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Brain Aging Mediating Heart Imaging-Derived Phenotypes and Mental and Nervous System Disorders.

Code ↔ Paper

2 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 2 matches
  1. [1] § Methods › Statistical Analysis › Brain Biological Age Prediction Model ↔ s1_predict_BA.R, lines 92–179 · score 0.83 · covariates regression model, training folds, age prediction, GLMnet, alpha, optimal
  2. [2] § Results › Brain Age Models and Associations of Heart IDPs With BAG ↔ s1_predict_BA.R, lines 92–179 · score 0.57 · LASSO model, regression model, MAE, predictive, S1, trained

Paper

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

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The authors' code

R · 181 lines · 7.7 KB · no license · 2 matches

  1. library(dplyr)
  2. library(readr)
  3. library(caret)
  4. library(glmnet)
  5. library(Metrics)
  7. # Define paths
  8. cova_data_path <- "cova.csv"
  9. cova_data <- read.csv(cova_data_path)
  10. eid_data_path <- "BA_health_eid.csv"
  11. allSiteInfo <- read.table('AllSiteInfo.csv', sep=',' ,header=T, check.names=F)
  12. HeightInfo <- read.table("AllHeightInfo.csv", sep=',', header=T, check.names=F)
  13. HeightInfo <- HeightInfo[,c('eid','50-0.0')]
  14. colnames(HeightInfo) <- c('eid', 'Height')
  15. # Define organs and their respective data files
  16. organs <- c('Brain_WM')
  17. ########################## model traning and testing on normal controls
  18. for (i in 1:length(organs)) {
  19. organ <- organs[i]
  21. # Load data
  22. df_GM <- read.csv("Brain_GM_clean.csv")
  23. df_WM <- read.csv("Brain_WM_clean.csv")
  24. df <- merge(df_GM, df_WM, by=c('eid'))
  26. # using only normal controls
  27. eid <- read.csv(eid_data_path)
  28. df <- inner_join(df, eid['eid'], by = 'eid')
  29. age <- read.csv(cova_data_path)
  30. age <- age[,c('eid','age2')]
  31. df <- inner_join(df, age, by = 'eid')
  32. df <- rename(df, Age = age2)
  34. # get confounding info
  35. df <- merge(df, allSiteInfo[,c('eid','54-2.0')])
  36. colnames(df)[which(colnames(df) == "54-2.0")] <- "Site"
  37. df$Site <- as.character(df$Site)
  38. df <- fastDummies::dummy_cols(df, select_columns = "Site", remove_first_dummy = TRUE)
  39. SiteCols <- unlist(sapply(colnames(df),function(x) if(grepl('Site_',x)) x),use.names = F)
  40. TIV <- cova_data
  41. TIV <- TIV[,c('eid','eTIV','Sex')]
  42. df <- merge(df, TIV, by = 'eid')
  43. df <- na.omit(df)
  44. table(df$Site)
  46. # Split data into training and testing sets
  47. set.seed(12345)
  48. folds <- createFolds(df$Age, k = 10)
  49. list_mae <- list()
  50. list_r <- list()
  51. list_models <- list()
  52. list_correctionModel <- list()
  53. list_lambda <- list()
  54. list_scaler <- list()
  55. list_plots <- list()
  56. list_coef <- list()
  57. list_covModles <- list()
  59. alldata <- df
  60. for (k in 1:10) {
  61. print(k)
  62. test_indices <- folds[[k]]
  63. train_indices <- setdiff(1:nrow(alldata), test_indices)
  65. x_train_data <- alldata[train_indices,]
  66. x_test_data <- alldata[test_indices,]
  67. data_matrix <- x_train_data
  68. data_matrix_test <- x_test_data
  70. residuals_matrix <- matrix(NA, nrow = nrow(data_matrix), ncol = ncol(data_matrix))
  71. residuals_matrix_test <- matrix(NA, nrow = nrow(data_matrix_test), ncol = ncol(data_matrix_test))
  72. colnames(residuals_matrix) <- colnames(data_matrix)
  73. colnames(residuals_matrix_test) <- colnames(data_matrix_test)
  74. covModels <- list()
  75. for (cc in 1:ncol(data_matrix)) {
  76. temp_data <- cbind(dependent_variable = data_matrix[, cc], x_train_data[,c('eTIV',SiteCols)])
  77. testing_data <- cbind(dependent_variable = data_matrix_test[, cc], x_test_data[,c('eTIV',SiteCols)])
  78. model <- lm(as.formula(paste0('dependent_variable ~ eTIV +',paste(SiteCols,collapse ='+'))), data = temp_data)
  79. residuals_matrix[, cc] <- residuals(model)
  80. # Predict the effect of covariates on the testing data using the training model
  81. predicted_values_test <- predict(model, newdata = testing_data)
  82. # Calculate residuals for the testing data
  83. residuals_matrix_test[,cc] <- testing_data$dependent_variable - predicted_values_test
  84. covModels[[cc]] <- model
  85. }
  86. list_covModles[[k]] <- covModels
  87. X_train_fold <- residuals_matrix
  88. y_train_fold <- alldata[train_indices, 'Age']
  89. X_test_fold <- residuals_matrix_test
  90. y_test_fold <- alldata[test_indices, 'Age']
  92. # normalizaion and standardziation
