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Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance.

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  1. [1] § Results › Longitudinal interrelations across neurocognitive domains of ageing-related atrophy, WMH, and cognition ↔ R/lgcm_syntax.R, lines 159–279 · score 0.51 · residual coupling, trivariate LGCM, cross, M48, variances, manifest

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The authors' code

R · 279 lines · 11 KB · GPL-3.0 · 1 match

  1. # brain-maintenance-lgcm: trivariate latent growth curve model and brain
  2. # maintenance index, companion code for Menze et al. (2026).
  3. #
  4. # Copyright (C) 2026 The authors of Menze et al. (2026).
  5. #
  6. # This program is free software: you can redistribute it and/or modify it
  7. # under the terms of the GNU General Public License as published by the
  8. # Free Software Foundation, either version 3 of the License, or (at your
  9. # option) any later version.
  10. #
  11. # This program is distributed in the hope that it will be useful, but
  12. # WITHOUT ANY WARRANTY; without even the implied warranty of
  13. # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  14. # General Public License for more details: <https://www.gnu.org/licenses/>.
  16. # =============================================================================
  17. # lgcm_syntax.R
  18. # -----------------------------------------------------------------------------
  19. # Builders for the lavaan model syntax of the trivariate latent growth curve
  20. # model (LGCM) described in:
  21. # Menze, Ziegler, ..., Düzel (2025/26). Joint trajectories of brain atrophy,
  22. # white matter hyperintensities and cognition quantify brain maintenance.
  23. #
  24. # Two construction helpers (`measurement_block` and `measurement_block_quad`)
  25. # emit the measurement / loading equations for one construct, with either a
  26. # linear or a linear-plus-quadratic latent slope. A third helper
  27. # (`cross_residuals`) emits time-matched residual covariances between
  28. # constructs ("cross-construct couplings").
  29. #
  30. # The top-level entry point `build_trivariate_lgcm()` assembles the full model
  31. # string from these blocks plus the structural equations for covariate
  32. # regressions and latent factor covariances.
  33. #
  34. # Design choice: all model text is generated programmatically rather than
  35. # hard-coded, so that changing the set of time points (e.g. T = 4 vs T = 5)
  36. # does not require manual edits. The slope identifiability constraints are
  37. # documented in docs/model_specification.md.
  38. # =============================================================================
  41. #' Build the measurement / loading block for one linear-slope construct.
  42. #'
  43. #' @param prefix Variable prefix common to all time points (e.g. "cog_z_").
  44. #' @param time_points Character vector of time-point suffixes (e.g. "M00").
  45. #' @param intercept_name Name of the latent intercept factor.
  46. #' @param slope_name Name of the latent linear-slope factor.
  47. #' @param residual_label A label used to constrain residual variances to be
  48. #' equal across time (homoscedastic residuals). Use distinct labels per
  49. #' construct (e.g. "a", "b", "c") to keep parameters separated.
  50. #' @param free_manifest_intercepts Logical. If FALSE (default), all manifest
  51. #' intercepts are fixed to 0 so that the latent intercept absorbs the mean.
  52. #' If TRUE, the first time point is fixed to 0 and the remaining time
  53. #' points share a free intercept "pr" — this corresponds to the
  54. #' practice-effect parameterisation used for cognition in the original
  55. #' manuscript.
  56. #' @return A character string of lavaan syntax.
  57. measurement_block <- function(prefix,
  58. time_points,
