Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Apr 19;36(3):e70100. doi: 10.1002/hipo.70100

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

Jennifer L Crawford 1,✉, Alex A Adornato 1, Johanna Matulonis 1, Xi Chen 2, Jacob M Hooker 3, Anne S Berry 1
PMCID: PMC13092649  PMID: 42002884

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.

Keywords: gray matter volume, hippocampus, neuropsychiatric symptoms, self‐reported memory, tau pathology

1. Introduction

Memory is a focal point of many studies of aging and Alzheimer's disease (AD). Although many investigations using neuropsychological assessments have shown that memory ability tends to decline with age (Nyberg and Pudas 2019; Reuter‐Lorenz and Park 2014), and is further impacted by AD pathology (Aschenbrenner et al. 2018; Chen et al. 2025; Maass et al. 2018; Pezzoli et al. 2025), assessments indexing metamemory—self‐reported assessments measuring the knowledge and awareness of one's own memory processes—do not always closely correspond to experimentally‐assessed measures of memory ability (Hertzog and Pearman 2013). Indeed, meta‐analyses have found that how participants perform on memory assessments was only weakly related to how they rated their own memory ability (i.e., metamemory) (Beaudoin and Desrichard 2011; Crumley et al. 2014). Though there is an apparent disconnect between objective and subjective measures of memory ability in aging, there is growing consensus that subjective cognitive complaints are clinically relevant. Namely, subjective cognitive decline is associated with increased likelihood of developing dementia, in addition to having the potential to dramatically affect well‐being (Jessen et al. 2014; Rabin et al. 2017). As such, understanding the mechanisms underlying distinct memory‐related processes in older adults, such as memory ability and metamemory, is important to be able to identify individuals at greater risk for developing dementia and to develop potential strategies to mitigate risk.

Affective function has been identified as a potential confounding variable when examining the linkages between memory ability and metamemory in older adults. Measures of both anxiety and depression symptoms consistently demonstrate robust relationships with self‐reported metamemory (Hill et al. 2016). In kind, multiple studies have found that measures of metamemory were more strongly associated with anxiety and depression symptoms than with measures of memory ability (Gustavson et al. 2021; Pearman et al. 2014; Tang et al. 2025; Zlatar et al. 2018). Depression symptoms also have been shown to moderate the effect size of the relationship between memory ability and metamemory; studies with greater numbers of participants with elevated levels of depression symptoms tended to observe smaller correlations between memory ability and metamemory (Crumley et al. 2014). Further, a coordinated analysis across four longitudinal datasets revealed that depression symptoms partially mediated the association between memory ability and metamemory (Hill et al. 2021), which suggests that older adults with worse self‐reported memory and elevated levels of depression symptoms are more likely to have greater objectively assessed memory deficits. Taken together, these findings highlight the importance of including affective measures in studies of memory function in older adults to more precisely understand the relationships between memory ability and metamemory and their underlying mechanisms.

Investigations into the neural substrates of memory ability and metamemory reveal both shared and distinct associations with brain structure. For example, there is growing evidence that estimates of gray matter volume in the medial temporal lobe are important for both memory ability and metamemory in older adults. Namely, gray matter volume in both the entorhinal cortex and hippocampus has been shown to be positively associated with measures of memory ability both cross‐sectionally and longitudinally (Aghjayan et al. 2025; Maass et al. 2018; Ottoy et al. 2019). In contrast, although some studies have observed associations between cross‐sectional estimates of hippocampal volume and metamemory (Hafkemeijer et al. 2013), other work has failed to observe this association after controlling for memory ability and anxiety and depression symptoms (Amariglio et al. 2015).

In parallel, investigations that have assessed AD pathology and its associations with memory ability and metamemory have also shown clear dissociations across the two memory‐related constructs. For example, studies examining the relationships between memory ability and AD pathology have found that tau pathology, relative to beta‐amyloid burden, is particularly important for our understanding of declines in memory ability in older adults (Chen et al. 2024, 2025; Fonseca et al. 2024; Pezzoli et al. 2025). On the other hand, subjective memory measures indexing metamemory show associations with both beta‐amyloid and tau pathology in which individuals with worse subjective memory have higher levels of pathological burden (Buckley et al. 2017; La Joie et al. 2016; Perrotin et al. 2012, 2017), which might suggest a more generalized AD pathological mechanism underlying metacognitive deficits, relative to measures of memory ability. Nevertheless, it is important to note that early tau PET tracers, including those used in the prior work assessing the relationships between tau burden and metamemory (Buckley et al. 2017), were unable to reliably quantify hippocampal tau burden (Betthauser et al. 2019). As such, there have been no studies to date that have examined the extent to which measures of metamemory are associated with tau pathology in the hippocampus, which represents a significant gap in our knowledge, given the importance of the hippocampus to memory processes in aging and AD.

Understanding the relationships between memory ability and metamemory, and their underlying mechanisms, also requires careful consideration of how self‐reported measures of metamemory are assessed. Although assessments of memory ability differ across studies, a far greater degree of heterogeneity has been noted in the literature on assessments of metamemory, and related processes, both regarding the types of measures used and the number of items used to assess metamemory. For example, a recent analysis across 19 large‐scale studies found that there was little overlap of the measures used to assess subjective estimates of cognition, with 75% of the self‐report measures used by only one study (Rabin et al. 2015). Moreover, this same analysis showed that the number of items used to assess subjective cognition varied widely—over a third of the instruments only contained a handful of items, with some only including a single item (Rabin et al. 2015). Using only one item, or a limited number of items, to assess metamemory not only risks failing to reliably assess this construct (Hayes et al. 2017), but it also eliminates the opportunity to understand different facets of metamemory in relation to objective assessments of memory ability. Multiple, psychometrically validated, questionnaires have shown that metamemory is a multifaceted construct; these instruments contain different sub‐scales indexing memory ability, capacity, contentment, strategy use, perceived change, and other similar constructs (Gilewski et al. 1990; Hertzog et al. 1987; Troyer and Rich 2002). Indeed, studies using these questionnaires have found that only specific facets of metamemory (e.g., capacity, perceived change) are related to longitudinal changes in objectively assessed memory ability (Chen et al. 2019; Zimprich and Kurtz 2015). Such findings highlight the importance of using measures of metamemory that can reliably assess multiple domains of this construct to rigorously understand its linkages to objective assessments of memory ability and other relevant variables (e.g., affective function, gray matter volume, AD pathology) in older adults.

The current study investigated the extent to which assessments of memory ability and metamemory were associated with each other and further examined the contribution of anxiety and depression symptoms, gray matter volume, and tau pathology towards these constructs. We also separately examined how distinct facets of metamemory—contentment, ability, and strategy use—are associated with objective assessments of memory ability and other relevant variables (e.g., anxiety and depression symptoms, gray matter value, tau burden). In line with prior work, we hypothesized that assessments of memory ability and metamemory would only be weakly associated with each other and that metamemory would show stronger associations with anxiety and depression symptoms than with objective assessments of memory ability. Further, we predicted that gray matter volume in the entorhinal cortex and hippocampus would have stronger associations with memory ability, but not metamemory, whereas tau pathology in the entorhinal cortex and hippocampus would be independently associated with both memory ability and metamemory.

2. Materials and Methods

2.1. Participants

Participants were adults ages 60 and older recruited from the greater Boston metropolitan area through advertisements, direct mail, and research participant registries. Inclusion criteria for participation included English language proficiency, no lifetime history of neurological disorders, a score ≥ 26 on the Mini Mental State Exam, no history of claustrophobia, no metallic objects or devices in the body deemed non‐compatible with MRI (e.g., pacemakers or other implanted cardiac devices), and no routine exposure to radiation. Individuals were not eligible to complete this study if they were currently taking psychotropic medication (or had taken any psychotropic medications in the past 6 months). A history of psychiatric illness was not exclusionary for the present study. All experimental procedures were approved by the Brandeis University Institutional Review Board and the Partners Human Research Committee of Massachusetts General Hospital (MGH) prior to data collection. Participants provided informed consent and were compensated for all study procedures. The sample consisted of 72 participants (39 females, 33 males; M age = 69.5; SDage = 5.5; 6 Asian, 7 Black or African American, 58 White, 1 more than one race; 0 Hispanic or Latinx; see Table 1).

