OSCR

Five-year associations among dopamine D2-like receptor loss, cognitive decline, education, and self-reported leisure activities in healthy older adults.

Code ↔ Paper

7 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 7 matches
  1. [1] § Method › Cognitive assessment ↔ Code/DataPreparation.R, lines 263–334 · score 0.92 · position recall, word recall, letter updating, letter comparison, perceptual speed, episodic memory
  2. [2] § Method › Statistical analyses ↔ Code/config.R, lines 47–134 · score 0.87 · right hemispheres, social activity frequency, exogenous variables, cognitive activity frequency, physical activity frequency, activity variables
  3. [3] § Method › Cognitive assessment ↔ Code/config.R, lines 47–134 · score 0.81 · position recall, word recall, letter updating, letter comparison, numerical, scores
  4. [4] § Method › Statistical analyses ↔ Code/DataPreparation.R, lines 44–132 · score 0.75 · sum scores, perceptual speed, episodic memory, working memory, ROI, putamen
  5. [5] § Method › Education and leisure activities ↔ Code/DataPreparation.R, lines 44–132 · score 0.74 · strength training, sum score, sociodemographic, jogging, walking, engagement
  6. [6] § Method › Data preparation ↔ Code/DataPreparation.R, lines 207–261 · score 0.62 · co registration, outliers, preparation, errors, segmentation, SD
  7. [7] § Method › Statistical analyses ↔ Code/config.R, lines 1–21 · score 0.57 · post burn, chains, iterations, variables

