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Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife.

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  1. [1] § Methods › Cognitive assessments and risk factors › The PREVENT cohort › Cognitive assessment ↔ risk_on_segregation_PREVENT.R, lines 1–42 · score 0.53 · cognitive domain, ratio, rotated, component, baseline

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The authors' code

R · 349 lines · 18 KB · no license · 1 match

  1. library(readxl)
  2. library(dplyr)
  3. library(psych)
  4. library(ggplot2)
  5. library(corrplot)
  6. library(Rmisc)
  7. library(lme4)
  8. library(lmerTest)
  9. library(effectsize)
  10. library(car)
  11. library(rstatix)
  12. library(tidyr)
  13. library(gghalves)
  15. ## Load cognition data
  16. Cog_t1 <- read_excel("Demo_risk_cog_t1_t2.xlsx", sheet = "baseline")
  17. Cog_t2 <- read_excel("Demo_risk_cog_t1_t2.xlsx", sheet = "follow_up")
  19. # recode data
  20. Cog_t1$FH1 <- factor(Cog_t1$FH1, levels=c(0,1), labels = c("FH-","FH+"))
  21. Cog_t2$FH2 <- factor(Cog_t2$FH2, levels=c(0,1), labels = c("FH-","FH+"))
  23. Cog_t1$apoe4 <- factor(Cog_t1$apoe4, levels=c(0,1), labels = c("non-carriers","carriers"))
  24. Cog_t2$apoe4 <- factor(Cog_t2$apoe4, levels=c(0,1), labels = c("non-carriers","carriers"))
  26. Cog_t1$Sex <- factor(Cog_t1$Sex, levels=c(1,2), labels = c("Male","Female"))
  27. Cog_t2$Sex <- factor(Cog_t2$Sex, levels=c(1,2), labels = c("Male","Female"))
  29. # Rotated PCA to extract main cognitive domains
  30. # parrallel analysis and scree plot to decide the number of components
  31. ParaAna_t1<-fa.parallel(Cog_t1[,9:21], fa="pc", n.iter = 500)
  32. ParaAna_t2<-fa.parallel(Cog_t2[,9:21], fa="pc", n.iter = 500)
  34. # rotated PCA
  35. PCs_t1<-principal(Cog_t1[,9:21], nfactors = 3, rotate="Varimax", eps=1e-7)
  36. PCs_t2<-principal(Cog_t2[,9:21], nfactors = 3, rotate="Varimax", eps=1e-7)
  38. # Visualise
  39. fa.diagram(PCs_t1)
  40. fa.diagram(PCs_t2)
  41. corrplot(PCs_t1$loadings,cl.ratio = 0.6,number.cex=0.8,number.digits=2, addCoef.col="black")
  42. corrplot(PCs_t2$loadings,cl.ratio = 0.6,number.cex=0.8,number.digits=2, addCoef.col="black")
  44. # Factor similarity
  45. fa.congruence(PCs_t1,PCs_t2)
  47. Cog_t1 <- cbind(Cog_t1,PCs_t1$scores)
  48. Cog_t2 <- cbind(Cog_t2,PCs_t2$scores) %>% rename(RC3=RC2,RC2=RC3)
  50. rm(ParaAna_t1,ParaAna_t2,PCs_t1,PCs_t2)
  52. Cog_t1$RC3_inv<-Cog_t1$RC3*(-1)
  53. Cog_t2$RC3_inv<-Cog_t2$RC3*(-1)
  55. # load functional data
  56. Seg_t1 <- read_excel("Segregation_Power214_noGSR.xlsx", sheet = "aucParameters_T1")
  57. Seg_t2 <- read_excel("Segregation_Power214_noGSR.xlsx", sheet = "aucParameters_T2")
  59. Seg_t1 <- inner_join(Seg_t1,Cog_t1,by="SubjID")