  59. intercept_name,
  60. slope_name,
  61. residual_label = "x",
  62. free_manifest_intercepts = FALSE) {
  64. intercept_lines <- paste0(
  65. intercept_name, " =~ ",
  66. paste0("1*", prefix, time_points, collapse = " + ")
  67. )
  69. slope_lines <- paste0(
  70. slope_name, " =~ ",
  71. paste0(seq_along(time_points) - 1L, "*", prefix, time_points,
  72. collapse = " + ")
  73. )
  75. residuals <- paste0(
  76. prefix, time_points, " ~~ ", residual_label, "*", prefix, time_points,
  77. collapse = "\n"
  78. )
  80. if (isTRUE(free_manifest_intercepts)) {
  81. intcpts <- paste0(
  82. prefix, time_points,
  83. ifelse(time_points == time_points[1L], " ~ 0", " ~ pr*1"),
  84. collapse = "\n"
  85. )
  86. } else {
  87. intcpts <- paste0(prefix, time_points, " ~ 0", collapse = "\n")
  88. }
  90. paste(intercept_lines, slope_lines, residuals, intcpts, sep = "\n")
  91. }
  94. #' Build the measurement / loading block for a construct with both linear
  95. #' and quadratic latent slopes.
  96. #'
  97. #' Loadings for the quadratic slope are fixed to (t - 1)^2 for the t-th time
  98. #' point, i.e. {0, 1, 4, 9, 16} for the five-occasion design.
  99. #'
  100. #' @inheritParams measurement_block
  101. #' @param slope_q_name Name of the latent quadratic-slope factor.
  102. #' @return A character string of lavaan syntax.
  103. measurement_block_quad <- function(prefix,
  104. time_points,
  105. intercept_name,
  106. slope_name,
  107. slope_q_name,
  108. residual_label = "x") {
  110. intercept_lines <- paste0(
  111. intercept_name, " =~ ",
  112. paste0("1*", prefix, time_points, collapse = " + ")
  113. )
  115. slope_lines <- paste0(
  116. slope_name, " =~ ",
  117. paste0(seq_along(time_points) - 1L, "*", prefix, time_points,
  118. collapse = " + ")
  119. )
  121. slopeq_lines <- paste0(
  122. slope_q_name, " =~ ",
  123. paste0((seq_along(time_points) - 1L)^2, "*", prefix, time_points,
  124. collapse = " + ")
  125. )
  127. residuals <- paste0(
  128. prefix, time_points, " ~~ ", residual_label, "*", prefix, time_points,
  129. collapse = "\n"
  130. )
  132. intcpts <- paste0(prefix, time_points, " ~ 0", collapse = "\n")
  134. paste(intercept_lines, slope_lines, slopeq_lines, residuals, intcpts,
  135. sep = "\n")
  136. }
  139. #' Build time-matched cross-construct residual covariances.
  140. #'
  141. #' For each occasion t, this adds a covariance between the residuals of
  142. #' prefix1_t and prefix2_t, constrained to be equal across time using the
  143. #' supplied label (e.g. "x*").
  144. #'
  145. #' @param prefix1,prefix2 Variable prefixes of the two constructs.
  146. #' @param time_points Time-point suffixes.
  147. #' @param residual_label Lavaan label, including the "*" multiplier
  148. #' (default "x*"). Use distinct labels per construct pair.
  149. #' @return A character string of lavaan syntax.
  150. cross_residuals <- function(prefix1, prefix2, time_points,
  151. residual_label = "x*") {
  152. paste0(
  153. prefix1, time_points, " ~~ ", residual_label, prefix2, time_points,
  154. collapse = "\n"
  155. )
  156. }
  159. #' Assemble the full trivariate LGCM syntax used in the manuscript.
  160. #'
  161. #' Defaults match the published model:
  162. #' - linear slope for cognition and WMH
  163. #' - linear + quadratic slope for the atrophy proxy
  164. #' - free practice-effect intercept ("pr") for cognition (manifest
  165. #' intercepts at all post-baseline occasions)
  166. #' - covariates: age_z, sex_z, edyrs_z, tiv_z (standardised)
  167. #' - homoscedastic residuals per construct (labels a / b / c)
  168. #' - time-matched cross-construct residual couplings (labels x* / y* / z*)
  169. #'
  170. #' The covariate model treats age, sex, education, and TIV as exogenous
  171. #' standardised variables (means fixed to 0, variances fixed to 1) and