Paper

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The authors' code

R · 334 lines · 13 KB · no license · 4 matches

  1. # ============================================================================
  2. # Data Preparation for Bayesian SEM Analysis
  3. # COBRA Study 1 — Dopamine D2 Receptors, Cognition, Education, and Leisure
  4. # Author: Amos Pagin
  5. #
  6. # Note: This script requires the raw COBRA data files, which are not publicly
  7. # available due to Swedish data-protection regulations. Data access can be
  8. # requested from the authors for well-defined analysis projects consistent
  9. # with the original ethical approval (see manuscript for details).
  10. #
  11. # File paths below reference the project's internal data directory structure
  12. # and are provided for documentation purposes.
  13. # ============================================================================
  15. # Setup -------------------------------------------------------------------
  17. library(tidyverse)
  18. library(haven)
  19. library(readxl)
  21. # Helper: set intensity to NA when corresponding frequency is 0
  22. # This ensures intensity means only reflect activities actually performed.
  23. filter_intensity_by_freq <- function(df, freq_cols, int_cols) {
  24. stopifnot(length(freq_cols) == length(int_cols))
  25. for (i in seq_along(freq_cols)) {
  26. fc <- freq_cols[i]
  27. ic <- int_cols[i]
  28. if (fc %in% names(df) && ic %in% names(df)) {
  29. mask <- !is.na(df[[fc]]) & !is.na(df[[ic]]) & df[[fc]] == 0
  30. df[[ic]][mask] <- NA
  31. }
  32. }
  33. df
  34. }
  36. # Helper: standardize T2 using T1 mean and SD
  37. scale_t2_by_t1 <- function(t2_values, t1_values) {
  38. t1_mean <- mean(t1_values, na.rm = TRUE)
  39. t1_sd <- sd(t1_values, na.rm = TRUE)
  40. (t2_values - t1_mean) / t1_sd
  41. }
  44. # =========================================================================
  45. # 1. PET DATA
  46. # =========================================================================
  48. # PET BP_ND estimates (PVE-corrected, bilateral caudate/putamen/hippocampus)
  49. pet_wide <- read_csv("<path_to>/PET_bp_wide.csv")
  51. # T1
  52. pet_t1 <- pet_wide %>%
  53. select(id, contains(c("hippocampus", "caudate", "putamen"))) %>%
  54. select(-contains("ROI"), -contains("t2"))
  55. pet_t1$id <- gsub("C", "", pet_t1$id)
  56. pet_t1 <- pet_t1[, c(1, 2, 3, 6, 7, 10, 11)]
  57. colnames(pet_t1) <- c("id", "hc_left", "hc_right", "cau_left", "cau_right",
  58. "put_left", "put_right")
  60. # T2
  61. pet_t2 <- pet_wide %>%
  62. select(id, contains(c("hippocampus", "caudate", "putamen"))) %>%
  63. select(-contains("ROI"), -contains("t1"))
  64. pet_t2$id <- gsub("C", "", pet_t2$id)
  65. pet_t2 <- pet_t2[, c(1, 2, 3, 6, 7, 10, 11)]
  66. colnames(pet_t2) <- c("id", "hc_left_t2", "hc_right_t2", "cau_left_t2",
  67. "cau_right_t2", "put_left_t2", "put_right_t2")
  69. # Anatomical data (T1 only, for ROI definition)
  70. anat <- read_csv("<path_to>/anatomical_subcortical.csv") %>%
  71. filter(TimePoint == "t1") %>%
  72. select(SubjectId,
  73. `aseg:Left-Putamen(Volume_mm3)`, `aseg:Right-Putamen(Volume_mm3)`,
  74. `aseg:Left-Caudate(Volume_mm3)`, `aseg:Right-Caudate(Volume_mm3)`)
  75. colnames(anat) <- c("id", "put_left_anat", "put_right_anat",
  76. "cau_left_anat", "cau_right_anat")
  77. anat$id <- gsub("C", "", anat$id)
  80. # =========================================================================
  81. # 2. COGNITIVE DATA
  82. # =========================================================================
  84. # Task sum scores for working memory, episodic memory, and perceptual speed
  85. wmsum <- read_csv("<path_to>/WM-sumscores-T1-T2.csv")
