Harvard Aging Brain Study Research Articles

Latent change-on-change between amyloid accumulation and cognitive decline.

While the influence of cross-sectional β-amyloid (Aβ) on longitudinal changes in cognition is well established, longitudinal change-on-change between Aβ and cognition is less explored. A series of bivariate latent change score models (LCSM) examined the relationship between changes in 11C-Pittsburgh Compound-B (PiB) positron emission tomography (PET) and the Preclinical Alzheimer's Cognitive Composite-5 (PACC-5) while adjusting for covariates, including cross-sectional medial temporal lobe (MTL) tau-PET burden. We selected 352 clinically normal older participants with up to 9 years of PiB-PET and PACC-5 data from the Harvard Aging Brain Study (HABS). Aβ accumulation was associated with subsequent cognitive decline beyond the effects of cross-sectional Aβ burden. Within this model including covariates such as age, sex, and apolipoprotein ε4 (APOEε4) status, we found no evidence supporting previously published associations between cross-sectional tau-PET and cognitive intercept/slope. Short-term Aβ changes are significantly associated with cognitive decline in clinically normal older adults and may eclipse the effect of cross-sectional Aβ and MTL tau. Aβ accumulation is associated with subsequent cognitive decline. High Aβ burden is not the sole metric signaling impending cognitive decline. Contrary to prior work, MTL tau-PET and cognition were not associated in our models. Models of bivariate latent Aβ and cognitive change may eclipse theeffects of MTL tau.

18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer’s disease clinical spectrum

Abstract Background With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC). Materials and Methods A 18F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (n = 129), and Harvard Aging Brain Study (HABS) NC (n = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and 18F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, 18F-FDG-PET SUVR, and amyloid PET Centiloids. Results DL output remained independently prognostic among other factors in all three datasets (P < .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [−0.005 to 0.044], and HABS-NC sets (0.059 [−0.003 to 0.126]). DL output showed independent (P = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [<0.001-0.036]) over clinical information, 18F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants. Conclusion The 18F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from 18F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.

Deep Learning-Based Amyloid PET Harmonization to Predict Cognitive Decline in Non-Demented Elderly

Abstract Background The robustness of conventional amyloid PET harmonization across tracers has been questioned. Purpose To evaluate deep learning-based harmonization of amyloid PET in predicting conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and MCI to Alzheimer’s disease (AD). Methods We developed an amyloid PET-based deep-learning model to classify participants with a clinical diagnosis of AD-dementia vs. CU across different tracers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Japanese ADNI, and Australian Imaging, Biomarker, and Lifestyle cohorts (n = 1,050). The model output (DL-ADprob), with other prognostic factors, was evaluated for predicting cognitive decline in ADNI-MCI (n = 451) and Harvard Aging Brain Study (HABS)-CU (n = 271) participants using Cox regression and area under time-dependent receiver operating characteristics curve (tdAUC) at 4-year follow-up. Subgroup analyses were performed in the ADNI-MCI group for conversion from amyloid-positive to AD and from amyloid negative to positive. Intraclass correlation coefficient (ICC) of DL-ADprob between tracers was calculated in the Global Alzheimer's Association Interactive Network dataset (n = 155). Results DL-ADprob was independently prognostic in both ADNI-MCI (P < 0.001) and HABS-CU (P = 0.048) sets. Adding DL-ADprob to other factors increased prognostic performances in both ADNI-MCI (tdAUC 0.758 [0.721–0.792] vs. 0.782 [0.742–0.818], tdAUC difference 0.023 [0.007–0.038]) and HABS-CU (tdAUC 0.846 [0.755–0.925] vs. 0.870 [0.773–0.943], tdAUC difference 0.022 [-0.004–0.053]). DL-ADprob was independently prognostic in amyloid-positive (P < 0.001) and amyloid-negative subgroups (P = 0.007). DL-ADprob showed incremental prognostic value in amyloid-positive (tdAUC 0.666 [0.623–0.713] vs. 0.706 [0.657–0.755], tdAUC difference 0.039 [0.016–0.064]), but not in amyloid-negative (tdAUC 0.818 [0.757–0.882] vs. 0.816 [0.751–0.880], tdAUC difference -0.002 [-0.031–0.029]) subgroup. The pairwise ICCs of DL-ADprob between Pittsburgh compound B and florbetapir, florbetaben, and flutemetamol respectively ranged from 0.913 to 0.935. Conclusion Deep learning-based harmonization of amyloid PET improves cognitive decline prediction in non-demented elderly, suggesting it could complement conventional amyloid PET measures.