TABLE 1.

Descriptive information for the study sample.

Dimension Mean (SD)
Age 69.63 (5.42)
MMSE 29.14 (1.03)
GDS 2.87 (3.22)
MASQ: Anhedonic Depression 25.31 (6.84)
MASQ: Anxious Arousal 11.85 (1.89)
MASQ: General Distress 14.18 (4.06)
PSWQ 35.27 (12.29)
Entorhinal Tau (SUVR) 1.37 (0.63)
Hippocampal Tau (SUVR) 0.96 (0.16)
Sex Percentage
Female 54.17
Male 45.83
Race Percentage
Asian or Pacific Islander 8.33
Black 8.33
White 81.94
More than One Race 1.39
Ethnicity Percentage
Hispanic 0
Not Hispanic 100
Open in a new tab

Abbreviations: GDS, Geriatric Depression Scale; MASQ, Mood and Anxiety Symptom Questionnaire; MMSE, Mini Mental State Exam; PSWQ, Penn State Worry Questionnaire.

2.2. Behavioral Assessments

To examine the relationships between measures of memory ability and metamemory we used assessments taken from the Brandeis Aging Brain Study. The Brandeis Aging Brain Study tests participants on an extensive neuropsychological battery containing experimental measures of learning and memory, executive function, and crystallized cognitive ability. Moreover, participants complete an extensive collection of self‐report questionnaires assessing a broad array of constructs including cognitive ability, anxiety and depression symptoms, activity participation, and sleep at each study time point. In the current study, we used experimental assessments and self‐report questionnaires measuring cognition in the domain of memory. As such, we had objective assessments of memory ability and self‐reports of metamemory for each participant. Moreover, these assessments were accompanied by self‐report questionnaires measuring anxiety and depression symptoms to understand the linkages between these constructs. All data used in the current study were taken from participants' initial behavioral assessment, which were collected from December 2019 through October 2024. Data and code used for analyses can be found on the Open Science Framework: https://osf.io/6et32.

2.2.1. Objective Cognitive Assessments

All objective cognitive assessments were performed in the laboratory during the same testing session. All tasks were performed in a fixed order across participants. Descriptive summaries for all measures described below can be found in the Supplement. Memory ability was assessed in using two different tasks: the California Verbal Learning Test (CVLT) (Woods et al. 2006) and a visual reproduction task (Wechsler 1997). The structure of each task is similar in that participants' memory performance is assessed across both long and short delays. As such, the free recall scores across both short and long delays were used in the current study as an index of memory ability, consistent with prior work examining the behavioral and neurobiological correlates of memory function in aging and AD (Chen et al. 2020, 2024, 2025; Tang et al. 2025).

2.2.2. Self‐Report Measures

Self‐report questionnaires were used to assess subjective cognitive ability (i.e., metamemory) and anxiety and depression symptoms. All questionnaires were completed at the same time for all participants and were completed prior to cognitive testing. If an item response was missing from a participant's questionnaire, we imputed the response using the median score for the particular scale from which the response was missing; if over 20% of the items were missing for any given scale, missing items were not imputed, and the participant was not included in the present analysis due to missingness. A summary of imputation and a general descriptive summary for each scale used in the current study can be found in the Supplement. To assess metamemory, we asked participants to complete the Multifactorial Memory Questionnaire (MMQ) (Troyer and Rich 2002). The MMQ has three sub‐scales that index participants' general perceptions about their memory ability (contentment score), how they think their memory functions across different daily life situations (ability score), and utilization of memory strategies across daily life contexts (strategy score); all scores were used to assess self‐perception of memory (i.e., metamemory).

Anxiety and depression symptoms were measured using the combination of the Geriatric Depression Scale (Yesavage et al. 1982) (GDS), the 30‐item Mood and Anxiety Symptom Questionnaire (Wardenaar et al. 2010) (MASQ), and the Penn State Worry Questionnaire (PSWQ) (Meyer et al. 1990). The GDS contains a single scale (GDS score), which captures depressive symptoms, with higher values indicating higher depressive symptoms. Further, the MASQ has three subscales: the anxious arousal scale measures levels of anxiety and somatization, the anhedonic depression score reflects levels of depression‐like symptoms, and the general distress score indexes levels of general psychological distress, depression, anxiety, and somatization. Across all three scales of the MASQ, higher scores indicate greater levels of symptoms or distress. Finally, the PSWQ has only one scale, with higher scores reflecting higher levels of worry. All of the aforementioned scales were included in our composite measure of anxiety and depression symptoms.

2.3. Neuroimaging Acquisition and Processing

2.3.1. MRI Acquisition and Processing

As a part of this study, all participants also completed a simultaneous PET‐MRI scan. Scanning was performed on a Siemens 3 T Biograph mMR, capable of simultaneous PET‐MRI acquisition. A T1‐weighted MPRAGE scan was acquired for each participant (TR = 2530 ms, TE = 1.69 ms, Flip angle = 7 degrees, 192 slices, slice thickness = 1.0 mm, FOV = 256 × 256 mm). Results included in this manuscript come from preprocessing performed using fMRIPrep 22.0.2 ((Esteban et al. 2019); RRID:SCR_016216), which is based on Nipype 1.8.5 ((Gorgolewski et al. 2011); RRID:SCR_002502). The following paragraphs describe the key stages of fMRIPrep preprocessing.

The T1‐weighted (T1w) image was corrected for intensity non‐uniformity (INU) with N4BiasFieldCorrection (Tustison et al. 2010), distributed with ANTs 2.3.3 ((Avants et al. 2008); RRID:SCR_004757), and was used as T1w‐reference throughout the workflow. The T1w‐reference was then skull‐stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white‐matter (WM) and gray‐matter (GM) was performed on the brain‐extracted T1w using fast (FSL 6.0.5.1, RRID:SCR_002823; (Zhang et al. 2001)). Brain surfaces were reconstructed using ‘recon‐all’ (FreeSurfer 7.2.0, RRID:SCR_001847, @fs_reconall), and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs‐derived and FreeSurfer‐derived segmentations of the cortical gray matter of Mindboggle (RRID:SCR_002438, @mindboggle). Volume‐based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain‐extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: ICBM 152 Nonlinear Asymmetrical template version 2009c [(Fonov et al. 2009); RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym].

Both the entorhinal cortex and hippocampus were used as regions of interest in the current study. These regions of interest were defined using the Desikan–Killiany atlas included as the default parcellation within FreeSurfer (Desikan et al. 2006; Fischl 2012); see Supplement for region of interest visualizations. Gray matter volumes were adjusted for total intracranial volume (ICV) using a covariance approach (Buckner et al. 2004) and summed across hemispheres as no a priori effects of hemisphere were expected.

2.3.2. Tau PET Acquisition and Processing

For the [18F]MK‐6240 tau PET scan, participants received approximately 5 mCi of tracer (5.28 ± 0.37) and were scanned 90–110 min post‐injection (4 × 5‐min frames) during the same scanning session in which the MRI data were acquired. Standardized uptake value ratio (SUVR) images for MK‐6240 were created by normalizing mean tracer uptake to the inferior cerebellar gray matter reference region (Baker et al. 2017; Harrison et al. 2023). Geometric transfer matrix partial volume correction (PVC) on FreeSurfer‐derived ROIs was applied to account for partial volume effects (Rousset et al. 1998). The entorhinal cortex and hippocampal ROIs, defined by the Desikan‐Killiany atlas, were specifically chosen for the present study because these regions are some of the earliest sites of tau pathology in AD and are known to be important for memory function and metamemory (Braak et al. 2011; Braak and Braak 1995; Braak and Tredici 2015; Buckley et al. 2017; Rabin et al. 2017; Rugg and Vilberg 2013). Unlike first generation tau‐PET tracers, MK‐6240 shows limited off‐target binding in nearby choroid plexus, ensuring an accurate estimation of hippocampal tau burden (Betthauser et al. 2019); see Supplement for visualization of raw PET images with ROI overlay and group averaged SUVR map.

