Association of CSF Biomarkers With Hippocampal-Dependent Memory in Preclinical Alzheimer Disease

Abstract

Objective

To determine whether memory tasks with demonstrated sensitivity to hippocampal function can detect variance related to preclinical Alzheimer disease (AD) biomarkers, we examined associations between performance in 3 memory tasks and CSF β-amyloid (Aβ)₄₂/Aβ₄₀ and phosopho-tau₁₈₁ (p-tau₁₈₁) in cognitively unimpaired older adults (CU).

Methods

CU enrolled in the Stanford Aging and Memory Study (n = 153; age 68.78 ± 5.81 years; 94 female) completed a lumbar puncture and memory assessments. CSF Aβ₄₂, Aβ₄₀, and p-tau₁₈₁ were measured with the automated Lumipulse G system in a single-batch analysis. Episodic memory was assayed using a standardized delayed recall composite, paired associate (word–picture) cued recall, and a mnemonic discrimination task that involves discrimination between studied “target” objects, novel “foil” objects, and perceptually similar “lure” objects. Analyses examined cross-sectional relationships among memory performance, age, and CSF measures, controlling for sex and education.

Results

Age and lower Aβ₄₂/Aβ₄₀ were independently associated with elevated p-tau₁₈₁. Age, Aβ₄₂/Aβ₄₀, and p-tau₁₈₁ were each associated with (1) poorer associative memory and (2) diminished improvement in mnemonic discrimination performance across levels of decreased task difficulty (i.e., target–lure similarity). P-tau mediated the effect of Aβ₄₂/Aβ₄₀ on memory. Relationships between CSF proteins and delayed recall were similar but nonsignificant. CSF Aβ₄₂ was not significantly associated with p-tau₁₈₁ or memory.

Conclusions

Tests designed to tax hippocampal function are sensitive to subtle individual differences in memory among CU and correlate with early AD-associated biomarker changes in CSF. These tests may offer utility for identifying CU with preclinical AD pathology.

Identifying cognitively unimpaired older adults (CU) who harbor Alzheimer disease (AD) pathology is critical for developing disease-modifying treatments, which may be most effective during the asymptomatic (preclinical) stage of the disease.¹ Decreases in CSF β-amyloid (Aβ₄₂) and increases in phospho-tau₁₈₁ (p-tau₁₈₁) may be the earliest detectable changes in the AD pathophysiologic cascade.^2,3 However, detecting cross-sectional associations between CSF and cognition using traditional standardized cognitive tests has posed a challenge.^4–8

Tests designed to tax core functions of the hippocampus and entorhinal cortex—areas of the medial temporal lobe (MTL) affected early on by tangle pathology^9,10—may be sensitive to subtle variations in memory that are associated with biomarker abnormalities, particularly elevations in CSF p-tau₁₈₁, which is known to associate with tangle pathology.¹¹ Associative memory (figure 1A) and mnemonic discrimination of studied target stimuli and perceptually similar lure stimuli¹² (figure 1B), tasks in which performance is tightly linked with hippocampal function,^13–15 show initial promise.^16–19 However, the ability of these tasks to detect CSF biomarker abnormalities in CU remains unclear.

Figure 1. Memory Paradigms and Discrimination Performance.

(A) Schematic of the associative memory paradigm, reproduced from reference 15 (creativecommons.org/licenses/by/4.0/). Participants intentionally studied word–picture pairs. At test, they viewed studied words intermixed with new words, and were asked to recall the picture associated with each word, if old. Participants responded “face” or “place” if they remembered the associated picture or picture category; “old” if they remembered the word but could not recollect the associate; “new” if they did not remember the word as studied. (B) Schematic of the mnemonic discrimination paradigm. Participants incidentally encoded objects and memory was assessed using a modified recognition memory test with perceptually similar lures, ranging from high (L1) to low similarity (L5), as well as novel (non-lure) foils. Correct responses are indicated next to each stimulus. (C) Performance on the associative memory task. Memory for studied words, irrespective of memory for the associate (old/new d′), was higher than memory for the associations (associative d′). (D, E) Mnemonic discrimination performance by target–lure similarity. Both lure/new d′ (D) and old/lure d′ (E) increased as target–lure similarity decreased. ISI = interstimulus interval.

This study leverages critical developments in CSF protein analysis, including fully automated^20,21 measurement of p-tau₁₈₁, Aβ₄₂, and Aβ₄₀^22–25 to quantify preclinical AD burden in CU. We examine associations between CSF Aβ₄₂/Aβ₄₀, p-tau₁₈₁, and memory performance on standardized memory tests and specialized hippocampal-dependent tests: associative memory and mnemonic discrimination. In an exploratory analysis, relationships between CSF proteins and MTL tau, measured by ¹⁸F-PI-2620,^26,27 were examined. We predicted that performance on specialized hippocampal-dependent tests would be associated with CSF biomarkers of AD, particularly p-tau₁₈₁.