Change in Depressive Symptoms and Longitudinal Regional Amyloid Accumulation in Unimpaired Older Adults

Depressive symptoms in older adults may be a harbinger of Alzheimer disease (AD), even in preclinical stages. It is unclear whether worsening depressive symptoms are manifestations of regional distributions of core AD pathology (amyloid) and whether cognitive changes affect this relationship. To evaluate whether increasing depressive symptoms are associated with amyloid accumulation in brain regions important for emotional regulation and whether those associations vary by cognitive performance. Participants from the Harvard Aging Brain Study, a longitudinal cohort study, underwent annual assessments of depressive symptoms and cognition alongside cortical amyloid positron emission tomography (PET) imaging at baseline and every 2 to 3 years thereafter (mean [SD] follow-up, 8.6 [2.2] years). Data collection was conducted from September 2010 to October 2022 in a convenience sample of community-dwelling older adults who were cognitively unimpaired with, at most, mild baseline depression. Data were analyzed from October 2022 to December 2023. Depression (Geriatric Depression Scale [GDS]-30-item), cognition (Preclinical Alzheimer Cognitive Composite-5 [PACC]), and a continuous measure of cerebral amyloid (Pittsburgh compound B [PiB] PET) examined in a priori-defined regions (medial orbitofrontal cortex [mOFC], lateral orbitofrontal cortex, middle frontal cortex [MFC], superior frontal cortex, anterior cingulate cortex, isthmus cingulate cortex [IC], posterior cingulate cortex, and amygdala). Associations between longitudinal GDS scores, regional amyloid slopes, and PACC slopes were assessed using linear mixed-effects models. In this sample of 154 individuals (94 [61%] female; mean [SD] age, 72.6 [6.4] years; mean (SD) education, 15.9 [3.1] years), increasing PiB slopes in the bilateral mOFC, IC, and MFC were associated with increasing GDS scores (mOFC: β = 11.07 [95% CI, 5.26-16.87]; t = 3.74 [SE, 2.96]; P = .004; IC: β = 12.83 [95% CI, 5.68-19.98]; t = 3.51 [SE, 3.65]; P = .004; MFC: β = 9.22 [95% CI, 2.25-16.20]; t = 2.59 [SE, 3.56]; P = .03). Even with PACC slope as an additional covariate, associations remained significant in these regions. In this cohort study of cognitively unimpaired older adults with, at most, mild baseline depressive symptoms, greater depressive symptoms over time were associated with amyloid accumulation in regions associated with emotional control. Furthermore, these associations persisted in most regions independent of cognitive changes. These results shed light on the neurobiology of depressive symptoms in older individuals and underscore the importance of monitoring for elevated mood symptoms early in AD.

Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease.

Spatial extent-based measures of how far amyloid beta (Aβ) has spread throughout the neocortex may be more sensitive than traditional Aβ-positron emission tomography (PET) measures of Aβ level for detecting early Aβ deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aβ's association with tau proliferation and cognitive decline. Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aβ level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+. EXT enabled earlier detection of Aβ deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aβ+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL. These findings indicate EXT may be more sensitive to Aβ's role in preclinical AD than level and improve targeting of individuals for AD prevention trials. Aβ spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aβ EXT improved detection of Aβ below traditional PET thresholds. Early regional Aβ deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aβ EXT than Aβ level. Neocortical tau onset aligned with reaching widespread neocortical Aβ.

Identifying longitudinal cognitive resilience from cross-sectional amyloid, tau, and neurodegeneration

BackgroundLeveraging Alzheimer’s disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.MethodsWe identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).ResultsThe favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.ConclusionThese findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.

Associations Between Self and Study Partner Report of Cognitive Decline With Regional Tau in a Multicohort Study.