2.4. Analysis

To facilitate comparisons across multiple tasks and scales measuring cognition and anxiety and depression symptoms, we created composite scores indexing each construct. As such, three different composites were calculated: objectively assessed memory ability, metamemory, and anxiety and depression symptoms. For each composite, we summed the z‐scores across all distinct scores from each instrument. Correlations between the individual scores used to create each composite and the distributions for each composite score can be found in the Supplement.

The relationships between assessments of memory ability and metamemory, along with anxiety and depression symptoms, gray matter volume, and tau pathology were all quantified using linear models. Estimates of gray matter volume and tau pathology were standardized to facilitate the interpretation of the model output. We included age, sex, and years of education as covariates in all linear models; age and years of education were entered as centered, continuous numeric variables and sex was entered as a dummy‐coded numeric variable (female = 0, male = 1). Sensitivity analyses were also conducted to better understand the relationships between memory ability, metamemory, and anxiety and depression symptoms. In one set of sensitivity analyses we removed cognitive items from the GDS and recomputed the anxiety and depression composite score to determine if we still observed effects of anxiety and depression symptoms after accounting for this potential confound. In another set of analyses, we added a dummy‐coded covariate pertaining to psychiatric history (0 = no history, 1 = history of psychiatric illness); a small proportion of our sample had a history of psychiatric illness (7/72). In all the models described in this manuscript, the effects are reported as the estimate from the model with 95% confidence intervals and the corresponding test statistic and p‐value. For exploratory analyses, we report p‐values both with and without correction for multiple comparisons using false discovery rate correction. Model diagnostics, including a summary of variance inflation factors across all models, are described in the Supplement to ensure all models are robust to concerns of multicollinearity. The Supplement also contains Bayesian tests of the Probability of Direction to provide a complementary lens from which to view the results by quantifying the probability that the observed effect is positive or negative (i.e., an index similar to a frequentist p‐value). Data analysis was conducted in R version 4.4.2 (Team 2023) using the ‘easystats’ ecosystem (Lüdecke et al. 2019, 2020; Lüdecke, Ben‐Shachar, et al. 2021; Lüdecke, Patil, et al. 2021; Makowski et al. 2020).

3. Results

3.1. Metamemory Is Related to Anxiety and Depression Symptoms, but Not Memory Ability

We observed effects of age, but not sex or education, on memory ability (B age = −0.96 [−1.71, −0.20], t = −2.53, p = 0.014; B sex = −0.73 [−2.22, 0.76], t = −0.98, p = 0.332; B edu = 0.59 [−0.16, 1.34], t = 1.57, p = 0.121), whereas we found that sex, but not age or education, was associated with metamemory (B age = −0.46 [−1.02, 0.09], t = −1.66, p = 0.102; B sex = 1.21 [0.11, 2.32], t = 2.19, p = 0.032; B edu = 0.31 [−0.24, 0.87], t = 1.13, p = 0.262). These results suggest that higher age is associated with worse memory ability and that men tend to have higher metamemory scores than women despite a lack of observable sex differences in objective measures of memory ability. Nevertheless, and most critically, when we tested for the associations between objective and subjective measures of memory, we did not find reliable evidence that memory ability was related to metamemory, B = 0.11 [−0.06, 0.29], t = 1.29, p = 0.202, even when controlling for age, sex, and years of education (Figure 1). Next, we assessed the relationships between anxiety and depression symptoms and objective and subjective assessments of cognition to better understand the mechanisms that underlie these distinct constructs. Indeed, we found that anxiety and depressive symptoms were associated with metamemory, B = −0.19 [−0.35, −0.03], t = −2.38, p = 0.020 (Figure 2b), but not memory ability, B = 0.01 [−0.22, 0.23], t = 0.05, p = 0.957 (Figure 2a). More specifically, higher levels of anxiety and depression symptoms were associated with worse metamemory, but there was no association between memory ability and anxiety and depression symptoms. Neither removing cognitive‐related items from the anxiety and depression symptom composite score, nor controlling for individual differences in psychiatric history, changed the above reported effects, ps < 0.039.

FIGURE 1.

FIGURE 1

Open in a new tab

Model estimates (residualized) of metamemory as they relate to memory ability, controlling for age, sex, and years of education. Larger values represent better memory ability and higher levels of self‐reported metamemory. Error bands represent 95% confidence intervals. Data points represent individual participants.

FIGURE 2.

FIGURE 2

Open in a new tab

Anxiety and depression symptoms are not related to (A) memory ability, but are negatively associated with (B) metamemory. The plots include residualized model estimates controlling for age, sex, and years of education. In each plot, larger values represent higher levels of anxiety and depression symptoms and better memory ability and self‐reported metamemory. Error bands represent 95% confidence intervals. Data points represent individual participants.

To better understand which measures contained within the anxiety and depression symptom composite help to explain variance in metamemory, we entered each facet of the anxiety and depression composite as simultaneous predictors in a linear model of metamemory. Interestingly, we found evidence for the importance of depressive symptoms, as measured by the GDS, on metamemory. Higher GDS scores were associated with worse self‐reported metamemory scores, B = −0.88 [−1.72, −0.03], t = −2.07, p = 0.043, Further, the anxious arousal sub‐scale from the MASQ was also a significant predictor, B = −0.77 [−1.35, −0.18], t = −2.62, p = 0.011, which suggests that somatic sensations are also a component that contributes to the relationship between negative affect and metamemory. There were no other significant predictors observed in the model, ps > 0.093. Taken together, these findings suggest that affective dimensions, such as anxiety and depression symptoms, play a role in participants' perceptions of their own memory, but neither metamemory, nor anxiety and depression symptoms are reliably associated with objectively assessed memory ability.

3.2. Hippocampal Volume Is Associated With Memory Ability, but Not Metamemory

Given the lack of robust associations between memory ability and metamemory, we sought to examine the potential neural substrates that are associated with these functions. To accomplish this, we first examined the relationships between gray matter volume in the entorhinal cortex and hippocampus with both memory ability and metamemory. Entorhinal cortex volume was not associated with either memory ability, B = 0.42 [−0.40, 1.23], t = 1.02, p = 0.313, or metamemory, B = −0.36 [−0.97, 0.24], t = −1.20, p = 0.236. In contrast, hippocampal volume was associated with memory ability, B = 0.96 [0.10, 1.82], t = 2.23, p = 0.029 (Figure 3a), but not metamemory, B = −0.29[−0.95, 0.36], t = −0.89, p = 0.377 (Figure 3b). Namely, higher ICV‐adjusted estimates of hippocampal volume were associated with better memory ability, whereas no such relationship was observed between hippocampal volume and metamemory. The relationships between hippocampal volume and memory ability, B = 0.98 [0.10, 1.88], t = 2.23, p = 0.029, and metamemory, B = −0.19[−0.84, 0.45], t = −0.60, p = 0.553, remained after adding anxiety and depression symptoms as a covariate into the models. When viewed together, these findings suggest that dissociable mechanisms are associated with memory ability and metamemory. More specifically, memory ability, but not metamemory, is positively associated with hippocampal volume, whereas metamemory, but not memory ability, is more tightly coupled with anxiety and depression symptoms.

FIGURE 3.

FIGURE 3

Open in a new tab

Estimates of hippocampal volume are associated (A) memory ability, but not (B) metamemory. In all plots, residualized model estimates are shown; larger values represent both greater gray matter volume and better memory ability and metamemory. Error bands represent 95% confidence intervals. Data points represent individual participants.

3.3. Associations Between Anxiety and Depression Symptoms and Metamemory Are Moderated by Hippocampal Tau Pathology

Given the relevance of both objective and subjective measures of cognition to AD, we also examined the extent to which tau burden in the entorhinal cortex and hippocampus was associated with memory ability and metamemory. Although both entorhinal, r = 0.31 [0.08, 0.50], t = 2.72, p = 0.008, and hippocampal tau burden, r = 0.26 [0.03, 0.406], t = 2.24, p = 0.028, were positively associated with age, we did not observe reliable associations between memory ability and tau burden in either the entorhinal cortex, B = −0.00 [−0.84, 0.83], t = −0.01, p = 0.992, or hippocampus, B = −0.43 [−1.21, 0.35], t = −1.11, p = 0.271, when controlling for age, sex, and education. Moreover, we also did not find evidence for direct associations between metamemory and tau burden in either the entorhinal cortex, B = −0.02 [−0.62, 0.59], t = −0.06, p = 0.953, or hippocampus, B = −0.22 [−0.78, 0.34], t = −0.79, p = 0.431; there also were no direct associations between anxiety and depression symptoms and entorhinal tau, B = −0.24 [−1.15, 0.67], t = −0.53, p = 0.597, or hippocampal tau, B = −0.31 [−1.17, 0.54], t = −0.74, p = 0.464, even when controlling for individual differences in psychiatric history or when removing cognitive items from the anxiety and depression symptom composite, ps > 0.322.