Methods

Participants

This study includes data from 153 CU (table 1; aged 60–88 years) of an initial 212 enrolled in the Stanford Aging and Memory Study (SAMS; see figure e-1 for participant flowchart, doi:10.5061/dryad.ngf1vhhrp). SAMS is a fluid and neuroimaging biomarker study focused on neuronal and behavioral measures of the MTL.¹⁵ SAMS eligibility included normal or corrected-to-normal vision/hearing, right-handedness, native English speaking, no history of neurologic or psychiatric disease, a Clinical Dementia Rating²⁸ global score of zero, and performance within the normal range on a standardized neuropsychological test battery.¹⁵ All participants were deemed cognitively unimpaired during a clinical consensus meeting consisting of neurologists and neuropsychologists.

Table 1.

Participant Demographics and Biomarker Summary

CSF Data

Participants underwent lumbar puncture at 8 or 9 am following overnight fasting. CSF was stored in either 1.0 or 0.5 mL aliquots at −80°C. A single aliquot for each participant was used to measure Aβ₄₂, Aβ₄₀, p-tau₁₈₁, and total tau using the fully automated Lumipulse G system (Fujirebio US, Inc., Malvern, PA) in a single-batch analysis using procedures previously described²⁹ by the Stanford Alzheimer's Disease Research Center Biomarker Core. Our primary measures of amyloid and tau were the Aβ₄₂/Aβ₄₀ ratio, due to greater specificity and sensitivity for detecting AD-related amyloid pathology than Aβ₄₂ alone,^22–25 and p-tau₁₈₁, due to greater specificity for AD than total tau.³⁰ For comparison, we also report CSF Aβ₄₂ levels.

We primarily examined CSF Aβ₄₂/Aβ₄₀ and p-tau₁₈₁ as continuous variables, but also examined amyloid by binary status. We used a Gaussian mixture modeling approach (R package mclust v4.1³¹) to classify CU as Aβ+ or Aβ− based on CSF Aβ₄₂/Aβ₄₀ values (figure 2D; see figure e-2 for more detail, doi:10.5061/dryad.ngf1vhhrp). Briefly, this procedure identified a 2-distribution model as optimal, yielding an Aβ₄₂/Aβ₄₀ cutoff of 0.0752. Participants were classified as Aβ+ if they had a greater than 0.5 probability of belonging to the Aβ+ distribution (Aβ₄₂/Aβ₄₀ <0.0752) or as Aβ− if they had a greater than 0.5 probability of belonging to the Aβ− distribution (Aβ₄₂/Aβ₄₀ > 0.0752). For visualization only, we also categorized participants into tau+ and tau− groups. The top quartile of CSF p-tau₁₈₁ concentration (>42 pg/mL) was used to define tau+, as the distribution showed a continuum of values. This binary classification was used to plot performance as a function of combined Aβ/tau status (Aβ−/tau−, Aβ+/tau−, Aβ−/tau+, Aβ+/tau+), as described in the biomarker framework of AD.³²

Figure 2. CSF Protein Distributions in Cognitively Unimpaired Older Adults.

Distributions of (A) CSF β-amyloid (Aβ)₄₂, (B) Aβ₄₀, (C) phospho-tau₁₈₁ (p-tau₁₈₁), and (D) Aβ₄₂/Aβ₄₀ in cognitively unimpaired older adults. Probability density functions for the estimated Gaussian distributions for the best fit model (a 2-cluster solution; see figure e-2, doi:10.5061/dryad.ngf1vhhrp) are superimposed on the Aβ₄₂/Aβ₄₀ distribution in (D); the turquoise curve represents the Aβ distribution and the coral curve represents the Aβ+ distribution.

Tau PET Data

An exploratory analysis of the relationship between CSF proteins and MTL tau, measured by ¹⁸F-PI-2620 PET, was conducted in 32 participants who had both CSF and tau PET data available (table 1). The data collection and image processing procedures, along with the exploratory outcomes, are reported in the supplement (method e-1; figure e-3, doi:10.5061/dryad.ngf1vhhrp).

Episodic Memory Measures

Standardized Neuropsychological Delayed Recall

The composite delayed recall score reflected delayed recall performance across (1) the logical memory subtest of the Wechsler Memory Scale, (2) the Hopkins Verbal Learning Test–Revised, and (3) the Brief Visuospatial Memory Test–Revised. Composite scores were computed by first z-scoring individual subtest scores using the full SAMS sample as reference and then averaging. Data were available from all 153 participants.

