Individuals with preclinical Alzheimer’s disease (AD) show reduced practice effects on annually repeated neuropsychological testing, suggesting a decreased ability to learn over repeated exposures. Remote, digital testing enables the assessment of learning over more frequent time intervals, thereby facilitating a more rapid detection of those early learning deficits. We previously showed that multi-day learning on the Boston Remote Assessment for Neurocognitive Health (BRANCH) was indeed diminished in Αβ+ cognitively unimpaired (CU) older adults. Here, we further investigated the impact of tau pathology on BRANCH multi-day learning curves (MDLCs). N = 136 CU older adults (age = 73.4±7.6, 66% female, 16.6±2.4 years education) from three well-characterized cohorts completed multi-day BRANCH on their personal device. The assessment includes two associative memory tests (Face Name and Groceries Prices) and a processing speed test with an associative memory component (Digit Signs) with identical stimuli repeated for seven consecutive days. An MDLC score is computed using an area under the curve method allowing for the combination of day 1 performance with a non-linear learning trajectory over the subsequent six days. All participants had [11C]Pittsburgh compound-B and [18F]flortaucipir PET within 0.7±0.5 years of BRANCH and were classified as A ± (global amyloid burden, DVR cutoff 1.14) and T ± (inferior-temporal tau SUVr, cutoff 1.30), resulting in n = 91 A-T-, n = 29 A+T- and n = 16 A+T+. Linear regression analyses adjusting for age, sex, and education were used to examine differences in BRANCH day 1 and MDLC scores across A/T groups. No A/T group differences were detected using day 1 scores. However, MDLC scores increasingly diminished across groups, with the A+T- group performing marginally worse (ß = -0.04,95%CI[-0.08–0.01], p = 0.11) and the A+T+ group significantly worse (ß = -0.06,95%CI[-0.11–0.01], p = 0.03) than A-/T- (Figure 1). A+ status regardless of T-status was associated with diminished Digit Signs MDLCs, whereas being T+ drove worse performance on Face Name and Groceries Prices MDLCs (Table 1). Subtle differences in learning among CU older adults with different A/T biomarker profiles are observable using MDLCs. These findings further support the notion that a multi-day learning paradigm can provide unique information about cognition that is not captured using a single time-point assessment and is particularly relevant in preclinical AD.

Remote, digital cognitive testing on an individual’s own device provides the opportunity to deploy previously understudied but promising cognitive paradigms in preclinical Alzheimer’s disease (AD). The Boston Remote Assessment for NeuroCognitive Health (BRANCH) captures a personalized learning curve for the same information presented over seven consecutive days. Here, we examined BRANCH multi-day learning curves (MDLCs) in 167 cognitively unimpaired older adults (age = 74.3 ± 7.5, 63% female) with different amyloid-β (A) and tau (T) biomarker profiles on positron emission tomography. MDLC scores decreased across ascending biomarker groups, with the A + T- group performing numerically worse (β = –0.24, 95%CI[–0.55,0.07], p = 0.128) and the A + T+ group performing significantly worse (β = –0.58, 95%CI[–1.06,–0.10], p = 0.018) than the A-T- group. Further, lower MDLC scores were associated with greater cortical thinning (β = 0.18, 95%CI[0.04,0.34], p = 0.013). Our results suggest that diminished MDLCs track with advanced AD pathophysiology, and demonstrate how a digital multi-day learning paradigm can provide novel insights about cognitive decline during preclinical AD.

Abstract Background Autopsy 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. Methods 142 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. Results At 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. Conclusions Our 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.