  172. #' freely estimates pairwise covariances among them. APOE-epsilon4 and
  173. #' vascular-risk covariates are commented in the syntax for transparency
  174. #' and can be enabled by extending the formula.
  175. #'
  176. #' @param time_points Character vector of T time-point suffixes (T >= 3 for
  177. #' identifiability of a linear slope, T >= 4 for a quadratic slope).
  178. #' @param prefix_cog,prefix_wmh,prefix_brain Variable prefixes.
  179. #' @return A length-one character string containing the lavaan model.
  180. build_trivariate_lgcm <- function(time_points = c("M00", "M12", "M24",
  181. "M36", "M48"),
  182. prefix_cog = "cog_z_",
  183. prefix_wmh = "wmh_z_",
  184. prefix_brain = "atrophy_z_") {
  186. stopifnot(length(time_points) >= 4L) # quadratic slope needs T >= 4
  188. cog_block <- measurement_block(prefix_cog, time_points,
  189. "intcept_cog", "slope_linear_cog",
  190. residual_label = "a",
  191. free_manifest_intercepts = TRUE)
  193. wmh_block <- measurement_block(prefix_wmh, time_points,
  194. "intcept_wmh", "slope_linear_wmh",
  195. residual_label = "b",
  196. free_manifest_intercepts = FALSE)
  198. brain_block <- measurement_block_quad(prefix_brain, time_points,
  199. "intcept_brain", "slope_linear_brain",
  200. "slope_q",
  201. residual_label = "c")
  203. cog_wmh <- cross_residuals(prefix_cog, prefix_wmh, time_points, "x*")
  204. cog_brain <- cross_residuals(prefix_cog, prefix_brain, time_points, "y*")
  205. wmh_brain <- cross_residuals(prefix_wmh, prefix_brain, time_points, "z*")
  207. glue::glue('
  208. # === COG ====================================================================
  209. {cog_block}
  211. intcept_cog ~ 1 + age_z + sex_z + edyrs_z
  212. slope_linear_cog ~ 1 + age_z + sex_z + edyrs_z
  214. intcept_cog ~~ slope_linear_cog
  215. intcept_cog ~~ intcept_cog
  216. slope_linear_cog ~~ slope_linear_cog
  218. # === WMH ====================================================================
  219. {wmh_block}
  221. intcept_wmh ~ 1 + age_z + sex_z + edyrs_z + tiv_z
  222. slope_linear_wmh ~ 1 + age_z + sex_z + edyrs_z + tiv_z
  224. intcept_wmh ~~ slope_linear_wmh
  225. intcept_wmh ~~ intcept_wmh
  226. slope_linear_wmh ~~ slope_linear_wmh
  228. # === BRAIN (linear + quadratic) =============================================
  229. {brain_block}
  231. intcept_brain ~ 1 + age_z + sex_z + edyrs_z + tiv_z
  232. slope_linear_brain ~ 1 + age_z + sex_z + edyrs_z + tiv_z
  233. slope_q ~ 1 + age_z + sex_z + edyrs_z + tiv_z
  235. intcept_brain ~~ slope_linear_brain + slope_q
  236. intcept_brain ~~ intcept_brain
  237. slope_linear_brain ~~ slope_linear_brain
  238. slope_q ~~ slope_q
  239. slope_linear_brain ~~ slope_q
  241. # === Domain couplings (latent factor covariances) ===========================
  242. intcept_wmh ~~ intcept_cog
  243. intcept_wmh ~~ slope_linear_cog
  244. intcept_cog ~~ slope_linear_wmh
  245. slope_linear_wmh ~~ slope_linear_cog
  247. intcept_wmh ~~ intcept_brain
  248. intcept_wmh ~~ slope_linear_brain
  249. intcept_brain ~~ slope_linear_wmh
  250. slope_linear_wmh ~~ slope_linear_brain
  252. intcept_brain ~~ intcept_cog
  253. intcept_brain ~~ slope_linear_cog
  254. intcept_cog ~~ slope_linear_brain
  255. slope_linear_brain ~~ slope_linear_cog
  257. slope_q ~~ intcept_cog + intcept_wmh + slope_linear_cog + slope_linear_wmh
  259. # === Covariate (exogenous) model ============================================
  260. age_z ~ 0*1
  261. sex_z ~ 0*1
  262. tiv_z ~ 0*1
  263. edyrs_z ~ 0*1
  265. age_z ~~ 1*age_z
  266. sex_z ~~ 1*sex_z
  267. tiv_z ~~ 1*tiv_z
  268. edyrs_z ~~ 1*edyrs_z
  270. age_z ~~ sex_z + tiv_z + edyrs_z
  271. sex_z ~~ tiv_z + edyrs_z
  272. tiv_z ~~ edyrs_z
  274. # === Cross-construct residual couplings (time-matched) ======================
  275. {cog_wmh}
  276. {cog_brain}
  277. {wmh_brain}
  278. ')
  279. }