Associative Memory

The associative memory task (figure 1A) was administered concurrent with fMRI as previously described.¹⁵ Briefly, this task assessed memory for word–picture pairs comprising concrete nouns (e.g., “banana,” “violin”) paired with pictures of famous faces (e.g., “Queen Elizabeth,” “Ronald Reagan”) or well-known places (e.g., “Golden Gate Bridge,” “Niagara Falls”). The task consisted of 5 alternating study and test blocks. Each study block included 12 word–face and 12 word–place pairs; participants were instructed to form a link between the word and picture presented. In each test block, participants saw a mix of 24 studied words and 6 novel (foil) words. Memory was assessed using an associative cued-recall test accompanied by a button response: participants selected “face” or “place” if they remembered the word and could recall the associated picture or picture category; they selected “old” if they remembered the word but could not recall the associated picture or picture category; they selected “new” if they did not remember studying the word.

Associative memory performance was estimated using a sensitivity index, associative d′, where hits were defined as correct associative category responses to studied words and false alarms were defined as incorrect category responses to new words. Thus, associative d′ = Z(“correct associate category”|old) − Z(“associate category”|new). To assess basic task comprehension and the ability to make discriminations between studied and novel words, we also calculated an old/new sensitivity index. Here, hits were defined as correct old responses to studied words, irrespective of successful memory for the associate, and false alarms rate was defined as any incorrect old response to novel words. Thus, old/new d ′= Z(“old” + “face” + “place”|old) − Z(“old” + “face” + “place”|new). Analyses included data from 128 participants (table 1; figure e-1, doi:10.5061/dryad.ngf1vhhrp).

Mnemonic Discrimination

The mnemonic discrimination task (figure 1B) was administered using previously described measures and instructions¹² (task and stimuli are available at github.com/celstark/MST). During an incidental encoding phase, participants made indoor/outdoor judgments for 128 pictures of everyday objects. Participants then performed a surprise memory test, in which half of the studied objects (64) were intermixed with 64 perceptually similar lure objects and 64 novel (dissimilar) objects. Participants were to respond “old” if they remembered the object as having been studied, “similar” if they remembered the object as similar, but not identical, to a studied object, or “new” if they remembered the object as not having been studied. Trials with a biologically implausible reaction time (<400 ms; M 1.68 trials/participant; SD 3.36) and trials in which participants did not respond (M 10.50 trials/participant; SD 9.47) were excluded from analysis.

Of particular interest in this task is the ability to correctly identify lures as “similar,” avoiding the tendency to label lures as “old”; this ability is thought to be hippocampal-dependent. Due to the 3-response task design, there are 2 measures of memory sensitivity (d′) that can been calculated to quantify lure discrimination ability: (1) lure/new d′—the ability to correctly classify perceptually similar lures and differentiate them from novel objects, as Z(“similar”|lure) − Z(“similar”|novel foil); and (2) old/lure d′—the ability to correctly endorse studied objects and avoid the propensity to incorrectly endorse lures as old, as Z(“old”|target) − Z(“old”|lure). Although related (r = 0.53), the former measure may be particularly sensitive to hippocampal function.¹⁴

The 64 lures systematically varied in perceptual similarity to the studied targets (figure 1B), ranging from level 1 (high perceptual similarity; most difficult) to 5 (low perceptual similarity; least difficult). For each of the 5 similarity levels, 13 lures were presented, except for level 1, in which 12 lures were presented. Thus, each lure discrimination measure was computed overall, as described above, as well as at each level of target–lure similarity (for example, Z[“similar”|lure bin 1] − Z[“similar”|novel foil]; Z[“old”|target] − Z[“old”|lure bin 1]). In addition to these lure discrimination measures of primary interest, we also computed old/new d′—the ability to differentiate between studied objects and novel objects, which is not selectively dependent on hippocampal function³³—as Z(“old”|target) − Z(“old”|novel foil). Analyses included data from 133 participants (table 1; figure e-1, doi:10.5061/dryad.ngf1vhhrp).

Statistical Analysis

Statistical analyses were conducted in R version 3.3.1. Multiple linear regression was used to examine the relationship between CSF proteins and memory. Prior to analysis, all continuous predictors were z scored across participants; standardized coefficients are reported. All models included age, sex, and years of education as nuisance regressors. Linear mixed-effects models were used to examine the relationship between CSF proteins and mnemonic discrimination as a function of target–lure similarity (5 levels, treated as ordinal variable and centered), with the inclusion of (1) an interaction term of CSF protein by similarity, (2) interaction terms for age, sex, and education by similarity, and (3) a random intercept and slope for each participant. To visualize interactions, we extracted the slope across lure bins for each participant and plotted against CSF proteins.

