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Oral microbiota dysbiosis related to the cortical thinning and cognitive impairment in cerebral small vessel disease.

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  1. [1] § Materials and methods › Individual cortical thinning biomarkers ↔ individual_brain_health_output_of_example/Brain_individual_report_output.Rmd, lines 689–759 · score 0.67 · subcortical volume, centile scores, 0–1, atlas, segmented, lifespan

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The authors' code

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  1. ---
  2. title: "Brain Health Report"
  3. author: " "
  4. date: "`r Sys.Date()`"
  5. output:
  6. html_document: default
  7. pdf_document: default
  8. word_document: default
  9. ---
  11. ```{r setup, include=FALSE}
  12. knitr::opts_chunk$set(echo = TRUE,error = FALSE,message = FALSE)
  13. ```
  16. ## Deviation of global features:
  17. #### lightblue=Male,darkred=Female,soldline=Meidan,dottedline=95%CI
  19. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  20. #load required packages
  21. #install.packages('gamlss')
  22. rm(list=ls())
  24. datapath='C:/ZZZ/work/manuscript/Lifespan-main'# change
  25. setwd(datapath)
  26. source("100.common-variables.r")
  27. source("101.common-functions.r")
  29. source("ZZZ_function.R")
  30. source("300.variables.r")
  31. source("301.functions.r")
  34. #install.packages('gamlss')
  35. rm(list=ls())
  36. library(glmnet)
  37. library(dplyr);
  38. library(ggplot2)
  39. library(ggplot2)
  40. library(stringr)
  41. library(robustbase)
  42. library(gamlss)
  43. library(ggplot2)
  44. library(reshape2)
  47. source("100.common-variables.r")
  48. source("101.common-functions.r")
  50. source("ZZZ_function.R")
  51. source("300.variables.r")
  52. source("301.functions.r")
  54. library(raincloudplots)
  55. library(commonmark)
  56. library(ggplot2)
  57. #library(ggstatsplot)
  58. library(lmerTest)
  59. library(effectsize)
  60. library(ggrain)
  61. library(ggsci)
  62. library(ggdist)
  63. library(hrbrthemes)
  65. ```
  67. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  70. #read individual freesurfer-derived phenotypes and calcuate the centiles
  71. datafile = 'C:/ZZZ/work/manuscript/Lifespan-main/Results1_individual.xlsx'
  74. datapath = 'C:/ZZZ/work/manuscript/Lifespan-main'# change
  75. setwd(datapath)#set the filepath
  76. path <- list()
  77. str_lab <- 'global_and_local_feature'# change
  78. path[[1]] <- paste0(datapath, '/test_zzz_update_20240116/aseg.vol.table')
  80. path[[2]] <-
  81. paste0(datapath, '/test_zzz_update_20240116/lh.aparc.thickness.table')
  83. path[[3]] <-
  84. paste0(datapath, '/test_zzz_update_20240116/rh.aparc.thickness.table')
  86. path[[4]] <-
  87. paste0(datapath, '/test_zzz_update_20240116/lh.aparc.area.table')
  89. path[[5]] <-
  90. paste0(datapath, '/test_zzz_update_20240116/rh.aparc.area.table')
  92. path[[6]] <-
  93. paste0(datapath, '/test_zzz_update_20240116/lh.aparc.volume.table')
  95. path[[7]] <-
  96. paste0(datapath, '/test_zzz_update_20240116/rh.aparc.volume.table')
  98. path[[8]] <- paste0(datapath, '/test_zzz_update_20240116/Global_feature')
  102. RDSfile_all <- NULL
  103. for (i in 1:length(path))
  104. {
  105. feature_path = path[[i]]
  107. setwd(feature_path)
  108. myfile <- list.files()
  109. RDSfile_all0 <-
  110. myfile[grep(myfile, pattern = "our_model.rds$")]
  111. for (j in RDSfile_all0)
  112. {
  113. #RDSfile_all0<-paste0(feature_path,'/',RDSfile_all0);
  114. RDSfile_all <- c(RDSfile_all, paste0(feature_path, '/', j))
  117. }
  118. }
  120. datapath = 'C:/ZZZ/work/manuscript/Lifespan-main'# change
  121. setwd(datapath)
  123. #loading for Global_feature
  124. var <-
  125. c(
  126. 'global.table',
  127. 'aseg.vol.table',
  128. 'lh.aparc.volume.table',
  129. 'rh.aparc.volume.table',
  130. 'lh.aparc.thickness.table',
  131. 'rh.aparc.thickness.table',
  132. 'lh.aparc.area.table',
  133. 'rh.aparc.area.table'
  134. )
  135. setwd(datapath)
  136. patient_index=5768;
  138. MRI <- data.frame(read_excel(datafile, sheet = 'global.table'))
  139. #individual_name<-c("20211201_WENGJINYUAN_029_M_p181")
  140. #individual_name<-c("20211201_ZHUOZHIZHENG_034_M_p253")
  141. #individual_name<-c("20220511_SUNJUN_033_F_p624")
  142. #individual_name<-c("20221230_LIJUNJIE_027_M_p2412")
  143. #individual_name<-c("20230411_LIYUNA_026_F_p577")
  144. #individual_name<-c("20230427_CHAILI_027_F_p696")
  145. #individual_name<-c("20220512_HUATIANTIAN_023_F_p626")
  146. #individual_name<-c("20230104_LIZHUYUERONG_025_F_p2464")
  147. #individual_name<-c("Lifespan%3A20210203_WEIREN_029_M_p1843")
  148. #individual_name<-c("20230120_XUXIAOLU_032_F_p89")
  149. #individual_name<-c("20220521_XUSIYAO_024_F_p735")
  150. #individual_name<-c("20230112_Sunting_033_F_p12")
  151. #individual_name<-c("20230420_GUOMIN_035_F_p648")
  152. #individual_name<-c("20220725_lvshan_030_F_p1120")
  153. #individual_name<-c("20230104_JINYING_024_F_p2462")
  154. #individual_name<-c("20230106_wuminghao_033_M_p2494")
  155. #individual_name<-c("20220523_QULIYING_028_F_p637")
  156. #individual_name<-c("20220717_LIUCHENGHAO_024_M_p1065")
  157. #individual_name<-c("20221227_SHIDONGLI_037_F_p2390")
  158. #individual_name<-c("20211226_LIYUXING_024_F_p342")
  159. #individual_name<-c("20211226_LIUWAN_026_F_p337")
  160. #individual_name<-c("20221014_LIUXINRU_023_F_p1828")
  161. #individual_name<-c("20221230_ZHUXIAOQIN_023_F_p2411")
  162. #individual_name<-c("20211226_LUQI_024_F_p341")
  163. #individual_name<-c("20220717_WUDI_024_F_p1064")
  164. #individual_name<-c("20210922_YANGWENJING_073_F_p1757")
  165. #individual_name<-c("20230408_TANGXIANZHANG_063_M_p556")
  166. #individual_name<-c("20220323_SHIJIAHUI_072_M_p288")
  167. #individual_name<-c("20220512_HUATIANTIAN_023_F_p626")
  168. #individual_name<-c("test3")
  169. individual_name<-c("20230518_WANGHONGGANG_049_M_p799")
  171. individual_index<-NULL
  172. individual_num=0;
  173. for(name in MRI$Freesufer_Path3)
  174. {
