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Long-term brain volume trajectories and lifestyle associations in cognitively normal adults: the BRAIN-STRIDE study.

Code ↔ Paper

4 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 4 matches
  1. [1] § Materials and methods › Associations with lifestyle and clinical factors ↔ brain_stride_lifestyle_analysis.Rmd, lines 37 to 47 · score 0.61 · lifestyle variables, lifestyle factors, glucose, HDL, LDL, BMI · Report a mismatch
  2. [2] § Materials and methods › Identification of brain atrophy subtypes in cognitively normal adults ↔ brain_stride_apply_lme.Rmd, lines 16 to 44 · score 0.60 · individual atrophy rates, linear mixed, individual slopes, brain region, predictor, harmonized · Report a mismatch
  3. [3] § Results › Atrophy group classification and longitudinal brain volume changes ↔ brain_stride_apply_lme.Rmd, lines 273 to 325 · score 0.59 · linear fits, volume trajectories, normalized volume, individual baseline, resistant, Age · Report a mismatch
  4. [4] § Materials and methods › Brain volume measurement and longitudinal harmonization ↔ brain_stride_apply_longcombat.Rmd, lines 366 to 429 · score 0.51 · random intercepts, metric, correlation, additive, batch, mixed · Report a mismatch

Paper

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The authors' code

R Markdown · 325 lines · 10 KB · MIT · 2 matches

  1. ---
  2. title: "Individual Brain Atrophy Trajectories: Mixed-Effects Modeling and Bootstrap Grouping"
  3. output:
  4. html_document:
  5. toc: true
  6. toc_float:
  7. toc_collapsed: true
  8. toc_depth: 3
  9. theme: paper
  10. ---
  12. ```{r setup, include=FALSE}
  13. knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
  14. ```
  16. # Overview
  18. This script estimates individual atrophy rates for each brain region using
  19. linear mixed-effects models, then uses a bootstrap procedure to stratify
  20. subjects into "rapid", "resistant", and "typical" atrophy groups based on the
  21. distribution of individual slopes within each decade of age. Summary statistics
  22. and figures are generated for selected regions.
  24. # Packages and Data
  26. ```{r load-packages-data}
  27. ##############################################################################
  28. ## 0. Packages and data
  29. ##############################################################################
  30. library(lme4)
  31. library(tidyverse)
  32. library(purrr)
  33. library(cowplot)
  35. df_norm <- read.csv("data/df_combat_norm.csv")
  37. ## Region columns to analyze (harmonized, normalized features)
  38. regions <- grep("\\.combat\\.norm$", names(df_norm), value = TRUE)
  40. ## Analysis parameters
  41. predictors <- c("BMI", "dBP") # Lifestyle factors included as fixed effects
  42. B <- 2000 # Number of bootstrap iterations
  43. set.seed(123)
  44. ```
  46. # Individual Slope Estimation
  48. ```{r individual-slopes}
  49. ##############################################################################
  50. ## 1. Per-region lmer -> individual slopes -> attach decade -> drop 30s/80s
  51. ##############################################################################
  52. get_indiv_coef <- function(region) {
  53. fml <- as.formula(
  54. sprintf("`%s` ~ time + %s + (time | subid)",
  55. region, paste(predictors, collapse = " + "))
  56. )
  57. m <- lmer(fml, data = df_norm, REML = FALSE)
  59. coef(m)$subid %>% # Intercept and time slope
  60. rownames_to_column("subid") %>%
  61. select(subid, time) %>% # Keep slope only
  62. left_join(df_norm %>% select(subid, decade) %>% distinct(),
  63. by = "subid") %>%
  64. filter(!decade %in% c("30s", "80s")) %>% # Exclude sparse decades
  65. mutate(decade = factor(decade, levels = sort(unique(decade))),
  66. region = region)
  67. }
  69. indiv_all <- map_dfr(regions, get_indiv_coef)
  70. ```
  72. # Group Assignment
  74. ```{r group-assignment}
  75. ##############################################################################
  76. ## 2. Flag rapid / resistant groups (10% / 90% thresholds)
  77. ##############################################################################
  78. flag_groups <- function(df) {
  80. # Regions where a positive slope reflects atrophy (inverted interpretation)