To mitigate the effect of influential data points, such as individuals with high p-tau₁₈₁, on the outcomes, we used bootstrap resampling with 5,000 iterations of data sampled with replacement to determine effect significance. Thus, for all analyses relating continuous CSF proteins to memory measures, we report 95% confidence intervals (CIs), and consider effects significant only if 0 does not fall within the 95% CI of the bootstrapped estimate of the effect. For mediation analyses, the coefficient of the indirect path was calculated as the product of direct effects a × b, and considered significant if 0 does not fall within the 95% CI of the bootstrapped estimate. All findings were replicated when log-transforming CSF p-tau₁₈₁ values as an alternative approach to mitigating the influence of extreme values (data not shown).

Standard Protocol Approvals, Registrations, and Patient Consents

All participants provided informed consent in accordance with a protocol approved by the Stanford institutional review board.

Data Availability

Anonymized data will be made available to any qualified investigator upon request.

Results

Sample Characteristics

Data subsamples did not differ from the full CSF sample with respect to demographics or CSF values (all p > 0.260; table 1). CSF Aβ₄₂, Aβ₄₀, and p-tau₁₈₁ distributions are plotted in figure 2, A–C. Gaussian mixture models fit to the Aβ₄₂/Aβ₄₀ ratio (figure 2D) identified a 2-distribution, equal variance model as optimal; the resulting Aβ₄₂/Aβ₄₀ cutoff was 0.0752 (figure e-2, doi:10.5061/dryad.ngf1vhhrp). This resulted in 40 CU classified as Aβ+ (M_age 70.45; SD 6.25) and 113 as Aβ− (M_age 68.19; SD 5.56). The Aβ+ group was significantly older than the Aβ− group (t[62.16] = 2.02, p = 0.048); groups did not differ in years of education (p = 0.461), sex (p = 0.727), or Mini-Mental State Examination score (p = 0.241).

CSF Protein Characteristics

Age was associated with lower CSF Aβ₄₂/Aβ₄₀ (β = −0.233, p = 0.004; figure 3A) and higher p-tau₁₈₁ (β = 0.342, p = 10⁻⁵; figure 3B), but not Aβ₄₂ (β = −0.029, p = 0.727; figure e-4A, doi:10.5061/dryad.ngf1vhhrp). Including age as a covariate, p-tau₁₈₁ was marginally greater in women (β = −0.302, p = 0.059), whereas CSF Aβ₄₂/Aβ₄₀ (β = 0.153, p = 0.353) and Aβ₄₂ (β = −0.253, p = 0.133) did not vary by sex.

Figure 3. CSF Protein Sample Characteristics.

Data are plotted for β-amyloid (Aβ)+ (coral) and Aβ− (turquoise) participants. (A) Age is associated with lower CSF Aβ₄₂/Aβ₄₀ and (B) higher phospho-tau (p-tau). (C) CSF Aβ₄₂/Aβ₄₀ is associated with p-tau. Plots show linear model predictions (black line) and 95% confidence intervals (shaded area).

CSF Aβ₄₂ and Aβ₄₀ were correlated (r = 0.64, p < 10⁻¹⁶; figure e-4B, doi:10.5061/dryad.ngf1vhhrp). A lower Aβ₄₂/Aβ₄₀ ratio was associated with elevated p-tau₁₈₁ (β = −0.494, p < 10⁻¹¹; figure 3C), but did not account for the effect of age on p-tau₁₈₁, which remained significant (β = 0.227, p = 0.001). Similarly, the Aβ+ group had elevated p-tau₁₈₁ relative to the Aβ− group (β = −1.146, p < 10⁻¹²). In contrast, CSF Aβ₄₂ was not significantly related to p-tau₁₈₁ (β = 0.027, p = 0.732; figure e-4C, doi:10.5061/dryad.ngf1vhhrp).

Standardized Neuropsychological Delayed Recall

Delayed recall composite score declined with age (table 2 and figure 4A), was lower in men (β = −0.447, p = 0.004), and was positively associated with education (β = 0.246, p = 0.001). Including these variables as covariates, delayed recall score did not vary as a function of CSF Aβ₄₂/Aβ₄₀ (table 2 and figure 4B) or amyloid status (β = −0.229, p = 0.182). An association with p-tau₁₈₁ was observed, but the bootstrapped effect was nonsignificant (table 2 and figure 4C). CSF Aβ₄₂ was not associated with delayed recall score (β = 0.103, p = 0.172). Similar outcomes were observed when analyses were restricted to individual delayed recall tests (table e-1, doi:10.5061/dryad.ngf1vhhrp).

Table 2.