Self-reported cognitive decline is an early behavioral manifestation of Alzheimer disease (AD) at the preclinical stage, often believed to precede concerns reported by a study partner. Previous work shows cross-sectional associations with β-amyloid (Aβ) status and self-reported and study partner-reported cognitive decline, but less is known about their associations with tau deposition, particularly among those with preclinical AD. This cross-sectional study included participants from the Anti-Amyloid Treatment in Asymptomatic AD/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration studies (N = 444) and the Harvard Aging Brain Study and affiliated studies (N = 231), which resulted in a cognitively unimpaired (CU) sample of individuals with both nonelevated (Aβ-) and elevated Aβ (Aβ+). All participants and study partners completed the Cognitive Function Index (CFI). Two regional tau composites were derived by averaging flortaucipir PET uptake in the medial temporal lobe (MTL) and neocortex (NEO). Global Aβ PET was measured in Centiloids (CLs) with Aβ+ >26 CL. We conducted multiple linear regression analyses to test associations between tau PET and CFI, covarying for amyloid, age, sex, education, and cohort. We also controlled for objective cognitive performance, measured using the Preclinical Alzheimer Cognitive Composite (PACC). Across 675 CU participants (age = 72.3 ± 6.6 years, female = 59%, Aβ+ = 60%), greater tau was associated with greater self-CFI (MTL: β = 0.28 [0.12, 0.44], p < 0.001, and NEO: β = 0.26 [0.09, 0.42], p = 0.002) and study partner CFI (MTL: β = 0.28 [0.14, 0.41], p < 0.001, and NEO: β = 0.31 [0.17, 0.44], p < 0.001). Significant associations between both CFI measures and MTL/NEO tau PET were driven by Aβ+. Continuous Aβ showed an independent effect on CFI in addition to MTL and NEO tau for both self-CFI and study partner CFI. Self-CFI (β = 0.01 [0.001, 0.02], p = 0.03), study partner CFI (β = 0.01 [0.003, 0.02], p = 0.01), and the PACC (β = -0.02 [-0.03, -0.01], p < 0.001) were independently associated with MTL tau, but for NEO tau, PACC (β = -0.02 [-0.03, -0.01], p < 0.001) and study partner report (β = 0.01 [0.004, 0.02], p = 0.002) were associated, but not self-CFI (β = 0.01 [-0.001, 0.02], p = 0.10). Both self-report and study partner report showed associations with tau in addition to Aβ. Additionally, self-report and study partner report were associated with tau above and beyond performance on a neuropsychological composite. Stratification analyses by Aβ status indicate that associations between self-reported and study partner-reported cognitive concerns with regional tau are driven by those at the preclinical stage of AD, suggesting that both are useful to collect on the early AD continuum.

Locus coeruleus integrity and left frontoparietal connectivity provide resilience against attentional decline in preclinical alzheimer’s disease

BackgroundAutopsy work reported that neuronal density in the locus coeruleus (LC) provides neural reserve against cognitive decline in dementia. Recent neuroimaging and pharmacological studies reported that left frontoparietal network functional connectivity (LFPN-FC) confers resilience against beta-amyloid (Aβ)-related cognitive decline in preclinical sporadic and autosomal dominant Alzheimer’s disease (AD), as well as against LC-related cognitive changes. Given that the LFPN and the LC play important roles in attention, and attention deficits have been observed early in the disease process, we examined whether LFPN-FC and LC structural health attenuate attentional decline in the context of AD pathology.Methods142 participants from the Harvard Aging Brain Study who underwent resting-state functional MRI, LC structural imaging, PiB(Aβ)-PET, and up to 5 years of cognitive follow-ups were included (mean age = 74.5 ± 9.9 years, 89 women). Cross-sectional robust linear regression associated LC integrity (measured as the average of five continuous voxels with the highest intensities in the structural LC images) or LFPN-FC with Digit Symbol Substitution Test (DSST) performance at baseline. Longitudinal robust mixed effect analyses examined associations between DSST decline and (i) two-way interactions of baseline LC integrity (or LFPN-FC) and PiB or (ii) the three-way interaction of baseline LC integrity, LFPN-FC, and PiB. Baseline age, sex, and years of education were included as covariates.ResultsAt baseline, lower LFPN-FC, but not LC integrity, was related to worse DSST performance. Longitudinally, lower baseline LC integrity was associated with a faster DSST decline, especially at PiB > 10.38 CL. Lower baseline LFPN-FC was associated with a steeper decline on the DSST but independent of PiB. At elevated PiB levels (> 46 CL), higher baseline LFPN-FC was associated with an attenuated decline on the DSST, despite the presence of lower LC integrity.ConclusionsOur findings demonstrate that the LC can provide resilience against Aβ-related attention decline. However, when Aβ accumulates and the LC’s resources may be depleted, the functioning of cortical target regions of the LC, such as the LFPN-FC, can provide additional resilience to sustain attentional performance in preclinical AD. These results provide critical insights into the neural correlates contributing to individual variability at risk versus resilience against Aβ-related cognitive decline.

