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Dissociable Mechanisms Underlie Differences Between Memory and Metamemory in Older Adults: The Differentiating Role of Anxiety and Depression Symptoms.

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  1. [1] § Materials and Methods › Behavioral Assessments › Self‐Report Measures ↔ Analysis/BABS_metamem_MK.Rmd, lines 242–249 · score 0.84 · anxious arousal, general distress, Penn State Worry, anhedonic depression, Geriatric Depression, PSWQ
  2. [2] § Materials and Methods › Behavioral Assessments › Objective Cognitive Assessments ↔ Analysis/BABS_metamem_MK_supplement.Rmd, lines 81–108 · score 0.73 · visual reproduction, free recall, long delays, short delays, CVLT, correlates
  3. [3] § Materials and Methods › Behavioral Assessments ↔ Analysis/BABS_metamem_MK.Rmd, lines 8–58 · score 0.63 · Brandeis Aging Brain, neuropsychological battery, metamemory, depression
  4. [4] § Materials and Methods › Behavioral Assessments ↔ Analysis/BABS_metamem_MK_supplement.Rmd, lines 8–53 · score 0.63 · Brandeis Aging Brain, neuropsychological battery, metamemory
  5. [5] § Results › Exploring the Contribution of Anxiety and Depression Symptoms and Tau Burden on Different Facets of Metamemory ↔ Analysis/BABS_metamem_MK.Rmd, lines 187–216 · score 0.58 · metamemory sub scales, psychiatric history, reported memory, depression symptom, models, memory ability