  175. individual_num<-individual_num+1;
  177. if(individual_name %in% name){
  178. individual_index<-individual_num;
  179. }else{next}
  180. }
  181. patient_index<-individual_index
  183. all_data1_raw<- MRI[c(patient_index), c(7,8,10,11)]
  184. all_data1_centile<- MRI[c(patient_index), c(7,8,10,11)]
  187. for (sheet in var)
  188. #for local feature
  189. {
  190. setwd(datapath)
  191. MRI <- read_excel(datafile, sheet = sheet)
  192. MRI <- MRI[c(patient_index, 5768, 5769), ]
  194. if (sheet == "aseg.vol.table")
  195. {
  196. MRI[, 'cerebellum_WM'] <-
  197. MRI$`Left-Cerebellum-White-Matter` + MRI$`Right-Cerebellum-White-Matter`
  198. MRI[, 'cerebellum_GM'] <-
  199. MRI$`Left-Cerebellum-Cortex` + MRI$`Right-Cerebellum-Cortex`
  200. MRI[, 'cerebellum_total'] <-
  201. MRI[, 'cerebellum_WM'] + MRI[, 'cerebellum_GM']
  203. MRI[, 'CC'] <- MRI$CC_Anterior + MRI$CC_Central + MRI$CC_Mid_Anterior +
  204. MRI$CC_Mid_Posterior + MRI$CC_Posterior
  205. }
  207. str = sheet
  211. if (sheet == "global.table")
  212. {
  213. for (i in 1:dim(MRI)[1])
  214. {
  215. MRI[i, 'mean_thickness'] <-
  216. (MRI[i, 'meanCT2_lhMeanThickness'] * MRI[i, 'meanCT2_lhVertex'] +
  217. MRI[i, 'meanCT2_rhMeanThickness'] * MRI[i, 'meanCT2_rhVertex']) /
  218. (MRI[i, 'meanCT2_lhVertex'] + MRI[i, 'meanCT2_rhVertex'])
  219. MRI[i, 'total_surface_arrea'] <-
  220. MRI[i, 'totalSA2_lh'] + MRI[i, 'totalSA2_rh']
  221. }
  222. str = 'Global_feature'
  224. }
  226. tem_feature <- colnames(MRI)
  229. all_data <- MRI
  231. Z_data_new <- list()
  233. Quant_data_new <- list()
  235. #for(i in tem_feature[1:length(tem_feature)])
  236. for (i in tem_feature[1:length(tem_feature)])
  237. {
  238. all_data[, 'feature'] <- all_data[, i]
  240. rads_file <- paste0(str, '_', i, '_loop_our_model.rds')
  242. #if (!exists(rads_file)) {next}
  245. rads_index <- NULL
  246. for (rads1 in RDSfile_all)
  247. {
  248. if (grepl(rads_file, rads1)) {
  249. rads_index <- rads1
  250. }
  251. }
  253. if (is.null(rads_index)) {
  254. next
  255. }
  256. else{
  257. results <- readRDS(rads_index)
  258. print(paste0(rads_index, '-vs-', i))
  259. }
  263. Z_score_sum <- NULL
  265. Quant_score_sum <- NULL
  267. m2 <- results$m2
  268. m0 <- results$m0
  270. #for mu
  271. # all_data[is.na(all_data$Sex),'Sex']<-'Male'
  272. # all_data[!is.na(all_data$feature)&!is.infinite(all_data$feature)&
  273. # !is.na(all_data$Age)&!is.na(all_data$Sex),]
  274. all_data$Sex <- as.factor(all_data$Sex)
  275. #all_data$Site_ZZZ<-as.factor(all_data$Site_ZZZ)
  276. all_data[2, 'Site'] <- 'CC'
  277. all_data$Site_ZZZ <- as.factor(all_data$Site)
  280. tem_rnd <- matrix(0, dim(all_data)[1], 1)
  282. Model.Frame <- model.frame(formula = m2$mu.formula, data = all_data)
  284. Model.Matrix <- model.matrix(m2$mu.formula, Model.Frame)
  285. Fit.fix <-
  286. matrix(m2$mu.coefficients[colnames(Model.Matrix)],
  287. ncol = 1,
  288. dimnames = list(colnames(Model.Matrix), 'Beta'))
  289. if (!is.null(m2$mu.coefSmo[[1]]))
  290. {
  291. Fit.fix[length(Fit.fix)] = 0
  293. for (iz in 1:dim(all_data)[1]) {
  294. if (all_data$Site_ZZZ[iz] %in% names(m2$mu.coefSmo[[1]]$coef)) {
  295. tem_rnd[iz] <-
  296. m2$mu.coefSmo[[1]]$coef[as.character(all_data$Site_ZZZ[iz])]
  297. } else{
  298. tem_rnd[iz] <- mean(m2$mu.coefSmo[[1]]$coef)
  299. }
  300. }
  301. } else{
  302. print('no random effects for this term')
  303. }
  306. mu <- exp(as.vector(Model.Matrix %*% Fit.fix) + as.vector(tem_rnd))
  307. #for sigma
  308. tem_rnd <- matrix(0, dim(all_data)[1], 1)
  310. Model.Frame <- model.frame(formula = m2$sigma.formula, data = all_data)
  312. Model.Matrix <- model.matrix(m2$sigma.formula, Model.Frame)
  313. Fit.fix <-
  314. matrix(
  315. m2$sigma.coefficients[colnames(Model.Matrix)],
  316. ncol = 1,
  317. dimnames = list(colnames(Model.Matrix), 'Beta')
  318. )
  319. if (!is.null(m2$sigma.coefSmo[[1]]))
  320. {
  321. Fit.fix[length(Fit.fix)] = 0
  323. for (iz in 1:dim(all_data)[1]) {
  324. if (all_data$Site_ZZZ[iz] %in% names(m2$sigma.coefSmo[[1]]$coef)) {
  325. tem_rnd[iz] <-
  326. m2$sigma.coefSmo[[1]]$coef[as.character(all_data$Site_ZZZ[iz])]
  327. } else{
  328. tem_rnd[iz] <- mean(m2$sigma.coefSmo[[1]]$coef)
  329. }
  330. }
  331. } else{
  332. print('no random effects for this term')
  333. }
  337. sigma <- exp(as.vector(Model.Matrix %*% Fit.fix) + as.vector(tem_rnd))
  339. #sigma<-(as.vector(Model.Matrix %*% Fit.fix))
  340. #for nu
  342. Model.Frame <- model.frame(formula = m2$nu.formula, data = all_data)
  344. Model.Matrix <- model.matrix(m2$nu.formula, Model.Frame)
  345. Fit.fix <-
  346. matrix(m2$nu.coefficients[colnames(Model.Matrix)],
  347. ncol = 1,
  348. dimnames = list(colnames(Model.Matrix), 'Beta'))
  349. if (!is.null(m2$nu.coefSmo[[1]]))
  350. {
  351. Fit.fix[length(Fit.fix)] = 0
  353. } else{
  354. print('no random effects for this term')
  355. }
  358. nu <- as.vector(Model.Matrix[, colnames(Model.Matrix)[1]] * Fit.fix[1])
  362. Z_score_sum <-
  363. zzz_cent(
  364. obj = m2,
  365. type = c("z-scores"),
  366. mu = mu,
  367. sigma = sigma,
  368. nu = nu,
  369. xname = 'Age',
  370. xvalues = all_data$Age,
  371. yval = all_data$feature,
  372. calibration = FALSE,
  373. lpar = 3
  374. )
  376. Quant_score_sum <-
  377. zzz_cent(
  378. obj = m2,
  379. type = c("z-scores"),
  380. mu = mu,
  381. sigma = sigma,
  382. nu = nu,
  383. xname = 'Age',
  384. xvalues = all_data$Age,
  385. yval = all_data$feature,
  386. calibration = FALSE,
  387. lpar = 3,
  388. cdf = TRUE
  389. )
  392. Z_score_sum <- data.frame(Z_score_sum)
  394. colnames(Z_score_sum) <- c('Z_score')
  397. Quant_score_sum <- data.frame(Quant_score_sum)
  399. colnames(Quant_score_sum) <- c('Quant_score')
  401. #
  402. # Z_data_new[[i]] <- Z_score_sum
  403. # Quant_data_new[[i]] <- Quant_score_sum
  404. #
  405. # results$Zscore_new <- Z_data_new
  406. # results$Quant_data_new <- Quant_data_new
  407. # results$all_data <- all_data
  408. # results$str <- str
  409. # results$i <- i
  411. all_data1_raw<- cbind(all_data1_raw,all_data$feature[1]);