Next, we sought to examine if tau pathology moderated the association between anxiety and depression symptoms and metamemory. In other words, because there is growing support for idea that depression and anxiety symptoms are related to tau pathology in the temporal lobe (Marchant et al. 2020; Markova et al. 2025), it is possible that the relationships between anxiety and depression symptoms and metamemory could be amplified in pathological aging. Indeed, we found that tau burden in the hippocampus moderated the association between anxiety and depression symptoms and metamemory, B = −0.26 [−0.46, −0.06], t = −2.61, p = 0.011 (Figure 4); this effect remained after controlling for individual differences in psychiatric history and after removing cognitive items from the anxiety and depression symptom composite, ps < 0.014. Specifically, individuals with high levels of both anxiety and depression symptoms and hippocampal tau burden had worse self‐reported metamemory. This effect was specific to hippocampal tau burden; the same pattern did not emerge for estimates of entorhinal tau burden and anxiety and depression symptoms, B = −0.01 [−0.22, 0.21], t = −0.05, p = 0.957. Interestingly, these analyses also revealed that when including both hippocampal tau burden and anxiety and depression symptoms, and their interaction, into the linear model of metamemory, there was a significant positive association between memory ability and metamemory, B = 0.022 [0.04, 0.40], t = 2.38, p = 0.020 (Table 2). This suggests that failing to account for measures of affect (e.g., anxiety and depression symptoms) and tau burden may obscure the positive association between objective and subjective memory measures in cognitively normal older adults. Collectively, these results suggest that how cognitively normal older adults think and feel about their memory is related not only to individual differences in anxiety and depression symptoms and underlying tau pathology, but also to objective assessments of memory ability, when controlling for affect and tau burden.

FIGURE 4.

FIGURE 4

Open in a new tab

Associations between anxiety and depression symptoms and metamemory are moderated by hippocampal tau pathology. In each plot, residualized values are shown; larger numeric values represent greater levels of hippocampal tau pathology, greater number of anxiety and depression symptoms, and better metamemory. Error bands represent 95% confidence intervals. Data points represent individual participants.

TABLE 2.

Relationships between metamemory and memory ability, hippocampal tau burden, hippocampal volume, anxiety and depression symptoms when controlling for age, sex, and years of education.

Predictors Metamemory
Estimates CI Statistic p
Intercept −0.56 −1.29 to 0.16 −1.56 0.125
Memory Ability 0.22 0.04 to 0.40 2.38 0.020
Anxiety and Depression Symptoms −0.21 −0.37 to −0.05 −2.68 0.009
Hippocampal Tau −0.15 −0.71 to 0.40 −0.55 0.581
Hippocampal Volume −0.43 −1.07 to 0.22 −1.33 0.189
Age −0.29 −0.94 to 0.36 −0.89 0.378
Sex 1.17 0.07 to 2.28 2.12 0.038
Education 0.21 −0.34 to 0.75 0.75 0.455
Anxiety and Depression Symptoms*Hippocampal Tau −0.26 −0.46 to −0.06 −2.61 0.011
R 2/R 2 adjusted 0.289/0.197
Open in a new tab

Note: Bolded p‐values represent significant effects.

3.4. Exploring the Contribution of Anxiety and Depression Symptoms and Tau Burden on Different Facets of Metamemory

As an extension of our findings detailed above, we sought to examine the extent to which the relationships between memory ability, metamemory, and anxiety and depression symptoms are consistent across each of the three dimensions of metamemory assessed in the current study. To accomplish this, we ran separate models using the contentment, ability, and strategy use sub‐scales of the MMQ. Replicating the finding that memory ability and metamemory are not reliably associated with each other, none of the metamemory sub‐scales were associated with memory ability, ps > 0.106 (Table 3). Interestingly, our findings suggest that observed associations between anxiety and depression symptoms and metamemory were likely driven by self‐reported memory contentment, B = −0.07 [−0.14, −0.01], t = −2.16, p = 0.035 (p corrected = 0.194), and ability, B = −0.08 [−0.15, −0.02], t = −2.55, p = 0.013 (p corrected = 0.194), but not strategy use, −0.03 [−0.10, 0.04], t = −0.96, p = 0.339 (p corrected = 0.636). This pattern of results remained after controlling for individual differences in psychiatric history and when removing cognitive items in the anxiety and depression symptom composite, ps < 0.040. In other words, participants who had higher levels of anxiety and depression symptoms tended to have lower levels of contentment and worse perceptions of their memory ability, whereas anxiety and depression symptoms were unrelated to their usage of memory strategies in daily life contexts.

TABLE 3.

Effects of memory ability on the three different facets of metamemory (contentment, ability, and strategy) when controlling for anxiety and depression symptoms, age, sex, and years of education.

Predictors Metamemory: contentment Metamemory: ability Metamemory: strategy
Estimates CI Statistic p Estimates CI Statistic p Estimates CI Statistic p
Intercept −0.14 −0.45 to 0.16 −0.94 0.350 −0.08 −0.38 to 0.22 −0.52 0.604 −0.23 −0.55 to 0.08 −1.48 0.145
Memory Ability 0.06 −0.01 to 0.13 1.64 0.106 0.05 −0.02 to 0.13 1.55 0.125 0.00 −0.07 to 0.08 0.06 0.956
Anxiety and Depression Symptoms −0.07 −0.14 to −0.01 −2.16 0.035 −0.08 −0.15 to −0.02 −2.55 0.013 −0.03 −0.10 to 0.04 −0.96 0.339
Age 0.01 −0.23 – 0.25 0.07 0.943 −0.13 −0.37 to 0.11 −1.09 0.281 −0.09 −0.34 to 0.16 −0.75 0.454
Sex 0.32 −0.14 to 0.78 1.37 0.174 0.17 −0.28 to 0.62 0.76 0.450 0.52 0.04 to 0.99 2.15 0.035
Education 0.10 −0.13 to 0.33 0.87 0.385 0.13 −0.09 to 0.36 1.17 0.247 −0.02 −0.26 to 0.22 −0.18 0.858
R 2/R 2 adjusted 0.156/0.093 0.197/0.137 0.101/0.034
Open in a new tab

Note: Bolded p‐values represent significant effects.

Next, we probed the degree to which the moderating role of hippocampal tau on the relationship between anxiety and depression symptoms and metamemory was more closely related to distinct facets of metamemory. Consistent with the findings above, these sub‐scale analyses are consistent with a specific role of self‐reported memory contentment, B = −0.09 [−0.18, −0.01], t = −2.15, p = 0.036 (p corrected = 0.122), and ability, B = −0.09 [−0.18, −0.01], t = −2.26, p = 0.027 (p corrected = 0.137), but not strategy use measures, B = −0.08 [−0.16, 0.01], t = −1.74, p = 0.087 (p corrected = 0.234) (Table 4). Taken together, these findings suggest that one's feelings of contentment about their memory and how they view their memory ability might be important facets of metamemory that have particular relevance to their anxiety and depression symptoms and underlying tau burden. Although these effects do not survive multiple comparisons correction, further exploring these facets of metamemory represents a prime target for future hypothesis testing.

TABLE 4.

Associations between facets of metamemory (contentment, ability, and strategy) and memory ability, hippocampal tau burden, hippocampal volume, anxiety and depression symptoms when controlling for age, sex, and years of education.

