OSCR

Thalamic volume alterations mediate glymphatic function and cognitive dysfunction in Alzheimer's disease.

Code ↔ Paper

5 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 5 matches
  1. [1] § RESULTS › Significant mediation of thalamic nucleus in the ALPS index and cognition ↔ 中介效应分析代码11-4更新.R, lines 269–313 · score 0.87 · ltAV, ltLP, ltMDl, rtMDl, ltMDm, ltVL
  2. [2] § RESULTS › ALPS‐related GM clusters ↔ 中介效应分析代码11-4更新.R, lines 269–313 · score 0.85 · ltAV, ltLP, ltMDl, rtMDl, ltMDm, ltVL
  3. [3] § RESULTS › Significant mediation of thalamic nucleus in the ALPS index and cognition ↔ 中介效应分析代码11-4更新.R, lines 216–267 · score 0.74 · ltMDm, ltVL, rtAV, rtMDm, rtVL, rtLP
  4. [4] § METHODS › Confirmatory factor analysis ↔ 1.R, lines 52–96 · score 0.69 · DSTback, N1N3, N4, N5, Stroop, BNT
  5. [5] § METHODS › Confirmatory factor analysis ↔ 1.R, lines 98–164 · score 0.66 · VFT, CFA, CFI, RMSEA, SRMR, TLI

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 · 391 lines · 15 KB · no license · 3 matches