  412. colnames(all_data1_raw)[dim(all_data1_raw)[2]]<-i
  413. all_data1_centile<- cbind(all_data1_centile,Quant_score_sum[1,1]);
  414. colnames(all_data1_centile)[dim(all_data1_centile)[2]]<-i
  418. }
  420. }
  422. setwd(paste0(datapath, '/test'))
  424. write.csv(all_data1_raw,'individual_raw_data.csv')
  425. write.csv(all_data1_centile,'individual_centile_score.csv')
  426. #
  428. ```
  430. ```{r, warning = FALSE, include = TRUE,echo=FALSE}
  431. # setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_quant_update/figure3')
  432. # results<-readRDS('BHI_predict_mdoel.rds')
  433. #
  434. # setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  435. #
  436. # individual_data<-read.csv('individual_centile_score.csv',header=TRUE,check.names=F);
  437. #
  438. #
  439. # predict_BHI<-predict(results$model,type='response',newdata=individual_data);
  440. #
  441. # predict_BHI<-(-(predict_BHI+results$W0))
  442. # predict_BHI<-round(predict_BHI,2)
  443. # options('scipen'=12);
  444. #cat(paste0("Brain Healthy Index is: ",round(predict_BHI,2)));
  445. ```
  448. ```{r,warning = FALSE, include = FALSE,echo=FALSE}
  449. library(ggplot2)
  450. library(ggpubr)
  451. #rm (list = ls ())
  452. width1 = 4; height1=3
  453. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  455. individual_data<-read.csv('individual_raw_data.csv',header=TRUE,check.names=F);
  458. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  459. results<-readRDS('Global_feature_GMV_loop_our_model.rds');
  460. p2<-results$p2
  461. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  462. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  463. individual_data[,results$i]<-individual_data[,results$i]/10000
  465. f1<-ggplot()+
  466. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  467. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  468. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  469. geom_line(data=Male_p2,aes(x=Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  471. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  472. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  473. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  474. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  476. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  477. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  478. labs(x='Age (years)',y=paste0(results$i,' (ml)'))+
  479. # legend("topright",
  480. # legend=c('Male','Lower 95%CI','Upper 95%CI','Female','Lower 95%CI','Upper 95%CI'),
  481. # fill = c('#00CCFF','#00CCFF','#00CCFF','darkred','darkred','darkred'),
  482. # col = c('#00CCFF','#00CCFF','#00CCFF','darkred','darkred','darkred'),
  483. # title = "cyl")
  484. theme_bw()+
  485. theme(axis.text = element_text(size = 14,colour = 'black'),
  486. axis.title = element_text(size=14,colour = 'black'))
  487. #scale_x_log10()
  488. print(f1)
  489. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  490. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  492. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  493. results<-readRDS('Global_feature_sGMV_loop_our_model.rds');
  494. p2<-results$p2
  495. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  496. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  497. individual_data[,results$i]<-individual_data[,results$i]/10000
  498. f2<-ggplot()+
  499. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  500. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  501. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  502. geom_line(data=Male_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  504. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  505. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  506. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  507. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  509. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  510. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  511. labs(x='Age (years)',y=paste0(results$i,' (ml)'))+
  512. theme_bw()+
  513. theme(axis.text = element_text(size = 14,colour = 'black'),
  514. axis.title = element_text(size=14,colour = 'black'))
  515. #scale_x_log10()
  517. print(f2)
  518. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  519. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  521. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  522. results<-readRDS('Global_feature_WMV_loop_our_model.rds');
  523. p2<-results$p2
  524. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  525. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  526. individual_data[,results$i]<-individual_data[,results$i]/10000
  527. f3<-ggplot()+
  528. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  529. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  530. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  531. geom_line(data=Male_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  533. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  534. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  535. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  536. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  538. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  539. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  540. labs(x='Age (years)',y=paste0(results$i,' (ml)'))+
  541. theme_bw()+
  542. theme(axis.text = element_text(size = 14,colour = 'black'),
  543. axis.title = element_text(size=14,colour = 'black'))
  544. #scale_x_log10()
  546. print(f3)
  547. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  548. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  550. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  551. results<-readRDS('Global_feature_Ventricles_loop_our_model.rds');
  552. p2<-results$p2
  553. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  554. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  555. individual_data[,results$i]<-individual_data[,results$i]/10000
  556. f4<-ggplot()+