Predictors Metamemory: contentment Metamemory: ability Metamemory: strategy
Estimates CI Statistic p Estimates CI Statistic p Estimates CI Statistic p
Intercept −0.15 −0.46 to 0.16 −0.94 0.350 −0.09 −0.39 to 0.21 −0.60 0.548 −0.33 −0.64 to −0.01 −2.08 0.042
Memory Ability 0.09 0.02 to 0.17 2.39 0.020 0.09 0.02 to 0.17 2.47 0.016 0.03 −0.05 to 0.11 0.78 0.438
Anxiety and Depression Symptoms −0.09 −0.16 to −0.02 −2.50 0.015 −0.09 −0.16 to −0.03 −2.83 0.006 −0.03 −0.10 to 0.03 −1.00 0.321
Hippocampal Tau −0.06 −0.29 to 0.18 −0.46 0.645 0.05 −0.18 to 0.28 0.45 0.656 −0.15 −0.39 to 0.09 −1.24 0.218
Hippocampal Volume −0.09 −0.36 to 0.19 −0.63 0.534 −0.06 −0.32 to 0.21 −0.43 0.671 −0.29 −0.56 to −0.01 −2.04 0.046
Age −0.01 −0.29 to 0.27 −0.04 0.967 −0.15 −0.42 to 0.11 −1.15 0.255 −0.13 −0.41 to 0.15 −0.91 0.365
Sex 0.33 −0.14 to 0.81 1.40 0.168 0.21 −0.25 to 0.67 0.92 0.360 0.63 0.15 to 1.11 2.64 0.011
Education 0.08 −0.15 to 0.32 0.72 0.473 0.12 −0.11 to 0.34 1.04 0.301 0.00 −0.23 to 0.24 0.02 0.981
Anxiety and Depression Symptoms*Hippocampal Tau −0.09 −0.18 to −0.01 −2.15 0.036 −0.09 −0.18 to −0.01 −2.26 0.027 −0.08 −0.16 to 0.01 −1.74 0.087
R 2/R 2 adjusted 0.228/0.129 0.283/0.190 0.204/0.102
Open in a new tab

Note: Bolded p‐values represent significant effects.

4. Discussion

A clear takeaway from the present study is that affective variables are important to measure in conjunction with assessments of metamemory when studying aging and AD. Not only did anxiety and depression symptoms demonstrate strong linkages with metamemory, but our results further demonstrated that underlying hippocampal tau pathology exacerbated this negative relationship. Prior work has already shown that failing to account for anxiety and depression symptoms risks occluding the relationships between memory ability and metamemory (Burmester et al. 2016; Crumley et al. 2014). Our findings extend this further; we show that affective disturbances (i.e., anxiety and depression symptoms) are increasingly influential on metamemory in people harboring tau. This distinction is important because prior work has shown that the strength of the relationship between metamemory and negative affect is preserved across the adult life span (Rowell et al. 2016). In other words, by showing that tau pathology exacerbates the association between negative affect and metamemory, the data in the present study provide a clear demonstration that metamemory is worsened by affective disturbances specifically in pathological aging processes.

Critically, these results add to a growing body of work which has revealed that AD pathology is associated with behavioral phenotypes closely linked to neuropsychiatric symptoms, which include both anxiety and depression symptoms. Namely, analyses of postmortem tissue have shown that early stages of tau pathology in the entorhinal cortex and hippocampus (i.e., Braak I and II) are associated with increased prevalence of neuropsychiatric symptoms, including anxiety and depression symptoms (Ehrenberg et al. 2018). A similar pattern of results is reflected across studies that use in vivo imaging of tau pathology to examine its associations with both anxiety and depression symptoms, revealing consistent positive associations between tau pathology in temporal regions and greater levels of depression and anxiety symptoms (Gatchel et al. 2017; Marchant et al. 2020; Markova et al. 2025; Tissot et al. 2021). Directly relevant to the present study, recent work has also found that both anxiety and depression symptoms act synergistically with AD pathology in predicting cognitive decline across multiple domains, including memory ability, in cognitively unimpaired older adults (Pink et al. 2023). As such, gaining a better understanding of the ways in which tau pathology is associated with both affective variables and metacognition will be crucial for future work to tackle, as it may help to identify people earlier in the disease before cognition has begun to decline and direct them towards appropriate interventions.

Although it is possible that the lack of reliable associations between measures of memory ability and metamemory could be explained by the differences in assessment type—memory ability is often assessed with neuropsychological tests, whereas metamemory is commonly measured via retrospective self‐report measures—there is likely more to the story. Prior work has shown that when older adults are asked to make metacognitive judgments about their memory while performing memory ability assessments in the laboratory (i.e., metamemory assessments not reliant on retrospective recall that are tightly linked to the contents of the neuropsychological assessment), the two constructs diverge both behaviorally and with regard to their underlying neural mechanisms (Uquillas et al. 2020; Zhuang et al. 2022). This suggests that retrospective response biases from self‐report are likely not a strong contributor to the differences often observed between memory ability and metamemory. Moreover, in the current study, we did observe a positive association between memory ability, measured with neuropsychological assessments, and metamemory, indexed via retrospective self‐report, only after controlling for individual differences in anxiety and depression symptoms and hippocampal tau burden. This provides further evidence that the lack of strong associations between measures of memory ability and metamemory more likely stem from key differences in the constructs themselves, rather than the type of measurement used to assess them.

Indeed, the lack of consistent, positive associations between assessments of memory ability and metamemory observed in the literature could be better attributed to the different facets of memory function that these measures are intended to measure. Put another way, many of the retrospective self‐report measures of metamemory, including the MMQ used in the present study, were specifically designed to index daily life memory function (Hertzog et al. 1987; Troyer and Rich 2002). This distinction is important because many of the neuropsychological tasks commonly used to assess objective measures of memory ability in older adults fail to meaningfully translate to daily life (T. A. Salthouse 2004, 2012) or demonstrate sensitivity to preclinical AD pathology (Rentz et al. 2013). In contrast, repeated daily life assessments of memory ability, which offer the promise of both improved reliability and generalizability relative to one‐time laboratory assessments (Öhman et al. 2021; Polk et al. 2025; Sliwinski et al. 2018), do show greater sensitivity to AD biomarkers (Allard et al. 2014). By extension, it is possible that repeated daily life assessments of memory ability could yield stronger and more reliable associations with metamemory in older adults and should be a priority for future directions in this research area.

Nevertheless, when considering the complex relationships between memory ability and metamemory in aging, there are several limitations that could not be directly addressed in the current study. For one, the relationships between cognitive and metacognitive processes were only assessed in the domain of memory. Although prior work has shown that self‐reported metamemory is associated with metacognition in other domains (Payne et al. 2017), it will be crucial for future studies to disentangle both the behavioral and neural mechanisms underlying these relationships. Multi‐domain investigations will be particularly important as they relate to AD pathology, given the recent work suggesting that there are dissociations between beta‐amyloid and tau pathology on executive function versus memory ability (Chen et al. 2025; Pezzoli et al. 2025). Further, anxiety and depression symptoms were assessed in a healthy older adult sample, with broad self‐report assessments. As such, we cannot draw conclusions about how the relationships between affective and subjective cognitive variables are affected by clinical diagnoses, such as depression or anxiety, or also how other dimensions of health and well‐being (e.g., physical health, sleep) play a role in these relationships. Work that can expand the present work to understand the contributions of multiple health‐related metrics will be critical. Moreover, our sensitivity to detect effects as they pertain to the relationships between subjective and objective memory measures could be improved with longitudinal assessments, in contrast to the cross‐sectional assessments used in the present study. Indeed, prior work has found that self‐reported memory is related to participants' change in objectively assessed memory ability across different timepoints, rather than the memory score assessed only at the time of administration of the self‐reported memory questionnaire (Chen et al. 2019). The present work is also limited both in its sample size and in its diversity across race, ethnicity, and levels of education. Future work should aim to assess a larger and more diverse pool of participants on multi‐domain cognitive and affective measures across time to better elucidate the linkages between these constructs and to more rigorously test for the generalizability of these effects.

Despite its limitations, the current study helps to contribute to a growing literature on the relationships between memory ability and metamemory in aging. Namely, we used psychometrically validated behavioral measures and multimodal imaging to show that both shared and distinct mechanisms underlie memory ability and metamemory processes. In particular, the results suggest that assessments of affect are important to measure when assessing the relationships between memory ability and metamemory, especially in the context of AD pathology, providing a strong foundation for future investigations.

