INTRODUCTION:Individuals with Down syndrome (DS) have a high prevalence of Alzheimer's disease (AD) and reveal an earlier age of amyloid beta (Aβ) onset compared to sporadic AD. Differences in amyloid accumulation rates between DS and sporadic AD populations have not been established. METHODS:Participants with ≥ 3 [C-11]PiB scans (spanning > 6 years) and transitioning to Aβ+ were included, resulting in 20 DS and 23 neurotypical (NT) participants. Amyloid accumulation was compared using global standardized uptake value ratio (SUVR) for Aβ deposition, with individual growth rates (r) estimated using the logistic growth model ( S U V R ( t ) = S U V R B L + K 1 + e - r ( t - t 50 ) $SUVR\ ( t ) = SUV{{R}_{BL}} + \frac{K}{{1 + {{e}^{ - r( {t - {{t}_{50}}} )}}}}$ ). RESULTS:The average growth rate in the DS cohort was 0.28 (0.08)/year versus 0.20 (0.08)/year for NT ( p = . 002 $p = .002$ ), an increase of 40%. DISCUSSION:Using individual longitudinal analyses, accelerated amyloid accumulation in DS is observed, This has important considerations for informing treatment trial design and monitoring beta-amyloid changes in future AD studies involving individuals with DS. HIGHLIGHTS:Aβ accumulation rate was estimated using a logistic growth model. There was no overlap in the age of amyloid positivity between DS and NT cohorts. Participants with DS accumulate amyloid 40% faster than those with sporadic AD.

INTRODUCTION:Adults with Down syndrome demonstrate striatum-first amyloid accumulation with [11C]Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging, which has not been replicated with [18F]florbetapir (FBP). Early striatal accumulation has not been temporally quantified with respect to global cortical measures. METHODS:Longitudinal PiB (n = 175 participants) and FBP (n = 92 participants) data from the Alzheimer Biomarkers Consortium-Down Syndrome (ABC-DS) were used to measure cortical and striatal binding. Generalized temporal models for cortical and striatal amyloid accumulation were created using the sampled iterative local approximation (SILA) method. RESULTS:PiB demonstrated greater striatal-to-cortical ratios than FBP. SILA analysis revealed striatal amyloid burden occurs 3.40 (2.39) years earlier than the cortex in PiB. There was no difference between the cortex and striatum in FBP. DISCUSSION:Among adults with Down syndrome, the striatum consistently accumulates amyloid earlier than the cortex when measured with PiB. This suggests the striatum is more sensitive to the onset of PiB PET-detectable amyloid in Down syndrome. HIGHLIGHTS:Striatal amyloid is detectable 3.4 years before the cortex using PiB PET in DS. Florbetapir PET does not detect early striatal amyloid accumulation in DS. White matter can be used as reference region in longitudinal florbetapir PET. SILA trajectory models can be used to compare regional estimates for age of onset.

Trisomy 21 in Down syndrome (DS) is associated with an earlier accumulation of beta-amyloid (Aβ) plaques and a higher rate of Alzheimer’s Disease due to the triplication of the amyloid precursor protein gene. In this study we compare accumulation rates of Aβ measured with [C-11]PiB PET between large longitudinal cohorts of DS and neurotypical (NT) participants at a single site. Participants imaged at the University of Wisconsin with ≥2 PiB scans and ≥2 years between scans were included in this study. DS participants were included from the Alzheimer’s Biomarker Consortium–DS (ABC-DS) study (n = 57) and NT participants from studies enriched with familial risk for AD (n = 162) with several participants having 10+ years of PiB data. An identical imaging procedure was conducted for all subjects and reconstructed PET images were processed using a standardized pipeline and converted into SUVR images using the cerebellar grey matter reference region. Global PiB SUVR, defined by grey matter regions from the AAL atlas, was used as a metric for Aβ deposition and input into a trajectory model (Betthauser, 2021) to estimate time from Aβ(+) (SUVR ≥ 1.40) for each scan (Figure 1). Using this model the PiB data were normalized in time and then fit using the logistic growth curve () with t 50 set to 0. Nonspecific binding (NS), found by taking the average SUVR of all Aβ(-) subject scans (n=491) at our site, and carrying capacity (K), represented by the highest total binding seen in any participant scanned, were set to 1.12 and 2.40 respectively. Aβ accumulation rate (r) was calculated by fitting all normalized participant data across each population. Rate of Aβ accumulation for DS was 0.25±0.02 and 0.17±0.01/yr for NT, corresponding to 0.065 and 0.038 SUVR/yr respectively when becoming Aβ(+). No overlap in 95% CI between the DS (0.21-0.29) and NT (0.15-0.19) populations was observed. There was a faster rate of Aβ accumulation observed in our DS cohort compared to NT by 47%. However, the high variability in Aβ rates requires further investigation towards understanding how genetic and lifestyle factors contribute to this process.