  557. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  558. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  559. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  560. geom_line(data=Male_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  562. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  563. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  564. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  565. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  567. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  568. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  569. labs(x='Age (years)',y=paste0(results$i,' (ml)'))+
  570. theme_bw()+
  571. theme(axis.text = element_text(size = 14,colour = 'black'),
  572. axis.title = element_text(size=14,colour = 'black'))
  573. #scale_x_log10()
  575. print(f4)
  576. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  577. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  579. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  580. results<-readRDS('aseg.vol.table_cerebellum_total_loop_our_model.rds');
  581. p2<-results$p2
  582. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  583. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  584. individual_data[,results$i]<-individual_data[,results$i]/10000
  586. f5<-ggplot()+
  587. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  588. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  589. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  590. geom_line(data=Male_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  592. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  593. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  594. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  595. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  597. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  598. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  599. labs(x='Age (years)',y=paste0('Cerebellum (ml)'))+
  600. theme_bw()+
  601. theme(axis.text = element_text(size = 14,colour = 'black'),
  602. axis.title = element_text(size=14,colour = 'black'))
  603. #scale_x_log10()
  605. print(f5)
  606. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  607. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  609. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/Global_feature')
  610. results<-readRDS('aseg.vol.table_Brain-Stem_loop_our_model.rds');
  611. p2<-results$p2
  612. Female_p2<-results$Female_p2;Female_p2[,c(2,3,4,5,6)]<-Female_p2[,c(2,3,4,5,6)]/10000
  613. Male_p2<-results$Male_p2;Male_p2[,c(2,3,4,5,6)]<-Male_p2[,c(2,3,4,5,6)]/10000
  614. individual_data[,results$i]<-individual_data[,results$i]/10000
  615. f6<-ggplot()+
  616. geom_line(data=Male_p2,aes(x=Age,y=median),color=c('#00CCFF'),linewidth=2,linetype=c('solid'))+
  617. #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  618. geom_line(data=Male_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  619. geom_line(data=Male_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  621. # geom_line(data=Female_p2,aes(x=Age,y=median),color=c('darkred'),linewidth=2,linetype=c('solid'))+
  622. # #geom_line(data=p2,aes(x=Age,y=lower99CI/10000),color=c('red'),linewidth=1,linetype=c('dashed'))+
  623. # geom_line(data=Female_p2,aes(x=Age,y=lower95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  624. # geom_line(data=Female_p2,aes(Age,y=upper95CI),color=c('darkred'),linewidth=1,linetype=c('dotted'))+
  626. #geom_line(data=p2,aes(Age,y=upper99CI/10000),color=c('#666666'),linewidth=1,linetype=c('dashed'))+
  627. geom_point(aes(individual_data$Age,individual_data[,results$i]),colour='red',size=2,stroke=5)+
  628. labs(x='Age (years)',y=paste0('Brainstem (ml)'))+
  629. theme_bw()+
  630. theme(axis.text = element_text(size = 14,colour = 'black'),
  631. axis.title = element_text(size=14,colour = 'black'))
  632. #scale_x_log10()
  633. print(f6)
  635. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  636. ggsave(paste0(results$i,".png"),width = width1, height = height1, dpi = 300)
  637. #library(cowplot)
  639. # library('png')
  640. # setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  641. #
  642. # figs<-c('GMV.png','sGMV.png','WMV.png','Ventricles.png','cerebellum_total.png','Brain-Stem.png')
  643. # f1<-readPNG('GMV.png')
  644. #ggarrange(f1,f2,f3,f4,f5,f6,ncol=6,nrow=1)
  646. ```
  648. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="50%",fig.align='center',fig.show='hold',fig.cap=' '}
  649. library(knitr)
  650. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/GMV.png')
  651. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/sGMV.png')
  652. p3<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/WMV.png')
  653. p4<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/Ventricles.png')
  655. p5<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/cerebellum_total.png')
  656. p6<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/Brain-Stem.png')
  659. knitr::include_graphics(c(p1,p2))
  660. #knitr::include_graphics(c(p3,p4))
  661. #knitr::include_graphics(c(p5,p6))
  663. ```
  665. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="50%",fig.align='center',fig.show='hold',fig.cap=' '}
  666. library(knitr)
  668. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/WMV.png')
  669. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/Ventricles.png')
  671. #knitr::include_graphics(c(p1,p2))
  672. knitr::include_graphics(c(p1,p2))
  673. #knitr::include_graphics(c(p5,p6))
  675. ```
  677. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="50%",fig.align='center',fig.show='hold',fig.cap=' '}
  678. library(knitr)
  679. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/cerebellum_total.png')
  680. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/Brain-Stem.png')
  683. #knitr::include_graphics(c(p1,p2))
  684. #knitr::include_graphics(c(p3,p4))
  685. knitr::include_graphics(c(p1,p2))
  687. ```
  689. \newpage
  690. ## Deviation of local features:
  691. #### lightblue=supernormal,darkred=abnormal
