citations: cited by 1 paper (Europe PMC); 66 references in the paper
Notices: A correction to this paper has been published (42337099, from Europe PMC)

Abstract

The cerebellum contains most of the brain’s neurons and supports many functions, yet how it changes with age remains unclear. Here we used three brain imaging studies spanning 47,000 adults and examined how different parts of the cerebellum age and their relation to cognition. We characterized cerebellar aging using volumetry and the T1-weighted/T2-weighted ratio, and corroborated these findings with quantitative magnetic resonance imaging in an independent sample. We show a spatially heterogeneous pattern of aging in which specific association and motor-related regions show steeper relationships with age than other lobules. Greater cerebellar volume was associated with higher cognitive scores with increasing age, suggesting that cerebellar structure may provide brain reserve that helps maintain function despite aging. In patients with Alzheimer’s disease, cerebellar volume was linked to cognition in individuals with lower amyloid burden, especially in those carrying two copies of the APOE-ε4 risk gene. This supports a threshold-reserve model, in which the cerebellum helps sustain cognition until pathology becomes widespread. These results show that the cerebellum has an active role in healthy cognitive aging and resilience.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

fdoleireuquillas/AgingCerebellum

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 346d62259d8980e433528d1d841e70a0f70440c5, 15 December 2025
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), easystats (1 file), ggplot2 (1 file), ggseg (1 file), patchwork (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1 file
At the source: github.com/fdoleireuquillas/AgingCerebellum

Code availability

Source code related to the statistical analysis and visualization can be found at https://github.com/fdoleireuquillas/AgingCerebellum.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 4 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

Data analyzed are available at public repositories, which may require appropriate data sharing agreements from each individual publicly accessible database, including LONI for ADNI and HCP data (https://ida.loni.usc.edu/), and biobank UK for UKB data (https://biobank.ctsu.ox.ac.uk). Deidentified data for Figs. 1–4 can be found at https://github.com/fdoleireuquillas/AgingCerebellum. 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 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 13 MeSH terms, 3 funders, 62 references, 1 integrity notice.

Cite

This paper

d’Oleire Uquillas, F., Sefik, E., Seidlitz, J., Daniel Hertz, E., Romero-Garcia, R., Warrier, V., Bethlehem, R. A. I., Alexander-Bloch, A. F., Cohen, J. D., Wang, S. S.-H., Sepulcre, J., Vannini, P., & Gomez, J. (2026). Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life. Nature neuroscience, 29(7), 1699-1710. https://doi.org/10.1038/s41593-026-02289-x

BibTeX

@article{doleireuquillas2026cerebellar,
author = {d’Oleire Uquillas, Federico and Sefik, Esra and Seidlitz, Jakob and Daniel Hertz, Edan and Romero-Garcia, Rafael and Warrier, Varun and Bethlehem, Richard A. I. and Alexander-Bloch, Aaron F. and Cohen, Jonathan D. and Wang, Samuel S.-H. and Sepulcre, Jorge and Vannini, Patrizia and Gomez, Jesse},
title = {{Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life}},
journal = {Nature neuroscience},
year = {2026},
month = jun,
volume = {29},
number = {7},
pages = {1699--1710},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02289-x},
url = {https://doi.org/10.1038/s41593-026-02289-x},
pmid = {42271047},
pmcid = {PMC13337503}
}

RIS

TY - JOUR
AU - d’Oleire Uquillas, Federico
AU - Sefik, Esra
AU - Seidlitz, Jakob
AU - Daniel Hertz, Edan
AU - Romero-Garcia, Rafael
AU - Warrier, Varun
AU - Bethlehem, Richard A. I.
AU - Alexander-Bloch, Aaron F.
AU - Cohen, Jonathan D.
AU - Wang, Samuel S.-H.
AU - Sepulcre, Jorge
AU - Vannini, Patrizia
AU - Gomez, Jesse
TI - Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/06/10
VL - 29
IS - 7
SP - 1699
EP - 1710
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02289-x
UR - https://doi.org/10.1038/s41593-026-02289-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41593-026-02289-x",
"type": "article-journal",
"title": "Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life",
"container-title": "Nature neuroscience",
"author": [
{
"family": "d’Oleire Uquillas",
"given": "Federico"
},
{
"family": "Sefik",
"given": "Esra"
},
{
"family": "Seidlitz",
"given": "Jakob"
},
{
"family": "Daniel Hertz",
"given": "Edan"
},
{
"family": "Romero-Garcia",
"given": "Rafael"
},
{
"family": "Warrier",
"given": "Varun"
},
{
"family": "Bethlehem",
"given": "Richard A. I."
},
{
"family": "Alexander-Bloch",
"given": "Aaron F."
},
{
"family": "Cohen",
"given": "Jonathan D."
},
{
"family": "Wang",
"given": "Samuel S.-H."
},
{
"family": "Sepulcre",
"given": "Jorge"
},
{
"family": "Vannini",
"given": "Patrizia"
},
{
"family": "Gomez",
"given": "Jesse"
}
],
"container-title-short": "Nat Neurosci",
"volume": "29",
"issue": "7",
"page": "1699-1710",
"DOI": "10.1038/s41593-026-02289-x",
"PMID": "42271047",
"PMCID": "PMC13337503",
"ISSN": "1097-6256",
"publisher": "Nature Portfolio",
"URL": "https://doi.org/10.1038/s41593-026-02289-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
10
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-73714-9 [code]
The genetic architecture of cortical similarity networks.
Journal: Nature communications
In common: patchwork, ggplot2, tidyverse, structural MRI / diffusion, 1 reference, 3 authors
[2] doi:10.1162/imag.a.1290 [code]
Calibration of MRI-based reference intervals to new samples.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Alzheimer's / dementia, structural MRI / diffusion, 1 reference, 3 authors
[3] doi:10.1038/s42003-026-10276-y [code]
The cellular correlates and adolescent reorganisation of cortical myelination networks in the common marmoset.
Journal: Communications biology
In common: patchwork, tidyverse, structural MRI / diffusion, 3 references, author Richard Bethlehem
[4] doi:10.7554/elife.103097 [code]
Canonical neurodevelopmental trajectories of structural and functional manifolds.
Journal: eLife
In common: ggseg, cowplot, ggplot2, 1 other tool, structural MRI / diffusion, author Richard Bethlehem
[5] doi:10.1038/s41593-026-02363-4 [code]
Cortical thickness changes precede high levels of amyloid by at least 7 years.
Journal: Nature neuroscience
In common: ggseg, easystats, cowplot, 3 other tools, Alzheimer's / dementia, structural MRI / diffusion
[6] doi:10.1111/jcpp.70178 [code]
What makes a lonely child: environmental, health, and multimodal neuroimaging correlates of prospective loneliness in the ABCD study.
Journal: Journal of child psychology and psychiatry, and allied disciplines
In common: ggseg, easystats, patchwork, 2 other tools, structural MRI / diffusion, 1 reference
[7] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: ggseg, easystats, cowplot, 3 other tools, cognitive
[8] doi:10.1126/sciadv.aec2348 [code]
Congenital blindness reduces myelination in human visual cortex.
Journal: Science advances
In common: ggseg, cowplot, ggplot2, 1 other tool, structural MRI / diffusion, 2 references
[9] doi:10.1038/s41467-026-72940-5 [code]
Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior.
Journal: Nature communications
In common: 6 references
[10] doi:10.1002/hbm.70605 [code]
BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.
Journal: Human brain mapping
In common: ggseg, easystats, cowplot, 3 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.