Vascular contributions to cognitive decline: Beyond amyloid and tau in the Harvard Aging Brain Study

In addition to amyloid and tau pathology, elevated systemic vascular risk, white matter injury, and reduced cerebral blood flow contribute to late-life cognitive decline. Given the strong collinearity among these parameters, we proposed a framework to extract the independent latent features underlying cognitive decline using the Harvard Aging Brain Study (N = 166 cognitively unimpaired older adults at baseline). We used the following measures from the baseline visit: cortical amyloid, inferior temporal cortex tau, relative cerebral blood flow, white matter hyperintensities, peak width of skeletonized mean diffusivity, and Framingham Heart Study cardiovascular disease risk. We used exploratory factor analysis to extract orthogonal factors from these variables and their interactions. These factors were used in a regression model to explain longitudinal Preclinical Alzheimer Cognitive Composite-5 (PACC) decline (follow-up = 8.5 ±2.7 years). We next examined whether gray matter volume atrophy acts as a mediator of factors and PACC decline. Latent factors of systemic vascular risk, white matter injury, and relative cerebral blood flow independently explain cognitive decline beyond amyloid and tau. Gray matter volume atrophy mediates these associations with the strongest effect on white matter injury. These results suggest that systemic vascular risk contributes to cognitive decline beyond current markers of cerebrovascular injury, amyloid, and tau.

The structural–functional-connectivity coupling of the aging brain

Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain’s resilience against aging-related cognitive decline. This study aims to examine the relationship between structural–functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.

Plasma VEGFA and PGF impact longitudinal tau and cognition in preclinical Alzheimer's disease.

Vascular dysfunction is increasingly recognized as an important contributor to the pathogenesis of Alzheimer's disease. Alterations in vascular endothelial growth factor (VEGF) pathways have been implicated as potential mechanisms. However, the specific impact of VEGF proteins in preclinical Alzheimer's disease and their relationships with other Alzheimer's disease and vascular pathologies during this critical early period remain to be elucidated. We included 317 older adults from the Harvard Aging Brain Study, a cohort of individuals who were cognitively unimpaired at baseline and followed longitudinally for up to 12 years. Baseline VEGF family protein levels (VEGFA, VEGFC, VEGFD, PGF and FLT1) were measured in fasting plasma using high-sensitivity immunoassays. Using linear mixed effects models, we examined the interactive effects of baseline plasma VEGF proteins and amyloid PET burden (Pittsburgh Compound-B) on longitudinal cognition (Preclinical Alzheimer Cognitive Composite-5). We further investigated if effects on cognition were mediated by early neocortical tau accumulation (flortaucipir PET burden in the inferior temporal cortex) or hippocampal atrophy. Lastly, we examined the impact of adjusting for baseline cardiovascular risk score or white matter hyperintensity volume. Baseline plasma VEGFA and PGF each showed a significant interaction with amyloid burden on prospective cognitive decline. Specifically, low VEGFA and high PGF were associated with greater cognitive decline in individuals with elevated amyloid, i.e. those on the Alzheimer's disease continuum. Concordantly, low VEGFA and high PGF were associated with accelerated longitudinal tau accumulation in those with elevated amyloid. Moderated mediation analyses confirmed that accelerated tau accumulation fully mediated the effects of low VEGFA and partially mediated (31%) the effects of high PGF on faster amyloid-related cognitive decline. The effects of VEGFA and PGF on tau and cognition remained significant after adjusting for cardiovascular risk score or white matter hyperintensity volume. There were concordant but non-significant associations with longitudinal hippocampal atrophy. Together, our findings implicate low VEGFA and high PGF in accelerating early neocortical tau pathology and cognitive decline in preclinical Alzheimer's disease. Additionally, our results underscore the potential of these minimally-invasive plasma biomarkers to inform the risk of Alzheimer's disease progression in the preclinical population. Importantly, VEGFA and PGF appear to capture distinct effects from vascular risks and cerebrovascular injury. This highlights their potential as new therapeutic targets, in combination with anti-amyloid and traditional vascular risk reduction therapies, to slow the trajectory of preclinical Alzheimer's disease and delay or prevent the onset of cognitive decline.

Amyloid beta-independent sleep markers associated with early regional tau burden and cortical thinning.

Sleep is crucial for memory consolidation and the clearance of toxic proteins associated with Alzheimer's disease (AD). We examined the association between sleep characteristics and imaging biomarkers of early amyloid beta (Aβ) and tau pathology as well as neurodegeneration in brain regions known to be affected in the incipient stages of AD. Thirty-nine cognitively unimpaired (CU) participants of the Harvard Aging Brain Study underwent at-home polysomnography as well as tau positron emission tomography (flortaucipir-PET), amyloid PET (Pittsburgh compound B [PiB]-PET), and magnetic resonance imaging-derived assessment of cortical thickness (CT). Increased N1 sleep was associated with a higher tau PET signal (β=0.009, p=0.001) and lower CT in the temporal composite region of interest (β=-0.017, p=0.007). Decreased slow-wave sleep (SWS) was associated with higher tau burden in the temporal composite (β=-0.008, p=0.005) and lower CT (β=0.008, p=0.002), even after controlling for global PiB-PET. In CU older adults, lower SWS and higher N1 sleep were associated with higher tau burden and lower CT in brain regions associated with early tau deposition and vulnerable to AD-related neurodegeneration through mechanisms dissociable from amyloid deposition. We report the results of an observational study, which leveraged -a well-characterized cohort of healthy aging (Harvard Aging Brain Study) by adding in-home full polysomnograms.By adding at-home polysomnograms to this unique and deeply phenotyped cohort, we examined variations in sleep architecture that are associated with Alzheimer's disease (AD) pathologic changes.Our results confirmed the association of sleep changes with early tau and cortical neurodegenerative changes that were independent of amyloid.The results will be of importance in monitoring sleep-related variations in relation to the natural history of AD pathology and in designing sleep-focused clinical trials.