Neuronal α4β2* nicotinic acetylcholine receptors (nAChRs) are stimulated by nicotine and are associated with tobacco dependence. [18F]Nifene is a PET radiotracer with high specificity for α4β2* nAChRs that can be used to investigate nAChR distribution in the human brain in vivo. In this study, we investigate the dependence of sex and age on the binding of [18F]nifene in nonsmoking healthy human participants. Cognitively normal participants (n = 31) were recruited into older versus younger and male versus female cohorts to investigate sex and age differences in [18F]nifene binding. Distribution volume ratios (DVRs) were calculated for brain regions with known nAChR expression and compared using a multiparameter linear regression model. There was a significant association between age and decreasing thalamic DVR (p = 0.01), with the most notable difference coming from the anterior nucleus of the thalamus (p < 0.001). Outside of the thalamus, a higher [18F]nifene DVR was observed with increasing age in the cerebellar grey matter (p = 0.01). No significant sex differences were observed using our linear model after multi-comparison correction. These results support including age in the experimental design and analysis of the α4β2* nAChR system in research and clinical applications.

Down syndrome (DS) is characterized by a higher risk and earlier onset of beta-amyloid (Aβ) plaque accumulation and Alzheimer’s disease (AD). The triplication of chromosome 21 containing the amyloid precursor protein (APP) gene is implicated in this process and in this study we compare the accumulation rates of Aβ between DS and neurotypical (NT) populations quantified by longitudinal [C-11]PiB imaged at the same site to minimize experimental variation. Individuals with three or more [C-11]PiB scans spanning eleven years our site with at least one Aβ+ (global SUVR>1.40) and Aβ- [C-11]PiB scan were selected. 12 of 74 DS and 15 of 246 NT participants fit these criteria. An identical imaging procedure was conducted for all participants imaged on either an ECAT HR+ or Biograph mCT PET scanner 50-70 minutes post-injection. Reconstructed PET images were summed and spatially normalized into template space and converted into 50-70min SUVR images using a cerebellar grey matter reference region. Global SUVR composed of grey matter regions from the AAL atlas was used as a metric for Aβ deposition. SUVR data were fit against age with a logistic growth curve (f(t) = NS+K/(1+e (-r(t-t50)) )) to determine Aβ accumulation as subjects transitioned to Aβ+. The rate of accumulation was found using the slope of the curve at Aβ positivity threshold based on the model fit. Our model fixed NS = 1.12, the average baseline SUVR of all Aβ- subject scans (n = 491), and K = 1.28, the highest specific binding in any participant. Plotting [C-11]PiB SUVR against age provides visual comparisons of cohorts (Fig 1) and transforming the x-axis to amyloid chronicity (t = 0 at Aβ+) illustrates differences in accumulation rates between groups (Fig 2). The average rate of amyloid accumulation (ΔSUVR/yr) when becoming Aβ+ in the DS cohort is 0.071(0.021) versus 0.043(0.009) for NT (p = 4.31×10 −4 ). Aβ accumulation rate was higher in the DS cohort compared to NT. This is consistent with the additional APP gene copy in DS, and we observed an accumulation rate 65% faster in this cohort. There was a higher variability in the DS population requiring further investigation towards understanding the influence of genetic and lifestyle factors on Aβ deposition.