Summary of Model Results Across Primary Episodic Memory Measures

Figure 4. Effects of Age, Amyloid, and Phospho-Tau (P-Tau) on Hippocampal-Dependent Memory.

Data are plotted for β-amyloid (Aβ)+ (coral) and Aβ− (turquoise) participants. (A) Delayed recall declined with age, but did not significantly vary with (B) Aβ₄₂/Aβ₄₀ or (C) p-tau₁₈₁ (bootstrapped effect nonsignificant). Associative d′ declined with (D) age, (E) lower Aβ₄₂/Aβ₄₀, and (F) p-tau₁₈₁. (G–I) Linear mixed effects models assessed the relationship between target–lure similarity and mnemonic discrimination performance. Each participant's slope (adjusted for sex and education), plotted on the y-axis, reflects the magnitude of the increase in performance as target–lure similarity moved from high to low. (G) The relationship between similarity and performance did not vary with age (bootstrapped effect nonsignificant), whereas (H) lower Aβ₄₂/Aβ₄₀ and (I) greater p-tau₁₈₁ are each associated with a diminished performance improvement as similarity decreased. Plots show linear model predictions (black line) and 95% confidence intervals (shaded area).

Associative Memory

The primary measure of interest from the associative memory task is associative d′—the ability to remember the category of the image initially paired with the cue word (figure 1C; see figure e-5 for results as a function stimulus category—i.e., face associations and place associations, doi:10.5061/dryad.ngf1vhhrp). Associative d′ declined with age (table 2 and figure 4D), but did not vary by sex or education (all p > 0.175). Including these variables as covariates, we found lower levels of CSF Aβ₄₂/Aβ₄₀ (table 2 and figure 4E) and Aβ+ status (β = −0.537, p = 0.007) were associated with poorer associative d′, whereas Aβ₄₂ was not (β = 0.049, p = 0.570). Similarly, p-tau₁₈₁ was negatively related to associative d′ and the bootstrapped effect was significant (table 2 and figure 4F). When CSF Aβ₄₂/Aβ₄₀ and p-tau₁₈₁ were combined in the same model, p-tau₁₈₁ remained a significant predictor of associative d′, while age and amyloid were no longer significant (table 2). A mediation analysis indicated that p-tau₁₈₁ mediated the relationship between Aβ₄₂/Aβ₄₀ and associative d′ (indirect effect: β = 0.165, CI = 0.019–0.323). To visualize this pattern another way, we plot performance as a function of binary amyloid (A)/tau (T) status (where T+ is the top 25% of the distribution; figure 5B), which demonstrates that pronounced deficits in performance are observed primarily in the A+/T+ group.

Figure 5. Memory Performance as a Function of Combined Amyloid (A) and Tau (T) Status.

Cognitively unimpaired individuals (CU) in the upper quartile of the phospho-tau₁₈₁ (p-tau₁₈₁) distribution (≥42 pg/mL; n = 38; 27 F) were classified as p-tau+, and the remainder as p-tau− (n = 125; 67 F), yielding 4 groups: A−T− (n = 98), A−T+ (N = 15), A+T− (n = 17), A + T+ (n = 23). Qualitatively, (A) while delayed recall performance was similar across groups, (B) associative d′, (C) lure/new d′, and (D) old/lure d′ revealed performance decrements predominantly in the A+/T+ group. Colors in C and D reflect target–lure similarity levels ranging from highest perceptual similarity (red) to lowest perceptual similarity (magenta).

The same pattern was observed with respect to old/new d′—the ability to discriminate studied from novel words irrespective of associative recall accuracy (figure 1C), which was highly correlated with associative d′ (r = 0.86). Performance declined with age (β = −0.262, p = 0.003, CI −0.45 to −0.09), with lower Aβ₄₂/Aβ₄₀ (β = 0.176, p = 0.049, CI 0.01–0.36) and Aβ+ status (β = −0.402, p = 0.044), but not Aβ₄₂ (β = 0.058, p = 0.510). An association with p-tau₁₈₁ was observed, and the bootstrapped effect was significant (β = −0.214, p = 0.020, CI −0.37 to −0.01).

Mnemonic Discrimination

Participants were able to successfully identify studied items as “old” (M 0.85, SD 0.10) and novel foils as “new” (M 0.83, SD 0.11). In contrast, the ability to identify lures as “similar,” avoiding the propensity to call lures “old,” varied systematically as a function of target–lure similarity, as reflected in the lure/new d′ and old/lure d′ scores (figure 1, D and E). Specifically, the probability of incorrectly calling a lure “old” (i.e., a false alarm) decreased as lures went from high (M 0.76, SD 0.16) to low similarity (M 0.35, SD 0.20). Likewise, the probability of correctly calling a lure “similar” was least likely when similarity was high (M 0.18, SD 0.14) and systematically improved as similarity decreased (M 0.49, SD 0.25).