Increased intraindividual variability in reaction time performance is associated with emerging cognitive decline in cognitively unimpaired adults.

To investigate whether intraindividual variability (IIV) in reaction time (RT) over monthly administered cognitive tasks is increased in cognitively unimpaired older adults who are at risk for cognitive decline, and whether this is independent of mean RT performance. N = 109 cognitively unimpaired individuals (age 77.4 ± 5.0, 61.5% female, Mini-Mental State Examination 29.1 ± 1.3) from the Harvard Aging Brain Study completed the self-administered Computerized Cognitive Composite (C3) monthly at home for up to 1 year (12.7 ± 3.2 C3 assessments). Baseline C3 assessment coincided with routine in-clinic visits, including amyloid and tau positron emission tomography imaging and standardized cognitive testing, with cognitive testing repeated annually (1.6 ± 1.2 years follow-up). The C3 includes two simple RT tasks and two complex RT tasks. IIV estimates were derived by computing intraindividual standard deviations on residual RT scores after regressing out age and session order effects. Cross-sectional associations of IIV with cognition (global cognition, memory, executive functions [EF], processing speed) and amyloid and tau burden were examined using linear regression analyses correcting for demographics and mean RT. The association between IIV and cognitive decline was assessed using linear mixed models correcting for demographic factors, mean RT, and amyloid burden. After adjusting for mean RT, increased IIV on complex RT tasks was independently associated with worse EF performance (β = -0.10, 95% CI [-.16, -0.03], p = .004), greater inferior-temporal tau deposition (β = 0.18, 95% CI [0.02, 0.34], p = .024), and faster cognitive decline in those with elevated amyloid (β = -0.62, 95% CI [-1.18, -0.06], p = .033). Increased variability in monthly RT may reflect subtle EF deficits and provide unique information about short-term cognitive decline in preclinical Alzheimer's disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Study Partner Report of Apathy in Older Adults is Associated with AD Biomarkers: Findings from the Harvard Aging Brain Study

ObjectivesWe examined relationships between apathy (self and study-partner-reported) and markers of Alzheimer's disease (AD) in older adults. DesignThe study utilized a well-characterized sample of participants from the Harvard Aging Brain Study (HABS), a longitudinal cohort study. Participants were cognitively unimpaired without clinically significant neuropsychiatric symptoms at HABS baseline. The dependent variables, apathy evaluation scale-self (AES-S) and informant (AES-I), were administered cross-sectionally between years 6–9 and compared to the independent variables, amyloid and tau PET neuroimaging, from the same year. SettingCommunity-dwelling participants assessed at research visits in an academic medical center. ParticipantsParticipants (n = 170) completed assessments within 1.5 years of their neuroimaging visit. At the time of apathy assessment, N = 156 were cognitively unimpaired and 14 had progressed to mild cognitive impairment (n = 8) or dementia (n = 6). MeasurementsWe utilized linear regression models to assess cross-sectional associations of AES-S and AES-I with AD PET imaging measures (beta-amyloid (Pittsburgh Compound B) and tau (Flortaucipir)), covarying for age, sex, education, and the time between PET scan-apathy assessment. ResultsAES-I was significantly associated with beta-amyloid and temporal lobe tau, and the associations were retained after further adjusting for depressive symptoms. The associations between AES-S and AD biomarkers were not significant. In an exploratory subgroup analysis of cognitively unimpaired individuals with elevated Aβ, we observed an association between AES-I and inferior temporal tau. ConclusionsStudy-partner-reported, but not self-reported, apathy in older adults is associated with AD pathology, and we observed this relationship starting from the preclinical stage. Our findings highlight the importance of collateral information in capturing AD-related apathy.

Spatiotemporal Correlation between Amyloid and Tau Accumulations Underlies Cognitive Changes in Aging.