  93. library(caret)
  94. normParam <- preProcess(X_train_fold)
  95. X_train_fold <- predict(normParam, X_train_fold)
  96. X_test_fold <- predict(normParam, X_test_fold)
  97. list_scaler[[k]] <- normParam
  99. set.seed(1010)
  100. lasso_reg = glmnet(as.matrix(X_train_fold), y_train_fold, alpha = 1)
  101. cv_lasso <- cv.glmnet(as.matrix(X_train_fold), y_train_fold, lambda = 10^seq(5, -8, by = -.1), alpha = 1, type.measure = 'mae',nfolds = 10,parallel = T)
  102. plot(cv_lasso)
  103. list_plots[[k]] <- recordPlot()
  104. optimal_lambda <- cv_lasso$lambda.min
  105. lasso_model = predict(lasso_reg, type = "coefficients", s = optimal_lambda)
  106. lasso_model_nonzero <- lasso_model[(lasso_model[,1]!= 0),]
  107. list_lambda[[k]] <- optimal_lambda
  108. lasso_predict <- predict(lasso_reg, newx = as.matrix(X_test_fold), s = optimal_lambda)
  110. df[test_indices, 'pre_age'] <- as.numeric(lasso_predict)
  112. # Age bias correction
  113. train_predicted_y <- predict(lasso_reg, newx = as.matrix(X_train_fold), s = optimal_lambda)
  114. model <- lm(train_predicted_y ~ y_train_fold)
  115. test <- data.frame(TrueAge = as.numeric(y_test_fold), age_LASSO = as.numeric(lasso_predict))
  116. test$age_correct_LASSO <- (test$age_LASSO - coef(model)[1]) / coef(model)[2]
  118. df[test_indices, 'delta_corrected'] <- unlist(test$age_correct_LASSO - test$TrueAge)
  119. df[test_indices, 'pre_age_corrected'] <- test$age_correct_LASSO
  121. list_mae[[k]] <- mae(test$TrueAge, test$age_LASSO)
  122. list_r[[k]] <- cor(test$TrueAge, test$age_LASSO)
  123. }
  125. best_model_index <- which.min(list_mae)
  127. ##################### Model evaluation
  128. r <- cor(df$Age, df$pre_age)
  129. r_corrected <- cor(df$Age, df$pre_age_corrected)
  130. mae <- mae(df$Age, df$pre_age)
  131. mae_corrected <- mae(df$Age, df$pre_age_corrected)
  132. corrdata <- data.frame(df$Age, df$pre_age)
  133. write.table(corrdata, 'brain_age_prediction_evaluation.csv', sep=',', row.names=F)
  135. # Get weights of the predictors
  136. brain_traits <- data.frame(rownames(best_coef),as.matrix(best_coef))
  137. colnames(brain_traits) <- c('field','coef')
  138. write_csv(brain_traits, 'Age_LASSO_weights.csv')
  140. ##################### Apply the trained model to other subjects (excluding normal controls)
  141. df <- merge(df_GM, df_WM, by=c('eid'))
  143. # exclude only normal controls
  144. eid <- read.csv(eid_data_path)
  145. df <- df[!(df$eid %in% eid$eid),]
  146. cnt <- rowSums(!is.na(df))
  147. df <- df[cnt == ncol(df), ]
  149. # apply the covariate regression model to other subjs
  150. x_test_data <- df
  151. data_matrix_test <- x_test_data[,unlist(sapply(colnames(x_test_data),function(x) grepl('^X',x)), use.names = F)]
  152. # Initialize a matrix to store residuals
  153. residuals_matrix_test <- matrix(NA, nrow = nrow(data_matrix_test), ncol = ncol(data_matrix_test))
  154. colnames(residuals_matrix_test) <- colnames(data_matrix_test)
  155. for (cc in 1:ncol(data_matrix_test)) {
  156. testing_data <- cbind(dependent_variable = data_matrix_test[, cc], x_test_data[,c('eTIV',SiteCols)])
  157. # Predict the effect of covariates on the testing data using the training model
  158. predicted_values_test <- predict(best_covModel[[cc]], newdata = testing_data)
  159. # Calculate residuals for the testing data
  160. residuals_matrix_test[,cc] <- testing_data$dependent_variable - predicted_values_test
  161. }
  162. df[,unlist(sapply(colnames(df),function(x) grepl('^X',x)), use.names = F)] <- residuals_matrix_test
  163. df <- df[, !(names(df) %in% c(unlist(sapply(colnames(df),function(x) {if(grepl('Site',x)) x}), use.names = F),'eTIV','Height','Sex'))]
  166. # normalization and standardization
  167. df[,2:ncol(df)-1] <- predict(best_scaler, df[,2:ncol(df)-1])
  168. X_all <- df[, -c(1, ncol(df))]
  169. y_all_pred <- as.numeric(predict(best_model, newx = as.matrix(X_all), s = best_lambda))
  170. y_all_pred_corrected <- (y_all_pred - coef(best_correction_model)[1]) / coef(best_correction_model)[2]
  171. all_delta_corrected <- y_all_pred_corrected - df$Age
  172. df$pre_age <- y_all_pred
  173. df$pre_age_corrected <- y_all_pred_corrected
  174. df$delta_corrected <- all_delta_corrected
  175. df_all <- rbind(df_HC, df)
  176. write_csv(data.frame(df_all), 'brain_age_prediction_all.csv')
  177. }
  178. selected_df_agegap <- df_all[, c("eid", "Age", "pre_age", "pre_age_corrected", "delta_corrected")]
  179. write_csv(data.frame(selected_df_agegap), 'agegap.csv')