  1. setwd("E:/ALPS/ALPS/mediation")
  2. library("mediation")
  3. data<-read.csv("ADNIdata需要统计的数据-mci59.csv")
  4. #### 6.加入协变量的线性回归中介效应分析
  5. # 设置随机种子
  6. set.seed(12345)
  7. # 按照lm(m ~ x + 协变量1 + 协变量2)格式加入协变量即可+ AGE + PTGENDER + PTEDUCAT +TIV(加上结果就更不显著了)
  8. mx = lm(rtLP ~ ALPS + AGE + PTGENDER+ PTEDUCAT, data = data)
  9. # 按照lm(y ~ x + m + 协变量1 + 协变量2)格式加入协变量即可
  10. ##左右丘脑显著
  11. yx = lm(ADNI_LAN ~ ALPS +rtLP+AGE + PTGENDER+ PTEDUCAT, data = data)
  12. mediate = mediate(mx, yx,
  13. treat = 'ALPS', # 处理变量（自变量），X
  14. mediator = 'rtLP', # 中介变量，这里为M
  15. sims = 1000, # 重抽样次数1000次
  16. boot = TRUE) # 是否使用引导法（Bootstrap）进行重抽样，这里设置为TRUE（使用引导法）
  18. summary(mediate)
  19. #ACME表示average causal mediation effects (indirect effect)，即间接效应，
  20. #ADE为average direct effects，即直接效应。
  21. #total effect为总的效应。
  22. #Prop.mediated为中介变量解释X与Y间关联所占的百分比。
  23. plot(mediate)
  24. #右图,线都与0线交叉显示结果无统计学意义。
  26. lmreg1<-lm(ADNI_LAN~ ALPS+AGE+PTGENDER+PTEDUCAT+factor(APOE4), data=data)
  27. summary(lmreg1)
  29. ##批量中介
  30. #### 7.批量中介效应分析####
  31. # 定义一个名为 "TS" 的引用类，并指定字段列表
  32. setRefClass("TS",
  33. fields = list(
  34. m = "character", # 中介变量名
  35. x = "character", # 处理变量名
  36. xm = "formula", # 中介模型公式
  37. xy = "formula" # 结果模型公式
  38. ))
  39. # 创建一个 "TS" 类的新实例
  40. test <- new("TS")
  41. # 定义一个函数，用于批量中介分析
  42. batch_mediate <- function(ms, y, xs, df) {
  43. # 设置中介变量名
  44. test$m <- ms
  45. # 设置处理变量名
  46. test$x <- xs
  47. # 创建中介模型公式
  48. test$xm <- as.formula(paste0(ms, "~", xs, "+ AGE + PTGENDER + PTEDUCAT + APOE4"))
  49. # 拟合中介模型
  50. model_xm <- lm(test$xm, data = df)
  51. # 创建结果模型公式
  52. test$xy <- as.formula(paste0(y, "~", ms, "+", xs, "+ AGE + PTGENDER + PTEDUCAT + APOE4"))
  53. # 拟合结果模型
  54. model_xy <- lm(test$xy, data = df)
  55. # 进行中介分析
  56. model_med <- mediate(model_xm,
  57. model_xy,
  58. treat = test$x,
  59. mediator = test$m,
  60. sims = 1000, # 设置模拟次数为1000次
  61. boot = TRUE) # 使用引导法（Bootstrap）进行重抽样
  62. # 获取中介分析的摘要结果
  63. med_sum <- summary(model_med)
  64. # 提取ACME（平均因果中介效应）及其P值
  65. ACME <- med_sum$d0
  66. ACME_P <- med_sum$d0.p
  67. ACME_CI <- med_sum$d0.ci
  68. # 提取ADE（平均直接效应）及其P值
  69. ADE <- med_sum$z0
  70. ADE_P <- med_sum$z0.p
  71. ADE_CI <- med_sum$z0.ci
  72. # 计算总效应（Total Effect）
  73. TE <- (ACME + ADE)
  74. # 计算总效应的P值
  75. # 这里我们假设使用bootstrap p-value，通常在summary中获得
  76. TE_P <- med_sum$tau.p
  77. # 计算中介效应所占比例
  78. Prop_Mediated <- ACME / TE
  79. # 将结果存储在数据框中
  80. result <- data.frame(
  81. 'Characteristics' = ms, # 中介变量名
  82. 'ACME' = ACME, # 平均因果中介效应
  83. 'ACME_P' = ACME_P, # 平均因果中介效应的P值
  84. 'ACME_95CI_lower' = ACME_CI[1], # ACME的95%CI下限
  85. 'ACME_95CI_upper' = ACME_CI[2], # ACME的95%CI上限
  86. 'ADE' = ADE, # 平均直接效应
  87. 'ADE_P' = ADE_P, # 平均直接效应的P值
  88. 'ADE_95CI_lower' = ADE_CI[1], # ADE的95%CI下限
  89. 'ADE_95CI_upper' = ADE_CI[2], # ADE的95%CI上限
  90. 'Total_Effect' = TE, # 总效应
  91. 'Total_Effect_P' = TE_P, # 总效应的P值
  92. 'Prop_Mediated' = Prop_Mediated) # 中介效应所占比例