It is poorly known how Aβ and tau accumulations associate at the spatiotemporal level in the in vivo human brain to impact cognitive changes in older adults prior to AD symptoms onset. In this study, we used a graph theory-based spatiotemporal analysis to characterize the cortical patterns of Aβ and tau deposits and their relationship with cognitive changes in the Harvard Aging Brain Study (HABS) cohort. We found that the temporal accumulations of interlinked Aβ and tau pathology display distinctive spatiotemporal correlations associated with early cognitive decline. Notably, we observed that baseline Aβ deposits-Thal amyloid phase Ⅱ-related to future increase of tau deposits, Braak stages Ⅰ-Ⅳ, both displaying linkage to the decline in multi-domain cognitive scores. We also found unimodal tau-to-tau and cognitive impairment associations in broad areas of Braak stages Ⅰ-Ⅳ. The unimodal Aβ-to-Aβ progressions were not associated with cognitive changes. Our results revealed a multifaceted correlation of the spatiotemporal Aβ and tau associations with cognitive decline over time, in which tau-to-tau and tau-Aβ interactions, and not Aβ independently, might be critical contributors to clinical trajectories toward AD in older adults.

Differential A/T/N Networks of Cerebrospinal Fluid and Neuroimaging Biomarkers and their Prediction of Cognition between Self‐reported Black/African American and Non‐Hispanic White Individuals

AbstractBackgroundDifferences in absolute levels of individual biomarkers from cerebrospinal fluid (CSF), amyloid PET, and MRI imaging between self‐reported Black and White individuals were reported. How the A/T/N network of AD biomarkers is biologically connected within a racialized group and whether it predicts cognition differentially between groups remain unknown.MethodThe Study of Race to Understand Alzheimer Biomarkers(SORTOUT‐AB) has centrally re‐processed the CSF samples and imaging scans collected at four AD Research Centers/studies: Washington University, University of Pennsylvania, Emory University, and the Harvard Aging Brain Study. A total of 286 Black and 2080 White participants had CSF biomarker data, 157 Black and 936 White participants provided PET amyloid biomarker data, 322 Black and 1530 White individuals had MRI structural data, and 67 Black and 492 White participants provided PET Tau biomarker data. Spearman correlations across biomarkers and cognition/memory were estimated within each race, then compared between groups, adjusting for age, sex, APOE e4 status, dementia status, and education.ResultThe absolute correlation between CSF Aß42 and the centiloid scale of PET amyloid mean cortical SUVR was significantly larger in White(r = ‐0.45) versus Black(r = ‐0.04) participants, similar to the absolute correlations between Aß42/40 ratio and each of below: PET amyloid centiloid(White r = ‐0.61, Black r = ‐0.12), CSF Tau(White r = ‐0.43, Black r = ‐0.25), and CSF pTau181(White r = ‐0.54, Black r = ‐0.33). Aß42 alone did not correlate with Tau or pTau181 in White individuals, but positively correlated with Tau(r = 0.15) and pTau181(r = 0.16) in Black participants. Significantly larger positive correlations were observed in Black versus White individuals between CSF Aß40 and pTau181(Black r = 0.68, White r = 0.55). PET amyloid centiloid, MRI hippocampal volume, and cortical thickness all predicted cognition, with no racial differences observed. However, cognition/memory was better predicted by Aß42/40 for White verses Black participants(Black r = 0.07, White r = 0.22), Tau(Black r = ‐0.06, White r = ‐0.20), and pTau181(Black r = ‐0.02, White r = ‐0.23).ConclusionBiological associations of CSF and imaging biomarkers differed between Black and White groups, suggesting that current A/T/N research framework of AD biomarkers may not apply across racialized groups. The prediction of CSF biomarkers to cognition also differed between groups. Use of biomarkers in studying AD and designing/analyzing AD clinical trials must appropriately address racial differences.

Spatial Extent as a more sensitive amyloid biomarker for early stages of Alzheimer’s disease