s1_predict_BA.R at commit a31a811, no license · at the source

Overview

Authors: Zairen Zhou1, Wenjing Su1, Yuna Li1,2, Bei Zhang3, Fang Lan1, Dawei Lin4, Jin‐Tai Yu1, Jianfeng Feng1,5,6,7, Yifei Jin1, Peng Ren1,5, Wei Cheng1,5,8
ORCID iDs: Peng Ren, Wei Cheng
  1. Institute of Science and Technology for Brain‐Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Brain Function and Disorders and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
  2. Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
  3. Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology, Shanghai, China
  4. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China
  5. Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
  6. School of Data Science, Fudan University, Shanghai, China
  7. Department of Computer Science, University of Warwick, Coventry, UK
  8. Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired Intelligence, Zhejiang Normal University, Jinhua, China
Institutions: Fudan University (China); Huashan Hospital (China); Frontiers Center for Brain Science of the Ministry of Education (China); Capital Medical University (China); Beijing Tian Tan Hospital (China); Shanghai Jiao Tong University (China); Shanghai Mental Health Center (China); Zhongshan Hospital (China); University of Warwick (United Kingdom); Zhejiang Normal University (China)
Journal: Aging cell, volume 25, issue 5, article e70499
Dates: received 19 August 2025; accepted 10 April 2026; published online 23 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/acel.70499 · PMID 42021636 · PMCID PMC13103654 · OpenAlex W7155399711
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics, Machine learning, fMRI & imaging, Preprocessing
Keywords: brain age, brain disorder, heart, MRI
MeSH: Aging*, Brain*, Heart*, Mental Disorders*, Nervous System Diseases*, Aged, Female, Humans, Male, Middle Aged, Phenotype, UK Biobank (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Higher Education Discipline Innovation Project (B18015); National Key Research and Development Program of China (2023YFC3605400); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0546300); Shanghai Science and Technology Commission Program (23JS1410100); Shanghai Pilot Program for Basic Research-Fudan University (25TQ010, 21TQ1400100); National Natural Science Foundation of China (82530047, 82271471, 82588301, 62433008, 82472055); Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2022ZD0211600); Shanghai Academy of Natural Sciences, New Cornerstone Science Foundation; Zhangjiang Lab; the State Key Laboratory of Brain Function and Disorders of Ministry of Education, Fudan University; Changping Laboratory (2025B-07-35, 2025B‐07‐35); China Postdoctoral Science Foundation (2025M772197); the Shanghai Center for Brain Science and Brain-Inspired Technology; National Key R&amp;D Program of China (2023YFC3605400); Leading Project of the Discipline Breakthrough Plan of the Ministry of Education (JYB2025XDXM604); Lin Gang Laboratory (LGL-3241-PDA030200); Program of Shanghai Academic Research Leader (23XD1420400); the 111 Project (B18015); Tianqiao and Chrissy Chen Institute
Citations: not cited yet (Europe PMC); 84 references in the paper