Background and Objectives 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 beta-amyloid (A beta) 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. Methods 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 (A beta-) and elevated A beta (A beta+). 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 A beta PET was measured in Centiloids (CLs) with A beta+ >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). Results Across 675 CU participants (age = 72.3 +/- 6.6 years, female = 59%, A beta+ = 60%), greater tau was associated with greater self-CFI (MTL: beta = 0.28 [0.12, 0.44], p < 0.001, and NEO: beta = 0.26 [0.09, 0.42], p = 0.002) and study partner CFI (MTL: beta = 0.28 [0.14, 0.41], p < 0.001, and NEO: beta = 0.31 [0.17, 0.44], p < 0.001). Significant associations between both CFI measures and MTL/NEO tau PET were driven by A beta+. Continuous A beta showed an independent effect on CFI in addition to MTL and NEO tau for both self-CFI and study partner CFI. Self-CFI (beta = 0.01 [0.001, 0.02], p = 0.03), study partner CFI (beta = 0.01 [0.003, 0.02], p = 0.01), and the PACC (beta = -0.02 [-0.03, -0.01], p < 0.001) were independently associated with MTL tau, but for NEO tau, PACC (beta = -0.02 [-0.03, -0.01], p < 0.001) and study partner report (beta = 0.01 [0.004, 0.02], p = 0.002) were associated, but not self-CFI (beta = 0.01 [-0.001, 0.02], p = 0.10). Discussion Both self-report and study partner report showed associations with tau in addition to A beta. Additionally, self-report and study partner report were associated with tau above and beyond performance on a neuropsychological composite. Stratification analyses by A beta 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.

INTRODUCTION: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. METHODS: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+. RESULTS: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. DISCUSSION: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. HIGHLIGHTS: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.

Importance: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. Objective: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. Design, Setting, and Participants: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. Main Outcomes and Measures: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. Results: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. Conclusions and Relevance: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.

Abstract Background Leveraging 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. Methods We 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). Results The 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. Conclusion These 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.

Clinically normal females exhibit higher 18F-flortaucipir (FTP)-PET signal than males across the cortex. However, these sex differences may be explained by neuroimaging idiosyncrasies such as off-target extracerebral tracer retention or partial volume effects (PVEs). 343 clinically normal participants (female = 58%; mean[SD]=73.8[8.5] years) and 55 patients with mild cognitive impairment (female = 38%; mean[SD] = 76.9[7.3] years) underwent cross-sectional FTP-PET. We parcellated extracerebral FreeSurfer areas based on proximity to cortical ROIs. Sex differences in cortical tau were then estimated after accounting for local extracerebral retention. We simulated PVE by convolving group-level standardized uptake value ratio means in each ROI with 6 mm Gaussian kernels and compared the sexes across ROIs post-smoothing. Widespread sex differences in extracerebral retention were observed. Although attenuating sex differences in cortical tau-PET signal, covarying for extracerebral retention did not impact the largest sex differences in tau-PET signal. Differences in PVE were observed in both female and male directions with no clear sex-specific bias. Our findings suggest that sex differences in FTP are not solely attributed to off-target extracerebral retention or PVE, consistent with the notion that sex differences in medial temporal and neocortical tau are biologically driven. Future work should investigate sex differences in regional cerebral blood flow kinetics and longitudinal tau-PET.

INTRODUCTION: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. METHODS: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). RESULTS: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. DISCUSSION: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. HIGHLIGHTS: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 the effects of MTL tau.