  81. invert_regions <- c("Ventricle.combat.norm", "Sulcus.combat.norm")
  83. df %>%
  84. group_by(region, decade) %>%
  85. mutate(
  86. q10 = quantile(time, 0.10),
  87. q90 = quantile(time, 0.90),
  88. grp = case_when(
  89. time <= q10 ~ if_else(region %in% invert_regions, "resistant", "rapid"),
  90. time >= q90 ~ if_else(region %in% invert_regions, "rapid", "resistant"),
  91. TRUE ~ "typical"
  92. )
  93. ) %>%
  94. ungroup()
  95. }
  96. ```
  98. # Bootstrap
  100. ```{r bootstrap}
  101. ##############################################################################
  102. ## 3. Bootstrap (stratified by region x decade)
  103. ##############################################################################
  104. boot_once <- function(data) {
  105. sampled <- data %>%
  106. group_by(region, decade) %>%
  107. sample_frac(replace = TRUE) %>%
  108. ungroup()
  110. grp_tbl <- flag_groups(sampled)
  112. diff_tbl <- grp_tbl %>%
  113. filter(grp != "typical") %>%
  114. group_by(region, decade, grp) %>%
  115. summarise(med = median(time), .groups = "drop") %>%
  116. pivot_wider(names_from = grp, values_from = med) %>%
  117. mutate(med_diff = resistant - rapid)
  119. list(diff = diff_tbl,
  120. subj = grp_tbl %>% select(subid, region, decade, grp))
  121. }
  123. boot_out <- map(1:B, ~ boot_once(indiv_all))
  124. ```
  126. # Final Labels and Per-Region Plots
  128. ```{r final-labels}
  129. ##############################################################################
  130. ## 4. Stable labels (probability >= 0.8) and per-region boxplots
  131. ##############################################################################
  132. subj_stab <- map_dfr(boot_out, "subj") %>%
  133. count(subid, region, grp) %>%
  134. pivot_wider(names_from = grp, values_from = n, values_fill = 0) %>%
  135. mutate(across(rapid:resistant, ~ .x / B)) # Convert counts to probabilities
  137. final_lab <- subj_stab %>%
  138. mutate(final_grp = case_when(
  139. rapid >= 0.8 ~ "rapid",
  140. resistant >= 0.8 ~ "resistant",
  141. TRUE ~ "typical"
  142. )) %>%
  143. select(subid, region, final_grp)
  145. plot_df <- indiv_all %>%
  146. left_join(final_lab, by = c("subid", "region")) %>%
  147. mutate(final_grp = factor(final_grp,
  148. levels = c("rapid", "typical", "resistant")))
  150. ## Color palette
  151. fill_pal <- c(rapid = "#E15759", typical = "grey60", resistant = "#4E79A7")
  152. point_pal <- fill_pal
  154. ## Save a jitter plot per region
  155. walk(unique(plot_df$region), function(reg) {
  156. p <- ggplot(filter(plot_df, region == reg),
  157. aes(x = decade, y = time * 100, fill = final_grp)) +
  158. geom_jitter(aes(colour = final_grp),
  159. width = 0.15, size = 1.5, alpha = 0.55) +
  160. geom_hline(yintercept = 0, linetype = "dashed") +
  161. scale_fill_manual(values = fill_pal, drop = FALSE) +
  162. scale_colour_manual(values = point_pal, drop = FALSE) +
  163. labs(title = paste(reg, ": Atrophy rate by decade/group"),
  164. x = "", y = "Annual slope (%)",
  165. fill = "Group", colour = "Group") +
  166. theme_cowplot(12) +
  167. theme(legend.position = "right")
  169. ggsave(sprintf("%s_boxplot.png", reg), p,
  170. width = 5, height = 4.5, dpi = 300)
  171. })
  173. cat("Analysis complete: PNG figures written to the working directory.\n")
  174. ```
  176. # Combined Faceted Plot
  178. ```{r faceted-plot, fig.width=14, fig.height=3}
  179. ## Display labels and ordering for selected regions
  180. region_labels <- c(
  181. "WholeBrain.combat.norm" = "Whole brain",
  182. "CerebralCortex.combat.norm" = "Cortical",
  183. "WhiteMatter.combat.norm" = "WM",
  184. "Ventricle.combat.norm" = "Ventricle",
  185. "Sulcus.combat.norm" = "Sulcus"
  186. )
  187. region_order <- names(region_labels)
  189. ## Fix region order and keep only selected regions
  190. plot_df_subset <- plot_df %>%
  191. filter(region %in% region_order) %>%
  192. mutate(region = factor(region, levels = region_order))
  194. p_all <- ggplot(
  195. plot_df_subset,
  196. aes(x = decade, y = time * 100,
  197. fill = final_grp, colour = final_grp)
  198. ) +
  199. geom_jitter(width = 0.15, size = 0.5, alpha = 0.55) +
  200. geom_hline(yintercept = 0, linetype = "dashed") +