AbstractBackgroundDespite considerable advances in beta amyloid (Aβ)‐PET imaging over the last decade, the standard approach of estimating the average neocortical Aβ burden remains largely unchanged. However, as research and clinical trials increasingly shift earlier in the disease process, measures of how far Aβ has spread throughout the cortex (spatial extent) may prove more sensitive than average neocortical magnitude for detecting and quantifying early Aβ deposits and their association with future tau proliferation and cognitive decline.MethodClinically‐normal individuals (n = 214) were included from the Harvard Aging Brain Study with longitudinal PIB‐PET (2‐4 scans, median = 4.7±2.7 years) and annual cognitive data (median = 5.2±2 years), as well as a subset (n = 181) with flortaucipir (FTP)‐PET. Spatial extent (EXT) was computed as the number of cortical ROIs (n = 62, Desikan atlas) above their ROI‐specific threshold for Aβ positivity. EXT was compared to a traditional mean neocortical DVR using logistic growth modeling. Receiver Operator Characteristic (ROC) curve analyses evaluated EXT’s ability to identify baseline PIB‐ individuals (&lt;1.19DVR/24CL) who progressed to PIB+ at 3‐year follow‐up. Linear Mixed Effects (LME) modeling assessed baseline EXT (or DVR) as a predictor of increasing inferior temporal tau (IT FTP SUVR) and cognitive decline on the Preclinical Alzheimer’s Cognitive Composite (PACC).ResultEXT begins rising below the neocortical DVR threshold (Figure1A), reaching a maximal growth rate of 14 ROIs per 0.1 DVR increase (∼10CL) and plateauing as full cortical EXT is achieved starting at ∼1.5DVR/68CL. A 3‐ROI EXT threshold predicts progression from PIB‐ to PIB+ in 3 years (AUC = .97, SE = .82,SP = .97), outperforming neocortical DVR (AUC = .92, SE = .65,SP = .94, Figure1B). EXT provides a stronger biomarker of Aβ change than DVR (lower coefficient of variation, Figure2) across the Aβ continuum due to its low variance, even after EXT has plateaued. Baseline EXT is also a stronger predictor of increases in IT FTP SUVR (Figure 3A, η2 EXT = .25, η2 DVR = .20) and PACC decline (Figure3B, η2 EXT = .28, η2 DVR = .22).ConclusionBy describing the spread of Aβ throughout the cortex rather than average neocortical Aβ burden, spatial extent provides a more sensitive measure of Aβ at early, preclinical stages of AD that may improve design of AD prevention trials and open new avenues for research into AD pathogenesis.

Reduced slow‐wave sleep is associated with greater tau accumulation and reduced cortical thickness in the temporal lobe

AbstractBackgroundSlow wave sleep (SWS) plays an important role in overnight memory consolidation and promotes overnight clearance of amyloid‐β and tau proteins. Reduced SWS has been associated with higher amyloid burden in CSF in preclinical stages of Alzheimer’s disease (AD). However, little is known about the relationship between SWS, tau burden and neurodegeneration in cognitively unimpaired (CU) adults. Thus, we investigated the relationships between slow‐wave sleep, regional tau PET (18F‐flortaucipir; FTP‐PET) and temporal lobe cortical thickness in cognitively unimpaired adults at risk of AD.MethodsThis study included 39 CU adults from the Harvard Aging Brain Study (mean age 74.6 years, 61.5% females) who underwent at‐home polysomnography (PSG; Compumedics Somte), tau PET (FTP‐PET, amyloid PET (PIB‐PET) and MRI (Table 1). We used separate multivariate linear regressions to assess associations between the percentage of slow‐wave sleep (SWS) and tau PET signal in the following ROIs: entorhinal cortex, inferior temporal cortex, and a temporal composite comprised of entorhinal, inferior temporal, middle temporal and fusiform cortices. Primary analyses were adjusted for age and sex. Secondary analyses also adjusted for global amyloid (PiB‐PET) burden. Lastly, we investigated the relationship between SWS and cortical thickness in the same ROIs, assessed using FreeSurfer v6.0.ResultsLower percentage of SWS was associated with greater tau burden in entorhinal (p = 0.036), inferior temporal (p = 0.011), and temporal composite ROIs (p = 0.005). Associations between reduced SWS and greater FTP‐PET signal in the inferior temporal cortex and temporal composite ROI remained after controlling for PiB‐PET (p = 0.028; p = 0.017, respectively; Table 2). Additionally, lower SWS was associated with reduced cortical thickness in the entorhinal (p = 0.031), inferior temporal (p = 0.023) and temporal composite ROIs (p = 0.002) (Table 3).ConclusionsLower SWS in CU adults was associated with higher tau burden in regions of the temporal lobe that are commonly sites of early tau accumulation. These effects were independent of amyloid burden, suggesting disrupted sleep may lead to increased tau accumulation via an amyloid‐independent pathway. Importantly, lower SWS was also associated with reduced cortical thickness in temporal regions, suggesting that disrupted sleep may be tied to early AD‐related neuronal loss in the temporal lobe.

