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Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts

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  1. [1] § Materials and Methods › Cognitive measures ↔ HABS-HD/HABS_g_factor.Rmd, lines 13 to 34 · score 0.61 · Digit Symbol Substitution, HABS HD, Trail, Animals · Report a mismatch

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The authors' code

R Markdown · 161 lines · 5.1 KB · no license · 1 match

  1. ---
  2. title: "HABS 'g' factor"
  3. output: html_document
  4. ---
  6. ```{r}
  7. library(dplyr)
  8. library(miceadds)
  9. library(lmerTest)
  10. library(gamm4)
  11. ```
  13. ```{r}
  14. mydata <- read.csv("data_raw/HABS_HD_7_Original.csv")
  16. mydata[mydata == "NULL"] <- NA
  17. mydata[mydata == "-9999"] <- NA
  19. mydata <- mydata %>%
  20. arrange(Med_ID, Age) %>%
  21. group_by(Med_ID) %>%
  22. mutate(
  23. A0 = first(Age),
  24. dA = Age - A0
  25. ) %>%
  26. ungroup()
  27. mydata <- mydata[mydata$dA != 12.00, ]
  28. names(mydata)[names(mydata) == "EducationYears"] <- "YoE"
  29. names(mydata)[names(mydata) == "Gender"] <- "Sex"
  31. mydata <- mydata[, c("Med_ID", "Visit_ID", "A0", "dA", "Sex", "YoE", "Trails_A_Time", "Trails_B_Time", "FAS_Total", "Animal_Total", "Digit_Symbol_Substitution", "DS_Total", "MMSE_Total", "Age")]
  33. write.csv(mydata, "COG_data/HABS_gCog_data.csv")
  34. ```
  36. ```{r}
  37. mydata <- read.csv("COG_data/HABS_gCog_data.csv")
  39. # plot(density(mydata$Trails_A_Time, na.rm = TRUE))
  40. # plot(density(mydata$Trails_B_Time, na.rm = TRUE))
  42. mydata$Trails_B_Time[mydata$Trails_B_Time >= 180] <- NA
  43. mydata$Trails_A_Time[mydata$Trails_A_Time >= 150] <- NA
  45. # plot(density(mydata$Trails_A_Time, na.rm = TRUE))
  46. # plot(density(mydata$Trails_B_Time, na.rm = TRUE))
  48. mydata$FAS_Total <- scale(as.numeric(mydata$FAS_Total))
  49. mydata$Animal_Total <- scale(as.numeric(mydata$Animal_Total))
  50. mydata$DSS <- scale(as.numeric(mydata$Digit_Symbol_Substitution))
  51. mydata$DS_Total <- scale(as.numeric(mydata$DS_Total))
  53. mydata$TMA <- (-1) * scale(log(as.numeric(mydata$Trails_A_Time)))
  54. mydata$TMB <- (-1) * scale(log(as.numeric(mydata$Trails_B_Time)))
  56. mydata <- mydata %>% arrange(Med_ID, dA)
  57. mydata$prac <- 1
  58. mydata$prac[!duplicated(mydata$Med_ID)] <- 0
  59. mydata$prac <- as.factor(mydata$prac)
  61. wave_counts <- mydata %>%
  62. group_by(Med_ID) %>%
  63. summarise(n_waves = n_distinct(Visit_ID)) %>%
  64. count(n_waves)%>%
  65. mutate(percent = 100 * n / sum(n))
  68. wave_counts
  69. ```
  71. ```{r}
  72. pca_vars <- c("TMA", "TMB", "FAS_Total", "Animal_Total", "DSS", "DS_Total")
  73. mydata[pca_vars] <- lapply(mydata[pca_vars], function(x) as.numeric(as.vector(x)))
  74. vars_for_mice <- c("Med_ID", "dA", "Sex", "YoE", "prac", pca_vars)
  75. data_mice <- mydata[, vars_for_mice]
  76. data_mice$Med_ID <- as.integer(as.factor(data_mice$Med_ID))
  78. ini <- mice(data_mice, maxit = 0)
  79. pred <- ini$predictorMatrix
  80. meth <- ini$method
  82. pca_rows <- match(pca_vars, colnames(pred))