BACKGROUND AND OBJECTIVES:Hippocampal volume (HV) atrophy is a well-known biomarker of memory impairment. However, compared with β-amyloid (Aβ) and tau imaging, it is less specific for Alzheimer disease (AD) pathology. This lack of specificity could provide indirect information about potential copathologies that cannot be observed in vivo. In this prospective cohort study, we aimed to assess the associations among Aβ, tau, HV, and cognition, measured over a 10-year follow-up period with a special focus on the contributions of HV atrophy to cognition after adjusting for Aβ and tau. METHODS:We enrolled 283 older adults without dementia or overt cognitive impairment in the Harvard Aging Brain Study. In this report, we only analyzed data from individuals with available longitudinal imaging and cognition data. Serial MRI (follow-up duration 1.3-7.0 years), neocortical Aβ imaging on Pittsburgh Compound B PET scans (1.9-8.5 years), entorhinal and inferior temporal tau on flortaucipir PET scans (0.8-6.0 years), and the Preclinical Alzheimer Cognitive Composite (3.0-9.8 years) were prospectively collected. We evaluated the longitudinal associations between Aβ, tau, volume, and cognition data and investigated sequential models to test the contribution of each biomarker to cognitive decline. RESULTS:We analyzed data from 128 clinically normal older adults, including 72 (56%) women and 56 (44%) men; median age at inclusion was 73 years (range 63-87). Thirty-four participants (27%) exhibited an initial high-Aβ burden on PET imaging. Faster HV atrophy was correlated with faster cognitive decline (R2 = 0.28, p < 0.0001). When comparing all biomarkers, HV slope was associated with cognitive decline independently of Aβ and tau measures, uniquely accounting for 10% of the variance. Altogether, 45% of the variance in cognitive decline was explained by combining the change measures in the different imaging biomarkers. DISCUSSION:In older adults, longitudinal hippocampal atrophy is associated with cognitive decline, independently of Aβ or tau, suggesting that non-AD pathologies (e.g., TDP-43, vascular) may contribute to hippocampal-mediated cognitive decline. Serial HV measures, in addition to AD-specific biomarkers, may help evaluate the contribution of non-AD pathologies that cannot be measured otherwise in vivo.

Optimizing longitudinal cognitive and biomarker trajectories can distill multiple observations from one individual into a single metric. Relative to other individuals, this metric can represent an individual’s distance from an anchor-point based on their rate and non-linearity of change. We have recently developed a cognitive time (c-time) based on the cognitive trajectories of clinically normal older adults. We examined the association between c-time and a previously published ‘time-to-Aβ+ threshold’ and how these metrics align with demographics and other biomarkers. We identified 135 clinically normal older adults from the Harvard Aging Brain Study (Age mean :73years(±5.9); Female:61%) with ≥3 neuropsychological assessments and PiB-PET, ≥1 Flortaucipir-PET, ≥2 volumetric MRI, and diagnostic follow up. We defined c-time using iterative non-linear least-squares optimization to define a curvilinear function that described the group-level Preclinical Alzheimer Cognitive Composite (PACC) trajectory (Fig1D). Each participant’s PACC trajectory was subsequently located on the curve using the same optimization framework (Fig1E). We identified the anchor-point of cognitive decline (c-time) using piecewise linear mixed-effects models. Time-to-Aβ+ was calculated using the published sampled iterative local approximation (SILA; Fig1A) algorithm with the anchor-point indicating Aβ+ threshold (Fig1B). We examined associations between c-time and time-to-Aβ+ using linear regression. Individuals were subsequently placed into groups depending on their position relative to the anchor-point on each axis, as well as the line-of-best-fit (Fig2). We compared the groups on demographics, and both cross-sectional and longitudinal indices of medial temporal (MTL) Flortaucipir-PET (entorhinal, parahippocampal, amygdala) and ICV-adjusted hippocampal volume. C-time and time-to-Aβ+ were significantly associated (r = 0.42, p< 0.001). Only one participant (who progressed to MCI/dementia) was post -c-time and remained pre -time-to-Aβ+, supporting the notion that time-to-Aβ+ occurs prior to cognitive inflection. Individuals post -c-time and post -time-to-Aβ+ (Group 1) were more likely to be APOEε 4 carriers, progressors to MCI/dementia, have significantly higher baseline MTL tau and lower hippocampal volume, and faster hippocampal atrophy (Fig3). Group 2 ( post -time-to-Aβ+/ pre -c-time) were more likely APOEε 4 carriers. Notably, no age or other effects were apparent between groups. Optimizing longitudinal cognitive and biomarker data to estimate a preclinical disease continuum can provide unique, and potentially age-independent, information about the distance an individual might be from disease-relevant events.

