PiB SUVR Research Articles

Quantitative Transport Mapping Derived Tracer Velocity for Alzheimer’s Disease

AbstractBackgroundThe Alzheimer’s disease (AD), featured with extracellular beta‐amyloid plaque and intracellular tau tangles, are considered caused by the brain clearance function deficits. One anatomical representation of the fluid stasis is the enlargement of the perivascular space. Our hypothesis is that the fluid stasis will reduce the fluid velocity, and the reduced fluid velocity will drive the PET tracer slower than normal. We used the velocity map from our proposed quantitative transport mapping (QTM, L. Zhou MRM 2020.) to evaluate the PET tracer velocity in Alzheimer’s disease.MethodFifty‐seven subjects (age: 69.7±9.1, Female/male = 37/20, NL/MCI = 45/12) went through both 11C‐PIB and 18F‐MK‐6240 PET scan for Aβ and tau deposit measurements, and MRI scan for ROI parcellation. PIB SUVR, MK vCSF‐Slope (Y. Li Fluids Barriers CNS 2022), MK QTM are processed for the following analysis. Specifically, vCSF‐Slope was computed as the tracer turnover rate in lateral ventricle using the dynamic MK data between 10 to 30min, normalized by the whole brain AUC in 1 to 4min. QTM velocity was processed using the dynamic MK data between 40 to 55min using mass conservation equation. Regression model for predicting Aβ using QTM was conducted by controlling for age and sex. The relationship between QTM and vCSF was evaluated. The use of QTM to distinguish subjects groups are tested.ResultOur preliminary results showed that quantitative transport mapping(QTM) based on fluid mechanics is significantly associate the ventricular CSF clearance(vCSF) measured by dynamic PET data and brain amyloid(PiB PET). The regression model shows that cerebral cortex (GM) QTM velocity (p<0.01, t = ‐2.76) and age (p<0.05, t = 2.55) are significantly associated with the GM PIB SUVR as shown in Figure 1. This indicates that high Aβ deposit is associated with lower QTM velocity, i.e., clearance deficits. Figure 2 shows that in GM QTM velocity is correlated with the vCSF‐Slope (p<0.05, r = 0.37). Figure 3 shows that QTM velocity in both GM and lateral ventricle are able to distinguish normal and MCI/AD groups.ConclusionThe tracer velocity in the brain tissue measured by QTM is associated with ventricular CSF turnover rate and brain amyloid burden. Acknowledgement: This study is supported by NIA grant AG057848

An accelerated rate of Beta‐amyloid Measured with [C‐11]PiB in Down Syndrome compared to Neurotypical Populations

AbstractBackgroundDown 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.MethodsIndividuals 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.ResultsPlotting [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).ConclusionsAβ 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.

In vivo [18F]SMBT‐1 imaging of reactive astrocytes in mouse models of Alzheimer’s disease ‐ temporospatial relationship with amyloid, tau and microgliosis

AbstractBackgroundReactive astrocytes play an important role in the pathogenesis of Alzheimer’s disease (AD). Here, we aim to investigate the temporospatial relationship between reactive astrocytes, microgliosis, tau and amyloid‐β by using in vivo multitracer imaging in AD transgenic mouse models.MethodPositron emission tomography (PET) imaging with [18F]SMBT‐1 (monoamine oxidase‐B for reactive astrocytes), [18F]DPA‐14 (translocator protein for microgliosis), [18F]PBB3 (tau), and [18F]florbetapir (Aβ) was carried out in 5‐ and 12‐month‐old APP/PS1, 12‐month‐old 3×Tg mice, and aged‐matched wild‐type mice. The brain regional referenced standard uptake value (SUVR) was computed with the cerebellum as reference region. Immunofluorescence staining was performed with antibodies against monoamine oxidase‐B, astrocytes, translocator protein, microglia, tau, and amyloid in mouse brain slices.Result[18F]SMBT‐1 SUVRs were higher in the cortex and hippocampus of 12‐month‐old APP/PS1 mice than age‐matched wild‐type mice. [18F]DPA‐14 SUVRs were higher in the brain stem and amygdala of 5 and 12‐month‐old APP/PS1 mice than in age‐matched wild‐type mice. [18F]PBB3 were higher in the cortex, striatum and hippocampus of APP/PS1 mice than in 5‐month‐old APP/PS1 mice and wild‐type mice. [18F]florbetapir SUVRs were higher in the brain stem, thalamus and midbrain of APP/PS1 mice at 5 months than in age‐matched wild‐type mice. No significant difference in the regional [18F]SMBT‐1, [18F]DPA‐14, [18F]PBB3, [18F]florbetapir or [11C]PIB SUVR was observed in 12‐month‐old 3×Tg mice compared to age‐matched wild‐type mice, which is validated by the limited 6E10‐positive amyloid plaques and AT8‐positive tau deposits.ConclusionThe findings provide in vivo evidence for different regional distribution of reactive astrocytes and microgliosis in animal models of AD.