  93. # 返回结果
  94. return(result)}
  96. #### 8.批量中介效应分析的具体应用
  97. # 设置需要探索是否为中介变量M的列名
  98. #"ltLP", "rtLP",
  99. #"lSMG", "rSMG",
  100. #"lROL", "rROL",
  101. #"lSTG","rSTG",
  102. #"lHES","rHES",
  103. #"ltAV","rtAV",
  104. #"ltVL","rtVL",
  105. #"ltMDm","rtMDm",
  106. #"ltMDl","rtMDl",
  107. #"lMTG","rMTG"
  108. cols <-c("ltLP", "rtLP",
  109. "ltAV","rtAV",
  110. "ltVL","rtVL",
  111. "ltMDm","rtMDm",
  112. "ltMDl","rtMDl")
  114. # 设置随机种子
  115. set.seed(12345)
  116. #ADNI_EF2 #ADNI_LAN #ADNI_MEM #ADNI_VS
  117. # 使用lapply函数对列列表(cols)中的每一列应用batch_mediate函数
  118. res2 <- lapply(cols, # 遍历列列表(cols)
  119. batch_mediate, # 应用batch_mediate函数
  120. y = "ADNI_VS", # y参数设为因变量名"status"
  121. x = "ALPS", # x参数设为自变量名"age"
  122. df = data) # df参数设为数据框data
  124. # 生成结果
  125. library("plyr")
  126. res2 <- ldply(res2,data.frame)
  127. View(res2)
  129. #####9.保存数据
  130. write.csv(res2, file = "adni_ADNI_VS_mediation.csv", row.names = FALSE)
  131. ##### 10.各个中介变量的值
  132. #"ltLP", "rtLP", "ltAV","rtAV",
  133. #"ltVL","rtVL","ltMDm","rtMDm","ltMDl","rtMDl",
  134. #a
  135. lmreg1<-lm(ltLP~ ALPS+AGE+PTGENDER+PTEDUCAT+factor(APOE4), data=data)
  136. summary(lmreg1)
  137. #b
  138. lmreg2<-lm(ADNI_EF2~ ltLP+AGE+PTGENDER+PTEDUCAT+factor(APOE4), data=data)
  139. summary(lmreg2)
  140. #c'
  141. lmreg3<-lm(ADNI_EF2~ ALPS+ltLP+AGE+PTGENDER+PTEDUCAT+factor(APOE4), data=data)
  142. summary(lmreg3)
  143. #c
  144. lmreg4<-lm(ADNI_EF2~ ALPS+AGE+PTGENDER+PTEDUCAT+factor(APOE4), data=data)
  145. summary(lmreg4)
  147. ####8. 批量中介 中日的数据####
  148. library(openxlsx)
  149. data1<-read.xlsx("中日用来进行分析的数据-优化版本.xlsx")
  150. #data1<-read.xlsx("中日用来进行分析的数据.xlsx")
  151. # 定义一个名为 "TS" 的引用类，并指定字段列表
  152. setRefClass("TS",
  153. fields = list(
  154. m = "character", # 中介变量名
  155. x = "character", # 处理变量名
  156. xm = "formula", # 中介模型公式
  157. xy = "formula" # 结果模型公式
  158. ))
  159. # 创建一个 "TS" 类的新实例
  160. test <- new("TS")
  161. # 定义一个函数，用于批量中介分析
  162. batch_mediate <- function(ms, y, xs, df) {
  163. # 设置中介变量名
  164. test$m <- ms
  165. # 设置处理变量名
  166. test$x <- xs
  167. # 创建中介模型公式
  168. test$xm <- as.formula(paste0(ms, "~", xs, "+ AGE + SEX + EDU+APOE4"))
  169. # 拟合中介模型
  170. model_xm <- lm(test$xm, data = df)
  171. # 创建结果模型公式
  172. test$xy <- as.formula(paste0(y, "~", ms, "+", xs, "+ AGE + SEX + EDU+APOE4"))
  173. # 拟合结果模型
  174. model_xy <- lm(test$xy, data = df)
  175. # 进行中介分析
  176. model_med <- mediate(model_xm,
  177. model_xy,
  178. treat = test$x,
  179. mediator = test$m,
  180. sims = 1000, # 设置模拟次数为1000次
  181. boot = TRUE) # 使用引导法（Bootstrap）进行重抽样
  182. # 获取中介分析的摘要结果
  183. med_sum <- summary(model_med)
  184. # 提取ACME（平均因果中介效应）及其P值
  185. ACME <- med_sum$d0
  186. ACME_P <- med_sum$d0.p
  187. ACME_CI <- med_sum$d0.ci
  188. # 提取ADE（平均直接效应）及其P值
  189. ADE <- med_sum$z0
  190. ADE_P <- med_sum$z0.p
  191. ADE_CI <- med_sum$z0.ci
  192. # 计算总效应（Total Effect）
  193. TE <- (ACME + ADE)
  194. # 计算总效应的P值
  195. # 这里我们假设使用bootstrap p-value，通常在summary中获得