  83. id_col <- match("Med_ID", colnames(pred))
  84. dA_col <- match("dA", colnames(pred))
  85. sex_col <- match("Sex", colnames(pred))
  86. yoe_col <- match("YoE", colnames(pred))
  87. prac_col <- match("prac", colnames(pred))
  89. pred[pca_rows, id_col] <- -2 # cluster variable
  90. pred[pca_rows, dA_col] <- 1 # include dA as predictor
  91. pred[pca_rows, sex_col] <- 1 # include sex as predictor
  92. pred[pca_rows, yoe_col] <- 1 # include YoE as predictor
  93. pred[pca_rows, prac_col] <- 1 # include YoE as predictor
  95. # Do not impute id, dx, sex, or YoE
  96. pred[id_col, ] <- 0
  97. pred[dA_col, ] <- 0
  98. pred[sex_col, ] <- 0
  99. pred[yoe_col, ] <- 0
  100. pred[prac_col, ] <- 0
  102. # Set method for PCA variables (multilevel predictive mean matching)
  103. meth[pca_rows] <- "2l.pmm"
  105. # Run MICE
  106. imp <- mice(data_mice, method = meth, predictorMatrix = pred,
  107. m = 30, maxit = 10, seed = 123)
  109. # Extract completed dataset
  110. cog_pca_imputed <- complete(imp, 1)
  111. cog_pca_imputed$Med_ID <- data_mice$Med_ID
  112. mydata[pca_vars] <- cog_pca_imputed[pca_vars]
  114. write.csv(mydata, "COG_data/HABS_gCog_data_imputed.csv")
  115. ```
  117. ```{r}
  118. mydata <- read.csv("COG_data/HABS_gCog_data_imputed.csv")
  120. pca_vars <- c("TMA", "TMB", "FAS_Total", "Animal_Total", "DSS", "DS_Total")
  122. cog_pca_data <- na.omit(mydata[mydata$Visit_ID == 1, pca_vars])
  123. pca_cog <- prcomp(cog_pca_data, center = TRUE, scale. = TRUE)
  124. pc1_loadings_cog <- -pca_cog$rotation[, 1]
  126. all_cog <- as.matrix(mydata[, pca_vars])
  127. mydata$gCog0 <- as.numeric(all_cog %*% pc1_loadings_cog)
  129. for (v in pca_vars) {
  130. ## A0
  131. f_A0 <- as.formula(paste0(v, " ~ prac + (1|Med_ID)"))
  132. idx <- complete.cases(mydata[, c(v, "A0", "prac", "Med_ID")])
  133. m_A0 <- lmer(f_A0, data = mydata[idx, ], REML = TRUE)
  135. mydata[[paste0(v, "_A0")]] <- NA
  136. mydata[[paste0(v, "_A0")]][idx] <- residuals(m_A0)
  138. ## A0:dA
  139. f_A0dA <- as.formula(paste0(v, " ~ A0:dA + prac + (1|Med_ID)"))
  140. idx <- complete.cases(mydata[, c(v, "A0", "dA", "prac", "Med_ID")])
  141. m_A0dA <- lmer(f_A0dA, data = mydata[idx, ], REML = TRUE)
  143. mydata[[paste0(v, "_A0dA")]] <- NA
  144. mydata[[paste0(v, "_A0dA")]][idx] <- residuals(m_A0dA)
  146. ## gamm
  147. f_gamm <- as.formula(paste0(v, " ~ s(Age) + prac"))
  148. idx <- complete.cases(mydata[, c(v, "Age", "prac", "Med_ID")])
  149. m_gamm <- gamm(f_gamm, data = mydata[idx, ], random = list(Med_ID = ~1), REML = TRUE)
  151. mydata[[paste0(v, "_gamm")]] <- NA
  152. mydata[[paste0(v, "_gamm")]][idx] <-
  153. residuals(m_gamm$gam, type = "response")
  154. }
  156. mydata$gCog_A0 <- as.numeric(as.matrix(mydata[, paste0(pca_vars, "_A0")]) %*% pc1_loadings_cog)
  157. mydata$gCog_A0dA <- as.numeric(as.matrix(mydata[, paste0(pca_vars, "_A0dA")]) %*% pc1_loadings_cog)
  158. mydata$gCog_gamm <- as.numeric(as.matrix(mydata[, paste0(pca_vars, "_gamm")]) %*% pc1_loadings_cog)
  160. write.csv(mydata,"COG_data/HABS_gCog_data_g_derived.csv",row.names = FALSE)
  161. ```