Funding

This manuscript is the result of funding by the National Institutes of Health (NIH): R01‐AG074330 to A.S.B.; T32‐NS007292 and F32‐AG085890 to J.L.C. As such, it is subject to the NIH Public Access Policy. Through acceptance of this federal funding, NIH has been given a right to make this manuscript publicly available in PubMed Central upon the Official Date of Publication, as defined by NIH.

Conflicts of Interest

J.M.H. is co‐founder of and equity holder in Eikonizo Therapeutics and Sensorium Therapeutics, where he also serves as CEO. He is currently an advisor to Rocket Science Health, Human Health, Delix Therapeutics, and Psy Therapeutics. All other authors report no competing interests.

Supporting information

Data S1: Supporting Information.

HIPO-36-0-s001.docx (7.6MB, docx)

Acknowledgments

We thank Emma Carlson, Claire Ciampa, Jordyn Cowan, Charlee Gordon, Mariah Lewis, Rachel Marcus, and Teodora Markova for their assistance with data collection. We additionally acknowledge the contributions of Megan Applegate‐Kenton and Katherine O'Malley towards project administration.

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.

References

  1. Aghjayan, S. L. , Polk S. E., Ripperger H. S., et al. 2025. “Associations Between Episodic Memory and Hippocampal Volume in Late Adulthood.” Hippocampus 35: e70010. 10.1002/hipo.70010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allard, M. , Husky M., Catheline G., et al. 2014. “Mobile Technologies in the Early Detection of Cognitive Decline.” PLoS One 9: e112197. 10.1371/journal.pone.0112197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Amariglio, R. E. , Mormino E. C., Pietras A. C., et al. 2015. “Subjective Cognitive Concerns, Amyloid‐Beta, and Neurodegeneration in Clinically Normal Elderly.” Neurology 85: 56–62. 10.1212/wnl.0000000000001712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aschenbrenner, A. J. , Gordon B. A., Benzinger T. L. S., Morris J. C., and Hassenstab J. J.. 2018. “Influence of Tau PET, Amyloid PET, and Hippocampal Volume on Cognition in Alzheimer Disease.” Neurology 91: e859–e866. 10.1212/wnl.0000000000006075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Avants, B. B. , Epstein C. L., Grossman M., and Gee J. C.. 2008. “Symmetric Diffeomorphic Image Registration With Cross‐Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” Medical Image Analysis 12: 26–41. 10.1016/j.media.2007.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Baker, S. L. , Maass A., and Jagust W. J.. 2017. “Considerations and Code for Partial Volume Correcting [18F]‐AV‐1451 Tau PET Data.” Data in Brief 15: 648–657. 10.1016/j.dib.2017.10.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Beaudoin, M. , and Desrichard O.. 2011. “Are Memory Self‐Efficacy and Memory Performance Related? A Meta‐Analysis.” Psychological Bulletin 137: 211–241. 10.1037/a0022106. [DOI] [PubMed] [Google Scholar]
  8. Betthauser, T. J. , Cody K. A., Zammit M. D., et al. 2019. “In Vivo Characterization and Quantification of Neurofibrillary Tau PET Radioligand 18F‐MK‐6240 in Humans From Alzheimer Disease Dementia to Young Controls.” Journal of Nuclear Medicine 60: 93–99. 10.2967/jnumed.118.209650. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Braak, H. , and Braak E.. 1995. “Staging of Alzheimer's Disease‐Related Neurofibrillary Changes.” Neurobiology of Aging 16: 271–278. 10.1016/0197-4580(95)00021-6. [DOI] [PubMed] [Google Scholar]
  10. Braak, H. , Thal D. R., Ghebremedhin E., and Tredici K. D.. 2011. “Stages of the Pathologic Process in Alzheimer Disease: Age Categories From 1 to 100 Years.” Journal of Neuropathology & Experimental Neurology 70: 960–969. 10.1097/nen.0b013e318232a379. [DOI] [PubMed] [Google Scholar]
  11. Braak, H. , and Tredici K. D.. 2015. “The Preclinical Phase of the Pathological Process Underlying Sporadic Alzheimer's Disease.” Brain 138: 2814–2833. 10.1093/brain/awv236. [DOI] [PubMed] [Google Scholar]
  12. Buckley, R. F. , Hanseeuw B., Schultz A. P., et al. 2017. “Region‐Specific Association of Subjective Cognitive Decline With Tauopathy Independent of Global β‐Amyloid Burden.” JAMA Neurology 74: 1455–1463. 10.1001/jamaneurol.2017.2216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Buckner, R. L. , Head D., Parker J., et al. 2004. “A Unified Approach for Morphometric and Functional Data Analysis in Young, Old, and Demented Adults Using Automated Atlas‐Based Head Size Normalization: Reliability and Validation Against Manual Measurement of Total Intracranial Volume.” NeuroImage 23: 724–738. 10.1016/j.neuroimage.2004.06.018. [DOI] [PubMed] [Google Scholar]
  14. Burmester, B. , Leathem J., and Merrick P.. 2016. “Subjective Cognitive Complaints and Objective Cognitive Function in Aging: A Systematic Review and Meta‐Analysis of Recent Cross‐Sectional Findings.” Neuropsychology Review 26: 376–393. 10.1007/s11065-016-9332-2. [DOI] [PubMed] [Google Scholar]
  15. Chen, X. , Cassady K. E., Adams J. N., Harrison T. M., Baker S. L., and Jagust W. J.. 2020. “Regional Tau Effects on Prospective Cognitive Change in Cognitively Normal Older Adults.” Journal of Neuroscience 41: 366–375. 10.1523/jneurosci.2111-20.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Chen, X. , Farrell M. E., Moore W., and Park D. C.. 2019. “Actual Memory as a Mediator of the Amyloid‐Subjective Cognitive Decline Relationship.” Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 11: 151–160. 10.1016/j.dadm.2018.12.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chen, X. , Juarez A., Mason S., et al. 2025. “Longitudinal Relationships Between Aβ and Tau to Executive Function and Memory in Cognitively Normal Older Adults.” Neurobiology of Aging 145: 32–41. 10.1016/j.neurobiolaging.2024.10.004. [DOI] [PubMed] [Google Scholar]
  18. Chen, X. , Toueg T. N., Harrison T. M., Baker S. L., and Jagust W. J.. 2024. “Regional Tau Deposition Reflects Different Pathways of Subsequent Neurodegeneration and Memory Decline in Cognitively Normal Older Adults.” Annals of Neurology 95: 249–259. 10.1002/ana.26813. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Crumley, J. J. , Stetler C. A., and Horhota M.. 2014. “Examining the Relationship Between Subjective and Objective Memory Performance in Older Adults: A Meta‐Analysis.” Psychology and Aging 29: 250–263. 10.1037/a0035908. [DOI] [PubMed] [Google Scholar]
  20. Desikan, R. S. , Ségonne F., Fischl B., et al. 2006. “An Automated Labeling System for Subdividing the Human Cerebral Cortex on MRI Scans Into Gyral Based Regions of Interest.” NeuroImage 31: 968–980. 10.1016/j.neuroimage.2006.01.021. [DOI] [PubMed] [Google Scholar]
  21. Ehrenberg, A. J. , Suemoto C. K., Resende E. d. P. F., et al. 2018. “Neuropathologic Correlates of Psychiatric Symptoms in Alzheimer's Disease.” Journal of Alzheimer's Disease 66, no. 1: 115–126. 10.3233/jad-180688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Esteban, O. , Markiewicz C. J., Blair R. W., et al. 2019. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” Nature Methods 16: 111–116. 10.1038/s41592-018-0235-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fischl, B. 2012. “FreeSurfer.” NeuroImage 62: 774–781. 10.1016/j.neuroimage.2012.01.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Fonov, V. , Evans A., McKinstry R., Almli C., and Collins D.. 2009. “Unbiased Nonlinear Average Age‐Appropriate Brain Templates From Birth to Adulthood.” NeuroImage 47: S102. 10.1016/s1053-8119(09)70884-5. [DOI] [Google Scholar]