Combined Digital Clock Drawing Test and Delayed Word Recall and their relationship with PET Amyloid Biomarker Status in Cognitively Unimpaired Older Adults

AbstractBackgroundDCTclock™ is an AI‐enabled digital cognitive assessment (DCA) that strongly discriminates between cognitively impaired and unimpaired individuals (receiver operating area under‐the‐curve AUC = 0.89). Among cognitively unimpaired individuals, DCTclock predicts greater PET Aβ burden and shows stronger discrimination (Cohen’s d = 0.76) between Aβ± individuals than the Preclinical Alzheimer’s Cognitive Composite (d = 0.30) (Rentz et al., 2021). The Digital Clock and Recall (DCR™) was recently created by adding 3‐word delayed recall to DCTclock to assess verbal memory impairment – an early hallmark of Alzheimer’s disease. We assessed whether recreating the DCR with the 3‐word delayed recall from the Mini‐Mental State Examination (MMSE) and DCTclock would improve classification of Aβ PET biomarker status among cognitively unimpaired older adults.MethodDCTclock, MMSE (including delayed recall), and Aβ PET imaging data were collected from 159 cognitively unimpaired older adults in the Harvard Aging Brain Study (age = 78.6±5.9; 98 females; MMSE = 28.9±1.4). Logistic regression classifiers were trained to classify Aβ biomarker status and were assessed with standard metrics and AUC as compared to age‐ and MMSE‐only models.ResultThe best‐performing model classified biomarker status among cognitively unimpaired individuals with an AUC of 0.76 (sensitivity = 0.65, specificity = 0.73, accuracy = 0.71)‐outperforming age‐only and total MMSE‐only models (AUCs = 0.61). Recursive feature elimination identified spatial reasoning, average speed, maximum speed on Copy Clock, oscillatory motion on Command Clock, and delayed recall score as key features. DCTclock or recreated DCR summary scores alone classified biomarker status with AUCs of 0.72 and 0.74, respectively.ConclusionThe DCR, which combines DCTclock with delayed verbal recall, may offer a means to classify Aβ biomarker status among cognitively unimpaired individuals in as little as 3 minutes. Classification models trained on a subset of DCR features performed better than all other models, including traditional neuropsychological testing. These results reinforce the potential for DCR solutions to streamline cognitive testing in the clinic and accelerate diagnosis for individuals with early‐stage Alzheimer’s and other dementias. Future work will examine the best possible combination of additional tests to further increase classification accuracy.

Differential A/T/N Networks of Cerebrospinal Fluid and Neuroimaging Biomarkers and their Prediction of Cognition between Self‐reported Black/African American and Non‐Hispanic White Individuals

AbstractBackgroundDifferences in absolute levels of individual biomarkers from cerebrospinal fluid (CSF), amyloid PET, and MRI imaging between self‐reported Black and White individuals were reported. How the A/T/N network of AD biomarkers is biologically connected within a racialized group and whether it predicts cognition differentially between groups remain unknown.MethodThe Study of Race to Understand Alzheimer Biomarkers(SORTOUT‐AB) has centrally re‐processed the CSF samples and imaging scans collected at four AD Research Centers/studies: Washington University, University of Pennsylvania, Emory University, and the Harvard Aging Brain Study. A total of 286 Black and 2080 White participants had CSF biomarker data, 157 Black and 936 White participants provided PET amyloid biomarker data, 322 Black and 1530 White individuals had MRI structural data, and 67 Black and 492 White participants provided PET Tau biomarker data. Spearman correlations across biomarkers and cognition/memory were estimated within each race, then compared between groups, adjusting for age, sex, APOE ε4 status, dementia status, and education.ResultsThe absolute correlation between CSF Aβ42 and the centiloid scale of PET amyloid mean cortical SUVR was significantly larger in White(r = ‐0.45) versus Black(r = ‐0.04) participants, similar to the absolute correlations between Aβ42/40 ratio and each of below: PET amyloid centiloid(White r = ‐0.61, Black r = ‐0.12), CSF Tau(White r = ‐0.43, Black r = ‐0.25), and CSF pTau181(White r = ‐0.54, Black r = ‐0.33). Aβ42 alone did not correlate with Tau or pTau181 in White individuals, but positively correlated with Tau(r = 0.15) and pTau181(r = 0.16) in Black participants. Significantly larger positive correlations were observed in Black versus White individuals between CSF Aβ40 and pTau181(Black r = 0.68, White r = 0.55). PET amyloid centiloid, MRI hippocampal volume, and cortical thickness all predicted cognition, with no racial differences observed. However, cognition/memory was better predicted by Aβ42/40 for White verses Black participants(Black r = 0.07, White r = 0.22), Tau(Black r = ‐0.06, White r = ‐0.20), and pTau181(Black r = ‐0.02, White r = ‐0.23).ConclusionBiological associations of CSF and imaging biomarkers differed between Black and White groups, suggesting that current A/T/N research framework of AD biomarkers may not apply across racialized groups. The prediction of CSF biomarkers to cognition also differed between groups. Use of biomarkers in studying AD and designing/analyzing AD clinical trials must appropriately address racial differences.