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The authors' code

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  1. ---
  2. title: "Dissociable mechanisms underlie differences between objective and subjective assessments of memory in older adults"
  3. author: "J.L. Crawford et al.,"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  8. ```{r setup, warning=FALSE, message=FALSE}
  9. rm(list=ls())
  11. # Packages
  12. library(knitr); library(easystats); library(ggpubr); library(sjPlot); library(tidyverse)
  14. #Create data directories
  15. demo.path<-"~/Library/CloudStorage/Box-Box/BABS_Neuropsych/Data/BABS_Demo.csv"
  16. NP.path<-"~/Library/CloudStorage/Box-Box/BABS_Neuropsych/Data/BABS_Neuropsych_Data_Validated.csv"
  17. sess.path <-"/Volumes/berry-lab/berry-lab/Brandeis_Aging_Brain_Study/BABS Data/"
  18. metamem.path <- "/Volumes/berry-lab/berry-lab/Brandeis_Aging_Brain_Study/BABS Data/Analysis/Qualtrics_Surveys/Metamemory_imputed_sum.csv"
  19. MASQ.path <- "/Volumes/berry-lab/berry-lab/Brandeis_Aging_Brain_Study/BABS Data/Analysis/Qualtrics_Surveys/MASQ_imputed_sum.csv"
  20. PSWQ.path <- "/Volumes/berry-lab/berry-lab/Brandeis_Aging_Brain_Study/BABS Data/Analysis/Qualtrics_Surveys/PSWQ_imputed_sum.csv"
  21. GDS.path <- "/Volumes/berry-lab/berry-lab/Brandeis_Aging_Brain_Study/BABS Data/Analysis/Qualtrics_Surveys/GDS_imputed_sum.csv"
  22. GDS.nonCog.path <-"~/Library/CloudStorage/Box-Box/BABS_Neuropsych/Data/GDS_noncog_imputed_sum.csv"
  23. psych.hx.path <- "~/Library/CloudStorage/Box-Box/BABS_Neuropsych/Data/BABS_Metamem_Dx_Hx.csv"
  24. scanTime.path <- "~/Library/CloudStorage/Box-Box/BABS_Neuropsych/Data/BABS_Tau_Dates.csv"
  25. SUVR.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_MK_SUVR/"
  26. MRI.vol.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_StructuralMRI/asegstats.csv"
  27. MRI.cort.vol.L.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_StructuralMRI/aparcstats_volume_lh.csv"
  28. MRI.cort.vol.R.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_StructuralMRI/aparcstats_volume_rh.csv"
  29. MRI.cort.thick.L.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_StructuralMRI/aparcstats_thickness_lh.csv"
  30. MRI.cort.thick.R.path <-"/Volumes/berry-lab/berry-lab/Aging_Norepinephrine_Tau/Data/ANETauTime01_StructuralMRI/aparcstats_thickness_rh.csv"
  32. #Make data frames for age and SUVR data
  33. BABS.demo <- read_csv(demo.path) %>% select(BABS_ID, Age_V1, Sex, Edu, Race, Ethnicity) %>%
  34. mutate(Subject = parse_number(BABS_ID),
  35. sexCode = factor(Sex, levels = c("Female","Male"), labels = c(0,1))) %>% select(-c(BABS_ID, Sex)) %>%
  36. rename(Age = Age_V1)
  37. BABS.demo$sexCode <- as.numeric(as.character(BABS.demo$sexCode))
  38. d.SUVR <- list.files(path = SUVR.path, pattern = ".csv", full.names = T) %>%
  39. lapply(read_csv) %>%
  40. bind_rows %>% as_tibble() %>% pivot_longer(values_to = "SUVR", names_to = "PVC", -c(BABSID,label, num_voxels)) %>%
  41. mutate(PVC = factor(PVC, levels = c("nonPVC_SUVR","PVC_SUVR"), labels = c("nonPVC","PVC")))
  42. d.tauTime <- read_csv(scanTime.path) %>% pivot_wider(id_cols = BABS_ID, names_from = Study, values_from = Date) %>%
  43. mutate(BABS = strptime(BABS, "%m/%d/%Y"),
  44. ANE = strptime(`ANE (LC)`, "%m/%d/%Y"),
  45. diffTime = as.numeric(difftime(ANE, BABS, units = "weeks"))) %>% select(-`ANE (LC)`) %>%
  46. mutate(diffTime_month = diffTime/4) %>%
  47. summarise(meanDelay = mean(diffTime_month), sdDelay = sd(diffTime_month))
  49. #Make data frames for neuropsych battery items
  50. BABS.NP.sess1 <- read.csv(NP.path, header = T) %>% filter(Session == 1)
  51. Metamem <- read.csv(metamem.path, header = T) %>% filter(Metamemory_Session == 1)
  52. MASQ <- read.csv(MASQ.path, header = T) %>% filter(MASQ_Session == 1)
  53. PSWQ <- read.csv(PSWQ.path, header = T) %>% filter(PSWQ_Session == 1)
  54. GDS <- read.csv(GDS.path, header = T) %>% filter(GDS_Session == 1)
  55. GDS.nonCog <- read.csv(GDS.nonCog.path, header = T) %>% filter(GDS_Session == 1) %>% rename(GDS_nonCog = GDS)
  56. psych.hx <- read.csv(psych.hx.path, header = T) %>% mutate(hxCode = if_else(Hx_Depression == "No", 0, 1)) %>%
  57. mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID)
  58. ```
  60. ```{r LearnMem, warning=FALSE, message=FALSE}
  61. sub.IDs <- c("BABS009", "BABS015","BABS019","BABS028", "BABS038","BABS040","BABS061", "BABS069", "BABS074", "BABS076", "BABS079", "BABS081", "BABS083", "BABS084", "BABS092", "BABS094", "BABS097", "BABS102", "BABS103", "BABS104", "BABS124", "BABS131", "BABS134", "BABS145", "BABS147", "BABS150", "BABS157", "BABS158", "BABS159", "BABS164", "BABS171", "BABS175", "BABS178", "BABS180", "BABS182", "BABS183", "BABS184", "BABS187", "BABS189", "BABS191", "BABS194", "BABS197", "BABS198", "BABS200", "BABS202", "BABS204", "BABS210", "BABS214", "BABS222", "BABS223", "BABS231", "BABS234", "BABS235", "BABS236", "BABS237", "BABS238", "BABS239", "BABS245", "BABS252", "BABS264", "BABS271", "BABS281", "BABS286", "BABS289", "BABS321", "BABS325", "BABS326", "BABS327", "BABS328", "BABS329", "BABS330", "BABS331", "BABS335")
  62. ```
  64. ```{r ROIs, message=FALSE, warning=FALSE}
  65. #Braak staging ROI groups
  66. BRAAK1 = c("L_entorhinal","R_entorhinal")
  67. BRAAK2 = c("L_hippocampus","R_hippocampus")
  68. ```
  70. ```{r tau_dataframes, warning=FALSE, message=FALSE}
  71. #creating data frame with relevant variables
  72. d.Braak1 <- d.SUVR %>% filter(label %in% BRAAK1) %>% group_by(BABSID, PVC) %>%
  73. summarise(mean_weighted_SUVR = (sum(num_voxels*SUVR)/sum(num_voxels))) %>% mutate(ROI = "Braak1") %>% filter(PVC == "PVC") %>% select(-PVC)