  25. Fonseca, C. S. , Baker S. L., Dobyns L., Janabi M., Jagust W. J., and Harrison T. M.. 2024. “Tau Accumulation and Atrophy Predict Amyloid Independent Cognitive Decline in Aging.” Alzheimer's & Dementia 20: 2526–2537. 10.1002/alz.13654. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gatchel, J. R. , Donovan N. J., Locascio J. J., et al. 2017. “Depressive Symptoms and Tau Accumulation in the Inferior Temporal Lobe and Entorhinal Cortex in Cognitively Normal Older Adults: A Pilot Ssudy.” Journal of Alzheimer's Disease 59: 975–985. 10.3233/jad-170001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gilewski, M. J. , Zelinski E. M., and Schaie K. W.. 1990. “The Memory Functioning Questionnaire for Assessment of Memory Complaints in Adulthood and Old Age.” Psychology and Aging 5: 482–490. 10.1037//0882-7974.5.4.482. [DOI] [PubMed] [Google Scholar]
  28. Gorgolewski, K. , Burns C. D., Madison C., et al. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5: 13. 10.3389/fninf.2011.00013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Gustavson, D. E. , Jak A. J., Elman J. A., et al. 2021. “How Well Does Subjective Cognitive Decline Correspond to Objectively Measured Cognitive Decline? Assessment of 10–12 Year Change.” Journal of Alzheimer's Disease 83: 291–304. 10.3233/jad-210123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hafkemeijer, A. , Altmann‐Schneider I., Oleksik A. M., et al. 2013. “Increased Functional Connectivity and Brain Atrophy in Elderly With Subjective Memory Complaints.” Brain Connectivity 3: 353–362. 10.1089/brain.2013.0144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Harrison, T. M. , Ward T. J., Murphy A., et al. 2023. “Optimizing Quantification of MK6240 Tau PET in Unimpaired Older Adults.” NeuroImage 265: 119761. 10.1016/j.neuroimage.2022.119761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hayes, J. M. , Tang L., Viviano R. P., van Rooden S., Ofen N., and Damoiseaux J. S.. 2017. “Subjective Memory Complaints Are Associated With Brain Activation Supporting Successful Memory Encoding.” Neurobiology of Aging 60: 71–80. 10.1016/j.neurobiolaging.2017.08.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Hertzog, C. , Dixon R. A., Schulenberg J. E., and Hultsch D. F.. 1987. “On the Differentiation of Memory Beliefs From Memory Knowledge: The Factor Structure of the Metamemory in Adulthood Scale.” Experimental Aging Research 13: 101–107. 10.1080/03610738708259308. [DOI] [PubMed] [Google Scholar]
  34. Hertzog, C. , and Pearman A.. 2013. “Memory Complaints in Adulthood and Old Age.” In The SAGE Handbook of Applied Memory, edited by Perfect T. J. and Lindsay D. S., 423–443. SAGE Publications Ltd. [Google Scholar]
  35. Hill, N. L. , Bhargava S., Bratlee‐Whitaker E., Turner J. R., Brown M. J., and Mogle J.. 2021. “Longitudinal Relationships Between Subjective Cognitive Decline and Objective Memory: Depressive Symptoms Mediate Between‐Person Associations.” Journal of Alzheimer's Disease 83: 1623–1636. 10.3233/jad-210230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hill, N. L. , Mogle J., Wion R., et al. 2016. “Subjective Cognitive Impairment and Affective Symptoms: A Systematic Review.” Gerontologist 56: e109–e127. 10.1093/geront/gnw091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Jessen, F. , Amariglio R. E., van Boxtel M., et al. 2014. “A Conceptual Framework for Research on Subjective Cognitive Decline in Preclinical Alzheimer's Disease.” Alzheimer's & Dementia 10: 844–852. 10.1016/j.jalz.2014.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. La Joie, R. , Perrotin A., Egret S., et al. 2016. “Qualitative and Quantitative Assessment of Self‐Reported Cognitive Difficulties in Nondemented Elders: Association With Medical Help Seeking, Cognitive Deficits, and β‐Amyloid Imaging.” Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 5: 23–34. 10.1016/j.dadm.2016.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lüdecke, D. , Ben‐Shachar M., Patil I., and Makowski D.. 2020. “Extracting, Computing and Exploring the Parameters of Statistical Models Using R.” Journal of Open Source Software 5: 2445. 10.21105/joss.02445. [DOI] [Google Scholar]
  40. Lüdecke, D. , Ben‐Shachar M., Patil I., Waggoner P., and Makowski D.. 2021. “Performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” Journal of Open Source Software 6: 3139. 10.21105/joss.03139. [DOI] [Google Scholar]
  41. Lüdecke, D. , Patil I., Ben‐Shachar M., Wiernik B., Waggoner P., and Makowski D.. 2021. “See: An R Package for Visualizing Statistical Models.” Journal of Open Source Software 6: 3393. 10.21105/joss.03393. [DOI] [Google Scholar]
  42. Lüdecke, D. , Waggoner P., and Makowski D.. 2019. “Insight: A Unified Interface to Access Information From Model Objects in R.” Journal of Open Source Software 4: 1412. 10.21105/joss.01412. [DOI] [Google Scholar]
  43. Maass, A. , Lockhart S. N., Harrison T. M., et al. 2018. “Entorhinal Tau Pathology, Episodic Memory Decline, and Neurodegeneration in Aging.” Journal of Neuroscience 38: 530–543. 10.1523/jneurosci.2028-17.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Makowski, D. , Ben‐Shachar M., Patil I., and Lüdecke D.. 2020. “Methods and Algorithms for Correlation Analysis in R.” Journal of Open Source Software 5: 2306. 10.21105/joss.02306. [DOI] [Google Scholar]
  45. Marchant, N. L. , Lovland L. R., Jones R., et al. 2020. “Repetitive Negative Thinking Is Associated With Amyloid, Tau, and Cognitive Decline.” Alzheimer's & Dementia 16: 1054–1064. 10.1002/alz.12116. [DOI] [PubMed] [Google Scholar]
  46. Markova, T. Z. , Fonseca C. S., Ciampa C. J., et al. 2025. “Defining the Contributions of Tau Pathology in the Amygdala to Increasing Depressive Symptoms in Aging.” Alzheimer's & Dementia 21: e70740. 10.1002/alz.70740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Meyer, T. , Miller M., Metzger R., and Borkovec T. D.. 1990. “Development and Validation of the Penn State Worry Questionnaire.” Behaviour Research and Therapy 28: 487–495. [DOI] [PubMed] [Google Scholar]
  48. Nyberg, L. , and Pudas S.. 2019. “Successful Memory Aging.” Annual Review of Psychology 70: 219–243. 10.1146/annurev-psych-010418-103052. [DOI] [PubMed] [Google Scholar]
  49. Öhman, F. , Hassenstab J., Berron D., Schöll M., and Papp K. V.. 2021. “Current Advances in Digital Cognitive Assessment for Preclinical Alzheimer's Disease.” Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 13: e12217. 10.1002/dad2.12217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ottoy, J. , Niemantsverdriet E., Verhaeghe J., et al. 2019. “Association of Short‐Term Cognitive Decline and MCI‐To‐AD Dementia Conversion With CSF, MRI, Amyloid‐ and 18F‐FDG‐PET Imaging.” NeuroImage: Clinical 22: 101771. 10.1016/j.nicl.2019.101771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Payne, B. R. , Gross A. L., Hill P. L., Parisi J. M., Rebok G. W., and Stine‐Morrow E. A. L.. 2017. “Decomposing the Relationship Between Cognitive Functioning and Self‐Referent Memory Beliefs in Older Adulthood: What's Memory Got to Do With It?” Aging, Neuropsychology, and Cognition 24: 345–362. 10.1080/13825585.2016.1218425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pearman, A. , Hertzog C., and Gerstorf D.. 2014. “Little Evidence for Links Between Memory Complaints and Memory Performance in Very Old Age: Longitudinal Analyses From the Berlin Aging Study.” Psychology and Aging 29: 828–842. 10.1037/a0037141. [DOI] [PubMed] [Google Scholar]
  53. Perrotin, A. , Joie R. L., Sayette V. d. L., et al. 2017. “Subjective Cognitive Decline in Cognitively Normal Elders From the Community or From a Memory Clinic: Differential Affective and Imaging Correlates.” Alzheimer's & Dementia 13: 550–560. 10.1016/j.jalz.2016.08.011. [DOI] [PubMed] [Google Scholar]