Down syndrome (DS) is characterized by earlier beta-amyloid (Aβ) plaque accumulation and an increased prevalence of Alzheimer’s disease (AD) due to trisomy 21 and triplication of the amyloid precursor protein (APP) gene. While the age of AD onset is earlier in DS, AD pathology progression in non-DS populations follows similar trends to the DS cohort. This study compares the accumulation rate of Aβ between DS and non-DS populations measured with [C-11]PiB at same site using the same scanning procedures. Individuals with at least one Aβ+ [C-11]PiB scan (Global SUVR>1.40), and at least one Aβ- scan from our site at the University of Wisconsin were selected for this study. 12 out of 58 DS participants in the Neurodegeneration in Aging DS (NiAD) study and 14 out of 246 non-DS subjects from the PREDICT study fit this criteria. All images were collected on an ECAT HR+ scanner from 50-70 minutes injection (i.d. = 15mCi). Reconstructed PET images were aligned and summed before being normalized into MNI-152 space and converted into an SUVR image using cerebellar grey matter as a reference region. We determined rate of Aβ accumulation as the change in global SUVR per year, defined as the average of grey matter regions in the anterior cingulate, frontal cortex, parietal cortex, precuneus, temporal cortex, and striatum, and focused on the period when participants became Aβ+. Plotting global [C-11]PiB SUVR and age (Fig 1) there is no age overlap between the two groups. The average rate of amyloid accumulation per year when becoming Aβ+ in the DS group was 0.058±0.026 compared to 0.059±0.028 for the non-DS sample. The two groups have notably similar mean values, although when organized into box plots (Fig 2), the interquartile range of the DS subjects is 0.044 compared to a tighter 0.019 for non-DS. The pattern of Aβ accumulation in DS and non-DS groups becoming Aβ+ have similar distributions. While we might expect a higher rate in the DS population due to the additional APP gene, our findings do not support this hypothesis. However, considerable variability in the rate of accumulation is observed requiring further investigation towards understanding this process.

INTRODUCTION:Almost all individuals with Down syndrome (DS) will develop neuropathological features of Alzheimer's disease (AD). Understanding AD biomarker trajectories is necessary for DS-specific clinical interventions and interpretation of drug-related changes in the disease trajectory. METHODS:A total of 177 adults with DS from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) underwent positron emission tomography (PET) and MR imaging. Amyloid-beta (Aβ) trajectories were modeled to provide individual-level estimates of Aβ-positive (A+) chronicity, which were compared against longitudinal tau change. RESULTS:Elevated tau was observed in all NFT regions following A+ and longitudinal tau increased with respect to A+ chronicity. Tau increases in NFT regions I-III was observed 0-2.5 years following A+. Nearly all A+ individuals had tau increases in the medial temporal lobe. DISCUSSION:These findings highlight the rapid accumulation of amyloid and early onset of tau relative to amyloid in DS and provide a strategy for temporally characterizing AD neuropathology progression that is specific to the DS population and independent of chronological age. HIGHLIGHTS:Longitudinal amyloid trajectories reveal rapid Aβ accumulation in Down syndrome NFT stage tau was strongly associated with A+ chronicity Early longitudinal tau increases were observed 2.5-5 years after reaching A.

Alzheimer’s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modeling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer’s disease continuum. Data were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer’s Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modeling amyloid accumulation using 10-fold cross-validation and hold-out validation where applicable. Estimated amyloid onset age was compared across all three modeling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E ( e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global Clinical Dementia Rating ≥1) in a subset of 595 ADNI participants that were not impaired prior to amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modeling methods. Sex and apolipoprotein E-e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was nonlinear and accelerated for amyloid onset age >65. These findings demonstrate the feasibility of modeling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer’s disease.