  86. emsum <- read_csv("<path_to>/EM-sumscores-T1-T2.csv")
  87. pssum <- read_csv("<path_to>/PS-sumscores-T1-T2.csv")
  90. # =========================================================================
  91. # 3. BACKGROUND DATA
  92. # =========================================================================
  94. # Sociodemographic variables (age, sex, education, BMI)
  95. background_data <- read_sav("<path_to>/background_data.sav")
  96. background_data$id <- str_sub(background_data$id, start = 3)
  97. background_data <- background_data %>%
  98. rename(bmi = BMI1) %>%
  99. select(id, age1, male, yearsofeducation, bmi)
  102. # =========================================================================
  103. # 4. ACTIVITY DATA — T1 and T2
  104. # =========================================================================
  105. #
  106. # Column mapping in the COBRA questionnaire:
  107. # Cognitive frequency: F_12 to F_29 (18 items)
  108. # Cognitive intensity: F_30 to F_47 (18 items; F_N+18 maps to F_N)
  109. # Physical frequency: F_48 to F_62 (15 items)
  110. # Physical intensity: F_63 to F_77 (15 items; F_N+15 maps to F_N)
  111. # Social frequency: F_78 to F_87 (10 items; no intensity)
  112. #
  113. # All activities are included (no engagement cutoff).
  115. # Define column names
  116. cog_freq_cols <- paste0("F_", 12:29)
  117. cog_int_cols <- paste0("F_", 30:47)
  118. phys_freq_cols <- paste0("F_", 48:62)
  119. phys_int_cols <- paste0("F_", 63:77)
  120. soc_freq_cols <- paste0("F_", 78:87)
  122. # 5-activity physical intensity subset (Kohncke et al., 2018):
  123. # Walking (F_51), Cycling (F_52), Jogging (F_53), Strength training (F_55), Sports (F_61)
  124. # Corresponding intensity items: F_66, F_67, F_68, F_70, F_76
  125. intensity_5act_freq_cols <- c("F_51", "F_52", "F_53", "F_55", "F_61")
  126. intensity_5act_int_cols <- c("F_66", "F_67", "F_68", "F_70", "F_76")
  128. all_activity_cols <- c(cog_freq_cols, cog_int_cols,
  129. phys_freq_cols, phys_int_cols,
  130. soc_freq_cols)
  132. # --- Import and clean raw questionnaire data ---
  134. clean_activity_data <- function(filepath) {
  135. df <- read_excel(filepath)
  136. df <- rename(df, id = `C-nr`)
  137. df$id <- str_sub(df$id, start = 3)
  139. # Keep only id and activity columns (some columns may not exist in file)
  140. available_cols <- intersect(all_activity_cols, colnames(df))
  141. df <- df %>% select(id, all_of(available_cols))
  143. # Clean values
  144. df <- df %>%
  145. mutate(across(-id, ~ gsub("15\\+", "15", .))) %>%
  146. mutate(across(-id, ~ gsub("999", NA_character_, .))) %>%
  147. mutate(across(-id, ~ gsub("vet ej", NA_character_, .))) %>% # Swedish: "don't know"
  148. mutate(across(-id, ~ as.numeric(.)))
  150. df
  151. }
  153. act_t1 <- clean_activity_data("<path_to>/activity_questionnaire_wave1.xlsx")
  154. act_t2 <- clean_activity_data("<path_to>/activity_questionnaire_wave2.xlsx")
  156. # --- Apply intensity filtering: set intensity to NA if frequency = 0 ---
  158. act_t1 <- filter_intensity_by_freq(act_t1, phys_freq_cols, phys_int_cols)
  159. act_t1 <- filter_intensity_by_freq(act_t1, cog_freq_cols, cog_int_cols)
  160. act_t1 <- filter_intensity_by_freq(act_t1, intensity_5act_freq_cols, intensity_5act_int_cols)
  162. act_t2 <- filter_intensity_by_freq(act_t2, phys_freq_cols, phys_int_cols)
  163. act_t2 <- filter_intensity_by_freq(act_t2, cog_freq_cols, cog_int_cols)
  164. act_t2 <- filter_intensity_by_freq(act_t2, intensity_5act_freq_cols, intensity_5act_int_cols)
  166. # --- Compute T1 summary scores ---
  168. # Frequency sums (0 values contribute nothing, NA handled by na.rm)
  169. act_t1$phys_freq_sum <- rowSums(act_t1[phys_freq_cols], na.rm = TRUE)