  693. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  694. library(ggplot2)
  695. library(ggpubr)
  696. width1 = 4; height1=3
  697. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  698. individual_data<-read.csv('individual_centile_score.csv',header=TRUE,check.names=F);
  699. del_str='_thickness'
  700. del_str='_area'
  704. library(ggseg)
  706. #for cortical volume
  707. del_str='_volume'
  708. feature_seg<-colnames(individual_data)[36:103];
  710. library(stringr)
  711. feature_seg<-str_remove_all(string = feature_seg,pattern = del_str)
  712. value=individual_data[1,36:103]
  713. value[,is.na(value[1,])]<-0
  715. seg_data=data.frame(label=feature_seg,
  716. Centile=as.numeric(value))
  718. library(ggsci)
  720. p<-ggplot(seg_data)+
  721. geom_brain(atlas=dk,
  722. size = 0.1,
  723. position=position_brain(hemi ~ side),
  724. #hemi=hemis,
  725. mapping = aes(fill = Centile))+
  726. theme_void()+
  727. theme(legend.title = element_text(size=14,colour = 'black'), #change legend title font size
  728. legend.text = element_text(size=10,colour = 'black'))+ #change legend text font size)
  729. #scale_fill_gradient(limits=c(4.5,max(seg_data$Peak_age)),low='grey',high='#990000')
  730. scale_fill_gradient2(limits=c(0,1),low='#990000',midpoint = 0.5, high='#00CCFF')
  731. p
  732. ggsave(paste0("cortical_volume.png"),width = width1, height = height1, dpi = 300)
  736. feature_seg<-colnames(individual_data)[c(14:17,19:22,25:32)];
  737. #feature_seg<-str_remove_all(string = feature_seg,pattern = del_str)
  738. value=individual_data[1,c(14:17,19:22,25:32)]
  739. value[,is.na(value[1,])]<-0
  740. feature_seg[1]<-c("Left-Thalamus-Proper")
  741. feature_seg[9]<-c("Right-Thalamus-Proper")
  742. seg_data=data.frame(label=feature_seg,
  743. Centile=as.numeric(value))
  744. library(ggsci)
  746. p<-ggplot(seg_data)+
  747. geom_brain(atlas=aseg,
  748. size = 0.1,
  749. side='coronal',
  750. mapping = aes(fill = Centile))+
  751. theme_void()+
  752. theme(legend.title = element_text(size=14,colour = 'black'), #change legend title font size
  753. legend.text = element_text(size=10,colour = 'black'))+ #change legend text font size)
  754. scale_fill_gradient2(limits=c(0,1),low='#990000',midpoint = 0.5, high='#00CCFF')
  756. print(p)
  757. ggsave(paste0("subcortical_volume.png"),width = width1, height = height1, dpi = 300)
  759. ```
  762. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="50%",fig.align='center',fig.show='hold',fig.cap=' '}
  763. library(knitr)
  764. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/subcortical_volume.png')
  765. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/cortical_volume.png')
  768. #knitr::include_graphics(c(p1,p2))
  769. #knitr::include_graphics(c(p3,p4))
  770. knitr::include_graphics(c(p1,p2))
  772. ```
  773. \newpage
  774. ## Summary of global and local features:
  775. #### lightblue=supernormal,darkred=abnormal
  776. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  777. library(ggplot2)
  778. library(ggpubr)
  779. width1 = 4; height1=3
  780. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  781. individual_data<-read.csv('individual_raw_data.csv',header=TRUE,check.names=F);
  782. individual_score<-read.csv('individual_centile_score.csv',header=TRUE,check.names=F);
  784. del_str='_thickness'
  785. del_str='_area'
  786. del_str='_volume'
  788. sub_var<-colnames(individual_data)[c(6:9,35,18,14:17,19:22,25:32)];
  789. table_sub<-cbind(t(individual_data[,sub_var]/1000),t(individual_score[,sub_var]))
  790. colnames(table_sub)<-c('Volume(ml)','centile')
  792. sub_var_volume<-colnames(individual_data)[c(36:103)];
  794. sub_var_thickness<-colnames(individual_data)[c(104:171)];
  796. sub_var_area<-colnames(individual_data)[c(172:239)];
  798. table_cor<-cbind(t(individual_data[,sub_var_volume]/1000),
  799. t(individual_score[,sub_var_volume]),
  800. t(individual_data[,sub_var_thickness]),
  801. t(individual_score[,sub_var_thickness]),
  802. t(individual_data[,sub_var_area]/100),
  803. t(individual_score[,sub_var_area])) #此处有问题，需要检查 20240128
  805. colnames(table_cor)<-c('volume(ml)','centile_volume',
  806. 'thickness(cm)','centile_thickness',
  807. 'surface_area(cm2)','centile_area')
  808. library("htmltools")
  809. library("webshot")
  811. export_formattable <- function(f, file, width = "100%", height = NULL,
  812. background = "white", delay = 0.2)
  813. {
  814. w <- as.htmlwidget(f, width = width, height = height)
  815. path <- html_print(w, background = background, viewer = NULL)
  816. url <- paste0("file:///", gsub("\\\\", "/", normalizePath(path)))
  817. webshot(url,
  818. file = file,
  819. selector = ".formattable_widget",
  820. zoom=4,
  821. delay = delay)
  822. }
  825. library(stringr)
  826. rownames(table_cor)<-str_remove_all(string = rownames(table_cor),pattern = del_str)
  828. table_cor<-round(table_cor,2)
  829. table_sub<-round(table_sub,2)
  831. library(formattable)
  832. #formattable(table_sub,list(centile=color_bar("pink",proportion)))
  833. table_sub<-data.frame(table_sub)
  835. p<-formattable(table_sub,
  836. list(centile = formatter("span",
  837. style = x ~ ifelse(x < 0.05,#条件1
  838. style(display = "block",
  839. color = "white",
  840. font.weight = "bold",
  841. "border-radius" = "10px",#圆角大小
  842. "padding-right" = "4px",#背景色框的大小
  843. "background-color" = "#990000"#背景色
  844. ),#满足条件1变绿加粗
  845. ifelse(x>=0.95,#条件2
  846. style(display = "block",
  847. color = "white",
  848. font.weight = "bold",
  849. "border-radius" = "10px",#圆角大小
  850. "padding-right" = "4px",#背景色框的大小
  851. "background-color" = "#00CCFF"#背景色
  852. ),#满足条件2变红加粗
  853. "color:orange")#都不满足橘黄色
  854. ))))
  856. export_formattable(p,"global_summary.png")
  858. table_cor<-data.frame(table_cor)
  859. p<-formattable(table_cor,
  860. list(centile_volume = formatter("span",
  861. style = x ~ ifelse(x < 0.05,#条件1
  862. style(display = "block",
  863. color = "white",
  864. font.weight = "bold",
  865. "border-radius" = "10px",#圆角大小
  866. "padding-right" = "4px",#背景色框的大小