  74. d.Braak2 <- d.SUVR %>% filter(label %in% BRAAK2) %>% group_by(BABSID, PVC) %>%
  75. summarise(mean_weighted_SUVR = (sum(num_voxels*SUVR)/sum(num_voxels))) %>% mutate(ROI = "Braak2") %>% filter(PVC == "PVC") %>% select(-PVC)
  76. d.Tau <- rbind(d.Braak1, d.Braak2) %>%
  77. pivot_wider(id_cols = "BABSID",names_from = "ROI", values_from = "mean_weighted_SUVR") %>% ungroup() %>%
  78. mutate(Subject = parse_number(BABSID)) %>% select(-BABSID)
  79. ```
  81. ## Creating memory composite score
  83. ```{r LM_comp, warning=FALSE, message=FALSE}
  84. #merging data frames and cleaning data
  85. d.CVLT <- BABS.NP.sess1 %>% select(BABS_ID, starts_with("CVLT")) %>%
  86. distinct(BABS_ID, .keep_all = T) %>%
  87. mutate_at(vars(starts_with("CVLT")), as.numeric) %>%
  88. mutate(Learning_sum = CVLT_listA__Trial1 + CVLT_listA__Trial2 + CVLT_listA__Trial3 + CVLT_listA__Trial4 + CVLT_listA__Trial5) %>%
  89. select(-starts_with("CVLT_listA"))
  91. d.CVLT.trim <- d.CVLT %>% filter(BABS_ID %in% sub.IDs) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(Subject, Learning_sum, CVLT_listB, starts_with("CVLT_SD"), starts_with("CVLT_LD"))
  92. d.VR.trim <- BABS.NP.sess1 %>% filter(BABS_ID %in% sub.IDs) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(Subject, starts_with("WMS_V"))
  94. #creating data frame with all WM measures
  95. d.LM <- d.CVLT.trim %>% inner_join(d.VR.trim) %>% drop_na()
  96. d.LM.comp <- d.LM %>% mutate(across(Learning_sum:WMS_VR2, ~ c(scale(.))),
  97. LM_comp = CVLT_SD__FR + CVLT_LD__FR + WMS_VR1 + WMS_VR2) %>% select(Subject, LM_comp, Learning_sum)
  98. d.cog.comp <- d.LM.comp
  99. ```
  101. ## Creating data frame for MMSE
  103. ```{r MMSE, warning=FALSE, message=FALSE}
  104. #merging data frames and cleaning data
  105. d.MMSE <- BABS.NP.sess1 %>% select(BABS_ID, MMSE) %>%
  106. distinct(BABS_ID, .keep_all = T) %>%
  107. filter(BABS_ID %in% sub.IDs) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID)
  108. ```
  110. ## Examining self-reported metacognition
  111. ### Meta-cognitive questionnaires: Metamemory
  112. Higher scores indicate better self-reported metamemory function.
  114. ```{r metacog_import, warning=FALSE, message=FALSE}
  115. #import metamemory and examine correlation across domains
  116. d.metamem.trim <- Metamem %>% filter(BABS_ID %in% sub.IDs & Metamemory_Session == 1) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(-Metamemory_Session)
  118. #Metamemory
  119. metamem.comp <- d.metamem.trim %>% mutate(across(Able:Strat, ~ c(scale(.))),
  120. metamem_comp = Able + Gen + Strat)
  121. d.metacog.comp <- metamem.comp
  122. ```
  124. ## Figure 1: Memory ability and metamemory are not reliably associated with each other
  126. ```{r metacog_cog_lms, warning=FALSE, message=FALSE}
  127. d.cog.meta <- d.cog.comp %>% inner_join(d.metacog.comp) %>% inner_join(BABS.demo) %>%
  128. mutate(Age = scale(Age),
  129. Edu = scale(Edu))
  130. #relationship between cognitive function and self-reported memory
  131. m.metamem.cog <- lm(data = d.cog.meta, metamem_comp ~ LM_comp + Age + sexCode + Edu)
  132. tab_model(m.metamem.cog, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Age", "Sex", "Education"), dv.labels = c("Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  133. #plot
  134. d.metamem.LM.plot <- get_datagrid(m.metamem.cog, by = c("LM_comp"), preserve_range = TRUE)
  135. LM.metamem.result <- estimate_relation(m.metamem.cog, include_random = F, data = d.metamem.LM.plot)
  136. fig.1 <- plot(LM.metamem.result,
  137. point = list(color = "royalblue", alpha = 0.2, size = 4),
  138. line = list(color = "royalblue3", size = 2),
  139. ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, y = "Metamemory (Z-Scored)", x = "Memory Ability (Z-Scored)")
  140. fig.1
  141. ```
  143. ## Adding self-reported measures of depression and anxiety symptoms
  144. ### Depression and anxiety symptom questionnaires: GDS, MASQ, PSWQ
  146. ```{r anxdep_import, warning=FALSE, message=FALSE}
  147. #import GDS
  148. d.GDS.trim <- GDS %>% filter(BABS_ID %in% sub.IDs) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(Subject, GDS) %>% mutate(GDS = as.numeric(GDS))
  149. #import GDS (cognitive items excluded)
  150. d.GDS.nonCog.trim <- GDS.nonCog %>% filter(BABS_ID %in% sub.IDs) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(Subject, GDS_nonCog) %>% mutate(GDS_nonCog = as.numeric(GDS_nonCog))
  151. #import MASQ and examine correlation across domains
  152. d.MASQ.trim <- MASQ %>% filter(BABS_ID %in% sub.IDs & MASQ_Session == 1) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(-MASQ_Session)
  153. #MASQ
  154. MASQ.comp <- d.MASQ.trim %>% mutate(across(AA:GD, ~ c(scale(.))),
  155. MASQ_comp = AA + AD + GD) %>% select(Subject, MASQ_comp)
  156. #import PSWQ
  157. d.PSWQ.trim <- PSWQ %>% filter(BABS_ID %in% sub.IDs & PSWQ_Session == 1) %>% mutate(Subject = parse_number(BABS_ID)) %>% select(-BABS_ID) %>% select(-PSWQ_Session)
  159. #combine dataframes and examine correlation across questionnaires
  160. d.anxdep <- d.GDS.trim %>% inner_join(d.MASQ.trim) %>% inner_join(d.PSWQ.trim)
  161. anxdep.comp <- d.anxdep %>% mutate(across(GDS:PSWQ, ~ c(scale(.))),
  162. anxdep_comp = GDS + AA + AD + GD + PSWQ) %>% select(Subject, anxdep_comp, GDS, AA, AD, GD, PSWQ) %>% drop_na()
  164. #combine dataframes and examine correlation across questionnaires
  165. d.anxdep.nonCog <- d.GDS.nonCog.trim %>% inner_join(d.MASQ.trim) %>% inner_join(d.PSWQ.trim)
  166. anxdep.comp.nonCog <- d.anxdep.nonCog %>% mutate(across(GDS_nonCog:PSWQ, ~ c(scale(.))),
  167. anxdep_comp = GDS_nonCog + AA + AD + GD + PSWQ) %>% select(Subject, anxdep_comp, GDS_nonCog, AA, AD, GD, PSWQ) %>% drop_na()