  201. scale_fill_manual(values = fill_pal, drop = FALSE) +
  202. scale_colour_manual(values = point_pal, drop = FALSE) +
  203. facet_wrap(
  204. ~ region,
  205. scales = "free_y",
  206. nrow = 1,
  207. labeller = as_labeller(region_labels)
  208. ) +
  209. labs(
  210. title = "Atrophy rate by decade / region / group",
  211. x = "",
  212. y = "Annual slope (%)",
  213. fill = "Group",
  214. colour = "Group"
  215. ) +
  216. theme_cowplot(12) +
  217. theme(legend.position = "right")
  219. print(p_all)
  220. ```
  222. # Summary Table
  224. ```{r summary-table}
  225. ##############################################################################
  226. ## 6. Summary statistics by region x decade x group (median [IQR])
  227. ##############################################################################
  228. region_labels <- c(
  229. "WholeBrain.combat.norm" = "Whole brain",
  230. "CerebralCortex.combat.norm" = "Cortical",
  231. "WhiteMatter.combat.norm" = "WM",
  232. "Ventricle.combat.norm" = "Ventricle",
  233. "Sulcus.combat.norm" = "Sulcus"
  234. )
  236. summary_tbl <- indiv_all %>% # subid, region, time, decade
  237. left_join(final_lab, by = c("subid", "region")) %>% # Add final_grp
  238. mutate(rate_pct = time * 100) %>% # Annual rate (%)
  239. group_by(region, decade, final_grp) %>%
  240. summarise(
  241. n = n(),
  242. median = median(rate_pct),
  243. p25 = quantile(rate_pct, 0.25),
  244. p75 = quantile(rate_pct, 0.75),
  245. .groups = "drop"
  246. ) %>%
  247. mutate(
  248. stat = sprintf("%.2f (%.2f to %.2f)", median, p25, p75)
  249. ) %>%
  250. select(region, decade, final_grp, stat) %>%
  251. pivot_wider(
  252. names_from = final_grp,
  253. values_from = stat
  254. ) %>%
  255. rename(
  256. `Typical volume change rate (annual %)` = typical,
  257. `Rapid-atrophy group` = rapid,
  258. `Atrophy-resistant group` = resistant
  259. ) %>%
  260. mutate(
  261. Structure = recode(region, !!!region_labels),
  262. Structure = factor(Structure, levels = region_labels)
  263. ) %>%
  264. arrange(Structure, decade) %>%
  265. select(Structure, decade,
  266. `Typical volume change rate (annual %)`,
  267. `Rapid-atrophy group`,
  268. `Atrophy-resistant group`)
  270. print(summary_tbl, n = Inf)
  271. ```
  273. # Age vs. Volume Trajectories
  275. ```{r age-volume-smooth, fig.width=14, fig.height=3}
  276. plot_lab <- final_lab
  278. regions_selected <- c("WholeBrain.combat.norm",
  279. "CerebralCortex.combat.norm",
  280. "WhiteMatter.combat.norm",
  281. "Sulcus.combat.norm",
  282. "Ventricle.combat.norm")
  284. plot_agevol <- df_norm %>%
  285. pivot_longer(cols = all_of(regions_selected),
  286. names_to = "region",
  287. values_to = "volume") %>%
  288. left_join(plot_lab, by = c("subid", "region")) %>%
  289. mutate(region = factor(region, levels = regions_selected))
  291. ## Version 1: per-subject linear fits for rapid / resistant groups
  292. ggplot(plot_agevol,
  293. aes(x = age, y = volume * 100, colour = final_grp)) +
  294. geom_jitter(alpha = 0.6, size = 0.3, width = 0.3) +
  295. geom_smooth(
  296. data = plot_agevol %>% filter(final_grp %in% c("rapid", "resistant")),
  297. aes(group = subid, colour = final_grp),
  298. method = "lm", se = FALSE,
  299. linewidth = 0.4, alpha = 0.7
  300. ) +
  301. facet_wrap(~ region, scales = "free_y", nrow = 1) +
  302. scale_colour_manual(values = point_pal) +
  303. labs(x = "Age (years)",
  304. y = "Normalized volume \n (% of individual baseline)",
  305. colour = "Group") +
  306. theme_cowplot(12)
  308. ## Version 2: connected observed points for rapid / resistant groups
  309. ggplot(plot_agevol,
  310. aes(x = age, y = volume * 100, colour = final_grp)) +
  311. geom_jitter(alpha = 0.6, size = 0.3, width = 0.3) +
  312. geom_line(
  313. data = plot_agevol %>%
  314. filter(final_grp %in% c("rapid", "resistant")) %>%
  315. arrange(subid, age),
  316. aes(group = subid),
  317. linewidth = 0.4, alpha = 0.7
  318. ) +
  319. facet_wrap(~ region, scales = "free_y", nrow = 1) +
  320. scale_colour_manual(values = point_pal) +
  321. labs(x = "Age (years)",
  322. y = "Normalized volume \n (% of individual baseline)",
  323. colour = "Group") +
  324. theme_cowplot(12)
  325. ```