  60. Seg_t2 <- inner_join(Seg_t2,Cog_t2,by="SubjID")
  62. rm(Cog_t1,Cog_t2)
  64. # exclude participants with nodes less than
  65. Seg_t1<-Seg_t1[!(Seg_t1$No_Nodes_retained<214*0.8),]
  66. Seg_t2<-Seg_t2[!(Seg_t2$No_Nodes_retained<214*0.8),]
  68. # exclude participants with headmovement larger than
  69. Seg_t1<-Seg_t1[!(Seg_t1$meanFD>0.4),]
  70. Seg_t2<-Seg_t2[!(Seg_t2$meanFD>0.4),]
  72. # load brain struct data
  73. BrainStruc_t1 <- read_excel("BrainStruc_t1_t2.xlsx", sheet = "T1")
  74. BrainStruc_t2 <- read_excel("BrainStruc_t1_t2.xlsx", sheet = "T2")
  76. Seg_t1 <- inner_join(Seg_t1,BrainStruc_t1,by="SubjID")
  77. Seg_t2 <- inner_join(Seg_t2,BrainStruc_t2,by="SubjID")
  79. rm(BrainStruc_t1,BrainStruc_t2)
  80. Seg_t1<-Seg_t1%>%rename(TICV=EstimatedTotalIntraCranialVol)
  81. Seg_t2<-Seg_t2%>%rename(TICV=EstimatedTotalIntraCranialVol)
  83. # load number of retained brain nodes per network
  84. No_Nodes_Net_t1 <-read_excel("No_Nodes_Net.xlsx", sheet = "baseline")
  85. No_Nodes_Net_t2 <-read_excel("No_Nodes_Net.xlsx", sheet = "followup")
  87. Seg_t1 <- inner_join(Seg_t1,No_Nodes_Net_t1,by="SubjID")
  88. Seg_t2 <- inner_join(Seg_t2,No_Nodes_Net_t2,by="SubjID")
  89. rm(No_Nodes_Net_t1,No_Nodes_Net_t2)
  91. # code some data
  92. Seg_t1[Seg_t1$Sex == 'Male','SexC'] = -1
  93. Seg_t1[Seg_t1$Sex == 'Female','SexC'] = 1
  95. Seg_t1[Seg_t1$FH == 'FH-','FHC'] = -1
  96. Seg_t1[Seg_t1$FH == 'FH+','FHC'] = 1
  98. Seg_t1_clean <- Seg_t1 %>% filter(!is.na(apoe4))
  99. Seg_t1_clean[Seg_t1_clean$apoe4 == 'non-carriers','apoe4C'] = -1
  100. Seg_t1_clean[Seg_t1_clean$apoe4 == 'carriers','apoe4C'] = 1
  102. # risk effect on global segregation
  103. lm_model_apoe4_1 <- lm(scale(Pc_Global) ~ apoe4C + scale(Age) + SexC + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1_clean)
  104. summary(lm_model_apoe4_1)
  105. round(confint(lm_model_apoe4_1), 2)
  107. lm_model_apoe4_2 <- lm(scale(Pc_Global) ~ apoe4C + scale(Age) + SexC + scale(TotalGrayVol) + scale(TICV) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1_clean)
  108. round(confint(lm_model_apoe4_2), 2)
  110. lm_model_FH <- lm(scale(Pc_Global) ~ FHC + scale(Age) + SexC + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1)
  111. summary(lm_model_FH)
  112. round(confint(lm_model_FH), 2)
  114. lm_model_CAIDE <- lm(scale(Pc_Global) ~ scale(CAIDE) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1_clean)
  115. summary(lm_model_CAIDE)
  116. round(confint(lm_model_CAIDE), 2)
  118. # risk effect on network segregation
  119. lm_Pc5<-lm(scale(Pc_5) ~ apoe4C + scale(Age) + SexC + scale(meanFD) + scale(Network5), data=Seg_t1_clean) # DMN
  120. lm_Pc7<-lm(scale(Pc_7) ~ apoe4C + scale(Age) + SexC + scale(meanFD) + scale(Network7), data=Seg_t1_clean) # FPN