Adults with Down syndrome (DS) are genetically predisposed to Alzheimer’s disease (AD) and accumulate beta-amyloid plaques (Aβ) early in life. Our previous work in this population comparing centiloids and amyloid load (Aβ L ) identified an amyloid-positive cutoff of 20 Aβ L . The aim of this study was to evaluate longitudinal Aβ L change across DS groups based on Aβ status. 175 adults with DS (age=39.8 (8.64) years) were recruited through the Alzheimer’s Biomarker Consortium - Down Syndrome study. Of the 175 adults, N=79 underwent up to four longitudinal [C-11]PiB scans (2.64 (0.71) years apart). Longitudinal Aβ change was calculated for each individual using Aβ L . Cluster analysis identified a group of A- individuals with significant Aβ increase at subthreshold detection levels, and was used to inform a subthreshold A+ cutoff. Rates of Aβ L change were compared across groups of A- (Aβ L < 13.3), subthreshold A+ (13.3 < Aβ L < 20) and A+ (Aβ L > 20) DS. Average SUVr images were then generated across all participants for each group and SUVrs were extracted to evaluate regional Aβ burden. The A- group showed longitudinal change of 0.32 (0.66) Aβ L /year, while the subthreshold A+ and A+ groups showed change of 2.60 (1.11) and 3.22 (1.26) Aβ L /year, respectively (Figure 1). Aβ L change was statistically different across all groups (ANCOVA F = 76.7, p < .0001) while adjusting for imaging site. Post hoc Student’s t-tests revealed that the subthreshold A+ and A+ groups had significantly greater Aβ L change compared to the A- group (Bonferroni-adjusted p < .0001). No significant difference in Aβ L change was observed between the subthreshold A+ and A+ groups. Figure 2 displays the average SUVr images for each Aβ group. Relative to the A- group, the subthreshold A+ group showed greater regional Aβ burden (Table 1). DS adults with a subthreshold A+ classification showed indistinguishable rates of Aβ change compared to those who are conventionally A+, suggesting that longitudinal PET imaging can identify significant Aβ change early during AD progression. These longitudinal findings can inform sample size estimates for AD clinical trials aimed at early intervention in this population.

[ 11 C]UCB-J selectively binds to synaptic vesicle glycoprotein 2A (SV2A) and may be used to measure synaptic density in synaptopathies. Along with the accumulation of amyloid-b plaques and neurofibrillary tau tangles, neurodegeneration is a hallmark of Alzheimer’s disease (AD) dementia. Synaptic density may be decreased due to AD pathology, resulting in cognitive dysfunction. We examined whether synaptic density was related to the accumulation of protein aggregates characteristic of AD as well as cognitive function. Thirty subjects (68.6±6.2 years old, 20F/10M) recruited from the Wisconsin ADRC and the Wisconsin Registry for Alzheimer’s Prevention study underwent [ 11 C]UCB-J dynamic PET imaging to assess synaptic density, T1w-MR imaging, and the Rey Auditory Verbal Learning Test (RAVLT). Twenty-four participants also completed [ 11 C]PiB dynamic imaging to assess amyloid-b plaque accumulation and [ 18 F]MK-6240 dynamic imaging to assess neurofibrillary tangle accumulation. T1w-MRI was used to derive subject-specific regions of interest (ROIs) as well as to calculate the matrices required to transform images into MNI152 space. Global amyloid burden was ascertained by taking the mean PiB DVR across eight bilateral AAL-based ROIs. Neurofibrillary tau tangle accumulation in the entorhinal cortex was quantified using the [ 18 F]MK-6240 SUV ratio. Subject-specific hippocampus ROIs for [ 11 C]UCB-J analysis were defined using FreeSurfer (v7) segmentation of the participant’s MR image. Using data from the n=24 subjects, [ 11 C]UCB-J binding in the hippocampus was significantly associated with [ 11 C]PiB global DVR (R 2 = 0.24, P = 0.01) and [ 18 F]MK-6240 uptake in the entorhinal cortex (R 2 = 0.17, P = 0.04). After correcting for hippocampus volume size, significance decreased for both measures (R 2 = 0.28, P = 0.05 for [ 11 C]PiB global DVR; R 2 = 0.17, P = 0.05 for [ 18 F]MK-6240 entorhinal cortex SUVR). [ 11 C]UCB-J binding in the hippocampus was not significantly related to RAVLT immediate recall (R 2 = 0.52, P = 0.08) but was significantly associated with RAVLT delayed recall (R 2 = 0.55, P = 0.04). Findings suggest that synaptic impairment is related to amyloid-b and neurofibrillary tau tangle accumulation as well as worse performance on RAVLT delayed recall. Future work will test the longitudinal relationship between accumulation of AD pathology, synapse loss, and cognitive decline.