  168. ```
  170. ## Table 1: Descriptive information
  171. ```{r Table1, warning=FALSE, message=FALSE}
  172. d.Table <- d.anxdep %>% inner_join(BABS.demo) %>% inner_join(d.MMSE) %>% inner_join(d.Tau) %>%
  173. select(-c(Race, Ethnicity, sexCode, Subject)) %>%
  174. pivot_longer(cols = everything()) %>%
  175. group_by(name) %>%
  176. summarize('mean' = mean(value),'sd' = sd(value))
  177. table.Race <- d.anxdep %>% inner_join(BABS.demo) %>%
  178. group_by(Race) %>% summarise(Percentage = n()/nrow(d.anxdep)*100)
  179. table.Ethnicity <- d.anxdep %>% inner_join(BABS.demo) %>%
  180. group_by(Ethnicity) %>% summarise(Percentage = n()/nrow(d.anxdep)*100)
  181. table.Sex <- d.anxdep %>% inner_join(BABS.demo) %>%
  182. group_by(sexCode) %>% summarise(Percentage = n()/nrow(d.anxdep)*100) %>%
  183. rename(Sex = sexCode) %>%
  184. mutate(Sex = factor(Sex, levels = c(0,1), labels = c("Female", "Male")))
  185. ```
  187. ## Testing for the associations between objectively assessed cognitive performance and self-reported cognition (with self-reported depression and anxiety symptoms)
  189. ```{r metacog_cog_anx_deplms, warning=FALSE, message=FALSE}
  190. d.cog.meta.anx <- d.cog.meta %>% inner_join(anxdep.comp) %>% inner_join(psych.hx)
  191. #basic descriptive models
  192. m.LM.covariates <- lm(data = d.cog.meta.anx, LM_comp ~ Age + sexCode + Edu)
  193. m.LM.learn.covariates <- lm(data = d.cog.meta.anx, LM_comp ~ Learning_sum + Age + sexCode + Edu)
  194. m.metamem.covariates <- lm(data = d.cog.meta.anx, metamem_comp ~ Age + sexCode + Edu)
  195. m.anxdep.covariates <- lm(data = d.cog.meta.anx, anxdep_comp ~ Age + sexCode + Edu)
  196. tab_model(m.LM.covariates, m.metamem.covariates, m.anxdep.covariates, digits = 2, show.stat = T, pred.labels = c("Intercept","Age", "Sex", "Education"), dv.labels = c("Memory Ability","Metamemory", "Anxiety and Depression Symptoms"), show.icc = F, show.re.var = F, show.obs = F)
  197. #relationship between anxiety and depression and memory ability
  198. m.LM.anx <- lm(data = d.cog.meta.anx, LM_comp ~ anxdep_comp + Age + sexCode + Edu)
  199. #relationship between anxiety and depression and memory ability controlling for psychiatric history
  200. m.LM.anx.psych <- lm(data = d.cog.meta.anx, LM_comp ~ anxdep_comp + Age + sexCode + Edu + hxCode)
  201. #relationship between cognitive function and self-reported memory
  202. m.metamem.cog.anx <- lm(data = d.cog.meta.anx, metamem_comp ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  203. #relationship between cognitive function and self-reported memory controlling for psychiatric history
  204. m.metamem.cog.anx.psych <- lm(data = d.cog.meta.anx, metamem_comp ~ LM_comp + anxdep_comp + Age + sexCode + Edu + hxCode)
  205. tab_model(m.metamem.cog.anx,m.metamem.cog.anx.psych, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Anxiety and Depression Symptoms", "Age", "Sex", "Education", "Psychiatric History"), dv.labels = c("Metamemory","Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  206. #testing for relationships between different metamemory sub-scales
  207. m.metamem.gen.cog.anx <- lm(data = d.cog.meta.anx, Gen ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  208. m.metamem.ability.cog.anx <- lm(data = d.cog.meta.anx, Able ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  209. m.metamem.strat.cog.anx <- lm(data = d.cog.meta.anx, Strat ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  210. tab_model(m.metamem.gen.cog.anx, m.metamem.ability.cog.anx, m.metamem.strat.cog.anx, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Anxiety and Depression Symptoms", "Age", "Sex", "Education"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  211. #testing for relationships between different metamemory sub-scales controlling for psychiatric history
  212. m.metamem.gen.cog.anx.psych <- lm(data = d.cog.meta.anx, Gen ~ LM_comp + anxdep_comp + Age + sexCode + Edu + hxCode)
  213. m.metamem.ability.cog.anx.psych <- lm(data = d.cog.meta.anx, Able ~ LM_comp + anxdep_comp + Age + sexCode + Edu + hxCode)
  214. m.metamem.strat.cog.anx.psych <- lm(data = d.cog.meta.anx, Strat ~ LM_comp + anxdep_comp + Age + sexCode + Edu + hxCode)
  215. tab_model(m.metamem.gen.cog.anx.psych, m.metamem.ability.cog.anx.psych, m.metamem.strat.cog.anx.psych, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Anxiety and Depression Symptoms", "Age", "Sex", "Education", "Psychiatric History"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  216. ```
  218. ## Figure 2: Metamemory, but not memory ability, is associated with anxiety and depression symptoms
  220. ```{r fig2, warning=FALSE, message=FALSE}
  221. #plotting relationships between memory ability & anxiety and depression symptoms
  222. d.LM.anx.plot <- get_datagrid(m.LM.anx, by = c("anxdep_comp"), preserve_range = TRUE)
  223. LM.anx.result <- estimate_relation(m.LM.anx, include_random = F, data = d.LM.anx.plot)
  224. fig.2a <- plot(LM.anx.result,
  225. point = list(color = "lightblue", alpha = 0.4, size = 4),
  226. line = list(color = "lightblue4", size = 2),
  227. ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, y = "Memory Ability (Z-Scored)", x = "Anxiety and Depression Symptoms (Z-Scored)")
  228. #relationship between anxiety and depression and subjective memory
  229. m.metamem.anx <- lm(data = d.cog.meta.anx, metamem_comp ~ anxdep_comp + Age + sexCode + Edu)
  230. #plotting relationships between memory ability & anxiety and depression symptoms
  231. d.metamem.anx.plot <- get_datagrid(m.metamem.anx, by = c("anxdep_comp"), preserve_range = TRUE)