  121. lm_Pc8<-lm(scale(Pc_8) ~ apoe4C + scale(Age) + SexC + scale(meanFD) + scale(Network8), data=Seg_t1_clean) # SN
  123. models <- list(lm_Pc5, lm_Pc7, lm_Pc8)
  125. results <- lapply(models, function(model) {
  126. tidy(model, conf.int = TRUE) %>%
  127. filter(term == "apoe4C") %>%
  128. select(term, estimate, conf.low, conf.high)
  129. })
  131. results_df <- do.call(rbind, results) %>%
  132. mutate(Network = rep(c("DMN", "FPN", "SN"), each = 1)) %>%
  133. mutate(term = "apoe4C")
  135. results_df$Network<-factor(results_df$Network,levels = c("SN","DMN","FPN"))
  137. # PLOT baseline results
  138. Seg_t1_clean$resid_Pc <- resid(lm(Pc_Global ~ scale(Age) + SexC + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1_clean))
  139. Seg_t1_clean$resid_Pc_5 <- resid(lm(Pc_5 ~ scale(Age) + SexC + scale(meanFD) + scale(Network5), data = Seg_t1_clean))
  141. Seg_t1_clean$apoe4n<-as.numeric(Seg_t1_clean$apoe4) #noncarriers=1; carriers=2
  142. set.seed(123)
  143. Seg_t1_clean$apoe4nj<-jitter(Seg_t1_clean$apoe4n,amount=0.09)
  144. GPc_apoe4_SE <- summarySE(Seg_t1_clean, measurevar="resid_Pc", groupvars="apoe4n")
  145. Pc5_apoe4_SE <- summarySE(Seg_t1_clean, measurevar="resid_Pc_5", groupvars="apoe4n")
  147. ggplot(data=filter(Seg_t1_clean, !is.na(apoe4n)), aes(y = resid_Pc))+
  148. geom_point(data=Seg_t1_clean %>% filter(apoe4n=="1"),aes(x = apoe4nj),color = "cornflowerblue", size=2,alpha=.4)+
  149. geom_point(data=Seg_t1_clean %>% filter(apoe4n=="2"),aes(x = apoe4nj),color = "salmon",size=2, alpha=.6)+
  150. geom_errorbar(data=filter(GPc_apoe4_SE,!is.na(apoe4n)), aes(x=apoe4n, ymin=resid_Pc-ci, ymax=resid_Pc+ci), width=.1) +
  151. geom_point(data=filter(GPc_apoe4_SE,!is.na(apoe4n)) %>% filter(apoe4n=="1"),aes(x = apoe4n,y=resid_Pc),shape=21, color="black", fill="cornflowerblue", size=2, stroke=1)+
  152. geom_point(data=filter(GPc_apoe4_SE,!is.na(apoe4n)) %>% filter(apoe4n=="2"),aes(x = apoe4n,y=resid_Pc),shape=21, color="black", fill="salmon", size=2, stroke=1)+
  153. geom_half_violin(data=filter(Seg_t1_clean, !is.na(apoe4n)) %>% filter(apoe4n=="1"), aes(x = apoe4n, y = resid_Pc), #CHANGE
  154. position=position_nudge(x=-.2),side="l",width=.5, fill="cornflowerblue",alpha=.4)+
  155. geom_half_violin(data=filter(Seg_t1_clean, !is.na(apoe4n)) %>% filter(apoe4n=="2"), aes(x = apoe4n, y = resid_Pc), #CHANGE
  156. position=position_nudge(x=.2),side="r",width=.5, fill="salmon",alpha=.6)+
  157. scale_x_continuous(breaks = c(1, 2),labels=c("non-carriers","carriers"))+ #limits = c(0,3)
  158. ylab("Global Pc")+
  159. scale_y_continuous(labels = scales::number_format(accuracy = 0.01))+#,limits = c(-2,2),limits = c(-0.35,-0.25)
  160. theme(axis.text=element_text(size=12,colour = "black"),
  161. axis.title=element_text(size=12,colour = "black"),