  170. act_t1$cog_freq_sum <- rowSums(act_t1[cog_freq_cols], na.rm = TRUE)
  171. act_t1$soc_freq_sum <- rowSums(act_t1[soc_freq_cols], na.rm = TRUE)
  173. # Intensity means (only activities with freq > 0, thanks to filtering above)
  174. act_t1$phys_int_mean <- rowMeans(act_t1[phys_int_cols], na.rm = TRUE)
  175. act_t1$cog_int_mean <- rowMeans(act_t1[cog_int_cols], na.rm = TRUE)
  177. # 5-activity intensity mean (sensitivity analysis)
  178. act_t1$phys_int_5act_mean <- rowMeans(act_t1[intensity_5act_int_cols], na.rm = TRUE)
  180. # --- Compute T2 summary scores ---
  182. act_t2$phys_freq_sum <- rowSums(act_t2[phys_freq_cols], na.rm = TRUE)
  183. act_t2$cog_freq_sum <- rowSums(act_t2[cog_freq_cols], na.rm = TRUE)
  184. act_t2$soc_freq_sum <- rowSums(act_t2[soc_freq_cols], na.rm = TRUE)
  186. act_t2$phys_int_mean <- rowMeans(act_t2[phys_int_cols], na.rm = TRUE)
  187. act_t2$cog_int_mean <- rowMeans(act_t2[cog_int_cols], na.rm = TRUE)
  189. # 5-activity intensity mean (sensitivity analysis)
  190. act_t2$phys_int_5act_mean <- rowMeans(act_t2[intensity_5act_int_cols], na.rm = TRUE)
  192. # --- Reduce to summary columns ---
  194. act_t1_summary <- act_t1 %>%
  195. select(id, phys_freq_sum, cog_freq_sum, soc_freq_sum,
  196. phys_int_mean, cog_int_mean, phys_int_5act_mean) %>%
  197. rename_with(~ paste0(., "_t1"), -id)
  199. act_t2_summary <- act_t2 %>%
  200. select(id, phys_freq_sum, cog_freq_sum, soc_freq_sum,
  201. phys_int_mean, cog_int_mean, phys_int_5act_mean) %>%
  202. rename_with(~ paste0(., "_t2"), -id)
  204. act_summary <- full_join(act_t1_summary, act_t2_summary, by = "id")
  207. # =========================================================================
  208. # 5. MERGE ALL DATASETS
  209. # =========================================================================
  211. df <- full_join(pet_t1, pet_t2, by = "id")
  212. df <- full_join(df, anat, by = "id")
  213. df <- full_join(df, background_data, by = "id")
  214. df <- full_join(df, wmsum, by = "id")
  215. df <- full_join(df, emsum, by = "id")
  216. df <- full_join(df, pssum, by = "id")
  217. df <- full_join(df, act_summary, by = "id")
  219. # Composite bilateral PET measures
  220. df$bp_cau <- df$cau_left + df$cau_right
  221. df$bp_put <- df$put_left + df$put_right
  222. df$bp_hc <- df$hc_right + df$hc_left
  223. df$bp_cau_t2 <- df$cau_left_t2 + df$cau_right_t2
  224. df$bp_put_t2 <- df$put_left_t2 + df$put_right_t2
  225. df$bp_hc_t2 <- df$hc_right_t2 + df$hc_left_t2
  228. # =========================================================================
  229. # 6. EXCLUSIONS AND OUTLIER REMOVAL
  230. # =========================================================================
  232. # Remove subjects with known data issues (segmentation/co-registration errors)
  233. df <- df %>% filter(!id %in% c("036", "037", "113"))
  235. # Remove PET outliers (> 3 SD below mean for any ROI)
  236. df <- df %>%
  237. filter(scale(bp_cau) > -3) %>%
  238. filter(scale(bp_put) > -3) %>%
  239. filter(scale(bp_hc) > -3)
  242. # =========================================================================
  243. # 7. SCALE VARIABLES (T2 scaled using T1 mean and SD)
  244. # =========================================================================
  246. # --- DRD2 indicators (bilateral, by hemisphere) ---
  248. df$cau_left_z_t1 <- scale(df$cau_left)[,1]
  249. df$cau_left_z_t2 <- scale_t2_by_t1(df$cau_left_t2, df$cau_left)
  250. df$cau_right_z_t1 <- scale(df$cau_right)[,1]
  251. df$cau_right_z_t2 <- scale_t2_by_t1(df$cau_right_t2, df$cau_right)
  253. df$put_left_z_t1 <- scale(df$put_left)[,1]
  254. df$put_left_z_t2 <- scale_t2_by_t1(df$put_left_t2, df$put_left)
  255. df$put_right_z_t1 <- scale(df$put_right)[,1]