A critical question for understanding the natural history of preclinical Alzheimer’s disease (AD) is the extent to which changes in pathology affect contemporaneous and/or subsequent (lagged) cognitive manifestations of the disease. The most common approach in the literature involves the extraction and correlation of time-demarcated slopes from different variables. Our objective was to employ a powerful and flexible structural equation modelling approach, latent change score models (LCSM), to directly address this question while also adjusting for demographics, a medial temporal lobe (MTL) tau burden composite, and an AD-relevant glucose metabolism composite. 131 Harvard Aging Brain Study participants (Age mean :73(6), Female:62%) had complete data for eight time-points of neuropsychological assessment (Preclinical Alzheimer’s Cognitive Composite; PACC) and three timepoints of global PiB-PET. In a contemporaneous bivariate LCSM (Fig1), we tested the influence of both cross-sectional PiB-PET(α) and change in PiB-PET(β) on changes in PACC in the following year. Within a bivariate lagged LCSM (Fig2), we examined the effect of both cross-sectional PiB-PET(α) and change in PiB-PET(β) on changes in PACC two years later. Regression coefficients were specified to be invariant over time. Both models included assumptions of cross-sectional PiB(λ) and PiB change(μ) to predict subsequent PiB changes, and the same for PACC. Baseline age, sex, education, APOE e4, an MTL tau (Flortaucipir)-PET composite (amygdala/entorhinal/parahippocampal), and an FDG-PET composite (hippocampal/inferior parietal/posterior cingulate) were included as covariates. All data were z-scored (PACC mean :0.21(0.6), PiB mean :1.16(0.19)). Higher cross-sectional PiB-PET was not significantly associated with contemporaneous cognitive decline (α;Table1), but PiB-PET accumulation was significantly associated with faster cognitive decline in the following year (β;Table1). Within the lagged model, PiB-PET accumulation, not cross-sectional, was significantly associated with faster PACC decline two years later (β;Table1). The lagged model was the better fitting model and demonstrated much larger change-on-change effect sizes (Table1). The strongest covariate relationships were between age/education and PACC intercept , and APOE e4/tau-PET burden with the global PiB-PET intercept and PiB-PET slope (Table1). Our findings suggest that change in PiB-PET is more strongly associated with subsequent changes in cognition rather than contemporaneous cognitive change. Further, this relationship exists above and beyond the influence of demographics, cross-sectional MTL tau burden, and glucose metabolism.

Despite 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. Clinically-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 (<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). EXT 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, η 2 EXT = .25, η 2 DVR = .20) and PACC decline (Figure3B, η 2 EXT = .28, η 2 DVR = .22). By 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.

Depression is a neuropsychiatric symptom of Alzheimer’s disease (AD) and has been linked to greater cortical amyloid burden in preclinical AD. We sought to examine whether amyloid accumulation in brain regions subserving emotional control and changes in cognitive performance may contribute to increased depressive symptomatology over time. 155 cognitively unimpaired participants who were below the cortical amyloid (<0.86DVR) and clinical depression thresholds (<12 on Geriatric Depression Scale (GDS); mean age = 72.6) at baseline were included from the Harvard Aging Brain Study, with annual GDS and Preclinical Alzheimer Cognitive Composite (PACC) assessment and MRI/PiB-PET every three years (mean years follow-up = 7.06). PiB slopes were calculated for FreeSurfer-defined bilateral regions of interest (ROI) implicated in emotional control: amygdala, medial and lateral orbitofrontal cortices (mOFC, lOFC), superior and middle frontal (MFC) cortices, anterior cingulate (ACC), posterior cingulate (PCC), and isthmus cingulate (IC). Linear mixed-effects models assessed whether main-effects of ROI PiB slope and PACC slope predicted longitudinal GDS scores for each ROI, covarying for age, sex, education, and random intercept/slope, adjusted for multiple comparisons. Post-hoc linear regression models assessed relationships between PiB and PACC slopes. Steeper PiB slopes in the mOFC, MFC, ACC, and IC were associated with increasing GDS scores over time, while a decreasing PACC slope was predictive of increasing GDS scores over time in all regions (Table 1). Post-hoc analyses indicated that PiB slopes were not significantly correlated with PACC slope (Figure 1). In a cohort of cognitively unimpaired older adults with low cortical amyloid and no/subclinical depressive symptoms at baseline, increasing depressive symptoms over time were significantly associated with both amyloid accumulation in specific regions associated with emotional control (i.e., mOFC, MFC, ACC, and IC) and worsening cognitive performance. Furthermore, changes in cognition were not significantly associated with changes in amyloid accumulation in regions involved in emotional control, suggesting that different factors may be independently influencing trajectories in depression versus cognition over time. These results shed light on the neurobiology of depression in older individuals and underscore the importance of monitoring new and increasing neuropsychiatric symptoms in addition to cognitive symptoms when screening for AD.