  162. panel.border = element_blank(),
  163. panel.grid.major = element_blank(),
  164. panel.grid.minor = element_blank(),
  165. panel.background = element_blank(),
  166. axis.line = element_line(colour = "black",linewidth = 0.5),
  167. legend.position = "none",
  168. axis.title.x=element_blank())
  170. rm(GPc_apoe4_SE,Pc5_apoe4_SE)
  172. # segregation - cognition relationships at baseline
  173. lm_model_RC1_G <- lm(RC1 ~ scale(Pc_Global) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1)
  174. lm_model_RC2_G <- lm(RC2 ~ scale(Pc_Global) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1)
  175. lm_model_RC3_G <- lm(RC3_inv ~ scale(Pc_Global) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1)
  176. summary(lm_model_RC1_G)
  177. summary(lm_model_RC2_G)
  178. summary(lm_model_RC3_G)
  180. lm_model_RC1_DMN <- lm(RC1 ~ scale(Pc_5) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(Network5), data = Seg_t1)
  181. lm_model_RC2_DMN <- lm(RC2 ~ scale(Pc_5) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(Network5), data = Seg_t1)
  182. lm_model_RC3_DMN <- lm(RC3_inv ~ scale(Pc_5) + scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(Network5), data = Seg_t1)
  183. summary(lm_model_RC1_DMN)
  184. summary(lm_model_RC2_DMN)
  185. summary(lm_model_RC3_DMN)
  187. # plot Pc~Cog relationship
  188. Seg_t1 = Seg_t1[complete.cases(Seg_t1$RC1), ]
  189. Seg_t1$RC1_resids <- resid(lm(RC1 ~ scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1))
  190. Seg_t1$Pc_Global_resids <- resid(lm(Pc_Global ~ scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(No_Nodes_retained), data = Seg_t1))
  192. Seg_t1$RC1_resids2 <- resid(lm(RC1 ~ scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(Network5), data = Seg_t1))
  193. Seg_t1$Pc5_resids <- resid(lm(Pc_5 ~ scale(Age) + SexC + scale(YeaoEdu) + scale(meanFD) + scale(Network5), data = Seg_t1))
  195. ggplot(data=Seg_t1,#filter(Seg_t1, !is.na(apoe4.x)
  196. aes(x=Pc5_resids, y = RC1_resids2))+ #, color=apoe4.x
  197. geom_smooth(method=lm)+
  198. geom_jitter(size = 2, alpha=.6)+
  199. xlab("DMN Pc")+
  200. ylab("Episodic and relational memory")+ #∆ Response time in working memory
  201. scale_x_continuous(labels = scales::number_format(accuracy = 0.01))+#,limits=c(0.10,0.35)
  202. scale_y_continuous(labels = scales::number_format(accuracy = 0.01))+ #,limits=c(-12,16)
  203. theme(axis.text=element_text(size=12,colour = "black"),
  204. axis.title=element_text(size=12,colour = "black"),
  205. panel.border = element_blank(),
  206. panel.grid.major = element_blank(),
  207. panel.grid.minor = element_blank(),
  208. panel.background = element_blank(),
  209. axis.line = element_line(colour = "grey",linewidth = 1),
  210. legend.position = "right")