  232. metamem.anx.result <- estimate_relation(m.metamem.anx, include_random = F, data = d.metamem.anx.plot)
  233. fig.2b <- plot(metamem.anx.result,
  234. point = list(color = "lightblue", alpha = 0.4, size = 4),
  235. line = list(color = "lightblue4", size = 2),
  236. ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, y = "Metamemory (Z-Scored)", x = "Anxiety and Depression Symptoms (Z-Scored)")
  237. #making paneled figure
  238. fig.2<- ggarrange(fig.2a, fig.2b)
  239. fig.2
  240. ```
  242. Sensitivity Analysis: Contribution of anxiety and depression facets towards metamemory
  244. ```{r sensivity_anxdep, warning=FALSE, message=FALSE}
  245. #relationship between cognitive function and self-reported memory
  246. m.metamem.cog.anx.facets <- lm(data = d.cog.meta.anx, metamem_comp ~ LM_comp + GDS + AA + AD + GD + PSWQ + Age + sexCode + Edu)
  247. tab_model(m.metamem.cog.anx.facets, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Geriatric Depression (GDS) Score", "MASQ: Anxious Arousal", "MASQ: Anhedonic Depression", "MASQ: General Distress", "Penn State Worry (PSWQ) Score", "Age", "Sex", "Education"), dv.labels = c("Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  248. check_model(m.metamem.cog.anx.facets)
  249. ```
  251. Sensitivity Analysis: Removing cognitive items from GDS
  253. ```{r sensivity_GDS, warning=FALSE, message=FALSE}
  254. d.cog.meta.anx.noncog <- d.cog.meta %>% inner_join(anxdep.comp.nonCog)
  255. #relationship between cognitive function and self-reported memory
  256. m.metamem.cog.anx.noncog <- lm(data = d.cog.meta.anx.noncog, metamem_comp ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  257. tab_model(m.metamem.cog.anx.noncog, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Anxiety and Depression Symptoms [GDS cognitive items removed]", "Age", "Sex", "Education", "Geriatric Depression (GDS) Score [cognitive items removed]", "MASQ: Anxious Arousal", "MASQ: Anhedonic Depression", "MASQ: General Distress", "Penn State Worry (PSWQ) Score"), dv.labels = c("Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  258. #testing for relationships between different metamemory sub-scales removing cognitive items from GDS
  259. m.metamem.gen.cog.anx.noncog <- lm(data = d.cog.meta.anx.noncog , Gen ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  260. m.metamem.ability.cog.anx.noncog <- lm(data = d.cog.meta.anx.noncog , Able ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  261. m.metamem.strat.cog.anx.noncog <- lm(data = d.cog.meta.anx.noncog , Strat ~ LM_comp + anxdep_comp + Age + sexCode + Edu)
  262. tab_model(m.metamem.gen.cog.anx.noncog, m.metamem.ability.cog.anx.noncog, m.metamem.strat.cog.anx.noncog, digits = 2, show.stat = T, pred.labels = c("Intercept","Memory Ability", "Anxiety and Depression Symptoms", "Age", "Sex", "Education"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  263. ```
  265. ```{r MRI_struct_cleaning, warning=FALSE, message=FALSE}
  266. #subcortical volume estimates
  267. d.MRI.vol <- read_csv(MRI.vol.path) %>%
  268. mutate(Subject = parse_number(BABSID)) %>% select(-BABSID) %>%
  269. select(Subject, ends_with("Hippocampus"), ends_with("Amygdala"), ends_with("IntraCranialVol")) %>%
  270. rename(total_ICV = EstimatedTotalIntraCranialVol, HC_R = `Right-Hippocampus`, HC_L = `Left-Hippocampus`,
  271. amygdala_R = `Right-Amygdala`, amygdala_L = `Left-Amygdala`)
  272. #calculating slopes for volume adjustment
  273. ##Hippocamppus
  274. m.HC.R <- lm(data = d.MRI.vol, HC_R ~ total_ICV)
  275. m.HC.R.coef <- m.HC.R$coefficients %>% as_data_frame() %>% slice(2) %>% as.numeric()
  276. m.HC.L <- lm(data = d.MRI.vol, HC_L ~ total_ICV)
  277. m.HC.L.coef <- m.HC.L$coefficients %>% as_data_frame() %>% slice(2) %>% as.numeric()
  278. #adjusted subcortical volume estimates
  279. d.MRI.vol.adj <- d.MRI.vol %>%
  280. mutate(HC_L_slope = m.HC.L.coef,
  281. HC_R_slope = m.HC.R.coef,
  282. mean_total_ICV = mean(total_ICV),
  283. ICV_diff = total_ICV - mean_total_ICV,
  284. HC_L_vol_adj = HC_L - (HC_L_slope*ICV_diff),
  285. HC_R_vol_adj = HC_R - (HC_R_slope*ICV_diff),
  286. HC_bilateral_sum_adj = HC_L_vol_adj + HC_R_vol_adj,
  287. HC_bilateral_mean_adj = (HC_L_vol_adj + HC_R_vol_adj)/2)
  288. #cortical volume estimates
  289. d.LH.cort.vol <- read_csv(MRI.cort.vol.L.path) %>%
  290. mutate(Subject = parse_number(BABSID)) %>% select(-BABSID) %>%
  291. select(Subject, starts_with("lh_entorhinal")) %>%
  292. rename(EC_L = lh_entorhinal_volume)
  293. d.RH.cort.vol <- read_csv(MRI.cort.vol.R.path) %>%
  294. mutate(Subject = parse_number(BABSID)) %>% select(-BABSID) %>%
  295. select(Subject, starts_with("rh_entorhinal")) %>%
  296. rename(EC_R = rh_entorhinal_volume)
  297. d.MRI.all.vol.adj <- d.LH.cort.vol %>% inner_join(d.RH.cort.vol) %>% inner_join(d.MRI.vol.adj, by = "Subject")
  298. #calculating slopes for volume adjustment
  299. ##Hippocamppus
  300. m.ERC.R <- lm(data = d.MRI.all.vol.adj, EC_R ~ total_ICV)
  301. m.ERC.R.coef <- m.ERC.R$coefficients %>% as_data_frame() %>% slice(2) %>% as.numeric()
  302. m.ERC.L <- lm(data = d.MRI.all.vol.adj, EC_L ~ total_ICV)
  303. m.ERC.L.coef <- m.ERC.L$coefficients %>% as_data_frame() %>% slice(2) %>% as.numeric()
  304. d.MRI.all.vol.adj <- d.MRI.all.vol.adj %>%
  305. mutate(ERC_L_slope = m.ERC.L.coef,
  306. ERC_R_slope = m.ERC.R.coef,
  307. ERC_L_vol_adj = EC_L - (ERC_L_slope*ICV_diff),
  308. ERC_R_vol_adj = EC_R - (ERC_R_slope*ICV_diff),
  309. ERC_bilateral_sum_adj = ERC_L_vol_adj + ERC_R_vol_adj,
  310. ERC_bilateral_mean_adj = (ERC_L_vol_adj + ERC_R_vol_adj)/2)
  311. ```
  313. ## Figure 3: Hippocampal (but not entorhinal cortex) volume is positively related to memory ability, but not metamemory