lgcm_syntax.R at commit 8dac647, under GPL-3.0 · at the source

Overview

Authors: Inga Menze1,2, Jose Bernal1,2,3,4,5, Rogier A. Kievit6, Kumar Parijat Tripathi7, Timo Kaleck7, Renat Yakupov1,2, Slawek Altenstein8,9, Claudia Bartels10, Katharina Buerger11,12, Michaela Butryn1,2, Peter Dechent13, Michael Ewers11,12, Klaus Fliessbach14,15, Ingo Frommann14,15, Maria Gemenetzi8,9, Wenzel Glanz1,2, Daria Gref16, Julian Hellmann-Regen8,16,17, Stefan Hetzer18, Enise I. Incesoy1,19
and 29 other authorsDaniel Janowitz11,12, Ingo Kiliman20,21, Luca Kleineidam14,15, Marie Theres Kronmüller14, Christoph Laske22,23, Debora Melo van Lent24, Falk Lüsebrink1, Robert Perneczky11,25,26,27, Oliver Peters8,16,17, Lukas Preis16, Josef Priller4,8,9,28, Boris-Stephan Rauchmann25,29,30, Ayda Rostamzadeh31, Sandra Roeske14, Klaus Scheffler32, Björn-Hendrik Schott10,33,34, Anja Schneider14,15, Sebastian Sodenkamp22,35, Annika Spottke14,36, Eike Jakob Spruth8,9, Melina Stark14,15, Stefan Teipel20,21, Michael Wagner14,15, Jens Wiltfang10,33,37, Frank Jessen14,31,38, Alfredo Ramirez7,14,15,24,38, Stefanie Schreiber1,39, Emrah Düzel1,2,40, Gabriel Ziegler1,2
ORCID iDs: Inga Menze, Jose Bernal, Kumar Parijat Tripathi, Renat Yakupov, Slawek Altenstein, Michael Ewers, Ingo Frommann, Maria Gemenetzi, Daria Gref, Julian Hellmann-Regen, Luca Kleineidam, Debora Melo van Lent, Robert Perneczky, Oliver Peters, Josef Priller, Boris-Stephan Rauchmann, Björn-Hendrik Schott, Anja Schneider, Melina Stark, Michael Wagner, Jens Wiltfang, Alfredo Ramirez, Stefanie Schreiber, Gabriel Ziegler
40 affiliations
  1. German Centre for Neurodegenerative Diseases (DZNE),Magdeburg, Germany
  2. Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University Magdeburg,Magdeburg, Germany
  3. Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),Erlangen, Germany
  4. Institute for Neuroscience and Cardiovascular Research, Row Fogo Centre for Research into Ageing and The Brain, Department of Neuroimaging Sciences, The University of Edinburgh,Edinburgh, UK
  5. Faculty of Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU),Erlangen, Germany
  6. Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Centre,Nijmegen, The Netherlands
  7. Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne,Cologne, Germany
  8. German Centre for Neurodegenerative Diseases (DZNE),Berlin, Germany
  9. Department of Psychiatry and Psychotherapy, Charité,Berlin, Germany
  10. Department of Psychiatry and Psychotherapy, University Medical Centre Goettingen, Georg August University of Goettingen,Goettingen, Germany
  11. German Centre for Neurodegenerative Diseases (DZNE),Munich, Germany
  12. Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich,Munich, Germany
  13. MR-Research in Neurosciences, Department of Cognitive Neurology, University Medical Centre Goettingen,Goettingen, Germany
  14. German Centre for Neurodegenerative Diseases (DZNE),Bonn, Germany
  15. Department of Old Age Psychiatry and Cognitive Disorders, University of Bonn, University Hospital Bonn,Bonn, Germany
  16. Department of Psychiatry and Neurosciences, Charité Universitätsmedizin Berlin,Berlin, Germany
  17. Charité Universitätsmedizin Berlin, ECRC Experimental and Clinical Research Center,Berlin, Germany
  18. Berlin Centre for Advanced Neuroimaging, Charité – Universitätsmedizin Berlin,Berlin, Germany
  19. Vivantes Klinikum Am Urban, Department of Psychiatry, Psychotherapy, and Psychosomatics, Vivantes Urban Hospital,Berlin, Germany
  20. German Centre for Neurodegenerative Diseases (DZNE),Rostock, Germany
  21. Department of Psychosomatic Medicine, Rostock University Medical Center,Rostock, Germany
  22. German Centre for Neurodegenerative Diseases (DZNE),Tübingen, Germany
  23. Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen,Tübingen, Germany
  24. Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio,San Antonio, TX USA
  25. Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich,Munich, Germany
  26. Munich Cluster for Systems Neurology (SyNergy),Munich, Germany
  27. Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, Charing Cross Hospital,London, UK