We modeled each d′ measure in a linear mixed model context to determine whether there was a linear relationship between lure similarity and performance and whether this relationship varied as a function of demographics and CSF variables. Target–lure similarity significantly affected performance (lure/new d′: β = 0.304, p < 10⁻¹⁶; old/lure d′: β = 0.435, p < 10⁻¹⁶), suggesting that each similarity bin was associated with a d′ increase of 0.30–0.43 (in z-score units) across the entire group (figure 1, D and E). We also observed an education × similarity interaction (lure/new d′: β = 0.056, p = 0.001; old/lure d′: β = 0.081, p < 10⁻⁵), such that as target–lure similarity decreased, fewer years of education were associated with a smaller enhancement in performance. An age × similarity interaction was also observed, but the bootstrapped effect was not significant for lure/new d′ (table 2 and figure 4G) or old/lure d′ (β = −0.044, p = 0.018, CI −0.11 to 0.02).

Turning first to lure/new d′, models including interactions of age, sex, and education with similarity revealed significant Aβ₄₂/Aβ₄₀ × similarity (table 2 and figure 4H), amyloid status × similarity (β = −0.083, p = 0.032), and p-tau₁₈₁ × similarity (table 2 and figure 4I) interactions. When combined in a single model, only the p-tau₁₈₁ × similarity interaction remained significant (table 2). A mediation analysis indicated that the p-tau₁₈₁ × similarity effect mediated the Aβ₄₂/Aβ₄₀ × similarity effect on lure/new d′ (indirect effect: β = 0.022, CI 0.004–0.043). To visualize this relationship, we plot performance as a function of similarity bin, with participants grouped based on binary amyloid/tau status (figure 5C). Examining the pattern of performance across similarity levels, these interactions reflect that the A+/T+ group shows the smallest increase in performance as target–lure similarity decreases, whereas the A−/T− group shows a clear increase in performance.

While a qualitatively similar pattern of results was observed for old/lure d′ (figure 5D), only the p-tau₁₈₁ × similarity effect was significant (β = −0.046, p = 0.017, CI −0.08 to −0.01), whereas nonsignificant interactions of Aβ₄₂/Aβ₄₀ (β = 0.032, p = 0.086, CI −0.002 to 0.066) and amyloid status (β = −0.061, p = 0.157) with similarity were observed. Across discrimination measures, Aβ₄₂ × similarity effects were nonsignificant (lure/new d′: β = 0.027, p = 0.107; old/lure d′: β = 0.032, p = 0.091).

While the preceding primary analyses considered how performance changes as a function of target–lure similarity (i.e., bins 1 through 5; see Statistical Analyses), similar results were observed when performance was analyzed as a difference in performance between low similarity (bins 3–5) and high similarity (bins 1–2) discrimination (see figure e-6, doi:10.5061/dryad.ngf1vhhrp). In contrast, when lure/new d′ or old/lure d′ were averaged across all target–lure similarity bins, we did not observe a significant effect of CSF Aβ₄₂/Aβ₄₀ (lure/new d′: β = −0.019, p = 0.829; old/lure d′: β = −0.038, p = 0.658) or p-tau₁₈₁ (lure/new d′: β = −0.030, p = 0.744; old/lure d′: β = −0.009, p = 0.919) on performance. Similarly, performance at any single level of target–lure similarity was not significantly associated with CSF Aβ₄₂/Aβ₄₀ or p-tau₁₈₁ (all p > 0.089). Finally, the ability to discriminate between studied and novel objects—old/new d′—did not significantly vary as a function of Aβ₄₂/Aβ₄₀ (β = 0.072, p = 0.384), Aβ status (β = −0.267, p = 0.155), or p-tau₁₈₁ (β = −0.134, p = 0.118). CSF Aβ₄₂ did not exhibit significant associations with any of these measures (all p > 0.557).