  213. # risk impact on longitudinal change of segregation
  214. Seg_all<-inner_join(Seg_t1_clean,Seg_t2,by="SubjID")
  216. Pc_Global<-gather(Seg_all[,c("SubjID","apoe4C","Age.x","SexC","Pc_Global.x","Pc_Global.y")], Time, Pc_Global, c("Pc_Global.x","Pc_Global.y"))
  217. Pc_Global$Time<-factor(Pc_Global$Time)
  218. levels(Pc_Global$Time)[levels(Pc_Global$Time)=="Pc_Global.x"] <- "Baseline"
  219. levels(Pc_Global$Time)[levels(Pc_Global$Time)=="Pc_Global.y"] <- "Follow_up"
  221. Pc_5<-gather(Seg_all[,c("SubjID","Pc_5.x","Pc_5.y")], Time, Pc_5, c("Pc_5.x","Pc_5.y"))
  222. Pc_5$Time<-factor(Pc_5$Time)
  223. levels(Pc_5$Time)[levels(Pc_5$Time)=="Pc_5.x"] <- "Baseline"
  224. levels(Pc_5$Time)[levels(Pc_5$Time)=="Pc_5.y"] <- "Follow_up"
  226. meanFD<-gather(Seg_all[,c("SubjID","meanFD.x","meanFD.y")], Time, meanFD, c("meanFD.x","meanFD.y"))
  227. meanFD$Time<-factor(meanFD$Time)
  228. levels(meanFD$Time)[levels(meanFD$Time)=="meanFD.x"] <- "Baseline"
  229. levels(meanFD$Time)[levels(meanFD$Time)=="meanFD.y"] <- "Follow_up"
  231. No_Nodes_retained<-gather(Seg_all[,c("SubjID","No_Nodes_retained.x","No_Nodes_retained.y")], Time, No_Nodes_retained, c("No_Nodes_retained.x","No_Nodes_retained.y"))
  232. No_Nodes_retained$Time<-factor(No_Nodes_retained$Time)
  233. levels(No_Nodes_retained$Time)[levels(No_Nodes_retained$Time)=="No_Nodes_retained.x"] <- "Baseline"
  234. levels(No_Nodes_retained$Time)[levels(No_Nodes_retained$Time)=="No_Nodes_retained.y"] <- "Follow_up"
  236. Network5<-gather(Seg_all[,c("SubjID","Network5.x","Network5.y")], Time, Network5, c("Network5.x","Network5.y"))
  237. Network5$Time<-factor(Network5$Time)
  238. levels(Network5$Time)[levels(Network5$Time)=="Network5.x"] <- "Baseline"
  239. levels(Network5$Time)[levels(Network5$Time)=="Network5.y"] <- "Follow_up"
  241. RC1<-gather(Seg_all[,c("SubjID","RC1.x","RC1.y")], Time, RC1, c("RC1.x","RC1.y"))
  242. RC1$Time<-factor(RC1$Time)
  243. levels(RC1$Time)[levels(RC1$Time)=="RC1.x"] <- "Baseline"
  244. levels(RC1$Time)[levels(RC1$Time)=="RC1.y"] <- "Follow_up"
  246. TotalGrayVol<-gather(Seg_all[,c("SubjID","TotalGrayVol.x","TotalGrayVol.y")], Time, TotalGrayVol, c("TotalGrayVol.x","TotalGrayVol.y"))
  247. TotalGrayVol$Time<-factor(TotalGrayVol$Time)
  248. levels(TotalGrayVol$Time)[levels(TotalGrayVol$Time)=="TotalGrayVol.x"] <- "Baseline"
  249. levels(TotalGrayVol$Time)[levels(TotalGrayVol$Time)=="TotalGrayVol.y"] <- "Follow_up"
  251. TICV<-gather(Seg_all[,c("SubjID","TICV.x","TICV.y")], Time, TICV, c("TICV.x","TICV.y"))
  252. TICV$Time<-factor(TICV$Time)
  253. levels(TICV$Time)[levels(TICV$Time)=="TICV.x"] <- "Baseline"
  254. levels(TICV$Time)[levels(TICV$Time)=="TICV.y"] <- "Follow_up"
  256. Pc<-left_join(Pc_Global,Pc_5)
  257. Pc<-left_join(Pc,meanFD)