  28. Department of Psychiatry and Psychotherapy, School of Medicine and Health, Technical University of Munich, and German Centre for Mental Health (DZPG),Munich, Germany
  29. Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield,Sheffield, UK
  30. Department of Neuroradiology, University Hospital LMU,Munich, Germany
  31. Department of Psychiatry, University of Cologne, Medical Faculty,Cologne, Germany
  32. Department for Biomedical Magnetic Resonance, University of Tübingen,Tübingen, Germany
  33. German Centre for Neurodegenerative Diseases (DZNE),Goettingen, Germany
  34. Leibniz Institute for Neurobiology,Magdeburg, Germany
  35. Department of Psychiatry and Psychotherapy, University of Tübingen,Tübingen, Germany
  36. Department of Neurology, University of Bonn,Bonn, Germany
  37. Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Campus Universitario de Santiago,Aveiro, Portugal
  38. Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne,Cologne, Germany
  39. Department of Neurology, Otto von Guericke University Magdeburg,Magdeburg, Germany
  40. Institute of Cognitive Neuroscience, University College London,London, UK
Institutions: German Center for Neurodegenerative Diseases (Germany); Otto-von-Guericke-Universität Magdeburg (Germany); Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany); University of Edinburgh (United Kingdom); Radboud University Medical Center (Netherlands); University Hospital Cologne (Germany); Charité - Universitätsmedizin Berlin (Germany); University of Göttingen (Germany); Institut für Schlaganfall- und Demenzforschung (Germany); Universitätsmedizin Göttingen (Germany); University Hospital Bonn (Germany); Vivantes Klinikum (Germany); Universitätsmedizin Rostock (Germany); University of Tübingen (Germany); The University of Texas at San Antonio Health Science Center (United States); Ludwig-Maximilians-Universität München (Germany); Munich Cluster for Systems Neurology (Germany); Charing Cross Hospital (United Kingdom); Technical University of Munich (Germany); University of Sheffield (United Kingdom); LMU Klinikum (Germany); University of Cologne (Germany); Leibniz Institute for Neurobiology (Germany); University of Bonn (Germany); University of Aveiro (Portugal); University College London (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 5846
Dates: received 24 June 2025; accepted 17 June 2026; published online 4 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74957-2 · PMID 42401584 · PMCID PMC13332871 · OpenAlex W7167339027
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), Alzheimer's / dementia (population), stroke (population), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Physiology & signal measures
Keywords: Neurology, Cognitive ageing, Neural ageing, Risk factors
MeSH: Aging*, Brain*, Cognition*, White Matter*, Aged, Aged, 80 and over, Atrophy, Cognitive Dysfunction, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Brain maintenance – the preservation of brain structure or function relevant to cognitive performance – remains challenging to quantify. Here, we propose a domain-general brain maintenance index derived by jointly modelling the longitudinal co-evolution of ageing-related atrophy (via medial temporal lobe to ventricle ratio, MTLV-ratio), white matter hyperintensities (WMH), and global cognition assessed by the preclinical Alzheimer’s cognitive composite (PACC5) using latent growth curve modelling. We demonstrate its utility in 543 cognitively unimpaired older adults from the DELCODE cohort, followed annually over four years. We show that changes in MTLV-ratio and WMH additively predict cognitive change. We further show that higher neuroticism, depressive symptoms, lower openness, and faster biological ageing are related to unfavourable domain-specific trajectories and poorer brain maintenance. Our findings highlight the combined relevance of WMH and ageing-related atrophy dynamics for brain maintenance. Maintaining cerebrovascular and mental health alongside cognitive engagement could promote brain maintenance, delay cognitive decline and dementia.