INTRODUCTION:Adults with Down syndrome are genetically predisposed to develop Alzheimer's disease and accumulate beta-amyloid plaques (Aβ) early in life. While Aβ has been heavily studied in Down syndrome, its relationship with neurofibrillary tau is less understood. The aim of this study was to evaluate neurofibrillary tau deposition in individuals with Down syndrome with varying levels of Aβ burden.METHODS:A total of 161 adults with Down syndrome (mean age = 39.2 (8.50) years) and 40 healthy, non-Down syndrome sibling controls (43.2 (12.6) years) underwent T1w-MRI, [C-11]PiB and [F-18]AV-1451 PET scans. PET images were converted to units of standardized uptake value ratios (SUVrs). Aβ burden was calculated using the amyloid load metric (AβL); a measure of global Aβ burden that improves quantification from SUVrs by suppressing the nonspecific binding signal component and computing the specific Aβ signal from all Aβ-carrying voxels from the image. Regional tau was assessed using control-standardized AV-1451 SUVr. Control-standardized SUVrs were compared across Down syndrome groups of Aβ-negative (A-) (AβL < 13.3), subthreshold A+ (13.3 ≤ AβL < 20) and conventionally A+ (AβL ≥ 20) individuals. The subthreshold A + group was identified as having significantly higher Aβ burden compared to the A- group, but not high enough to satisfy a conventional A + classification.RESULTS:A large-sized association that survived adjustment for chronological age, mental age (assessed using the Peabody Picture Vocabulary Test), and imaging site was observed between AβL and AV-1451 within each Braak region (p < .05). The A + group showed significantly higher AV-1451 retention across all Braak regions compared to the A- and subthreshold A + groups (p < .05). The subthreshold A + group showed significantly higher AV-1451 retention in Braak regions I-III compared to an age-matched sample from the A- group (p < .05).DISCUSSION:These results show that even the earliest detectable Aβ accumulation in Down syndrome is accompanied by elevated tau in the early Braak stage regions. This early detection of tau can help characterize the tau accumulation phase during preclinical Alzheimer's disease progression in Down syndrome and suggests that there may be a relatively narrow window after Aβ accumulation begins to prevent the downstream cascade of events that leads to Alzheimer's disease.

119 Objectives: [11C]UCB-J is a PET radioligand which specifically binds to synaptic vesicle protein SV2A and is designed to reflect synaptic density. Synaptic loss along with beta-amyloid accumulation is a hallmark of Alzheimer’s disease. One of the aims of this ongoing study is to determine the relationship over time between synaptic density degradation as determined by lower [11C]UCB-J binding and higher beta-amyloid burden as determined by [11C]PiB binding. Methods: Eighteen participants (69 ± 6 years old, 11 female/7 male) were recruited from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention study. Participants underwent comprehensive clinical and cognitive evaluation to determine the presence or absence of cognitive impairment according to NINDS/ADRDA criteria (McKahnn et al, 2011), and confirmed by a multidisciplinary consensus diagnostic panel. Each participant underwent [11C]UCB-J dynamic imaging to assess synaptic density, [11C]PiB dynamic imaging to assess beta-amyloid plaque accumulation. Volumetric MR imaging was performed to derive subject-specific regions of interest (ROIs) as well as to calculate the matrices required to transform the subject T1-w MR into MNI152 space. The PET images were smoothed, dynamically denoised, and registered to the T1-w MRI. Both scans were conducted over 70 minutes and binned into 5x2min and 12x5min frames. [11C]PiB DVR images were created using Logan graphical analysis (t* = 30min, k2’ = 0.15min-1, cerebellum grey matter reference region). Global amyloid burden was ascertained by taking the mean PiB DVR across eight bilateral regions of interest from the AAL atlas. Parametric [11C]UCB-J DVR images were created using SRTM2 (cerebellum reference region, k2’ between 0.01 and 1.0 min-1 with an increment of 0.01). Subject-specific hippocampus ROIs were defined using FreeSurfer (v7) segmentation of the participant’s MR image. Results: Among participants with cognitive impairment, [11C]UCB-J DVR was lower in the hippocampus (0.54 ± 0.07) relative to the cognitively unimpaired group (0.85 ± 0.17) (P = 0.04). When[c1] the global [11C]PiB DVR for all participants was compared to the [11C]UCB-J DVR in the hippocampus, an overall inverse relationship was exhibited irrespective of cognitive status (R2 = 0.10, P = 0.003) (Figure 1). Conclusions: These preliminary data suggest a relationship between synaptic density and beta-amyloid accumulation as defined using [11C]UCB-J and [11C]PiB DVR measures, respectively. Future work will further elucidate this relationship in the context of Alzheimer’s disease progression. Figure 1: Cognitively impaired participants showed decreased [11C]UCB-J uptake with variable [11C]PiB binding while the overall group showed a relationship that suggests increased [11C]PiB binding relates to decreased [11C]UCB-J uptake, even among participants who were cognitively unimpaired.