Behavioral Predictors of Preclinical AD Pathology

Given that qualitatively similar relationships were observed between memory performance and CSF biomarkers across multiple memory measures, we next assessed which measures might be most sensitive to individual differences in CSF Aβ₄₂/Aβ₄₀ and p-tau₁₈₁ using stepwise regression. The set of possible predictors entered into the model included age, sex, education, delayed recall composite score, associative d′, lure/new d′ similarity slope, and old/lure d′ similarity slope, with CSF variables as the outcome variables. The final model retained only significant predictors (at p < 0.05). For Aβ₄₂/Aβ₄₀, the final model (R²_adj = 0.101, F_3,112 = 7.28, p = 0.001) included age (β = −0.225, p = 0.018, CI −0.411 to −0.039) and associative d′ (β = 0.200, p = 0.035, CI 0.014–0.385). For p-tau₁₈₁, the final model (R²_adj = 0.228, F_3,112 = 12.05, p < 0.001) included age (β = 0.200, p = 0.035, CI 0.025–0.384), associative d′ (β = −0.250, p = 0.006, CI −0.426 to −0.074), and lure/new d′ similarity slope (β = −0.224, p = 0.015, CI −0.403 to −0.044). Thus, associative memory and mnemonic discrimination are stronger predictors, relative to delayed recall, of variance in CSF biomarkers in CU. Moreover, these tasks are not redundant, but explain unique variance in p-tau₁₈₁.

Discussion

This study tested the hypothesis that in a CU population, memory assays designed to tax hippocampal function are sensitive to variations in performance associated with underlying preclinical AD pathology. In the present cohort, over a quarter of individuals were identified as Aβ+ based on CSF Aβ₄₂/Aβ₄₀ distribution. We observed significant associations between CSF Aβ₄₂/Aβ₄₀, p-tau₁₈₁, and 2 specialized assays of hippocampal-dependent memory, where effects of Aβ₄₂/Aβ₄₀ were mediated by associated increases in p-tau₁₈₁. Although relationships were qualitatively similar, a standardized clinical memory measure—delayed recall composite score—did not exhibit significant associations with CSF proteins. Together, these results suggest that assays that are designed to incisively tax hippocampal function have promise for detecting variance in memory related to the presence of preclinical AD pathology in CU.

These findings have relevance for efforts to identify specialized cognitive screening tools for detecting biomarker positivity in CU, which are gaining increasing interest and popularity^18,34–36 due to limitations of standardized neuropsychological assays for detecting subtle variations in cognition among CU, especially when examining cross-sectional associations. Our results indicate that both associative cued recall and mnemonic discrimination of perceptually similar objects outperform standardized delayed recall tests with respect to detecting variance related to CSF biomarkers of preclinical AD in CU. Thus, although all 3 memory tests explored here are supported by the hippocampus and surrounding medial temporal lobe structures, tasks designed to tax hippocampal computations (e.g., pattern separation, pattern completion) may offer enhanced sensitivity to detect initial changes in performance related to AD pathology. Notably, we found that associative memory and mnemonic discrimination measures explained unique variance in p-tau₁₈₁, suggesting that these tasks may be used in combination to improve detection of preclinical AD pathology in older adults.

Critically, however, such relationships within the mnemonic discrimination task were observed only as a function of change in performance (i.e., slope) across levels of target–lure similarity: whereas all participants performed poorly on the most difficult discriminations (i.e., high lure–target similarity), only biomarker-positive individuals failed to systematically improve as discriminations became easier (i.e., lower lure–target similarity; figure 5). The sensitivity of an individual's slope across similarity bins to variance in AD biomarkers replicates recent work using a spatial mnemonic discrimination task and amyloid PET to measure preclinical AD burden.¹⁹ Notably, this pattern also parallels boundary conditions of the ability of the hippocampus to differentiate similar inputs: at extremely high levels of perceptual overlap, even a functional hippocampus will often fail to distinguish between events³⁷; this level therefore lacks utility for measuring differences across CU. As overlap decreases across lure bins, but events nevertheless share overlapping features, performance improves in a linear fashion, reflecting hippocampal-dependent computations supporting performance. The magnitude of improvement across similarity levels, or lack thereof, may provide an index of hippocampal functional integrity. This pattern highlights important boundary conditions regarding the use of mnemonic discrimination tasks for detecting variance related to AD biomarkers in CU, suggesting it may be optimal to measure change in performance across successive levels of difficulty.

More broadly, this pattern is consistent with current hypotheses that the sensitivity of these tasks to biomarker levels in CU is related to links between performance and functional integrity of the hippocampus–entorhinal circuit, areas that are particularly vulnerable to early tangle pathology in CU. For example, we previously demonstrated in the SAMS cohort that the magnitude of hippocampal activity was tightly coupled with the likelihood of accurate associative cued recall on individual trials, and predicted associative d′ across individuals.¹⁵ Similarly, prior work indicates that discrimination of perceptually similar lures from studied objects engages the hippocampus and anterolateral entorhinal cortex, and that functional imbalances of this circuit are associated with worse performance.^13,14 Thus, performance may also be sensitive to alterations in hippocampal–entorhinal functional integrity, such as those arising due to tangle pathology. Consistent with this possibility, prior work in CU has observed associations between MTL tau, altered activity,¹⁸ and functional connectivity^38,39 in these areas and memory performance. The present observation that CSF p-tau₁₈₁ was more proximal to behavior, mediating relationships between Aβ₄₂/Aβ₄₀ and performance, is compatible with these hypotheses.