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 1 match between paragraphs and lines of code.

neuroprognosis/brain-maintenance-lgcm

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8dac647ebc7e88a098f6a70d89cd47e1de0671ef, 22 May 2026
Languages: R (11)
Size: 17 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, tests, documentation
Not found: CITATION.cff, environment file, continuous integration
Tools: lavaan (4 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/neuroprognosis/brain-maintenance-lgcm

Code availability

The R code implementing the trivariate latent growth curve model (lavaan syntax), factor-score extraction, and robust regression for the brain maintenance index is openly available at https://github.com/neuroprognosis/brain-maintenance-lgcm. The repository contains a synthetic example dataset reproducing the analysis pipeline end-to-end so that the framework is usable on alternative cohorts without access to DELCODE raw data.

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

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

The raw data collected in the study DELCODE—DZNE-Longitudinal Cognitive Impairment and Dementia Study (BN012) cannot be made openly available without violation of the data protection concept of the DZNE. Access to the relevant study data can be obtained by submitting an application to the Clinical Research Platform of the DZNE. The template for the application for the submission of data and biomaterial samples is available on the DZNE homepage (https://www.dzne.de/en/research/research-areas/clinical-research/for-researchers/). The expected timeframe for response to access requests is 1 month. Access will be granted for 10 years. Source data are provided with this paper.

Reproduced under the paper's license (CC BY), 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, 49 authors, 4 keywords, 12 MeSH terms, 95 references.

Cite

This paper

Menze, I., Bernal, J., Kievit, R. A., Tripathi, K. P., Kaleck, T., Yakupov, R., Altenstein, S., Bartels, C., Buerger, K., Butryn, M., Dechent, P., Ewers, M., Fliessbach, K., Frommann, I., Gemenetzi, M., Glanz, W., Gref, D., Hellmann-Regen, J., Hetzer, S., . . . Ziegler, G. (2026). Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance. Nature communications, 17(1), 5846. https://doi.org/10.1038/s41467-026-74957-2

BibTeX

@article{menze2026joint,
author = {Menze, Inga and Bernal, Jose and Kievit, Rogier A. and Tripathi, Kumar Parijat and Kaleck, Timo and Yakupov, Renat and Altenstein, Slawek and Bartels, Claudia and Buerger, Katharina and Butryn, Michaela and Dechent, Peter and Ewers, Michael and Fliessbach, Klaus and Frommann, Ingo and Gemenetzi, Maria and Glanz, Wenzel and Gref, Daria and Hellmann-Regen, Julian and Hetzer, Stefan and Incesoy, Enise I. and Janowitz, Daniel and Kiliman, Ingo and Kleineidam, Luca and Kronmüller, Marie Theres and Laske, Christoph and van Lent, Debora Melo and Lüsebrink, Falk and Perneczky, Robert and Peters, Oliver and Preis, Lukas and Priller, Josef and Rauchmann, Boris-Stephan and Rostamzadeh, Ayda and Roeske, Sandra and Scheffler, Klaus and Schott, Björn-Hendrik and Schneider, Anja and Sodenkamp, Sebastian and Spottke, Annika and Spruth, Eike Jakob and Stark, Melina and Teipel, Stefan and Wagner, Michael and Wiltfang, Jens and Jessen, Frank and Ramirez, Alfredo and Schreiber, Stefanie and Düzel, Emrah and Ziegler, Gabriel},
title = {{Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance}},
journal = {Nature communications},
year = {2026},
month = jul,
volume = {17},
number = {1},
pages = {5846},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74957-2},
url = {https://doi.org/10.1038/s41467-026-74957-2},
pmid = {42401584},
pmcid = {PMC13332871}
}