Adults with Down syndrome (DS) are genetically predisposed to Alzheimer’s disease (AD) and accumulate beta‐amyloid plaques (Aβ) early in life. The aim of this study was to evaluate neurofibrillary tau deposition in DS between groups of Aβ negative (A‐) and subthreshold Aβ accumulators (subthreshold A+).

42 Introduction: Adults with Down syndrome (DS) are genetically predisposed to Alzheimer’s disease (AD) and accumulate beta-amyloid plaques (Aβ) early in life. The aim of this study was to evaluate neurofibrillary tau deposition in DS adults that are classified as PET amyloid-negative (A-). Methods: A total of 130 A- adults with DS (mean age: 36.5 [6.77] years) and 40 healthy, non-DS sibling controls (43.2 [12.6] years) underwent T1w-MRI, [C-11]PiB and [F-18]AV-1451 PET scans. MRI images were processed using FreeSurfer v5.3.0 to generate ROI masks encompassing the Braak staging of tau pathology. PiB and AV-1451 SUVr images were generated using a cerebellar gray matter reference region. Global Aβ burden was calculated using the amyloid load metric (AβL). Regional tau was assessed using AV-1451 SUVr Z-scores relative to the control group. Partial volume correction was performed on AV-1451 SUVrs in the Braak I-II regions using the geometric transfer matrix method. N=26 DS adults had elevated Aβ at typical subthreshold detection levels (13.3 < AβL < 20) and were re-classified as subthreshold A+. AV-1451 images were averaged for the A- and subthreshold A+ groups, and the difference was taken between the two groups to generate a ΔSUVr image. Braak regional SUVr Z-scores were then compared across the two groups using Student’s t-tests while adjusting for imaging site. The analysis was then repeated using an age-matched sample of A- (N=68) and subthreshold A+ (N=26) DS. Results: The SUVr difference image revealed higher AV-1451 uptake in Braak regions I-III for the subthreshold A+ group (Figure 1). Student’s t-tests revealed significantly higher AV-1451 uptake in Braak regions I-III for the subthreshold A+ group (Table 1). No difference in AV-1451 uptake was observed between groups in Braak regions IV-VI (Table 1). Higher AV-1451 uptake in Braak regions I-III was also observed in the subthreshold A+ group when compared to the age-matched A- group (Table 2). Discussion: These results show that subthreshold Aβ in DS is accompanied by elevated tau in the early Braak stage regions. These findings indicate that there is a short latency between the onset of Aβ and the spread of neurofibrillary tau in DS.