brain_stride_apply_lme.Rmd, under MIT · at the source

Overview

Authors: Shohei Fujita1,2,3,4, Susumu Mori5,6, Kengo Onda5, Shouhei Hanaoka1, Yukihiro Nomura7,8, Takahiro Nakao7, Akifumi Hagiwara1,2, Yasuhiro Hagiwara9, Hidemasa Takao1, Takeharu Yoshikawa7, Osamu Abe1
ORCID iDs: Shohei Fujita, Kengo Onda, Akifumi Hagiwara, Yasuhiro Hagiwara, Osamu Abe
  1. Department of Radiology, The University of Tokyo, Tokyo, 113-8655, Japan
  2. Department of Radiology, Juntendo University, Tokyo, 113-8421, Japan
  3. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129, USA
  4. Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
  5. Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
  6. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD 21205, USA
  7. Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, 113-8655, Japan
  8. Center for Frontier Medical Engineering, Chiba University, Chiba, 263-8522, Japan
  9. Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, 113-0033, Japan
Institutions: Harvard University (United States); Juntendo University (Japan); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States); The University of Tokyo (Japan); Kennedy Krieger Institute (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States); Chiba University (Japan); University of Tokyo Hospital (Japan)
Journal: Brain communications, volume 8, issue 5, article fcag343
Dates: received 29 January 2026; accepted 27 August 2026; published online 7 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag343 · PMID 42765092 · PMCID PMC13590224 · OpenAlex W7211919747
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), healthy (population), cognitive (subfield)
Methods: Connectivity, Statistics
Keywords: ageing, longitudinal studies, lifestyle, brain volume, cognition
Topic: Dementia and Cognitive Impairment Research (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: University of Tokyo
Citations: not cited yet (Europe PMC); 37 references in the paper

Abstract

Brain ageing is a major determinant of cognitive health, yet the extent of inter-individual variability in structural trajectories and its links to lifestyle factors remain incompletely understood. We conducted a single-centre, prospective longitudinal cohort study within an urban academic health screening programme in Japan (November 2006 to April 2021), with up to 15 years of follow-up. Among 10 209 individuals who underwent screening, those with more than 10 years of serial visits were eligible; 186 participants were excluded for major intracranial findings, 3 for cognitive decline during follow-up and 3 for contraindications or ineligibility, yielding 653 cognitively normal adults [207 women (31.6%); mean (SD) age at baseline, 55.1 (9.3) years]. Participants underwent serial magnetic resonance imaging and Mini-Mental State Examination, along with baseline assessments of lifestyle and vascular risk factors including body mass index, blood pressure, smoking exposure and lipid profile. Annual percentage change in whole-brain and regional volumes was estimated using linear mixed-effects models after harmonization for scanner effects, and participants were categorized as rapid atrophy, typical, or atrophy-resistant based on percentile thresholds. Across 7915 MRI scans, whole-brain volume decline accelerated from 0.24% per year in the 40s to 0.44% per year in the 70s. Individuals classified as having rapid atrophy in their 40s exhibited rates of decline of 0.46% per year, comparable to population averages in the 70s, whereas atrophy-resistant individuals in their 70s demonstrated rates as low as 0.27% per year. Faster whole-brain atrophy was associated with higher body mass index [β = 0.027; 95% confidence interval (CI), 0.0058–0.049], greater smoking exposure (β = 0.032; 95% CI, 0.012–0.053), elevated diastolic blood pressure (β = 0.044; 95% CI, 0.021–0.067) and higher low-density lipoprotein cholesterol (β = 0.043; 95% CI, 0.021–0.065) (all P < 0.01). Utilizing an uncommonly long and densely sampled longitudinal design, this study demonstrates that structural brain ageing among cognitively normal adults follows highly heterogeneous trajectories, spanning from marked decline to near-complete resistance. Modifiable lifestyle and vascular risk factors were associated with faster atrophy, supporting opportunities for risk stratification and targeted prevention strategies.