  196. TE_P <- med_sum$tau.p
  197. # 计算中介效应所占比例
  198. Prop_Mediated <- ACME / TE
  199. # 将结果存储在数据框中
  200. result <- data.frame(
  201. 'Characteristics' = ms, # 中介变量名
  202. 'ACME' = ACME, # 平均因果中介效应
  203. 'ACME_P' = ACME_P, # 平均因果中介效应的P值
  204. 'ACME_95CI_lower' = ACME_CI[1], # ACME的95%CI下限
  205. 'ACME_95CI_upper' = ACME_CI[2], # ACME的95%CI上限
  206. 'ADE' = ADE, # 平均直接效应
  207. 'ADE_P' = ADE_P, # 平均直接效应的P值
  208. 'ADE_95CI_lower' = ADE_CI[1], # ADE的95%CI下限
  209. 'ADE_95CI_upper' = ADE_CI[2], # ADE的95%CI上限
  210. 'Total_Effect' = TE, # 总效应
  211. 'Total_Effect_P' = TE_P, # 总效应的P值
  212. 'Prop_Mediated' = Prop_Mediated) # 中介效应所占比例
  213. # 返回结果
  214. return(result)}
  216. # 设置需要探索是否为中介变量M的列名
  217. cols <-c("ltLP", "rtLP",
  218. "ltAV","rtAV",
  219. "ltVL","rtVL",
  220. "ltMDm","rtMDm",
  221. "ltMDl","rtMDl")
  223. # 设置随机种子
  224. set.seed(12345)
  225. #execution #language #visuo #memory
  226. # 使用lapply函数对列列表(cols)中的每一列应用batch_mediate函数
  227. res4<- lapply(cols, # 遍历列列表(cols)
  228. batch_mediate, # 应用batch_mediate函数
  229. y = "memory", # y参数设为因变量
  230. x = "ALPS", # x参数设为自变量
  231. df = data1) # df参数设为数据框data
  233. # 生成结果
  234. library("plyr")
  235. res4 <- ldply(res4,data.frame)
  236. View(res4)
  237. #保存结果
  238. write.csv(res4, file = "hospital_visuo_mediation.csv", row.names = FALSE)
  240. res5<- lapply(cols, # 遍历列列表(cols)
  241. batch_mediate, # 应用batch_mediate函数
  242. y = "memory", # y参数设为因变量名"status"
  243. x = "ALPS", # x参数设为自变量名"age"
  244. df = data1) # df参数设为数据框data
  246. # 生成结果
  247. library("plyr")
  248. res5 <- ldply(res5,data.frame)
  249. View(res5)
  250. ###中日数据保存
  251. write.csv(res5, file = "ALPSHospitalMemory.csv", row.names = FALSE)
  253. data1$SEX <- factor(data1$SEX)
  254. mx = lm(rtMDl ~ ALPS+ AGE + SEX + EDU + APOE4, data = data1)
  255. # 按照lm(y ~ x + m + 协变量1 + 协变量2)格式加入协变量即可
  256. #左右丘脑显著 #language #execution #visuo#memory
  257. yx = lm(execution ~ ALPS +rtMDl+ AGE+ SEX + EDU + APOE4, data = data1)
  258. mediate = mediate(mx, yx,
  259. treat = 'ALPS', # 处理变量（自变量），X
  260. mediator = 'rtMDl', # 中介变量，这里为M
  261. sims =1000, # 重抽样次数1000次
  262. boot = TRUE) # 是否使用引导法（Bootstrap）进行重抽样，这里设置为TRUE（使用引导法）
  264. summary(mediate)
  265. #language #execution #visuo#memory
  266. model1<- lm(visuo ~ ALPS + AGE + factor(SEX) + EDU +factor(APOE4),data=data1)
  267. summary(model1)
  269. #####三、合并数据进行分析,结果显著####
  270. ######3.1 处理数据#####
  271. library(dplyr)
  273. df_ADNI <- data %>%
  274. select(PTID, AGE, PTGENDER, PTEDUCAT, TIV, APOE4,
  275. ADNI_MEM, ADNI_LAN, ADNI_VS, ADNI_EF2,
  276. ALPS, group, lROL, rROL, lSMG, rSMG,
  277. lHES, rHES, lSTG, rSTG, ltAV, rtAV, ltLP, rtLP,
  278. ltVL, rtVL, ltMDm, rtMDm, ltMDl, rtMDl, lMTG, rMTG)
  279. #重命名列
  280. df_ADNI <- df_ADNI %>%
  281. rename(SEX = PTGENDER,
  282. EDU = PTEDUCAT,
  283. GROUP = group,
  284. execution=ADNI_EF2,
  285. language=ADNI_LAN,
  286. visuo=ADNI_VS,
  287. memory=ADNI_MEM)
  289. df_hospital<- data1 %>%