  258. Pc<-left_join(Pc,No_Nodes_retained)
  259. Pc<-left_join(Pc,Network5)
  260. Pc<-left_join(Pc,RC1)
  261. Pc<-left_join(Pc,TotalGrayVol)
  262. Pc<-left_join(Pc,TICV)
  264. Pc[Pc$Time == 'Baseline','TimeC'] = -1
  265. Pc[Pc$Time == 'Follow_up','TimeC'] = 1
  267. model <- lmer(scale(Pc_Global) ~ TimeC*apoe4C + scale(Age.x) + SexC + scale(meanFD) + scale(No_Nodes_retained) + (1 | SubjID), data = Pc)
  268. model_5 <- lmer(scale(Pc_5) ~ TimeC*apoe4C+ scale(Age.x) + SexC + scale(meanFD) + scale(Network5) + (1 | SubjID), data = Pc)
  270. summary(model)
  271. round(confint(model), 2)
  272. summary(model_5)
  273. round(confint(model_5), 2)
  275. model_cog <- lmer(RC1 ~ TimeC*scale(Pc_Global) + scale(Age.x) + SexC + scale(meanFD) + scale(No_Nodes_retained) + (1 | SubjID), data = Pc)
  276. model_cog_5 <- lmer(RC1 ~ TimeC*scale(Pc_5)+ scale(Age.x) + SexC + scale(meanFD) + scale(Network5) + (1 | SubjID), data = Pc)
  278. summary(model_cog)
  279. summary(model_cog_5)
  281. # simple effects testing
  282. noncarriers<-filter(Seg_all,apoe4.x=="non-carriers")
  283. carriers<-filter(Seg_all,apoe4.x=="carriers")
  285. t.test(carriers$Pc_5.x,carriers$Pc_5.y,paired = T)#p=0.007
  286. t.test(noncarriers$Pc_5.x,noncarriers$Pc_5.y,paired = T)#p=0.49
  288. # plot longitudinal results
  289. Pc$resid_Pc <- resid(lmer(Pc_Global ~ scale(Age.x) + SexC + scale(meanFD) + scale(No_Nodes_retained) + (1 | SubjID), data = Pc))
  290. Pc$resid_Pc_5 <- resid(lmer(Pc_5 ~ scale(Age.x) + SexC + scale(meanFD) + scale(Network5) + (1 | SubjID), data = Pc))
  292. Pc$Timen<-as.numeric(Pc$Time)
  293. set.seed(123)
  294. Pc$Timenj<-jitter(Pc$Timen,amount=0.09)
  296. noncarriers<-filter(Pc,apoe4C==-1)
  297. carriers<-filter(Pc,apoe4C==1)
  299. library(Rmisc)
  300. noncarriers_SE <- summarySE(noncarriers, measurevar="resid_Pc", groupvars="Timen")
  301. carriers_SE <- summarySE(carriers, measurevar="resid_Pc", groupvars="Timen")
  303. ggplot(data=carriers, aes(y = resid_Pc))+ #CHANGE
  304. geom_point(data=carriers%>%filter(Timen=="1"), aes(x = Timenj), color = "salmon", size=2, alpha=.6)+
  305. geom_point(data=carriers%>%filter(Timen=="2"), aes(x = Timenj), color = "salmon", size=2, alpha=.6)+
  306. geom_line(data=carriers, aes(x=Timenj, group=SubjID), color='gray', alpha=.5)+
  307. geom_errorbar(data=carriers_SE, aes(x=Timen, ymin=resid_Pc-ci, ymax=resid_Pc+ci), width=.1) +
  308. geom_point(data=carriers_SE, aes(x=Timen, y=resid_Pc), shape=21, color="black", fill="salmon", size=2, stroke=1) +
  309. geom_line(data=carriers_SE, aes(x=Timen), color='salmon', linewidth=1, alpha=.5) +
  310. geom_half_violin(data=carriers %>% filter(Timen=="1"), aes(x = Timen, y = resid_Pc), #CHANGE
  311. position=position_nudge(x=1.3),side="r",width=.5, fill="salmon",alpha=.6)+