  867. "background-color" = "#990000"#背景色
  868. ),#满足条件1变绿加粗
  869. ifelse(x>=0.95,#条件2
  870. style(display = "block",
  871. color = "white",
  872. font.weight = "bold",
  873. "border-radius" = "10px",#圆角大小
  874. "padding-right" = "4px",#背景色框的大小
  875. "background-color" = "#00CCFF"#背景色
  876. ),#满足条件2变红加粗
  877. "color:orange")#都不满足橘黄色
  878. )),
  879. centile_thickness = formatter("span",
  880. style = x ~ ifelse(x < 0.05,#条件1
  881. style(display = "block",
  882. color = "white",
  883. font.weight = "bold",
  884. "border-radius" = "10px",#圆角大小
  885. "padding-right" = "4px",#背景色框的大小
  886. "background-color" = "#990000"#背景色
  887. ),#满足条件1变绿加粗
  888. ifelse(x>=0.95,#条件2
  889. style(display = "block",
  890. color = "white",
  891. font.weight = "bold",
  892. "border-radius" = "10px",#圆角大小
  893. "padding-right" = "4px",#背景色框的大小
  894. "background-color" = "#00CCFF"#背景色
  895. ),#满足条件2变红加粗
  896. "color:orange")#都不满足橘黄色
  897. )),
  898. centile_area = formatter("span",
  899. style = x ~ ifelse(x < 0.05,#条件1
  900. style(display = "block",
  901. color = "white",
  902. font.weight = "bold",
  903. "border-radius" = "10px",#圆角大小
  904. "padding-right" = "4px",#背景色框的大小
  905. "background-color" = "#990000"#背景色
  906. ),#满足条件1变绿加粗
  907. ifelse(x>=0.95,#条件2
  908. style(display = "block",
  909. color = "white",
  910. font.weight = "bold",
  911. "border-radius" = "10px",#圆角大小
  912. "padding-right" = "4px",#背景色框的大小
  913. "background-color" = "#00CCFF"#背景色
  914. ),#满足条件2变红加粗
  915. "color:orange")#都不满足橘黄色
  916. ))))
  918. export_formattable(p,"local_summary.png")
  921. ```
  924. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="100%",fig.align='left',fig.show='hold',fig.cap=paste0('Note:lh,left hemisphere;rh:right hemisphere')}
  925. library(knitr)
  926. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/global_summary.png')
  927. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/local_summary.png')
  928. knitr::include_graphics(c(p1))
  930. ```
  931. \newpage
  933. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="100%",fig.align='left',fig.show='hold',fig.cap=paste0('Note:lh,left hemisphere;rh:right hemisphere')}
  934. library(knitr)
  935. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/global_summary.png')
  936. p2<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/local_summary.png')
  937. knitr::include_graphics(c(p2))
  939. ```
  941. \newpage
  942. ## Disease propensity score:
  943. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  944. library(ggplot2)
  945. library(ggpubr)
  947. width1 = 4; height1=3
  948. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  949. individual_data<-read.csv('individual_centile_score.csv',header=TRUE,check.names=F);
  952. tem_data<-individual_data[1,individual_data[1,]<0.05]
  955. ```
  957. ```{r, warning = FALSE, include = FALSE,echo=FALSE}
  958. library(ggplot2)
  959. library(ggpubr)
  960. rm (list = ls ())
  961. width1 = 4; height1=3
  962. setwd('C:/ZZZ/work/manuscript/Lifespan-main/test');
  963. individual_data<-read.csv('individual_centile_score.csv',header=TRUE,check.names=F);
  965. model<-readRDS('C:/ZZZ/work/manuscript/Lifespan-main/test_zzz_update_20240116/figure3/global_and_local_feature/global_and_local_feature_ROC_loop_lasso_selection.rds')
  967. model$model$MCI
  968. if(individual_data$Sex=='Male')
  969. {
  970. individual_data[1,'Sex']<-1
  971. }
  972. if(individual_data$Sex=='Female')
  973. {
  974. individual_data[1,'Sex']<-0
  975. }
  976. individual_data$Sex<-as.numeric(individual_data$Sex)
  978. Pre_MCI<-predict(model$model$MCI,type='response',newdata=individual_data);
  979. Pre_AD<-predict(model$model$AD,type='response',newdata=individual_data);
  980. Pre_PD<-predict(model$model$PD,type='response',newdata=individual_data);
  981. Pre_CSVD<-predict(model$model$SVD,type='response',newdata=individual_data);
  982. Pre_MS<-predict(model$model$MS,type='response',newdata=individual_data);
  983. Pre_NMOSD<-predict(model$model$AQP4Pos_NMOSD,type='response',newdata=individual_data);
  986. library(fmsb)
  988. rad_data0<-data.frame(matrix(NA,3,6));
  989. rad_data0[1,] <- data.frame(t(c(1,1,1,1,1,1)));
  990. rad_data0[2,] <- data.frame(t(c(0,0,0,0,0,0)));
  991. rad_data0[3,] <- data.frame(t(c(Pre_MCI,Pre_AD,Pre_PD,Pre_CSVD,Pre_MS,Pre_NMOSD)));
  992. colnames(rad_data0)<-c('MCI','AD','PD','CSVD','MS','NMOSD')
  993. rownames(rad_data0)<-c('Max','Min','value')
  995. # rad_data0<-data.frame(matrix(NA,3,4));
  996. # rad_data0[1,] <- data.frame(t(c(1,1,1,1)));
  997. # rad_data0[2,] <- data.frame(t(c(0,0,0,0)));
  998. # rad_data0[3,] <- data.frame(t(c(Pre_MCI,Pre_AD,Pre_PD,Pre_CSVD)));
  999. # colnames(rad_data0)<-c('MCI','AD','PD','CSVD')
  1000. # rownames(rad_data0)<-c('Max','Min','value')
  1002. png(filename = paste0("disease_risk.png"),
  1003. width = 1500,
  1004. height = 1500,
  1005. units = "px",
  1006. bg = "white",
  1007. res = 300)
  1010. p<-radarchart(rad_data0,
  1011. axistype = 1,
  1012. seg=5,
  1013. centerzero=TRUE,
  1014. # Customize the polygon
  1015. pcol = "black",
  1016. pfcol = scales::alpha("lightgrey", 0.5),
  1017. plwd = 2, plty = 1,
  1018. # Customize the grid
  1019. cglcol = "lightgrey", cglty = 2,
  1020. #cglwd = 0.8,
  1021. # Customize the axis
  1022. axislabcol = "black",
  1023. cglwd=1,
  1024. # Variable labels
  1025. vlcex = 1, palcex = 1,
  1026. vlabels = colnames(rad_data0),
  1027. caxislabels = c(0,1/5,
  1028. 1/5*2,
  1029. 1/5*3,
  1030. 1/5*4,
  1031. 1/5*5))
  1032. print(p)
  1033. dev.off()
  1035. write.csv(rad_data0,'Disease_propensity_score.csv')
  1036. #ggsave(paste0("disease_risk.png"),width = width1, height = height1, dpi = 300)
  1037. #dev.off()
  1039. ```
  1041. ```{r warning = FALSE, include = TRUE,echo=FALSE, out.width="80%",fig.align='center',fig.show='hold',fig.cap=' '}
  1042. library(knitr)
  1043. p1<-file.path('C:/ZZZ/work/manuscript/Lifespan-main/test/disease_risk.png')
  1045. knitr::include_graphics(c(p1))
  1047. ```
  1050. Note that this quantitative report is just for clinical research currently.