  315. ```{r metacog_leq_deplms_gmvol, warning=FALSE, message=FALSE}
  316. #import tau data
  317. d.cog.meta.tau <- d.cog.meta.anx %>% inner_join(d.Tau, by = "Subject") %>% inner_join(d.MRI.all.vol.adj, by = "Subject") %>%
  318. mutate(Braak1 = scale(Braak1, center = T, scale = T),
  319. Braak2 = scale(Braak2, center = T, scale = T),
  320. ERC_bilateral_sum_adj = scale(ERC_bilateral_sum_adj, center = T, scale = T),
  321. HC_bilateral_sum_adj = scale(HC_bilateral_sum_adj, center = T, scale = T))
  322. #relationship between cognitive ability and gray matter volume
  323. m.LM.gm.EC <- lm(data = d.cog.meta.tau, LM_comp ~ ERC_bilateral_sum_adj + Age + sexCode + Edu)
  324. m.LM.gm.HC <- lm(data = d.cog.meta.tau, LM_comp ~ HC_bilateral_sum_adj + Age + sexCode + Edu)
  325. tab_model(m.LM.gm.EC, m.LM.gm.HC, digits = 2, show.stat = T, pred.labels = c("Intercept", "Entorhinal Volume","Age", "Sex", "Education", "Hippocampal Volume"), dv.labels = c("Memory Ability", "Memory Ability"), show.icc = F, show.re.var = F, show.obs = F)
  326. #relationship between metamemory and gray matter volume
  327. m.metamem.gm.EC <- lm(data = d.cog.meta.tau, metamem_comp ~ ERC_bilateral_sum_adj + Age + sexCode + Edu)
  328. m.metamem.gm.HC <- lm(data = d.cog.meta.tau, metamem_comp ~ HC_bilateral_sum_adj + Age + sexCode + Edu)
  329. #relationship between metamemory and gray matter volume controlling for anxiety and depression symptoms
  330. m.metamem.gm.EC.anx <- lm(data = d.cog.meta.tau, metamem_comp ~ ERC_bilateral_sum_adj + Age + sexCode + Edu + anxdep_comp)
  331. m.metamem.gm.HC.anx <- lm(data = d.cog.meta.tau, metamem_comp ~ HC_bilateral_sum_adj + Age + sexCode + Edu + anxdep_comp)
  332. tab_model(m.metamem.gm.EC, m.metamem.gm.HC,m.metamem.gm.EC.anx,m.metamem.gm.HC.anx, digits = 3, show.stat = T, pred.labels = c("Intercept", "Entorhinal Volume","Age", "Sex", "Education", "Hippocampal Volume", "Anxiety and Depression Symptoms"), dv.labels = c("Metamemory", "Metamemory", "Metamemory", "Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  333. #plot
  334. d.HC.plot <- get_datagrid(m.LM.gm.HC, by = c("HC_bilateral_sum_adj"), preserve_range = TRUE)
  335. HC.result <- estimate_relation(m.LM.gm.HC, include_random = F, data = d.HC.plot)
  336. fig.3a <- plot(HC.result,
  337. point = list(color = "plum", alpha = 0.4, size = 4),
  338. line = list(color = "plum4", size = 2),
  339. ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, x = "Hippocampal Volume (Z-Scored)", y = "Memory Ability (Z-Scored)")
  340. d.HC.metamem.plot <- get_datagrid(m.metamem.gm.HC.anx, by = c("HC_bilateral_sum_adj"), preserve_range = TRUE)
  341. HC.vol.result <- estimate_relation(m.metamem.gm.HC.anx, include_random = F, data = d.HC.metamem.plot)
  342. fig.3b <- plot(HC.vol.result,
  343. point = list(color = "plum", alpha = 0.4, size = 4),
  344. line = list(color = "plum4", size = 2),
  345. ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, x = "Hippocampal Volume (Z-Scored)", y = "Metamemory (Z-Scored)")
  346. fig.3 <- ggarrange(fig.3a, fig.3b)
  347. fig.3
  348. ```
  350. ```{r metacog_deplms_tau, warning=FALSE, message=FALSE}
  351. #relationship between cognitive ability and tau burden
  352. m.LM.tau.EC <- lm(data = d.cog.meta.tau, LM_comp ~ Braak1 + Age + sexCode + Edu)
  353. m.LM.tau.HC <- lm(data = d.cog.meta.tau, LM_comp ~ Braak2 + Age + sexCode + Edu)
  354. tab_model(m.LM.tau.EC, m.LM.tau.HC, digits = 2, show.stat = T, pred.labels = c("Intercept", "Entorhinal Tau", "Age", "Sex", "Education", "Hippocampal Tau"), dv.labels = c("Memory Ability", "Memory Ability"), show.icc = F, show.re.var = F, show.obs = F)
  355. ```
  357. ```{r anxdep_tau, warning=FALSE, message=FALSE}
  358. #relationship between anxiety and depression symptoms and tau burden
  359. m.anxdep.tau.EC <- lm(data = d.cog.meta.tau, anxdep_comp ~ Braak1 + Age + sexCode + Edu)
  360. m.anxdep.tau.HC <- lm(data = d.cog.meta.tau, anxdep_comp ~ Braak2 + Age + sexCode + Edu)
  361. tab_model(m.anxdep.tau.EC, m.anxdep.tau.HC, digits = 2, show.stat = T, pred.labels = c("Intercept", "Entorhinal Tau", "Age", "Sex", "Education", "Hippocampal Tau"), dv.labels = c("Anxiety and Depression Symptoms", "Anxiety and Depression Symptoms"), show.icc = F, show.re.var = F, show.obs = F)
  362. ```
  364. ## Cognitive items from GDS removed
  366. ```{r anxdep_tau_noncog, warning=FALSE, message=FALSE}
  367. d.cog.meta.tau.noncog <- d.cog.meta.anx.noncog %>% inner_join(d.Tau, by = "Subject") %>%
  368. inner_join(d.MRI.all.vol.adj, by = "Subject") %>%
  369. mutate(Braak1 = scale(Braak1, center = T, scale = T),
  370. Braak2 = scale(Braak2, center = T, scale = T),
  371. ERC_bilateral_sum_adj = scale(ERC_bilateral_sum_adj, center = T, scale = T),
  372. HC_bilateral_sum_adj = scale(HC_bilateral_sum_adj, center = T, scale = T))
  373. #relationship between anxiety and depression symptoms and tau burden
  374. m.anxdep.tau.EC <- lm(data = d.cog.meta.tau.noncog, anxdep_comp ~ Braak1 + Age + sexCode + Edu)
  375. m.anxdep.tau.HC <- lm(data = d.cog.meta.tau.noncog, anxdep_comp ~ Braak2 + Age + sexCode + Edu)
  376. tab_model(m.anxdep.tau.EC, m.anxdep.tau.HC, digits = 2, show.stat = T, pred.labels = c("Intercept", "Entorhinal Tau", "Age", "Sex", "Education", "Hippocampal Tau"), dv.labels = c("Anxiety and Depression Symptoms", "Anxiety and Depression Symptoms"), show.icc = F, show.re.var = F, show.obs = F)
  377. ```
  379. ## Figure 4: Anxiety and depression symptoms and hippocampal tau pathology interact to predict metamemory
  381. ```{r fig4, warning=FALSE, message=FALSE}