1592 Introduction: Adults with Down syndrome (DS) have a high incidence of Alzheimer’s disease (AD). In late-onset AD, tau has been shown to have greater impact on cognition compared to Aβ, but the effects of tau on cognition have not yet been evaluated in DS. The aim of this study was to examine the association between Aβ and tau with cognition in a large DS cohort. Methods: A total of 161 adults with DS (mean age: 39.2 [8.46] years) and 40 healthy, non-DS sibling controls (43.2 [12.6] years) underwent T1w-MRI, [C-11]PiB and [F-18]AV-1451 PET scans at four imaging sites. MRI images were processed using FreeSurfer v5.3.0 to generate ROI masks encompassing the Braak staging of tau pathology. PiB and AV-1451 SUVr images were generated using a cerebellar gray matter reference region. Global Aβ burden was calculated using the amyloid load metric (AβL). A low-threshold amyloid-positivity (A+) cutoff was defined as 13.3 AβL (~18 Centiloids). Regional tau was assessed using AV-1451 SUVr Z-scores. A Z-score cutoff for tau-positivity (T+) was defined as 3.90 from the sibling control data (97.5th percentile of maximum Z-score values). No corrections for the partial volume effect were performed, so analyses were restricted to Braak regions III-VI. Z-score composite measures of episodic memory and overall cognition (includes measures of episodic memory, dementia symptoms/mental status, visual perception, executive functioning, motor planning and coordination, and verbal fluency) were compared between adults with DS who were A+T- versus A+T+ across each Braak region using two-sample t-tests while adjusting for age, premorbid level of intellectual disability, and imaging site. Results: Of the 20 A+T+ DS individuals, six were cognitively stable (CS-DS), five had mild cognitive impairment (MCI-DS), seven had AD, and two had a consensus of unable to be determined (Figure 1). The cognitive consensus was determined independent of imaging data. There were no participants classified as A-T+. T+ classification resulted in fewer CS-DS cases when compared to A+ classification. Compared to those that were only Braak III T+, Braak IV T+ revealed fewer CS-DS cases. Figure 2 displays the associations between cognition, Aβ, and regional tau. Participants with an A+T+ classification displayed significantly worse cognitive functioning compared to those with an A+T- classification (Table 1). Discussion: These findings reveal that T+ classification was better able to distinguish cases of MCI-DS and AD from CS-DS compared to A+ classification alone. The A+T+ individuals displayed worse cognitive functioning compared to the A+T- individuals, suggesting T+ presence in DS is a better indicator of cognitive decline, similar to the observations in late-onset AD.

INTRODUCTION:Adults with Down syndrome (DS) are predisposed to Alzheimer's disease (AD) and reveal early amyloid beta (Aβ) pathology in the brain. Positron emission tomography (PET) provides an in vivo measure of Aβ throughout the AD continuum. Due to the high prevalence of AD in DS, there is need for longitudinal imaging studies of Aβ to better characterize the natural history of Aβ accumulation, which will aid in the staging of this population for clinical trials aimed at AD treatment and prevention.METHODS:Adults with DS (N = 79; Mean age (SD) = 42.7 (7.28) years) underwent longitudinal [C-11]Pittsburgh compound B (PiB) PET. Global Aβ burden was quantified using the amyloid load metric (AβL). Modeled PiB images were generated from the longitudinal AβL data to visualize which regions are most susceptible to Aβ accumulation in DS. AβL change was evaluated across Aβ(-), Aβ-converter, and Aβ(+) groups to assess longitudinal Aβ trajectories during different stages of AD-pathology progression. AβL change values were used to identify Aβ-accumulators within the Aβ(-) group prior to reaching the Aβ(+) threshold (previously reported as 20 AβL) which would have resulted in an Aβ-converter classification. With knowledge of trajectories of Aβ(-) accumulators, a new cutoff of Aβ(+) was derived to better identify subthreshold Aβ accumulation in DS. Estimated sample sizes necessary to detect a 25% reduction in annual Aβ change with 80% power (alpha 0.01) were determined for different groups of Aβ-status.RESULTS:Modeled PiB images revealed the striatum, parietal cortex and precuneus as the regions with earliest detected Aβ accumulation in DS. The Aβ(-) group had a mean AβL change of 0.38 (0.58) AβL/year, while the Aβ-converter and Aβ(+) groups had change of 2.26 (0.66) and 3.16 (1.34) AβL/year, respectively. Within the Aβ(-) group, Aβ-accumulators showed no significant difference in AβL change values when compared to Aβ-converter and Aβ(+) groups. An Aβ(+) cutoff for subthreshold Aβ accumulation was derived as 13.3 AβL. The estimated sample size necessary to detect a 25% reduction in Aβ was 79 for Aβ(-) accumulators and 59 for the Aβ-converter/Aβ(+) group in DS.CONCLUSION:Longitudinal AβL changes were capable of distinguishing Aβ accumulators from non-accumulators in DS. Longitudinal imaging allowed for identification of subthreshold Aβ accumulation in DS during the earliest stages of AD-pathology progression. Detection of active Aβ deposition evidenced by subthreshold accumulation with longitudinal imaging can identify DS individuals at risk for AD development at an earlier stage.