Brain_individual_report_output.Rmd at commit adca4ca, no license · at the source

Overview

Authors: Yuerong Lizhu1,2, Yiyi Chen3,4, Xinze Zhang5, Yuqi Luo1,2, Zhizheng Zhuo1,2, Jinhui Wang6,7,8, Yunyun Duan1,2, Li Chai1,2, Jun Qiu1,9, Ziyi Gao10, Tingting Wang11, Hongyi Yan12, Xiaoyun Liang13, Yilong Wang11,12,14, Yingying Su5, Ling Guan10,11,15, Yaou Liu1,2
ORCID iDs: Xiaoyun Liang
15 affiliations
  1. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  2. Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
  3. Department of Neurology, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
  4. Shenzhen Clinical Research Center for Neurological Diseases, Shenzhen, China
  5. Department of Stomatology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  6. Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, China
  7. Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
  8. Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
  9. Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
  10. School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China
  11. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  12. China National Clinical Research Center for Neurological Diseases, Beijing, China
  13. Institute of Research and Clinical Innovations, Neusoft Medical Systems Co., Ltd., Shanghai, China
  14. National Center for Neurological Disorders, Beijing, China
  15. Department of Medicine, The University of British Columbia, Vancouver, Canada
Institutions: Capital Medical University (China); Beijing Tian Tan Hospital (China); Shenzhen University (China); Shenzhen Second People's Hospital (China); South China Normal University (China); University of Science and Technology of China (China); Beijing Institute of Technology (China); University of British Columbia (Canada)
Journal: Journal of oral microbiology, volume 18, issue 1, article 2705667
Dates: received 16 January 2026; accepted 14 July 2026; published online 5 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1080/20002297.2026.2705667 · PMID 42564713 · PMCID PMC13446054 · OpenAlex W7172537838
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), stroke (population)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging, Preprocessing
Keywords: Oral microbiota, CSVD, MRI, brain atrophy, cortical thinning, brain charts
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (82271516, 82330057, 82425101, 2023ZD0504800, 2023ZD0504803, 2023ZD0504802, 2023ZD0504804, 2023ZD0504801, 82330057, 82271516, 82425101); National Science and Technology Major Project (2023ZD0504800, 2023ZD0504802, 2023ZD0504803, 2023ZD0504804, 2023ZD0504801)
Citations: not cited yet (Europe PMC); 47 references in the paper

Abstract

Background: Prior studies have linked the microbiota to brain diseases, whereas the longitudinal effects of the oral microbiota on cortical thinning and cognitive impairments in cerebral small vessel disease (CSVD) remain unexplored.

Methods: We recruited 120 CSVD patients and 40 healthy controls (HCs). The subgingival plaque microbiota was sequenced by a metagenomic approach. Cortical thickness was assessed using GM-centile, an age- and sex-normalized MRI metric. Differential microbial taxa and KEGG orthologs (KOs) between groups were identified using MaAsLin2. Associations between key differential taxa with CSVD-specific cortical thinning were examined using the Spearman test, and those with MoCA score and plasma inflammatory markers (CRP and lymphocyte counts) were examined by linear regression models. Mediation models evaluated the indirect role of cortical thinning in the relationship between microbial abundance and cognitive function. Generalized estimation equations validated the longitudinal effects of the microbiota on cortical thinning progression.