  54. Perrotin, A. , Mormino E. C., Madison C. M., Hayenga A. O., and Jagust W. J.. 2012. “Subjective Cognition and Amyloid Deposition Imaging: A Pittsburgh Compound B Positron Emission Tomography Study in Normal Elderly Individuals.” Archives of Neurology 69: 223–229. 10.1001/archneurol.2011.666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Pezzoli, S. , Giorgio J., Chen X., Ward T. J., Harrison T. M., and Jagust W. J.. 2025. “Cognitive Aging Outcomes Are Related to Both Tau Pathology and Maintenance of Cingulate Cortex Structure.” Alzheimer's & Dementia 21: e14515. 10.1002/alz.14515. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Pink, A. , Krell‐Roesch J., Syrjanen J. A., et al. 2023. “Interactions Between Neuropsychiatric Symptoms and Alzheimer's Disease Neuroimaging Biomarkers in Predicting Longitudinal Cognitive Decline.” Psychiatric Research and Clinical Practice 5: 4–15. 10.1176/appi.prcp.20220036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Polk, S. E. , Öhman F., Hassenstab J., et al. 2025. “A Scoping Review of Remote and Unsupervised Digital Cognitive Assessments in Preclinical Alzheimer's Disease.” Npj Digital Medicine 8: 266. 10.1038/s41746-025-01583-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Rabin, L. A. , Smart C. M., and Amariglio R. E.. 2017. “Subjective Cognitive Decline in Preclinical Alzheimer's Disease.” Annual Review of Clinical Psychology 13: 369–396. 10.1146/annurev-clinpsy-032816-045136. [DOI] [PubMed] [Google Scholar]
  59. Rabin, L. A. , Smart C. M., Crane P. K., et al. 2015. “Subjective Cognitive Decline in Older Adults: An Overview of Self‐Report Measures Used Across 19 International Research Studies.” Journal of Alzheimer's Disease 48: S63–S86. 10.3233/jad-150154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Rentz, D. M. , Rodriguez M. A. P., Amariglio R., Stern Y., Sperling R., and Ferris S.. 2013. “Promising Developments in Neuropsychological Approaches for the Detection of Preclinical Alzheimer's Disease: A Selective Review.” Alzheimer's Research & Therapy 5: 58. 10.1186/alzrt222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Reuter‐Lorenz, P. A. , and Park D. C.. 2014. “How Does It STAC Up? Revisiting the Scaffolding Theory of Aging and Cognition.” Neuropsychology Review 24: 355–370. 10.1007/s11065-014-9270-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Rousset, O. G. , Ma Y., and Evans A. C.. 1998. “Correction for Partial Volume Effects in PET: Principle and Validation.” Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine 39: 904–911. [PubMed] [Google Scholar]
  63. Rowell, S. F. , Green J. S., Teachman B. A., and Salthouse T. A.. 2016. “Age Does Not Matter: Memory Complaints Are Related to Negative Affect Throughout Adulthood.” Aging & Mental Health 20: 1255–1263. 10.1080/13607863.2015.1078284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Rugg, M. D. , and Vilberg K. L.. 2013. “Brain Networks Underlying Episodic Memory Retrieval.” Current Opinion in Neurobiology 23: 255–260. 10.1016/j.conb.2012.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Salthouse, T. 2012. “Consequences of Age‐Related Cognitive Declines.” Annual Review of Psychology 63: 201–226. 10.1146/annurev-psych-120710-100328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Salthouse, T. A. 2004. “What and When of Cognitive Aging.” Current Directions in Psychological Science 13: 140–144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Sliwinski, M. J. , Mogle J. A., Hyun J., Munoz E., Smyth J. M., and Lipton R. B.. 2018. “Reliability and Validity of Ambulatory Cognitive Assessments.” Assessment 25: 14–30. 10.1177/1073191116643164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Tang, R. , Bell T. R., Elman J. A., et al. 2025. “Subjective Memory Concern, Negative Affect, and Cortical Microstructural Integrity in Community‐Dwelling Middle‐Aged Men.” GeroScience 48: 1–14. 10.1007/s11357-025-01778-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Team, R.C . 2023. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. [Google Scholar]
  70. Tissot, C. , Therriault J., Pascoal T. A., et al. 2021. “Association Between Regional Tau Pathology and Neuropsychiatric Symptoms in Aging and Dementia due to Alzheimer's Disease.” Alzheimer's & Dementia: Translational Research & Clinical Interventions 7: e12154. 10.1002/trc2.12154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Troyer, A. K. , and Rich J. B.. 2002. “Psychometric Properties of a New Metamemory Questionnaire for Older Adults.” Journals of Gerontology Series B: Psychological Sciences and Social Sciences 57: P19–P27. 10.1093/geronb/57.1.p19. [DOI] [PubMed] [Google Scholar]
  72. Tustison, N. J. , Avants B. B., Cook P. A., et al. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29: 1310–1320. 10.1109/tmi.2010.2046908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Uquillas, F. d.'O. , Jacobs H. I. L., Schultz A. P., et al. 2020. “Functional and Aathological Correlates of Judgments of Learning in Cognitively Unimpaired Older Adults.” Cerebral Cortex 30: 1974–1983. 10.1093/cercor/bhz217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Wardenaar, K. J. , van Veen T., Giltay E. J., de Beurs E., Penninx B. W. J. H., and Zitman F. G.. 2010. “Development and Validation of a 30‐Item Short Adaptation of the Mood and Anxiety Symptoms Questionnaire (MASQ).” Psychiatry Research 179: 101–106. 10.1016/j.psychres.2009.03.005. [DOI] [PubMed] [Google Scholar]
  75. Wechsler, D. 1997. Administration and Scoring Guide, WAIS—III, Wechsler Adult Intelligence Scale—Third Edition. Psychological Corporation. [Google Scholar]
  76. Woods, S. P. , Delis D. C., Scott J. C., Kramer J. H., and Holdnack J. A.. 2006. “The California Verbal Learning Test – Second Edition: Test‐Retest Reliability, Practice Effects, and Reliable Change Indices for the Standard and Alternate Forms.” Archives of Clinical Neuropsychology 21: 413–420. 10.1016/j.acn.2006.06.002. [DOI] [PubMed] [Google Scholar]
  77. Yesavage, J. A. , Brink T. L., Rose T. L., et al. 1982. “Development and Validation of a Geriatric Depression Screening Scale: A Preliminary Report.” Journal of Psychiatric Research 17: 37–49. 10.1016/0022-3956(82)90033-4. [DOI] [PubMed] [Google Scholar]
  78. Zhang, Y. , Brady M., and Smith S.. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation‐Maximization Algorithm.” IEEE Transactions on Medical Imaging 20: 45. 10.1109/42.906424. [DOI] [PubMed] [Google Scholar]
  79. Zhuang, K. , Chen X., Cassady K. E., Baker S. L., and Jagust W. J.. 2022. “Metacognition, Cortical Thickness, and Tauopathy in Aging.” Neurobiology of Aging 118: 44–54. 10.1016/j.neurobiolaging.2022.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Zimprich, D. , and Kurtz T.. 2015. “Subjective and Objective Memory Changes in Old Age Across Five Years.” Gerontology 61: 223–231. 10.1159/000369927. [DOI] [PubMed] [Google Scholar]
  81. Zlatar, Z. Z. , Muniz M., Galasko D., and Salmon D. P.. 2018. “Subjective Cognitive Decline Correlates With Depression Symptoms and Not With Concurrent Objective Cognition in a Clinic‐Based Sample of Older Adults.” Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 73: 1198–1202. 10.1093/geronb/gbw207. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting Information.

HIPO-36-0-s001.docx (7.6MB, docx)

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.

Articles from Hippocampus are provided here courtesy of Wiley

RESOURCES