[18F]MK-6240 meningeal/extracerebral off-target binding may impact tau quantification. We examined the kinetics and longitudinal changes of extracerebral and reference regions. [18F]MK-6240 PET was performed in 24 cognitively-normal and eight cognitively-impaired subjects, with arterial samples in 13 subjects. Follow-up scans at 6.1 ± 0.5 (n = 25) and 13.3 ± 0.9 (n = 16) months were acquired. Extracerebral and reference region (cerebellar gray matter (CerGM)-based, cerebral white matter (WM), pons) uptake were evaluated using standardized uptake values (SUV90-110), spectral analysis, and distribution volume. Longitudinal changes in SUV90-110 were examined. The impact of reference region on target region outcomes, partial volume correction (PVC) and regional erosion were evaluated. Eroded WM and pons showed lower variability, lower extracerebral contamination, and lower longitudinal changes than CerGM-based regions. CerGM-based regions resulted larger cross-sectional effect sizes for group differentiation. Extracerebral signal was high in 50% of subjects and exhibited irreversible kinetics and nonsignificant longitudinal changes over one-year but was highly variable at subject-level. PVC resulted in higher variability in reference region uptake and longitudinal changes. Our results suggest that eroded CerGM may be preferred for cross-sectional, whilst eroded WM or pons may be preferred for longitudinal [18F]MK-6240 studies. For CerGM, erosion was necessary (preferred over PVC) to address the heterogenous nature of extracerebral signal.

The locus coeruleus (LC) is one of the first regions to accumulate tau in Alzheimer’s disease (AD). As the disease progresses, tau in the LC has been related to increasing allocortical tau. Recent autopsy work reported that LC neurodegeneration correlated with parietal amyloid, suggesting that the LC may impact both Ab and tau, but with regionally varying contributions. We investigated whether cross-sectional and longitudinal relationships between in vivo LC integrity and regional tau or Ab are uniquely determined by one pathology or exhibit shared vulnerabilities. 213 individuals from the Harvard Aging Brain Study (mean age:71.6 years, 58% female; 11% cognitively impaired; Figure 1) who underwent 3T LC-MRI, Ab- and tau-PET imaging were included. Of these, 62 individuals received a second MRI and PET session. For the LC, we extracted the 5 highest normalized intensity voxels. PET-data was referenced to cerebellar gray and partial volume corrected. Linear regressions associated LC integrity to tau or Ab and variance contributions were quantified. Mixed effects models examined LC changes to changes in tau or Ab. Mediation analyses examined whether local Ab mediated relationships between LC integrity and local tau. Analyses were adjusted for age, sex and multiple comparisons using FDR-correction. LC integrity was negatively associated with medial-lateral temporal tau, and widespread Ab. Multivariable analyses demonstrated that LC integrity associated uniquely with tau in medial temporal lobe (MTL) regions and with Ab in frontoparietal regions. LC integrity was associated with both tau and Ab in inferior temporal (IT) and posterior cingulate cortices, and mediation analyses showed that LC integrity – tau associations in these regions were Ab-mediated (Figure 2). Longitudinal analyses revealed stronger local associations between LC integrity and tau changes, compared to Ab. LC integrity changes were uniquely associated with tau changes in MTL, but longitudinal LC integrity-IT tau correlations were mediated by local Ab (Figure 3). The LC may have anatomically distinct cortical tau and Ab-pathways in AD, with MTL correlations being almost uniquely tau-related, frontoparietal associations uniquely Ab-related and lateral temporal regions showing Ab-mediated tau accumulation. Potential underlying mechanisms can include synaptic plasticity alterations, glial activation or neuronal hyperactivation.