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 4 matches between paragraphs and lines of code.

Zenodo 20819668

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 4 files, 3 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), lme4 (2 files), broom (1 file), cowplot (1 file), ggplot2 (1 file), nlme (1 file), patchwork (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
4 files
At the source: doi.org/10.5281/zenodo.20819668

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;
  • 3 scripts, each with its path and the digest of its content;
  • 4 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

MRI data and individual measurements are currently unavailable because the hospital has withheld them to protect participant privacy. The scripts used for analyses are available via code repository link: https://doi.org/10.5281/zenodo.20819668.

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 3, 28 September 2026

  • Funding: added University of Tokyo

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 5 keywords, 33 references.

Cite

This paper

Fujita, S., Mori, S., Onda, K., Hanaoka, S., Nomura, Y., Nakao, T., Hagiwara, A., Hagiwara, Y., Takao, H., Yoshikawa, T., & Abe, O. (2026). Long-term brain volume trajectories and lifestyle associations in cognitively normal adults: the BRAIN-STRIDE study. Brain communications, 8(5), fcag343. https://doi.org/10.1093/braincomms/fcag343

BibTeX

@article{fujita2026long,
author = {Fujita, Shohei and Mori, Susumu and Onda, Kengo and Hanaoka, Shouhei and Nomura, Yukihiro and Nakao, Takahiro and Hagiwara, Akifumi and Hagiwara, Yasuhiro and Takao, Hidemasa and Yoshikawa, Takeharu and Abe, Osamu},
title = {{Long-term brain volume trajectories and lifestyle associations in cognitively normal adults: the BRAIN-STRIDE study}},
journal = {Brain communications},
year = {2026},
month = sep,
volume = {8},
number = {5},
pages = {fcag343},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag343},
url = {https://doi.org/10.1093/braincomms/fcag343},
pmid = {42765092},
pmcid = {PMC13590224}
}

RIS

TY - JOUR
AU - Fujita, Shohei
AU - Mori, Susumu
AU - Onda, Kengo
AU - Hanaoka, Shouhei
AU - Nomura, Yukihiro
AU - Nakao, Takahiro
AU - Hagiwara, Akifumi
AU - Hagiwara, Yasuhiro
AU - Takao, Hidemasa
AU - Yoshikawa, Takeharu
AU - Abe, Osamu
TI - Long-term brain volume trajectories and lifestyle associations in cognitively normal adults: the BRAIN-STRIDE study
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/09/07
VL - 8
IS - 5
SP - fcag343
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag343
UR - https://doi.org/10.1093/braincomms/fcag343
LA - en
ER -

CSL-JSON

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{
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"container-title-short": "Brain Commun",
"volume": "8",
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"page": "fcag343",
"DOI": "10.1093/braincomms/fcag343",
"PMID": "42765092",
"PMCID": "PMC13590224",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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Journal: Nature
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[6] doi:10.1038/s41467-025-63967-1 [code]
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[7] doi:10.1002/hbm.70546 [code]
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Journal: Human brain mapping
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[8] doi:10.1038/s41514-024-00171-3 [code]
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Journal: npj aging
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[9] doi:10.1038/s41467-024-53548-z [code]
Brain change trajectories in healthy adults correlate with Alzheimer's related genetic variation and memory decline across life.
Journal: Nature communications
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[10] doi:10.1186/s13024-023-00688-3 [code]
Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease.
Journal: Molecular neurodegeneration
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