  256. df$put_right_z_t2 <- scale_t2_by_t1(df$put_right_t2, df$put_right)
  258. df$hc_left_z_t1 <- scale(df$hc_left)[,1]
  259. df$hc_left_z_t2 <- scale_t2_by_t1(df$hc_left_t2, df$hc_left)
  260. df$hc_right_z_t1 <- scale(df$hc_right)[,1]
  261. df$hc_right_z_t2 <- scale_t2_by_t1(df$hc_right_t2, df$hc_right)
  263. # --- Working Memory indicators ---
  264. # ll = letter updating, su = spatial updating, nb = numerical 3-back
  266. df$llsc1 <- scale(df$llsum1)[,1]
  267. df$llsc2 <- scale_t2_by_t1(df$llsum2, df$llsum1)
  268. df$susc1 <- scale(df$susum1)[,1]
  269. df$susc2 <- scale_t2_by_t1(df$susum2, df$susum1)
  270. df$nbsc1 <- scale(df$nbsum1)[,1]
  271. df$nbsc2 <- scale_t2_by_t1(df$nbsum2, df$nbsum1)
  273. # --- Episodic Memory indicators ---
  274. # rc = word recall, nrc = number-word recall, orc = object-position recall
  276. df$rcsc1 <- scale(df$rcsum)[,1]
  277. df$rcsc2 <- scale_t2_by_t1(df$rcsum2, df$rcsum)
  278. df$nrcsc1 <- scale(df$nrcsum)[,1]
  279. df$nrcsc2 <- scale_t2_by_t1(df$nrcsum2, df$nrcsum)
  280. df$orcsc1 <- scale(df$orcsum)[,1]
  281. df$orcsc2 <- scale_t2_by_t1(df$orcsum2, df$orcsum)
  283. # --- Perceptual Speed indicators ---
  284. # num = number comparison, fig = figure comparison, ver = letter comparison
  286. df$numsc1 <- scale(df$numsum)[,1]
  287. df$numsc2 <- scale_t2_by_t1(df$numsum2, df$numsum)
  288. df$figsc1 <- scale(df$figsum)[,1]
  289. df$figsc2 <- scale_t2_by_t1(df$figsum2, df$figsum)
  290. df$versc1 <- scale(df$versum)[,1]
  291. df$versc2 <- scale_t2_by_t1(df$versum2, df$versum)
  293. # --- Education ---
  295. df$education_sc <- scale(df$yearsofeducation)[,1]
  297. # --- Covariates for supplemental analyses ---
  299. # male is already 0/1; scale for consistency with other variables in the models
  300. df$male <- scale(df$male)[,1]
  301. df$bmi <- scale(df$bmi)[,1]
  303. # --- Activity measures (T1 scaled, T2 scaled by T1) ---
  305. df$physfreq_sc <- scale(df$phys_freq_sum_t1)[,1]
  306. df$physint_sc <- scale(df$phys_int_mean_t1)[,1]
  307. df$cogfreq_sc <- scale(df$cog_freq_sum_t1)[,1]
  308. df$cogint_sc <- scale(df$cog_int_mean_t1)[,1]
  309. df$socsum_sc <- scale(df$soc_freq_sum_t1)[,1]
  311. # 5-activity physical intensity (sensitivity analysis)
  312. df$physint_k5_sc <- scale(df$phys_int_5act_mean_t1)[,1]
  314. df$physfreq_t2_sc <- scale_t2_by_t1(df$phys_freq_sum_t2, df$phys_freq_sum_t1)
  315. df$physint_t2_sc <- scale_t2_by_t1(df$phys_int_mean_t2, df$phys_int_mean_t1)
  316. df$cogfreq_t2_sc <- scale_t2_by_t1(df$cog_freq_sum_t2, df$cog_freq_sum_t1)
  317. df$cogint_t2_sc <- scale_t2_by_t1(df$cog_int_mean_t2, df$cog_int_mean_t1)
  318. df$socsum_t2_sc <- scale_t2_by_t1(df$soc_freq_sum_t2, df$soc_freq_sum_t1)
  320. # 5-activity T2
  321. df$physint_k5_t2_sc <- scale_t2_by_t1(df$phys_int_5act_mean_t2, df$phys_int_5act_mean_t1)
  324. # =========================================================================
  325. # 8. MEDIAN SPLITS FOR MODERATION ANALYSES
  326. # =========================================================================
  328. df$education_group <- ifelse(df$education_sc > median(df$education_sc, na.rm = TRUE), "High", "Low")
  329. df$physfreq_group <- ifelse(df$physfreq_sc > median(df$physfreq_sc, na.rm = TRUE), "High", "Low")
  330. df$physint_group <- ifelse(df$physint_sc > median(df$physint_sc, na.rm = TRUE), "High", "Low")
  331. df$physint_k5_group <- ifelse(df$physint_k5_sc > median(df$physint_k5_sc, na.rm = TRUE), "High", "Low")
  332. df$cogfreq_group <- ifelse(df$cogfreq_sc > median(df$cogfreq_sc, na.rm = TRUE), "High", "Low")
  333. df$cogint_group <- ifelse(df$cogint_sc > median(df$cogint_sc, na.rm = TRUE), "High", "Low")
  334. df$socsum_group <- ifelse(df$socsum_sc > median(df$socsum_sc, na.rm = TRUE), "High", "Low")