As Alzheimer’s clinical trials shift earlier and earlier in the disease process, current global PET measures of beta-amyloid (Aβ) positivity may be insufficient for detecting the earliest Aβ deposits. Regional PET measures may better detect the earliest deposits. Previous efforts to identify early-accumulating regions have inferred which regions may be most vulnerable based on older adults. Our aim was to identify early vulnerable areas by looking earlier in the lifespan. 235 clinically-normal adults ages 33-74 from the Framingham Heart Study (FHS) underwent one time-point of dynamic Pittsburgh Compound B (PIB) PET (Tab.1). PIB was regionally quantified in Desikan regions using distribution volume ratio (DVR, cerebellar reference). Linear and quadratic (Age + Age 2 ) models were run to determine association of age with region of interest (ROI) PIB DVR, and ROIs with significantly increasing DVRs were selected for the generation of candidate early vulnerable area (EVA) aggregates. Multiple aggregate EVA DVRs (EVA2-EVA11) were generated by sequential addition of regions in rank order of descending Age 2 estimate. EVA positivity was derived from both full-sample GMM and < 45 sample mean + 2SD thresholds. Finally, EVA aggregates were tested using an independent cohort of older adults from the Harvard Aging Brain Study (HABS) that were imaged under an identical protocol (n=250, ages 50-92), computing sensitivity and specificity of each aggregate at baseline to predict progression to global PIB positivity 3 years later. Of 35 Desikan regions each evaluated on the left and right, 11 emerged as quadratic age relationships ( p <0.05; Fig.1) and were used to generate EVA aggregates (Fig.2). Independent validation in baseline global PIB- individuals from HABS indicated EVA9 and EVA10 best predicted future accumulation (Fig.3). GMM-based thresholds provided excellent specificity (SP=1.0) but weak sensitivity (SE=0.41) to predict progression to global PIB positivity 3 years later, while the more liberal mean+2SD thresholds improved sensitivity but decreased specificity (SE=0.88, SP=0.88). Utilizing lifespan data from FHS, we identified a set of early vulnerable regions that are predictive of future accumulation in an independent sample of older adults. These findings are potentially useful in identifying the earliest deposits of Aβ for use in clinical trial design.

The Framingham Heart Study (FHS), a three-generation community-based cohort studying cardiovascular disease across the adult lifespan, has expanded to include positron emission tomography (PET) of beta-amyloid (Aβ) and tau. Our aim is to characterize these pathologies in this unique sample and utilize the wide age range to assess the spatiotemporal ordering of emerging Aβ and tau. 211 clinically-normal adults aged 33-74 from FHS underwent Pittsburgh Compound B (PIB) and Flortaucipir (FTP) PET. PIB and FTP were regionally quantified in Desikan regions using distribution volume ratio (DVR) for PIB and standardized uptake volume ratio (SUVR) for FTP. Three approaches were used to identify early accumulating regions: 1. A series of Age*APOE linear models to identify regions accumulating earlier in the lifespan in ε4 carriers. 2. Identification of early regions as those more frequently elevated as previously used in older adults, with Gaussian mixture models (GMM) conducted to evaluate bimodality and set biomarker positivity thresholds for each region. 3. Applied the same frequency approach using well-validated PIB GMM thresholds based on older adults from the Harvard Aging Brain Study (HABS). For PIB, significant Age*APOE interactions (Fig. 1) were seen across multiple regions previously implicated as early-accumulating based on elevation frequencies from older adult samples, as well as the pars opercularis (p=0.005) and lateral parietal cortices (p<0.04). PIB was significantly bimodal across all ROIs (LRT>36.1, p<0.001; Fig. 1). Application of GMM-based regional positivity frequencies derived within FHS versus from HABS thresholds led to substantial discrepancies in the spatiotemporal ordering of PIB ( r s =0.30, p=0.17). Age*APOE effects were not detected for any tau regions, but inferior temporal (IT), middle temporal (MT), and amygdala (AM) increased with age (Fig. 2). Higher global Aβ was also associated with higher FTP SUVR in IT, MT, and a trend for AM, as well as inferior parietal and precuneus (Fig. 2). Aβ and tau mainly follow spatiotemporal patterns consistent with prior evidence in older adult samples. GMM-based methods used in older adult samples to dichotomize biomarker positivity may be suboptimal in younger samples. Continuous approaches may better capture early Aβ and tau in younger populations.