Abstract

Mental and nervous system disorders often co‐occur with cardiovascular diseases in aging populations, yet the biological relationships underlying these associations remain incompletely understood. Using heart and brain imaging data from 33,573 UK Biobank (UKB) participants, we developed a brain age prediction model to estimate the brain age gap (BAG), an imaging‐based marker of brain aging. We then examined BAG as a mediator between 82 cardiac imaging‐derived phenotypes (IDPs) and 11 disorders. Sixty‐one cardiac IDPs, particularly those related to the atria and left ventricle, were significantly associated with BAG, with several also related to mental and nervous system disorders. Mediation analyses revealed that BAG significantly mediated 18 associations between heart and substance abuse, mood, and neurotic disorders. Furthermore, we observed a total of 49 significant associations, where lifestyle factors, including smoking and physical activity, were related to heart‐brain aging‐disorder relationships. These findings highlight brain aging as a potential pathway linking cardiovascular health to diverse brain disorders in aging populations.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

WenJSu/Heart-brain-aging-disorders-pathways

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a31a811414b5f9ed7f39c7a2ff03e9c89f293a19, 21 July 2025
Languages: R (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lavaan (2 files), caret (1 file), data.table (1 file), glmnet (1 file), survival (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/WenJSu/Heart-brain-aging-disorders-pathways

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;
  • 6 scripts, each with its path and the digest of its content;
  • 2 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 individual level data analyzed in this study are available from the UK Biobank (https://biobank.ctsu.ox.ac.uk) upon application and approval. The code for the main analysis of this study is publicly available at https://github.com/WenJSu/Heart‐brain‐aging‐disorders‐pathways (https://github.com/WenJSu/Heart-brain-aging-disorders-pathways).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 4 keywords, 12 MeSH terms, 19 funders, 84 references.

Cite

This paper

Zhou, Z., Su, W., Li, Y., Zhang, B., Lan, F., Lin, D., Yu, J., Feng, J., Jin, Y., Ren, P., & Cheng, W. (2026). Brain Aging Mediating Heart Imaging-Derived Phenotypes and Mental and Nervous System Disorders. Aging cell, 25(5), e70499. https://doi.org/10.1111/acel.70499

BibTeX

@article{zhou2026brain,
author = {Zhou, Zairen and Su, Wenjing and Li, Yuna and Zhang, Bei and Lan, Fang and Lin, Dawei and Yu, Jin‐Tai and Feng, Jianfeng and Jin, Yifei and Ren, Peng and Cheng, Wei},
title = {{Brain Aging Mediating Heart Imaging-Derived Phenotypes and Mental and Nervous System Disorders}},
journal = {Aging cell},
year = {2026},
month = may,
volume = {25},
number = {5},
pages = {e70499},
publisher = {Wiley},
issn = {1474-9718},
doi = {10.1111/acel.70499},
url = {https://doi.org/10.1111/acel.70499},
pmid = {42021636},
pmcid = {PMC13103654}
}

RIS

TY - JOUR
AU - Zhou, Zairen
AU - Su, Wenjing
AU - Li, Yuna
AU - Zhang, Bei
AU - Lan, Fang
AU - Lin, Dawei
AU - Yu, Jin‐Tai
AU - Feng, Jianfeng
AU - Jin, Yifei
AU - Ren, Peng
AU - Cheng, Wei
TI - Brain Aging Mediating Heart Imaging-Derived Phenotypes and Mental and Nervous System Disorders
T2 - Aging cell
J2 - Aging Cell
PY - 2026
DA - 2026/05/01
VL - 25
IS - 5
SP - e70499
SN - 1474-9718
PB - Wiley
DO - 10.1111/acel.70499
UR - https://doi.org/10.1111/acel.70499
LA - en
ER -

CSL-JSON

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"container-title": "Aging cell",
"author": [
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"family": "Zhou",
"given": "Zairen"
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"family": "Su",
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{
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"container-title-short": "Aging Cell",
"volume": "25",
"issue": "5",
"page": "e70499",
"DOI": "10.1111/acel.70499",
"PMID": "42021636",
"PMCID": "PMC13103654",
"ISSN": "1474-9718",
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"language": "en",
"issued": {
"date-parts": [
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1
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