HABS_g_factor.Rmd at commit 4f02fa7, no license · at the source

Overview

Authors: Ina Demetriou1, Marta Correia1, Didac Vidal-Piñeiro2, Dace Apšvalka1, Adam Attaheri1, Tina Emery1, Richard N. Henson1,3
ORCID iDs: Ina Demetriou, Marta Correia, Didac Vidal-Piñeiro, Dace Apšvalka, Adam Attaheri, Richard N. Henson
  1. Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  2. Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
  3. Department of Psychiatry, University of Cambridge, Cambridge, UK
Institutions: MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); University of Oslo (Norway)
Journal: bioRxiv (preprint)
Dates: published online 22 March 2026
Type: Preprint · Language: English
License: CC BY
Identifiers: DOI 10.64898/2026.03.20.713139 · OpenAlex W7140041764
Open access: green, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), cognitive (subfield)
Methods: Smoothing, state filtering, decompositions, Connectivity, Statistics, Preprocessing
Keywords: ageing, cognition, cortical thickness, longitudinal, surface area
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council Grant (BB/H008217/1); Biotechnology and Biological Sciences Research Council (BB/H008217/1); the European Union’s Horizon 2020 research and innovation programme (732592); the National Institute on Aging of the National Institutes of Health (R01AG054073, R01AG058533, R01AG070862, P41EB015922, U19AG078109); NIH (P30 AG066444, P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352); Medical Research Council (SUAG/046/G101400)
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Cortical volume, a widely-used marker of brain ageing, is the product of two genetically and developmentally dissociable morphometric features: thickness and area. However, it remains unclear whether these two features have dissociable consequences for cognitive ageing. To address this, we analyse cross-sectional and longitudinal neuroimaging and cognitive data from one discovery cohort (Cam-CAN) and two independent, pre-registered replication cohorts (OASIS-3 and HABS-HD), leveraging wide age ranges across adulthood, different follow-up intervals and diverse population backgrounds. We show that thickness declines more steeply with age than does area, and shows stronger associations with longitudinal change in fluid cognitive abilities, fairly uniformly across the cortex. Cognitive change is also dependent on baseline thickness, independent of thickness change and independent of baseline cognitive ability. In contrast, area is comparatively stable across adulthood, at least until old age, and shows weaker and more heterogeneous associations with cognitive change, despite being a stronger mediator of the effect of polygenic scores on baseline cognitive ability. Together, these findings help to reconcile inconsistencies in the literature, and indicate that thickness provides a more sensitive marker of dynamic neurobiological processes underlying cognitive ageing, whereas area seems to reflect primarily stable, trait-like variation in cognitive ability.

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.

InaDemetriou/GrayMatter_THK_SA_Dissociation

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 4f02fa7052cb688f07f65f7a1b30ec571c12695b, 9 June 2026
Languages: R (7)
Size: 40 files, 7 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: 7 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: lmerTest (7 files), tidyverse (7 files), car (3 files), easystats (3 files), ggplot2 (3 files), lavaan (3 files), patchwork (2 files), broom (1 file), data.table (1 file), ggpubr (1 file), lme4 (1 file), psych (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
7 files
At the source: github.com/InaDemetriou/GrayMatter_THK_SA_Dissociation

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 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);
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Data

No dataset and no data link were found in the paper.

Data availability

R code (for all analyses) and data (for Cam-CAN only) are available here: https://github.com/InaDemetriou/GrayMatter_THK_SA_Dissociation

Raw data for OASIS-3 and HABS-HD can be requested from: https://www.nitrc.org/projects/oasis3 and https://ida.loni.usc.edu/home/projectPage.jsp?project=HABS_HD respectively.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, dates, 7 authors, 5 keywords, 6 funders, 39 references.

Cite

This paper

Demetriou, I., Correia, M., Vidal-Piñeiro, D., Apšvalka, D., Attaheri, A., Emery, T., & Henson, R. N. (2026). Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts. bioRxiv (preprint). https://doi.org/10.64898/2026.03.20.713139

BibTeX

@article{demetriou2026dissociable,
author = {Demetriou, Ina and Correia, Marta and Vidal-Piñeiro, Didac and Apšvalka, Dace and Attaheri, Adam and Emery, Tina and Henson, Richard N.},
title = {{Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts}},
journal = {bioRxiv (preprint)},
year = {2026},
month = mar,
publisher = {bioRxiv},
issn = {2692-8205},
doi = {10.64898/2026.03.20.713139},
url = {https://doi.org/10.64898/2026.03.20.713139}
}

RIS

TY - JOUR
AU - Demetriou, Ina
AU - Correia, Marta
AU - Vidal-Piñeiro, Didac
AU - Apšvalka, Dace
AU - Attaheri, Adam
AU - Emery, Tina
AU - Henson, Richard N.
TI - Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts
T2 - bioRxiv (preprint)
J2 - bioRxiv
PY - 2026
DA - 2026/03/22
SN - 2692-8205
PB - bioRxiv
DO - 10.64898/2026.03.20.713139
UR - https://doi.org/10.64898/2026.03.20.713139
LA - en
ER -

CSL-JSON

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