DataPreparation.R, no license · at the source

Overview

Authors: Amos Pagin1, Nina Karalija2,3, Micael Andersson2,3, Lars Nyberg2,3,4, Lars Bäckman5, Katrine Riklund2,4, Ulman Lindenberger6,7, Martin Lövdén1
ORCID iDs: Amos Pagin
  1. Department of Psychology, University of Gothenburg, Gothenburg, Sweden
  2. Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
  3. Department of Medical and Translational Biology, Umeå University, Umeå, Sweden
  4. Department of Diagnostics and Intervention, Umeå University, Umeå, Sweden
  5. Aging Research Center, Karolinska Institute and Stockholm University, Solna, Sweden
  6. Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
  7. Max Planck Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
Institutions: University of Gothenburg (Sweden); Umeå University (Sweden); Stockholm University (Sweden); Karolinska Institutet (Sweden); Max Planck Institute for Human Development (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1302
Dates: received 13 November 2025; accepted 24 June 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1302 · PMID 42488356 · PMCID PMC13389743 · OpenAlex W7166731962
Open access: diamond, a free copy (OpenAlex)
Preprint: osf.io/pwy8f
Status: code verified
Categories: PET / SPECT (modality), human (organism)
Methods: Connectivity, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: dopamine, cognition, aging, education, leisure activities, cognitive reserve
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Vetenskapsrådet (VR) (421-2012-648, 2017-02217, 2022-01804); Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation) (2015.0277); Torsten Söderbergs Stiftelse; Alzheimerfonden (AF-967710); Riksbankens Jubileumsfond (P20-0779); Swedish Research Council (2018-05973)
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Age-related loss of dopamine (DA) integrity has been linked to cognitive decline. Relatedly, education and leisure activity engagement have been highlighted as neurocognitive protective factors, but their associations with DA integrity remain poorly understood. Using Bayesian structural equation modeling, we analyzed longitudinal data from the Cognition, Brain, and Aging (COBRA) prospective cohort study with 181 older adults at baseline to examine correlations among DA D2-like receptor (DRD2) availability in the caudate and putamen, measured using [11C]raclopride positron emission tomography (PET), cognition (working memory, episodic memory, and perceptual speed), education, and self-reported physical, cognitive, and social leisure activity measures. Our research questions target whether (i) education or leisure activities are associated with baseline levels or 5-year changes in DRD2 availability; (ii) changes in leisure activities covary with DRD2 changes; and (iii) education or leisure activities moderate DRD2–cognition change–change correlations. Results showed declines in DRD2 availability in the caudate and putamen, with weak overall DRD2–cognition change–change correlations. For both baseline levels and changes in DRD2 availability, the associations with education and leisure activities were uniformly negligible or small and not strongly supported. Neither education nor leisure activities moderated DRD2–cognition change–change correlations.

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

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OSF pwy8f

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State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (3)
Size: 212 files, 3 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/pwy8f/

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

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Data and Code Availability

Swedish data-protection laws prohibit us from providing the data in the public domain, but data can be requested from the authors and subsequently transferred for well-defined analysis projects that are in line with the one covered by the original ethical approval. Complete model outputs and code used for the analyses are provided at the OSF repository: https://osf.io/pwy8f/.

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Pagin, A., Karalija, N., Andersson, M., Nyberg, L., Bäckman, L., Riklund, K., Lindenberger, U., & Lövdén, M. (2026). Five-year associations among dopamine D2-like receptor loss, cognitive decline, education, and self-reported leisure activities in healthy older adults. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1302. https://doi.org/10.1162/imag.a.1302

BibTeX

@article{pagin2026five,
author = {Pagin, Amos and Karalija, Nina and Andersson, Micael and Nyberg, Lars and Bäckman, Lars and Riklund, Katrine and Lindenberger, Ulman and Lövdén, Martin},
title = {{Five-year associations among dopamine D2-like receptor loss, cognitive decline, education, and self-reported leisure activities in healthy older adults}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {4},
pages = {IMAG.a.1302},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1302},
url = {https://doi.org/10.1162/imag.a.1302},
pmid = {42488356},
pmcid = {PMC13389743}
}

RIS

TY - JOUR
AU - Pagin, Amos
AU - Karalija, Nina
AU - Andersson, Micael
AU - Nyberg, Lars
AU - Bäckman, Lars
AU - Riklund, Katrine
AU - Lindenberger, Ulman
AU - Lövdén, Martin
TI - Five-year associations among dopamine D2-like receptor loss, cognitive decline, education, and self-reported leisure activities in healthy older adults
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/07/21
VL - 4
SP - IMAG.a.1302
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1302
UR - https://doi.org/10.1162/imag.a.1302
LA - en
ER -

CSL-JSON

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"title": "Five-year associations among dopamine D2-like receptor loss, cognitive decline, education, and self-reported leisure activities in healthy older adults",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Pagin",
"given": "Amos"
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{
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"given": "Nina"
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{
"family": "Andersson",
"given": "Micael"
},
{
"family": "Nyberg",
"given": "Lars"
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{
"family": "Bäckman",
"given": "Lars"
},
{
"family": "Riklund",
"given": "Katrine"
},
{
"family": "Lindenberger",
"given": "Ulman"
},
{
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"given": "Martin"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1302",
"DOI": "10.1162/imag.a.1302",
"PMID": "42488356",
"PMCID": "PMC13389743",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1302",
"language": "en",
"issued": {
"date-parts": [
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2026,
7,
21
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}
}

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