  290. select(PTID, AGE, SEX, EDU, TIV, APOE4,GROUP,
  291. execution,language,visuo,memory,
  292. ALPS, lROL, rROL, lSMG, rSMG,
  293. lHES, rHES, lSTG, rSTG, ltAV, rtAV, ltLP, rtLP,
  294. ltVL, rtVL, ltMDm, rtMDm, ltMDl, rtMDl, lMTG, rMTG)
  295. df_hospital <- df_hospital %>%
  296. mutate(SEX = recode(SEX, "女" = "Female", "男" = "Male"))
  298. head(df_ADNI)
  299. head(df_hospital)
  301. combined_df <- bind_rows(df_ADNI, df_hospital) #合并数据
  304. ######3.2 进行批量中介分析#####
  305. setRefClass("TS",
  306. fields = list(
  307. m = "character", # 中介变量名
  308. x = "character", # 处理变量名
  309. xm = "formula", # 中介模型公式
  310. xy = "formula" # 结果模型公式
  311. ))
  312. # 创建一个 "TS" 类的新实例
  313. test <- new("TS")
  314. # 定义一个函数，用于批量中介分析
  315. batch_mediate <- function(ms, y, xs, df) {
  316. # 设置中介变量名
  317. test$m <- ms
  318. # 设置处理变量名
  319. test$x <- xs
  320. # 创建中介模型公式
  321. test$xm <- as.formula(paste0(ms, "~", xs, "+ AGE + SEX + EDU+APOE4"))
  322. # 拟合中介模型
  323. model_xm <- lm(test$xm, data = df)
  324. # 创建结果模型公式
  325. test$xy <- as.formula(paste0(y, "~", ms, "+", xs, "+ AGE + SEX + EDU+APOE4"))
  326. # 拟合结果模型
  327. model_xy <- lm(test$xy, data = df)
  328. # 进行中介分析
  329. model_med <- mediate(model_xm,
  330. model_xy,
  331. treat = test$x,
  332. mediator = test$m,
  333. sims = 1000, # 设置模拟次数为1000次
  334. boot = TRUE) # 使用引导法（Bootstrap）进行重抽样
  335. # 获取中介分析的摘要结果
  336. med_sum <- summary(model_med)
  337. # 提取ACME（平均因果中介效应）及其P值
  338. ACME <- med_sum$d0
  339. ACME_P <- med_sum$d0.p
  340. # 提取ADE（平均直接效应）及其P值
  341. ADE <- med_sum$z0
  342. ADE_P <- med_sum$z0.p
  343. # 计算总效应（Total Effect）
  344. TE <- (ACME + ADE)
  345. # 计算总效应的P值
  346. # 这里我们假设使用bootstrap p-value，通常在summary中获得
  347. TE_P <- med_sum$tau.p
  348. # 计算中介效应所占比例
  349. Prop_Mediated <- ACME / TE
  350. # 将结果存储在数据框中
  351. result <- data.frame(
  352. 'Characteristics' = ms, # 中介变量名
  353. 'ACME' = ACME, # 平均因果中介效应
  354. 'ACME_P' = ACME_P, # 平均因果中介效应的P值
  355. 'ADE' = ADE, # 平均直接效应
  356. 'ADE_P' = ADE_P, # 平均直接效应的P值
  357. 'Total_Effect' = TE, # 总效应
  358. 'Total_Effect_P' = TE_P, # 总效应的P值
  359. 'Prop_Mediated' = Prop_Mediated) # 中介效应所占比例
  360. # 返回结果
  361. return(result)}
  363. # 设置需要探索是否为中介变量M的列名
  364. cols <-c("ltLP", "rtLP",
  365. "lSMG", "rSMG",
  366. "lROL", "rROL",
  367. "lSTG","rSTG",
  368. "lHES","rHES",
  369. "ltAV","rtAV",
  370. "ltVL","rtVL",
  371. "ltMDm","rtMDm",
  372. "ltMDl","rtMDl",
  373. "lMTG","rMTG")
  375. # 设置随机种子
  376. set.seed(12345)
  377. #execution #language #visuo #memory
  378. # 使用lapply函数对列列表(cols)中的每一列应用batch_mediate函数
  379. res<- lapply(cols, # 遍历列列表(cols)
  380. batch_mediate, # 应用batch_mediate函数
  381. y = "memory", # y参数设为因变量名"status"
  382. x = "ALPS", # x参数设为自变量名"age"
  383. df = combined_df) # df参数设为数据框data
  385. # 生成结果
  386. library("plyr")
  387. res <- ldply(res,data.frame)
  388. View(res)
  389. ###结果显著
  390. model1 <-lm(execution~ALPS+AGE+factor(SEX)+EDU+APOE4,data=combined_df)
  391. summary(model1)