RIS

TY - JOUR
AU - Menze, Inga
AU - Bernal, Jose
AU - Kievit, Rogier A.
AU - Tripathi, Kumar Parijat
AU - Kaleck, Timo
AU - Yakupov, Renat
AU - Altenstein, Slawek
AU - Bartels, Claudia
AU - Buerger, Katharina
AU - Butryn, Michaela
AU - Dechent, Peter
AU - Ewers, Michael
AU - Fliessbach, Klaus
AU - Frommann, Ingo
AU - Gemenetzi, Maria
AU - Glanz, Wenzel
AU - Gref, Daria
AU - Hellmann-Regen, Julian
AU - Hetzer, Stefan
AU - Incesoy, Enise I.
AU - Janowitz, Daniel
AU - Kiliman, Ingo
AU - Kleineidam, Luca
AU - Kronmüller, Marie Theres
AU - Laske, Christoph
AU - van Lent, Debora Melo
AU - Lüsebrink, Falk
AU - Perneczky, Robert
AU - Peters, Oliver
AU - Preis, Lukas
AU - Priller, Josef
AU - Rauchmann, Boris-Stephan
AU - Rostamzadeh, Ayda
AU - Roeske, Sandra
AU - Scheffler, Klaus
AU - Schott, Björn-Hendrik
AU - Schneider, Anja
AU - Sodenkamp, Sebastian
AU - Spottke, Annika
AU - Spruth, Eike Jakob
AU - Stark, Melina
AU - Teipel, Stefan
AU - Wagner, Michael
AU - Wiltfang, Jens
AU - Jessen, Frank
AU - Ramirez, Alfredo
AU - Schreiber, Stefanie
AU - Düzel, Emrah
AU - Ziegler, Gabriel
TI - Joint trajectories of brain atrophy, white matter hyperintensities and cognition quantify brain maintenance
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/07/04
VL - 17
IS - 1
SP - 5846
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74957-2
UR - https://doi.org/10.1038/s41467-026-74957-2
LA - en
ER -

CSL-JSON

{
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"container-title": "Nature communications",
"author": [
{
"family": "Menze",
"given": "Inga"
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},
{
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},
{
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"given": "Kumar Parijat"
},
{
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"given": "Timo"
},
{
"family": "Yakupov",
"given": "Renat"
},
{
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"given": "Slawek"
},
{
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},
{
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},
{
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"given": "Michaela"
},
{
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"given": "Peter"
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{
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"given": "Michael"
},
{
"family": "Fliessbach",
"given": "Klaus"
},
{
"family": "Frommann",
"given": "Ingo"
},
{
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"given": "Maria"
},
{
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"given": "Wenzel"
},
{
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"given": "Daria"
},
{
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"given": "Julian"
},
{
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"given": "Stefan"
},
{
"family": "Incesoy",
"given": "Enise I."
},
{
"family": "Janowitz",
"given": "Daniel"
},
{
"family": "Kiliman",
"given": "Ingo"
},
{
"family": "Kleineidam",
"given": "Luca"
},
{
"family": "Kronmüller",
"given": "Marie Theres"
},
{
"family": "Laske",
"given": "Christoph"
},
{
"family": "van Lent",
"given": "Debora Melo"
},
{
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"given": "Falk"
},
{
"family": "Perneczky",
"given": "Robert"
},
{
"family": "Peters",
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},
{
"family": "Preis",
"given": "Lukas"
},
{
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{
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"given": "Anja"
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{
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"given": "Sebastian"
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{
"family": "Spottke",
"given": "Annika"
},
{
"family": "Spruth",
"given": "Eike Jakob"
},
{
"family": "Stark",
"given": "Melina"
},
{
"family": "Teipel",
"given": "Stefan"
},
{
"family": "Wagner",
"given": "Michael"
},
{
"family": "Wiltfang",
"given": "Jens"
},
{
"family": "Jessen",
"given": "Frank"
},
{
"family": "Ramirez",
"given": "Alfredo"
},
{
"family": "Schreiber",
"given": "Stefanie"
},
{
"family": "Düzel",
"given": "Emrah"
},
{
"family": "Ziegler",
"given": "Gabriel"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5846",
"DOI": "10.1038/s41467-026-74957-2",
"PMID": "42401584",
"PMCID": "PMC13332871",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74957-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
4
]
]
}
}

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