Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis.
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- [1] § Methods › Data ↔ 01_Create_MRI_Cohort.R, the whole file · a weak match · score 0.65 · MRI scans, missing race, covariates, Americans, written, cohorts
Paper
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The authors' code
R · 73 lines · 1.9 KB · no license · 1 match
- ####################################################################
- ## Add MRI Measures to the Cohort Data Cleaned for SENAS Analysis ##
- ####################################################################
- #written by: Emma Gause
- #Date: 07/19/23
- #Last updated: 11/08/23
- #load libraries:
- library("tidyverse")
- library("dplyr")
- #Create path to directory
- datadir1 <- "[directory path]"
- datadir2 <- "[directory path]"
- dataexp <- "[directory path]"
- #read in cohort data with covariates
- dat <- readRDS(paste0(datadir1, "Analysis_Data_081823.rds"))
- #read in mri data
- kmri <- readRDS(paste0(datadir2, "KHANDLE/Data/Imaging/khandle_T1_analysis_052322_age.rds"))
- smri <- readRDS(paste0(datadir2, "STAR/Data/Imaging/k-star_T1_analysis_052322_age.rds"))
- ##------------------------------------------------------------------------##
- str(dat)
- str(kmri)
- str(smri)
- #create the study IDs to be compatible
- head(dat$id)
- head(dat$STUDYID)
- head(kmri$StudyID)
- tail(smri$StudyID)
- #pad study IDs for cohort data to match MRI
- #[redacted to preserve ID anonymity]
- ##------------------------------------------------------------------------##
- #rbind mri measures and then merge to cohort data
- colnames(kmri)
- colnames(smri)
- mri <- rbind(kmri, smri)
- #prepare cohort data to merge to MRI set
- #We want to compare m:1 so we can assess the difference in time between depression and MRI scan
- str(dat)
- str(mri)
- # XXXXX and XXXXX exist in MRI but not in cohort data...
- # these are Native American or missing race exclusions - they will be removed in inner_join
- data <- inner_join(dat, mri, by = "StudyID", relationship = "many-to-one")
- #see if they all merged --> race_fact should have no missingness
- table(data$race_fact, useNA = "ifany")
- #looks good!
- #how many unique IDs do we have?
- ids <- data %>% select(StudyID) %>% unique()
- #560 - this is what we expect
- saveRDS(data, paste0(dataexp, "MRI_Long_110823.rds"))
- ##------------------------------------------------------------------------##
01_Create_MRI_Cohort.R at commit 6743d82, no license · at the source
Overview
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Center for Climate and Health, Boston University School of Public Health, Boston, MA, United States
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, United States
- Department of Epidemiology, Brown University, Providence, RI, United States
- Department of Neurology, Columbia University, New York, NY, United States
- Department of Neurology, University of California, Davis, CA, United States
- Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, United States
- Department of Public Health Sciences and Neurology, University of California Davis School of Medicine, Davis, CA, United States
Abstract
Depression has been associated with magnetic resonance imaging (MRI) measures of larger white matter hyperintensity (WMH) volumes and smaller cerebral gray matter volumes (GMV) in predominantly White samples. Recent findings suggest that some race/
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 1 match between paragraphs and lines of code.
SpatialHealth/KHANDLE-STAR_Depression_MRI
6743d82e50ea4d2ea8633755209c88b5876ea4a2, 15 April 2025Availability: 1 check, the latest on 26 September 2026: the link answers
- 26 September 2026: the link answers
18 files
- 01_Create_MRI_Cohort.R, R, 73 lines, 1 match
- 02_Create_Analysis_Set.R
, R, 71 lines - 03_Table1.R, R, 72 lines
- 04_Frequentist_analyses.
R , R, 204 lines - 05_Bayes_Grey_uprior.R, R, 375 lines
- 05_Bayes_WMH_uprior.R, R, 375 lines
- 06a_Bayes_Grey_apriori.R
, R, 425 lines - 06a_Bayes_WMH_apriori.R, R, 390 lines
- 06b_Bayes_Grey_apriori_t
iered_030525.R , R, 1,143 lines - 06b_Bayes_WMH_apriori_ti
ered_030525.R , R, 1,109 lines - 07_Bayes_Grey_addladj.R, R, 395 lines
- 07_Bayes_WMH_addladj.R, R, 394 lines
- 08_Bayes_Grey_65plus.R, R, 193 lines
- 08_Bayes_WMH_65plus.R, R, 188 lines
- 09_Grey_Sensitivity.R, R, 868 lines
- 09_WMH_Sensitivity.R, R, 867 lines
- 10_make_sensitivity_plot
s.R , R, 236 lines - README.md, Text, 3 lines
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:
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Data
No dataset and no data link were found in the paper.
Data availability
The data underlying this article cannot be shared publicly due to the privacy of individuals who participated in the KHANDLE and STAR cohorts. All access inquiries for the datasets used in this study should be directed to the Rachel Whitmer Lab: https://
Reproduced under the paper's license (CC BY-NC), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 7 keywords, 19 MeSH terms, 3 funders, 61 references.
Cite
This paper
Jenson, T. E., Gause, E. L., Wang, J., Ackley, S. F., Manly, J., Fletcher, E., Gilsanz, P., Whitmer, R. A., Glymour, M. M., & Jimenez, M. P. (2026). Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis. American journal of epidemiology, 195(9), 2355-2363. https://
BibTeX
@article{jenson2026depre
author = {Jenson, Tara E and Gause, Emma L and Wang, Jingxuan and Ackley, Sarah F and Manly, Jennifer and Fletcher, Evan and Gilsanz, Paola and Whitmer, Rachel A and Glymour, M Maria and Jimenez, Marcia Pescador},
title = {{Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis}},
journal = {American journal of epidemiology},
year = {2026},
month = sep,
volume = {195},
number = {9},
pages = {2355--2363},
publisher = {Oxford University Press},
issn = {0002-9262},
doi = {10.1093/
url = {https://
pmid = {42299687},
pmcid = {PMC13537852}
}
RIS
TY - JOUR
AU - Jenson, Tara E
AU - Gause, Emma L
AU - Wang, Jingxuan
AU - Ackley, Sarah F
AU - Manly, Jennifer
AU - Fletcher, Evan
AU - Gilsanz, Paola
AU - Whitmer, Rachel A
AU - Glymour, M Maria
AU - Jimenez, Marcia Pescador
TI - Depressive symptoms and neuroimaging markers of brain aging in an ethno-racially diverse sample: a Bayesian analysis
T2 - American journal of epidemiology
J2 - Am J Epidemiol
PY - 2026
DA - 2026/
VL - 195
IS - 9
SP - 2355
EP - 2363
SN - 0002-9262
PB - Oxford University Press
DO - 10.1093/
UR - https://
LA - en
ER -
CSL-JSON
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