  312. geom_half_violin(data=carriers %>% filter(Timen=="2"), aes(x = Timen, y = resid_Pc), #CHANGE
  313. position=position_nudge(x=.3),side="r",width=.5, fill="salmon",alpha=.6)+
  314. scale_x_continuous(breaks = c(1,2),labels=c("Baseline","Follow-up"),limits = c(0,3))+ #,limits = c(0.5,3)
  315. scale_y_continuous(labels = scales::number_format(accuracy = 0.01),limits=c(-0.035,0.035))+ #,,limits=c(-0.35,-0.25)
  316. labs(title = "APOE ɛ4 carriers", y="Global Pc")+
  317. theme(axis.text=element_text(size=12,colour = "black"),
  318. axis.title=element_text(size=12,colour = "black"),
  319. panel.border = element_blank(),
  320. panel.grid.major = element_blank(),
  321. panel.grid.minor = element_blank(),
  322. panel.background = element_blank(),
  323. axis.line = element_line(colour = "black",linewidth = 0.5),
  324. legend.position = "none",
  325. axis.title.x=element_blank())
  327. ggplot(data=noncarriers, aes(y = resid_Pc))+ #CHANGE
  328. geom_point(data=noncarriers%>%filter(Timen=="1"), aes(x = Timenj), color = "cornflowerblue", size=2, alpha=.4)+
  329. geom_point(data=noncarriers%>%filter(Timen=="2"), aes(x = Timenj), color = "cornflowerblue", size=2, alpha=.4)+
  330. geom_line(data=noncarriers, aes(x=Timenj, group=SubjID), color='gray', alpha=.5)+
  331. geom_errorbar(data=noncarriers_SE, aes(x=Timen, ymin=resid_Pc-ci, ymax=resid_Pc+ci), width=.1) +
  332. geom_point(data=noncarriers_SE, aes(x=Timen, y=resid_Pc), shape=21, color="black", fill="cornflowerblue", size=2, stroke=1) +
  333. geom_line(data=noncarriers_SE, aes(x=Timen), color='cornflowerblue', linewidth=1, alpha=.5) +
  334. geom_half_violin(data=noncarriers %>% filter(Timen=="1"), aes(x = Timen, y = resid_Pc), #CHANGE
  335. position=position_nudge(x=-.3),side="l",width=.5, fill="cornflowerblue",alpha=.4)+
  336. geom_half_violin(data=noncarriers %>% filter(Timen=="2"), aes(x = Timen, y = resid_Pc), #CHANGE
  337. position=position_nudge(x=-1.3),side="l",width=.5, fill="cornflowerblue",alpha=.4)+
  338. scale_x_continuous(breaks = c(1,2),labels=c("Baseline","Follow-up"),limits = c(0,3))+ #,limits = c(0.5,3)
  339. scale_y_continuous(labels = scales::number_format(accuracy = 0.01),limits=c(-0.035,0.035))+ #,,limits=c(-0.35,-0.25) c(-0.35, -0.10)
  340. labs(title = "APOE ɛ4 non-carriers", y="Global Pc")+
  341. theme(axis.text=element_text(size=12,colour = "black"),
  342. axis.title=element_text(size=12,colour = "black"),
  343. panel.border = element_blank(),
  344. panel.grid.major = element_blank(),
  345. panel.grid.minor = element_blank(),
  346. panel.background = element_blank(),
  347. axis.line = element_line(colour = "black",linewidth = 0.5),
  348. legend.position = "none",
  349. axis.title.x=element_blank())