  382. #relationship between self-reported metacognition and tau
  383. m.metamem.tau.braak1 <- lm(data = d.cog.meta.tau, metamem_comp ~ LM_comp + anxdep_comp*Braak1 + ERC_bilateral_sum_adj + Age + sexCode + Edu)
  384. m.metamem.tau.braak2 <- lm(data = d.cog.meta.tau, metamem_comp ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  385. m.metamem.tau.braak1.psych <- lm(data = d.cog.meta.tau, metamem_comp ~ LM_comp + anxdep_comp*Braak1 + ERC_bilateral_sum_adj + Age + sexCode + Edu + hxCode)
  386. m.metamem.tau.braak2.psych <- lm(data = d.cog.meta.tau, metamem_comp ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu + hxCode)
  387. tab_model(m.metamem.tau.braak2, m.metamem.tau.braak2.psych, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Anxiety & Depression Symptoms", "Hippocampal Tau", "Hippocampal Volume", "Age", "Sex", "Education", "Anxiety and Depression Symptoms*Hippocampal Tau", "Psychiatric History"), dv.labels = c("Metamemory", "Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  388. #plot
  389. d.tau.plot <- get_datagrid(m.metamem.tau.braak2, by = c("anxdep_comp*Braak2"), preserve_range = TRUE, length = 5)
  390. tau.result <- estimate_relation(m.metamem.tau.braak2, include_random = F, data = d.tau.plot)
  391. fig.4 <- plot(tau.result,
  392. point = list(size = 3, alpha = 0.85),
  393. line = list(size = 1), ribbon = list(alpha = 0.2)) + theme_classic() + labs(title = NULL, x = "Anxiety and Depression Symptoms (Z-Scored)", y = "Metamemory (Z-Scored)", colour = "Hippocampal Tau Burden (Z-Scored)", fill = "Hippocampal Tau Burden (Z-Scored)")
  394. fig.4
  395. #testing for effects on metamemory across different domains
  396. m.metamem.gen.tau.braak2 <- lm(data = d.cog.meta.tau, Gen ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  397. m.metamem.ability.tau.braak2 <- lm(data = d.cog.meta.tau, Able ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  398. m.metamem.strat.tau.braak2 <- lm(data = d.cog.meta.tau, Strat ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  399. tab_model(m.metamem.gen.tau.braak2 ,m.metamem.ability.tau.braak2, m.metamem.strat.tau.braak2, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Anxiety and Depression Symptoms", "Hippocampal Tau", "Hippocampal Volume", "Age", "Sex", "Education", "Anxiety and Depression Symptoms*Hippocampal Tau"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  400. #testing for effects on metamemory across different domains controlling for psychiatric history
  401. m.metamem.gen.tau.braak2.psych <- lm(data = d.cog.meta.tau, Gen ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu + hxCode)
  402. m.metamem.ability.tau.braak2.psych <- lm(data = d.cog.meta.tau, Able ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu + hxCode)
  403. m.metamem.strat.tau.braak2.psych <- lm(data = d.cog.meta.tau, Strat ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu + hxCode)
  404. tab_model(m.metamem.gen.tau.braak2.psych, m.metamem.ability.tau.braak2.psych, m.metamem.strat.tau.braak2.psych, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Anxiety and Depression Symptoms", "Hippocampal Tau", "Hippocampal Volume", "Age", "Sex", "Education", "Psychiatric History", "Anxiety and Depression Symptoms*Hippocampal Tau"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  405. ```
  407. ### Cognitive items from GDS removed
  409. ```{r fig4_noncog, warning=FALSE, message=FALSE}
  410. #relationship between self-reported metacognition and tau
  411. m.metamem.tau.braak1 <- lm(data = d.cog.meta.tau.noncog, metamem_comp ~ LM_comp + anxdep_comp*Braak1 + ERC_bilateral_sum_adj + Age + sexCode + Edu)
  412. m.metamem.tau.braak2 <- lm(data = d.cog.meta.tau.noncog, metamem_comp ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  413. tab_model(m.metamem.tau.braak2, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Anxiety and Depression Symptoms [cognitive GDS items removed]", "Hippocampal Tau", "Hippocampal Volume", "Age", "Sex", "Education", "Anxiety and Depression Symptoms [cognitive GDS items removed]*Hippocampal Tau"), dv.labels = c("Metamemory"), show.icc = F, show.re.var = F, show.obs = F)
  414. #plot
  415. d.tau.plot <- get_datagrid(m.metamem.tau.braak2, by = c("anxdep_comp*Braak2"), preserve_range = TRUE, length = 5)
  416. tau.result <- estimate_relation(m.metamem.tau.braak2, include_random = F, data = d.tau.plot)
  417. fig.4 <- plot(tau.result,
  418. line = list(size = 1),
  419. point = list(size = 3, alpha = 0.85)) + theme_classic() + labs(title = NULL, x = "Anxiety and Depression Symptoms [cognitive GDS items removed]", y = "Metamemory", colour = "Hippocampal Tau Burden", fill = "Hippocampal Tau Burden")
  420. fig.4
  421. #testing for effects on metamemory across different domains
  422. m.metamem.gen.tau.braak2 <- lm(data = d.cog.meta.tau, Gen ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  423. m.metamem.ability.tau.braak2 <- lm(data = d.cog.meta.tau, Able ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  424. m.metamem.strat.tau.braak2 <- lm(data = d.cog.meta.tau, Strat ~ LM_comp + anxdep_comp*Braak2 + HC_bilateral_sum_adj + Age + sexCode + Edu)
  425. tab_model(m.metamem.gen.tau.braak2 ,m.metamem.ability.tau.braak2, m.metamem.strat.tau.braak2, digits = 2, show.stat = T, pred.labels = c("Intercept", "Memory Ability", "Anxiety and Depression Symptoms [cognitive GDS items removed]", "Hippocampal Tau", "Hippocampal Volume", "Age", "Sex", "Education", "Anxiety and Depression Symptoms [cognitive GDS items removed]*Hippocampal Tau"), dv.labels = c("Metamemory: Contentment", "Metamemory: Ability", "Metamemory: Strategy"), show.icc = F, show.re.var = F, show.obs = F)
  426. ```

