#-------------------------------------------------------------------- #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #Change working directory to the folder with downloaded files #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #-------------------------------------------------------------------- #-----Call required libraries--------- library(stats) library(sjstats) library(Rcpp) library(ggplot2) library(ggpubr) library(dplyr) library(caTools) library(overlapping) library(lattice) library(pwr) library(stringr) #--------Read in data------------------ whole_cc <- read.csv("data_with_corpus_callosum_segmented_as_single_ROI.csv", header = TRUE) #cohort HC whole corpus callosum data regions_cc <- read.csv("data_for_corpus_callosum_segmented_by_subregions.csv", header = TRUE) #cohort HC sub-region data gender_cc <- read.csv("data_of_gender_matched_group_HC_sub.csv", header = TRUE) #data for cohort HC_sub (whole corpus callosum) seg_cc <- read.csv("data_for_segmentation_methods_comparison.csv", header = TRUE) #data for Figure 2: 10 matched subjects from HC_sub dataHC_whole<-data.frame(subset(whole_cc, group %in% c("HC"))) dataHC_region<-data.frame(subset(regions_cc, group %in% c("HC"))) #============================================================================================================================================================================================ #============================================================================================================================================================================================ #----------Supplementary table S1------------------------------------------------ #----------testing effects of gender on ageing using cohort HC_sub---------------------------- #compute linear regression models by stratifying HC by gender gender_cc_M <- gender_cc[gender_cc$gender=="M",] gender_cc_F <- gender_cc[gender_cc$gender=="F",] faModelHC1_M <- lm(fa~age,data=gender_cc_M) faModelHC1_F <- lm(fa~age,data=gender_cc_F) mdModelHC1_M <- lm(md~age,data=gender_cc_M) mdModelHC1_F <- lm(md~age,data=gender_cc_F) drModelHC1_M <- lm(rd~age,data=gender_cc_M) drModelHC1_F <- lm(rd~age,data=gender_cc_F) daModelHC1_M <- lm(ad~age,data=gender_cc_M) daModelHC1_F <- lm(ad~age,data=gender_cc_F) #display the linear regression models summary(faModelHC1_M) summary(faModelHC1_F) summary(mdModelHC1_M) summary(mdModelHC1_F) summary(drModelHC1_M) summary(drModelHC1_F) summary(daModelHC1_M) summary(daModelHC1_F) #============================================================================================================================================================================================ #============================================================================================================================================================================================ #------------------Analysis for tables 2, S2, and S3-------------------------------------------------- #------------------Using data from cohort HC------------------------------------------------------------------ #----------------------------Table 2----------------------------------------- #ANCOVA controlling for age between HC regions dataHC_region$region<-as.factor(dataHC_region$region) fage<-cut(dataHC_region$age, breaks=4) # convert numerical 'age' to categories for TukeyHSD ancovaFA3<-aov(fa~region+fage,data=dataHC_region) summary(ancovaFA3) TukeyHSD(ancovaFA3) omega_sq(ancovaFA3, partial = TRUE) ancovaMD3<-aov(md~region+fage,data=dataHC_region) summary(ancovaMD3) TukeyHSD(ancovaMD3) omega_sq(ancovaMD3, partial = TRUE) ancovaDR3<-aov(rd~region+fage,data=dataHC_region) summary(ancovaDR3) TukeyHSD(ancovaDR3) omega_sq(ancovaDR3, partial = TRUE) ancovaDA3<-aov(ad~region+fage,data=dataHC_region) summary(ancovaDA3) TukeyHSD(ancovaDA3) omega_sq(ancovaDA3, partial = TRUE) #------------------Table S2-------------------------------------------------- #compute linear regression models with interaction between age and gender faModelHC2 <- lm(fa~age,data=dataHC_whole) mdModelHC2 <- lm(md~age,data=dataHC_whole) drModelHC2 <- lm(rd~age,data=dataHC_whole) daModelHC2 <- lm(ad~age,data=dataHC_whole) #display the linear regression models summary(faModelHC2) summary(mdModelHC2) summary(drModelHC2) summary(daModelHC2) #-----------------------Table S3---------------------------------------------- dt<-data.frame(subset(dataHC_region, (group %in% c("HC") & region == 1))) # vary region = 1, 2, 3, 4, 5 faModelHC2 <- lm(fa~age,data=dt) summary(faModelHC2) mdModelHC2 <- lm(md~age,data=dt) summary(mdModelHC2) drModelHC2 <- lm(rd~age,data=dt) summary(drModelHC2) daModelHC2 <- lm(ad~age,data=dt) summary(daModelHC2) #========================================Tables S4 and S5==================================================================================================================================================== #============================================================================================================================================================================================ #-------------------------------Table S4---------------------------------- whole_cc_MCI <- data.frame(subset(whole_cc, (group %in% c("MCI")))) # regression faModel4<-lm(fa~age,data=whole_cc_MCI) summary(faModel4) mdModel4<-lm(md~age,data=whole_cc_MCI) summary(mdModel4) rdModel4<-lm(rd~age,data=whole_cc_MCI) summary(rdModel4) adModel4<-lm(ad~age,data=whole_cc_MCI) summary(adModel4) # omega-squared and p-value in table S4 #ANCOVA test, correcting for the effects of age ancovaFA4<-aov(fa~group+age,data=whole_cc) ancovaMD4<-aov(md~group+age,data=whole_cc) ancovaRD4<-aov(rd~group+age,data=whole_cc) ancovaAD4<-aov(ad~group+age,data=whole_cc) #displays the models and values of omega squared (effect size measure) summary(ancovaFA4) omega_sq(ancovaFA4, partial = TRUE) summary(ancovaMD4) omega_sq(ancovaMD4, partial = TRUE) summary(ancovaRD4) omega_sq(ancovaRD4, partial = TRUE) summary(ancovaAD4) omega_sq(ancovaAD4, partial = TRUE) #-------------------------------Table S5-------------------------------------- dt<-data.frame(subset(regions_cc, (group %in% c("MCI") & region == 1))) # vary region == 1, 2, 3, 4, 5 for table S5 faModelHC2 <- lm(fa~age,data=dt) summary(faModelHC2) mdModelHC2 <- lm(md~age,data=dt) summary(mdModelHC2) drModelHC2 <- lm(rd~age,data=dt) summary(drModelHC2) daModelHC2 <- lm(ad~age,data=dt) summary(daModelHC2) # omega_squared and p-value in table s5 # ------------------------------------------- dt<-data.frame(subset(regions_cc, (group %in% c("HC", "MCI") & region == 5))) # vary region = 1, 2, 3, 4, 5 #ANCOVA analysis, controlling for the effects of age ancovaFA<-aov(fa~group+age,data=dt) ancovaMD<-aov(md~group+age,data=dt) ancovaDR<-aov(rd~group+age,data=dt) ancovaDA<-aov(ad~group+age,data=dt) #display the results summary(ancovaFA) summary(ancovaMD) summary(ancovaDR) summary(ancovaDA) omega_sq(ancovaFA, partial = TRUE) omega_sq(ancovaMD, partial = TRUE) omega_sq(ancovaDR, partial = TRUE) omega_sq(ancovaDA, partial = TRUE)