The ATN framework is defined by cross-sectional biomarkers of β-amyloid (Aβ), tau and neurodegeneration. Given that prevention trials, e.g., AHEAD 3-45, are focused on individuals who have lower Aβ than established thresholds, we investigated whether defining the ATN framework using longitudinal biomarker trajectories might better identify an at-risk sample within this boundary. Here, we applied a data-driven method to re-define the ATN with longitudinal biomarker data from the Harvard Aging Brain Study (HABS) and we then replicated this longitudinal framework in ADNI. 157 HABS participants were clinically-normal at baseline and underwent at least two Pittsburgh Compound-B [PiB]-PET, Flortaucipir-PET, and T1-weighted MRI scans. To define longitudinal ATN, we applied latent class mixture models (LCMM) to each biomarker (global Aβ DVR, entorhinal tau SUVr, ICV-adjusted hippocampal volume) separately, adjusting for age, and including random intercept and slopes. Akaike information criteria (AIC) determined the best-fitting models out of two-group or three-group solutions with linear or spline-link functions. We compared longitudinal ATN profiles on demographics and an optimized estimate of cognitive change (derived from longitudinal Preclinical Alzheimer Cognitive Composite (PACC) data). Aβ trajectories (Fig.1a) were best categorized by one stable (A→) and two accumulating subgroups, a predominantly amyloid-negative at baseline subgroup (A-↑) and an entirely amyloid positive at baseline subgroup (A+↑). Tau (Fig.1b) and neurodegeneration (Fig.1c) were optimally defined by stable (T→/N→) vs accumulating/atrophying (T↑/N↑) groups, respectively. These groups were replicated in ADNI (Fig.2). The entire A-↑ subgroup were stable on T and N (A-↑/T→/N→) and were predominantly A-/T-/N- at baseline (86%; Table 1). By contrast, 38% of A+↑ individuals changed on T, or T&N. A-↑/T→/N→ demographically most closely resembled the longitudinally-stable ATN group (A→/T→/N→), but were older, more likely to carry e4+ and exhibited higher baseline Aβ (Table 2). Although demonstrating Aβ accumulation, A-↑/T→/N→ did not exhibit greater cognitive decline versus the stable group (A→/T→/N→; Fig. 3). Our findings suggest that a longitudinal biomarker run-in of Aβ-PET may be useful for the identification of early-risk groups for prevention trials. Future work will establish whether other features (e.g. genetics, neuroinflammatory markers, functional imaging) can help to distinguish this cohort.

In Alzheimer’s disease, cognitive decline is associated with a rapid increase in tau pathology, making tau‐PET a potentially useful outcome in prevention trials. To identify individuals most at‐risk of tau accumulation, we investigated the longitudinal associations between amyloid and tau in clinically normal (CN) and impaired (MCI/AD) participants from the Harvard Aging Brain Study.