中介效应分析代码11-4更新.R, no license · at the source

Overview

Authors: Leian Chen1, Xiao Zhou2, Bin Zhang3, Ying Hou4,5, Yu Sun5, Dantao Peng5, for the Alzheimer's Disease Neuroimaging Initiative (ADNI)
ORCID iDs: Leian Chen, Dantao Peng
  1. Epilepsy Center, Department of Neurology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
  2. Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
  3. Department of Neurology, Peking University First Hospital, Peking University, Beijing, China
  4. Peking University China‐Japan Friendship School of Clinical Medicine, Beijing, China
  5. Department of Neurology, China‐Japan Friendship Hospital, Beijing, China
Institutions: Second Affiliated Hospital of Zhejiang University (China); Zhejiang University (China); Capital Medical University (China); Beijing Friendship Hospital (China); Peking University First Hospital (China); Peking University (China); China-Japan Friendship Hospital (China)
Journal: Alzheimer's & dementia (New York, N. Y.), volume 12, issue 3, article e70304
Dates: received 3 February 2026; accepted 1 July 2026; published online 6 August 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/trc2.70304 · PMID 42564689 · PMCID PMC13445249 · OpenAlex W7196939744
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), cognitive (subfield)
Methods: Statistics, fMRI & imaging, Preprocessing
Keywords: Alzheimer's disease, cognitive function, diffusion tensor image analysis along the perivascular space, glymphatic system, thalamus
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (2022ZD0213300, 82201570)
Citations: not cited yet (Europe PMC); 48 references in the paper

Abstract

INTRODUCTION: Glymphatic dysfunction is implicated in Alzheimer's disease (AD), but its mechanism remains unclear. This study investigated the associations of the analysis along the perivascular space (ALPS) index, brain reserve, and cognitive outcomes across the AD continuum.

METHODS: This study enrolled two cohorts from the local hospital (n = 95) and the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 178). We calculated the diffusion tensor image ALPS to assess the whole‐brain glymphatic function and evaluated its associations with cognition and brain structure.

RESULTS: A higher ALPS index was positively associated with better executive, memory, and language abilities (p < 0.05). A lower ALPS index correlated with thalamic atrophy, involving the anterior, lateral, ventral, and midline thalamic nuclei. Thalamic atrophy mediated the link between glymphatic dysfunction and worse cognition via structural association of full or part mediation.

CONCLUSIONS: Glymphatic system deterioration correlates with cognitive decline, potentially mediated by thalamic atrophy. The thalamic nuclei may be critical in this pathway.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF wgtn7

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 2 files, 2 scripts
Software Heritage: not checked
Found in: “DATA AVAILABILITY STATEMENT”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (2 files), ggplot2 (1 file), lavaan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/wgtn7/

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

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;
  • 2 scripts, each with its path and the digest of its content;
  • 5 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 statement

The code of this article is available in the “open science framework” repository (https://osf.io/wgtn7/). The datasets analysed in this study are available from the corresponding author on reasonable request.

Reproduced under the paper's license (CC BY-NC), 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

  • Funding: added National Natural Science Foundation of China: 2022ZD0213300, 82201570

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 48 references.

Cite

This paper

Chen, L., Zhou, X., Zhang, B., Hou, Y., Sun, Y., Peng, D., & for the Alzheimer's Disease Neuroimaging Initiative (ADNI). (2026). Thalamic volume alterations mediate glymphatic function and cognitive dysfunction in Alzheimer's disease. Alzheimer's & dementia (New York, N. Y.), 12(3), e70304. https://doi.org/10.1002/trc2.70304

BibTeX

@article{chen2026thalamic,
author = {Chen, Leian and Zhou, Xiao and Zhang, Bin and Hou, Ying and Sun, Yu and Peng, Dantao and {for the Alzheimer's Disease Neuroimaging Initiative (ADNI)}},
title = {{Thalamic volume alterations mediate glymphatic function and cognitive dysfunction in Alzheimer's disease}},
journal = {Alzheimer's \& dementia (New York, N. Y.)},
year = {2026},
month = jul,
volume = {12},
number = {3},
pages = {e70304},
publisher = {Wiley},
issn = {2352-8737},
doi = {10.1002/trc2.70304},
url = {https://doi.org/10.1002/trc2.70304},
pmid = {42564689},
pmcid = {PMC13445249}
}