Result: We identified distinct oral microbiota dysbiosis in CSVD, including depletion of g_Selenomonas and g_Leptotrichia and enrichment of g_Treponema. The abundance of these microbes was correlated with longitudinal cortical thinning in the frontal gyrus, insular lobes, and inferotemporal gyrus. Enrichment analysis revealed that CSVD-enriched KOs were linked to the upregulation of LPS-mediated pro-inflammatory pathways, while those depleted were associated with the reduced biosynthesis of neuroprotective short-chain fatty acids (SCFAs). g_Leptotrichia abundance showed negatively correlation with CRP (p = 0.045). Mediation analyses indicated that the association between g_Leptotrichia depletion and baseline cognitive impairment was mediated by bilateral insular cortical thinning (both p < 0.05). Additionally, the association between g_Leptotrichia depletion and one-year cognitive decline was mediated by superior frontal cortical thinning (p = 0.033).

Conclusions: Oral microbiota dysbiosis in CSVD patients reflects a pro-inflammatory state, characterized by enhanced LPS synthesis and reduced SCFAs production. This dysbiosis is associated with CSVD-specific cortical thinning in regions vulnerable to neuroinflammation, which in turn mediates cognitive impairment.

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

Repository

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zhuozhizheng/Chinese_normative_reference_and_downstream_clinical_applications

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: adca4ca6c1c0f79f1914b22913ba898ed2714b28, 10 September 2024
Languages: R (28)
Size: 62 files, 28 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, 2 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: reshape2 (22 files), ggplot2 (21 files), tidyverse (21 files), easystats (20 files), lmerTest (20 files), glmnet (19 files), pROC (9 files), ggpubr (7 files), ggseg (7 files), caret (3 files), pheatmap (2 files), survival (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
29 files
At the source: github.com/zhuozhizheng/Chinese_normative_reference_and_downstream_clinical_applications

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

Tracing map

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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;
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  • 1 match 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 statement

The data involving participant-derived information (including brain MRI scans, oral microbiota metagenomic sequencing, and clinical and cognitive assessments) are available on request from Prof. Yaou Liu and Prof. Yilong Wang. The normative reference MRI dataset for quantifying the degree of brain atrophy used the data and models from the Chinese Human Brain Project. The source data and main codes for the statistical analysis are openly available online at GitHub: https://github.com/zhuozhizheng/Chinese_normative_reference_and_downstream_clinical_applications.

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

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Version 3, 28 September 2026

  • Funding: added National Natural Science Foundation of China: 82271516, 82330057, 82425101, 2023ZD0504800, 2023ZD0504803, 2023ZD0504802, 2023ZD0504804, 2023ZD0504801, 82330057, 82271516, 82425101; National Science and Technology Major Project: 2023ZD0504800, 2023ZD0504802, 2023ZD0504803, 2023ZD0504804, 2023ZD0504801

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 6 keywords, 47 references.

Cite

This paper

Lizhu, Y., Chen, Y., Zhang, X., Luo, Y., Zhuo, Z., Wang, J., Duan, Y., Chai, L., Qiu, J., Gao, Z., Wang, T., Yan, H., Liang, X., Wang, Y., Su, Y., Guan, L., & Liu, Y. (2026). Oral microbiota dysbiosis related to the cortical thinning and cognitive impairment in cerebral small vessel disease. Journal of oral microbiology, 18(1), 2705667. https://doi.org/10.1080/20002297.2026.2705667

BibTeX

@article{lizhu2026oral,
author = {Lizhu, Yuerong and Chen, Yiyi and Zhang, Xinze and Luo, Yuqi and Zhuo, Zhizheng and Wang, Jinhui and Duan, Yunyun and Chai, Li and Qiu, Jun and Gao, Ziyi and Wang, Tingting and Yan, Hongyi and Liang, Xiaoyun and Wang, Yilong and Su, Yingying and Guan, Ling and Liu, Yaou},
title = {{Oral microbiota dysbiosis related to the cortical thinning and cognitive impairment in cerebral small vessel disease}},
journal = {Journal of oral microbiology},
year = {2026},
month = aug,
volume = {18},
number = {1},
pages = {2705667},
publisher = {Taylor \& Francis},
issn = {2000-2297},
doi = {10.1080/20002297.2026.2705667},
url = {https://doi.org/10.1080/20002297.2026.2705667},
pmid = {42564713},
pmcid = {PMC13446054}
}

RIS

TY - JOUR
AU - Lizhu, Yuerong
AU - Chen, Yiyi
AU - Zhang, Xinze
AU - Luo, Yuqi
AU - Zhuo, Zhizheng
AU - Wang, Jinhui
AU - Duan, Yunyun
AU - Chai, Li
AU - Qiu, Jun
AU - Gao, Ziyi
AU - Wang, Tingting
AU - Yan, Hongyi
AU - Liang, Xiaoyun
AU - Wang, Yilong
AU - Su, Yingying
AU - Guan, Ling
AU - Liu, Yaou
TI - Oral microbiota dysbiosis related to the cortical thinning and cognitive impairment in cerebral small vessel disease
T2 - Journal of oral microbiology
J2 - J Oral Microbiol
PY - 2026
DA - 2026/08/05
VL - 18
IS - 1
SP - 2705667
SN - 2000-2297
PB - Taylor & Francis
DO - 10.1080/20002297.2026.2705667
UR - https://doi.org/10.1080/20002297.2026.2705667
LA - en
ER -

CSL-JSON

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"id": "10.1080/20002297.2026.2705667",
"type": "article-journal",
"title": "Oral microbiota dysbiosis related to the cortical thinning and cognitive impairment in cerebral small vessel disease",
"container-title": "Journal of oral microbiology",
"author": [
{
"family": "Lizhu",
"given": "Yuerong"
},
{
"family": "Chen",
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{
"family": "Zhang",
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},
{
"family": "Luo",
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{
"family": "Zhuo",
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"given": "Jinhui"
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{
"family": "Duan",
"given": "Yunyun"
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{
"family": "Chai",
"given": "Li"
},
{
"family": "Qiu",
"given": "Jun"
},
{
"family": "Gao",
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},
{
"family": "Wang",
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{
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"container-title-short": "J Oral Microbiol",
"volume": "18",
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"DOI": "10.1080/20002297.2026.2705667",
"PMID": "42564713",
"PMCID": "PMC13446054",
"ISSN": "2000-2297",
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