CSF p-tau₁₈₁ is an indirect measure of tangle pathology, and longitudinal data indicate that CSF p-tau₁₈₁ becomes abnormal relatively early in the disease course, years prior to significant regional uptake using tau PET imaging,^3,40 which provides a measure of focal tangle accumulation.⁴¹ The present results therefore build on findings from Aβ-PET and tau-PET imaging suggesting sensitivity of associative cued recall tasks^16,17 and mnemonic discrimination tasks^18,19,36 to preclinical AD pathology, and provide novel evidence for such relationships using CSF to measure biomarker abnormality in a large sample of CU. Moreover, by measuring amyloid and tau simultaneously, they also provide insights into how these 2 proteins relate to one another and behavior. While the present data are cross-sectional, they suggest that amyloid-dependent increases in p-tau₁₈₁ are necessary to observe decrements in memory performance using the present measures (i.e., they are not observed in Aβ+ alone or T+ alone, and overt tau elevations are not present in the Aβ− group). This observation is consistent with prior work highlighting a correlation between amyloid plaque and neurofibrillary tangle deposition, as well as work showing that tangles are a closer proxy of cognitive decline and clinical status compared to amyloid plaques.^42–44

Beyond the adoption of specialized tasks, the ability to detect meaningful variability in CSF protein levels within CU may be particularly affected by methodologic precision, given the more limited range in analyte values. We employed fully automated CSF analysis, which reduces experimenter-introduced noise and intralaboratory and interlaboratory variability⁴⁵ in the data through incorporation of standardized reference material.⁴⁶ We also normalized Aβ₄₂ by Aβ₄₀, which improves sensitivity and specificity for detecting Aβ burden related to AD^22–25 by adjusting for individual differences in Aβ production. Notably, the Aβ₄₂/Aβ₄₀ ratio provided a basis for establishing Aβ positivity within CU in the absence of Aβ-PET or a patient comparison group, yielding a cutoff (<0.0752) that corresponds remarkably well with that from an independent CU cohort (<0.075),⁴⁷ despite the use of a different analysis platform (Elecsys). Furthermore, the ratio enabled detection of relationships between amyloid and both p-tau₁₈₁ and memory, neither of which was achieved using CSF Aβ₄₂ alone. These results demonstrate the value of Aβ₄₀ measurement and suggest it may be particularly critical for detecting initial changes in Aβ in CU.

Importantly, while the present results provide evidence for significant relationships between CSF proteins and specialized tests of hippocampal-dependent memory, we observed qualitatively similar, though nonsignificant, relationships with standardized clinical memory tests. Future work is needed to further define the task characteristics that optimize sensitivity to preclinical AD pathology in CU. Moreover, although these tasks are readily implemented in laboratory contexts, there may be challenges for integration into clinical contexts in their current form. Nevertheless, the present findings add to a growing body of evidence encouraging further exploration of assays that tax hippocampal function for early detection of biomarker positivity in cognitively unimpaired older adults.

Glossary

Aβ

β-amyloid

AD

Alzheimer disease

CI

confidence interval

CU

cognitively unimpaired older adults

MTL

medial temporal lobe

p-tau181

phospho-tau181

SAMS

Stanford Aging and Memory Study

Appendix. Authors

Study Funding

Supported by the National Institute on Aging (R01AG048076, R21AG058111, R21AG058859, and P30AG066515), Stanford Wu Tsai Neurosciences Institute, and Stanford Center for Precision Health and Integrated Diagnostics (PHIND).

Disclosure

A.N. Trelle, V.A. Carr, E.N. Wilson, M.S. Swarovski, M.P. Hunt, T.N. Toueg, T.T. Tran, D. Channappa, N.K. Corso, M.K. Thieu, M. Jayakumar, A. Nadiadwala, W. Guo, N.J. Tanner, J.D. Bernstein, C.P. Litovsky, S.A. Guerin, A.M. Khazenzon, M.B. Harrison, B.K. Rutt, G.K. Deutsch, F.T. Chin, G.A. Davidzon, J.N. Hall, S.J. Sha, C.A. Fredericks, and K.I. Andreasson report no disclosures relevant to the manuscript. G.A. Kerchner is a full-time employee and shareholder of F. Hoffmann-La Roche, Ltd. A.D. Wagner reports no disclosures relevant to the manuscript. E.C. Mormino has served as a paid consultant to F. Hoffmann-La Roche and Alector. Go to Neurology.org/Nhttps://n.neurology.org/lookup/doi/10.1212/WNL.0000000000011477 for full disclosures.