BABS_metamem_MK.Rmd, no license · at the source

Overview

Authors: Jennifer L Crawford1, Alex A Adornato1, Johanna Matulonis1, Xi Chen2, Jacob M Hooker3, Anne S Berry1
ORCID iDs: Jennifer L Crawford
  1. Department of Psychology, Brandeis University, Waltham, Massachusetts, USA
  2. Department of Psychology, Stony Brook University, Stony Brook, New York, USA
  3. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
Institutions: Brandeis University (United States); Stony Brook University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States)
Journal: Hippocampus, volume 36, issue 3, article e70100
Dates: received 17 November 2025; accepted 9 April 2026; published online 19 April 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hipo.70100 · PMID 42002884 · PMCID PMC13092649 · OpenAlex W7154948550
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), PET / SPECT (modality), human (organism), other condition (population), Alzheimer's / dementia (population), depression (population)
Methods: Statistics, fMRI & imaging
Keywords: gray matter volume, hippocampus, neuropsychiatric symptoms, self‐reported memory, tau pathology
MeSH: Aging*, Anxiety*, Depression*, Hippocampus*, Memory*, Metacognition*, Aged, Aged, 80 and over, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, Positron-Emission Tomography, tau Proteins (* major topic)
Topic: Memory Processes and Influences (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIA NIH HHS (F32-AG085890, R01-AG074330, F32 AG085890, R01 AG074330); NINDS NIH HHS (T32-NS007292); National Institute on Aging (R01‐AG074330, F32‐AG085890); National Institute of Neurological Disorders and Stroke (T32‐NS007292)
Citations: not cited yet (Europe PMC); 81 references in the paper
Research resources: RRID:SCR_001847, RRID:SCR_002438, which is based on Nipype 1.8.5 RRID:SCR_002502, RRID:SCR_002823, distributed with ANTs 2.3.3 RRID:SCR_004757, RRID:SCR_008796, RRID:SCR_016216

Abstract

The ability to remember (i.e., memory ability) and to accurately discern memory function (i.e., metamemory) are both important facets of cognition. In the present study, we examined the shared and distinct sources of variance across memory ability and metamemory using psychometrically validated measures of memory ability, metamemory, and anxiety and depression symptoms in conjunction with multimodal imaging (i.e., structural MRI, tau PET) in a sample of cognitively normal older adults (N = 72). Replicating a growing body of work, we found that metamemory was more tightly linked to anxiety and depression symptoms relative to objective measures of memory ability. Our results also revealed that the hippocampus was a critical locus of both memory ability and metamemory—hippocampal volume was positively associated with memory ability, but not metamemory, whereas increased hippocampal tau pathology exacerbated the negative effect of anxiety and depression symptoms on metamemory. Importantly, we also found that after controlling for anxiety and depression symptoms and tau burden, there was a positive association between memory ability and metamemory. Our findings also demonstrated the importance of assessing different facets of metamemory; self‐reported memory contentment and ability, but not strategy use, showed the strongest relationships with both anxiety and depression symptoms and hippocampal tau burden. Together, these results suggest that both shared and distinct mechanisms underlie memory ability and metamemory processes in older adults. Chiefly, this work highlights the potential of metamemory measures as sensitive tools to understand affective processes that occur in both healthy and pathological aging, independent of memory ability.

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

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OSF 6et32

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Size: 5 files, 2 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: easystats (2 files), ggpubr (2 files), tidyverse (2 files), ggplot2 (1 file), psych (1 file)
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At the source: osf.io/6et32

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;
  • 2 scripts, each with its path and the digest of its content;
  • 5 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 Statement

All data and analysis code are publicly available on the Open Science Framework: https://osf.io/6et32. Supporting Information provide additional descriptions and auxiliary analyses.

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 16 MeSH terms, 4 funders, 78 references, 7 RRIDs.

Cite

This paper

Crawford, J. L., Adornato, A. A., Matulonis, J., Chen, X., Hooker, J. M., & Berry, A. S. (2026). Dissociable Mechanisms Underlie Differences Between Memory and Metamemory in Older Adults: The Differentiating Role of Anxiety and Depression Symptoms. Hippocampus, 36(3), e70100. https://doi.org/10.1002/hipo.70100

BibTeX

@article{crawford2026dissociable,
author = {Crawford, Jennifer L and Adornato, Alex A and Matulonis, Johanna and Chen, Xi and Hooker, Jacob M and Berry, Anne S},
title = {{Dissociable Mechanisms Underlie Differences Between Memory and Metamemory in Older Adults: The Differentiating Role of Anxiety and Depression Symptoms}},
journal = {Hippocampus},
year = {2026},
month = may,
volume = {36},
number = {3},
pages = {e70100},
publisher = {Wiley},
issn = {1050-9631},
doi = {10.1002/hipo.70100},
url = {https://doi.org/10.1002/hipo.70100},
pmid = {42002884},
pmcid = {PMC13092649}
}

RIS

TY - JOUR
AU - Crawford, Jennifer L
AU - Adornato, Alex A
AU - Matulonis, Johanna
AU - Chen, Xi
AU - Hooker, Jacob M
AU - Berry, Anne S
TI - Dissociable Mechanisms Underlie Differences Between Memory and Metamemory in Older Adults: The Differentiating Role of Anxiety and Depression Symptoms
T2 - Hippocampus
J2 - Hippocampus
PY - 2026
DA - 2026/05/01
VL - 36
IS - 3
SP - e70100
SN - 1050-9631
PB - Wiley
DO - 10.1002/hipo.70100
UR - https://doi.org/10.1002/hipo.70100
LA - en
ER -

CSL-JSON

{
"id": "10.1002/hipo.70100",
"type": "article-journal",
"title": "Dissociable Mechanisms Underlie Differences Between Memory and Metamemory in Older Adults: The Differentiating Role of Anxiety and Depression Symptoms",
"container-title": "Hippocampus",
"author": [
{
"family": "Crawford",
"given": "Jennifer L"
},
{
"family": "Adornato",
"given": "Alex A"
},
{
"family": "Matulonis",
"given": "Johanna"
},
{
"family": "Chen",
"given": "Xi"
},
{
"family": "Hooker",
"given": "Jacob M"
},
{
"family": "Berry",
"given": "Anne S"
}
],
"container-title-short": "Hippocampus",
"volume": "36",
"issue": "3",
"page": "e70100",
"DOI": "10.1002/hipo.70100",
"PMID": "42002884",
"PMCID": "PMC13092649",
"ISSN": "1050-9631",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/hipo.70100",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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