risk_on_segregation_PREVENT.R at commit 868dbc2, no license · at the source

Overview

Authors: Feng Deng1, Richard N. Henson2,3, Graciela Muniz-Terrera4,5, Paresh Malhotra6,7, John T. O’Brien3,8, Craig W. Ritchie4,9, Brian Lawlor10,11, Lorina Naci10,11
ORCID iDs: Richard N. Henson, Paresh Malhotra, John T. O’Brien, Lorina Naci
  1. School of Psychology, Shenzhen University,Shenzhen, China
  2. Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge,Cambridge, UK
  3. Department of Psychiatry, School of Clinical Medicine, University of Cambridge,Cambridge, UK
  4. Edinburgh Dementia Prevention, University of Edinburgh,Edinburgh, UK
  5. Department of Social Medicine, Ohio University, Ohio,Athens, OH USA
  6. Department of Brain Science, Imperial College London,London, UK
  7. UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey,London, UK
  8. Cambridgeshire and Peterborough NHS Foundation Trust,Cambridge, UK
  9. Scottish Brain Sciences, Edinburgh, UK
  10. Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin,Dublin, Ireland
  11. Global Brain Health Institute, Trinity College Dublin,Dublin, Ireland
Institutions: Shenzhen University (China); University of Cambridge (United Kingdom); University of Edinburgh (United Kingdom); Ohio University (United States); Imperial College London (United Kingdom); Cambridgeshire and Peterborough NHS Foundation Trust (United Kingdom); Trinity College Dublin (Ireland)
Journal: Communications biology, volume 9, issue 1, article 1067
Dates: received 11 June 2025; accepted 6 May 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10282-0 · PMID 42157001 · PMCID PMC13454165 · OpenAlex W7161748663
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Graphs, fMRI & imaging
Keywords: Risk factors, Predictive markers, Alzheimer's disease, Cognitive ageing
MeSH: Alzheimer Disease*, Brain*, Genetic Predisposition to Disease*, Nerve Net*, Adolescent, Adult, Aged, Aged, 80 and over, Apolipoprotein E4, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Risk Factors, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Health Research Board (HRB) (ILP-POR-2024-057); National Natural Science Foundation of China (National Science Foundation of China) (32400902)
Citations: not cited yet (Europe PMC); 105 references in the paper

Abstract

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

fengdeng315/Segregation_risk_age

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 868dbc2431e114e03bbc09f7ab4ec2e21724b920, 1 April 2025
Languages: R (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: easystats (2 files), ggplot2 (2 files), tidyverse (2 files), broom (1 file), car (1 file), lme4 (1 file), lmerTest (1 file), psych (1 file), rstatix (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/fengdeng315/Segregation_risk_age

Code availability statement

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Tracing map

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Data

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Data availability statement

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Read it in the paper: doi.org/10.1038/s42003-026-10282-0.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 18 MeSH terms, 2 funders, 102 references.

Cite

This paper

Deng, F., Henson, R. N., Muniz-Terrera, G., Malhotra, P., O’Brien, J. T., Ritchie, C. W., Lawlor, B., & Naci, L. (2026). Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife. Communications biology, 9(1), 1067. https://doi.org/10.1038/s42003-026-10282-0

BibTeX

@article{deng2026genetic,
author = {Deng, Feng and Henson, Richard N. and Muniz-Terrera, Graciela and Malhotra, Paresh and O’Brien, John T. and Ritchie, Craig W. and Lawlor, Brian and Naci, Lorina},
title = {{Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1067},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10282-0},
url = {https://doi.org/10.1038/s42003-026-10282-0},
pmid = {42157001},
pmcid = {PMC13454165}
}

RIS

TY - JOUR
AU - Deng, Feng
AU - Henson, Richard N.
AU - Muniz-Terrera, Graciela
AU - Malhotra, Paresh
AU - O’Brien, John T.
AU - Ritchie, Craig W.
AU - Lawlor, Brian
AU - Naci, Lorina
TI - Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/19
VL - 9
IS - 1
SP - 1067
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10282-0
UR - https://doi.org/10.1038/s42003-026-10282-0
LA - en
ER -

CSL-JSON

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"id": "10.1038/s42003-026-10282-0",
"type": "article-journal",
"title": "Genetic risk of Alzheimer's disease is associated with loss of brain network segregation in midlife",
"container-title": "Communications biology",
"author": [
{
"family": "Deng",
"given": "Feng"
},
{
"family": "Henson",
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{
"family": "Muniz-Terrera",
"given": "Graciela"
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{
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}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1067",
"DOI": "10.1038/s42003-026-10282-0",
"PMID": "42157001",
"PMCID": "PMC13454165",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10282-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
19
]
]
}
}

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