RIS

TY - JOUR
AU - Chen, Leian
AU - Zhou, Xiao
AU - Zhang, Bin
AU - Hou, Ying
AU - Sun, Yu
AU - Peng, Dantao
AU - for the Alzheimer's Disease Neuroimaging Initiative (ADNI)
TI - Thalamic volume alterations mediate glymphatic function and cognitive dysfunction in Alzheimer's disease
T2 - Alzheimer's & dementia (New York, N. Y.)
J2 - Alzheimers Dement (N Y)
PY - 2026
DA - 2026/07/01
VL - 12
IS - 3
SP - e70304
SN - 2352-8737
PB - Wiley
DO - 10.1002/trc2.70304
UR - https://doi.org/10.1002/trc2.70304
LA - en
ER -

CSL-JSON

{
"id": "10.1002/trc2.70304",
"type": "article-journal",
"title": "Thalamic volume alterations mediate glymphatic function and cognitive dysfunction in Alzheimer's disease",
"container-title": "Alzheimer's & dementia (New York, N. Y.)",
"author": [
{
"family": "Chen",
"given": "Leian"
},
{
"family": "Zhou",
"given": "Xiao"
},
{
"family": "Zhang",
"given": "Bin"
},
{
"family": "Hou",
"given": "Ying"
},
{
"family": "Sun",
"given": "Yu"
},
{
"family": "Peng",
"given": "Dantao"
},
{
"literal": "for the Alzheimer's Disease Neuroimaging Initiative (ADNI)"
}
],
"container-title-short": "Alzheimers Dement (N Y)",
"volume": "12",
"issue": "3",
"page": "e70304",
"DOI": "10.1002/trc2.70304",
"PMID": "42564689",
"PMCID": "PMC13445249",
"ISSN": "2352-8737",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/trc2.70304",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

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:10.1073/pnas.2516601123 [code]
Unveiling the glymphatic system's role in brain aging: A comprehensive biomarker and modifiable intervention target.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: ggplot2, tidyverse, structural MRI / diffusion, 7 references
[2] doi:10.1016/j.isci.2026.116581
Peripheral inflammation impairs glymphatic function, contributing to neurodegeneration in Alzheimer's disease.
Journal: iScience
In common: Alzheimer's / dementia, structural MRI / diffusion, cognitive, 7 references
[3] doi:10.1186/s12916-026-04948-z [code]
Putative glymphatic dysfunction links extracellular fluid dysregulation to white matter degeneration and clinical impairment in amyotrophic lateral sclerosis.
Journal: BMC medicine
In common: structural MRI / diffusion, 6 references
[4] doi:10.3390/ijms27135758
Diffusion Tensor Imaging Along the Perivascular Space (DTI-ALPS) as a Neuroimaging Biomarker of Glymphatic Function in Neurodegenerative Diseases: A Systematic Review.
Journal: International journal of molecular sciences
In common: Alzheimer's / dementia, structural MRI / diffusion, 5 references
[5] doi:10.1038/s42003-026-10388-5 [code]
Hierarchical organization of tau topography across the Alzheimer's disease continuum integrates amyloid, connectome, and intrinsic molecular vulnerability.
Journal: Communications biology
In common: Alzheimer's / dementia, 5 references
[6] doi:10.1002/alz.71530 [code]
Differential associations of plasma biomarkers with Alzheimer's disease and small vessel disease: A multimodal imaging study.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: Alzheimer's / dementia, structural MRI / diffusion, 4 references
[7] doi:10.1038/s41467-026-74957-2 [code]
Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.
Journal: Nature communications
In common: lavaan, Alzheimer's / dementia, structural MRI / diffusion, cognitive, 1 reference
[8] doi:10.3389/fneur.2026.1879894
Perivascular diffusion and white matter free water improve identification of cognitive impairment in cerebral small vessel disease.
Journal: Frontiers in neurology
In common: Alzheimer's / dementia, structural MRI / diffusion, 3 references
[9] doi:10.1002/alz.71811
Female vulnerability to microvascular transport dysfunction emerges early across the Alzheimer's disease continuum.
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association
In common: Alzheimer's / dementia, structural MRI / diffusion, 3 references
[10] doi:10.1007/s42761-026-00394-5 [code]
Amygdalar Pattern Similarity to Negative and Neutral Images, Ratings of the Images, and PET-Measured Amyloid and Tau Levels in Older Adults Without Dementia.
Journal: Affective science
In common: lavaan, ggplot2, tidyverse, Alzheimer's / dementia, cognitive

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.