Effect of SUVR reference region selection on sampled iterative local approximation estimates of amyloid onset age

AbstractBackgroundAmyloid PET imaging quantifies Aß plaques informing longitudinal modeling methods such as sampled iterative local approximation (SILA) that provide individualized estimated amyloid onset age (EAOA). EAOA is defined as the transition to abnormal Aß accumulation that occurs in Alzheimer’s disease. This work investigates the effect of SUVR reference region selection on EAOA by comparing DVR‐trained versus SUVR‐trained SILA models with three different SUVR reference regions.MethodN = 585 participants (mean (SD) baseline age = 66.40 (7.89) years, 512 unimpaired, 44 MCI, 28 dementia, 1 unknown) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center underwent T1‐weighted MRI and dynamic 11C‐Pittsburgh Compound B (PiB) amyloid PET imaging (0‐70 minutes after bolus injection). Reconstructed dynamic PET time series were processed and quantified using MR‐guided imaging pipelines optimized for DVR (Logan, k2’ = 0.149 min‐1, cerebellum gray matter (CBLM‐GM) reference region) and SUVR (50‐70 min frames) quantification with CBLM‐GM, whole cerebellum (WHOLE‐CBLM), or subcortical white matter (WM) as a reference region. Mean cortical PiB DVR and SUVR were each calculated by averaging across eight bilateral ROIs. Group‐based trajectory modeling was applied to longitudinal DVR data to establish an amyloid positivity (A+) threshold (DVR>1.14; equivalent to 14 centiloids). LME was used to define the relationship between DVR and SUVR and translate the DVR threshold to SUVR (SUVRCBLM GM>1.28; SUVRWHOLE CBLM>1.07; SUVRWM>0.68). Within‐person paired differences between EAOADVR and EAOASUVR_REFERENCE_REGION are reported as (mean; [95% CI]).ResultEAOA derived from DVR and SUVR were compared for n = 198 (34%) DVR A+ participants. Amyloid vs. age data are shown for each quantification approach in Figure 1. EAOASUVR‐WHOLE‐CBLM (‐1.29 years; [‐1.51, ‐1.08]) had the highest agreement with EAOADVR compared to EAOASUVR‐CBLM‐GM (‐1.44 years; [‐1.75, ‐1.12]) and EAOASUVR‐WM (‐0.67 years; [‐2.13, 0.79]; Figure 2). EAOA variability was highest for DVR>1.8 where fewer longitudinal observations were available for model training.ConclusionSUVR with whole cerebellum as a reference region resulted in the smallest difference in EAOA compared to DVR from the SILA model. Future work is ongoing to define SUVR thresholds that will optimize agreement with DVR‐trained EAOA results for harmonization of temporal estimates.

Effect of SUVR reference region selection on sampled iterative local approximation estimates of amyloid onset age

AbstractBackgroundAmyloid PET imaging quantifies Aβ plaques informing longitudinal modeling methods such as sampled iterative local approximation (SILA) that provide individualized estimated amyloid onset age (EAOA). EAOA is defined as the transition to abnormal Aβ accumulation that occurs in Alzheimer’s disease. This work investigates the effect of SUVR reference region selection on EAOA by comparing DVR‐trained versus SUVR‐trained SILA models with three different SUVR reference regions.MethodN = 585 participants (mean (SD) baseline age = 66.40 (7.89) years, 512 unimpaired, 44 MCI, 28 dementia, 1 unknown) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center underwent T1‐weighted MRI and dynamic 11C‐Pittsburgh Compound B (PiB) amyloid PET imaging (0‐70 minutes after bolus injection). Reconstructed dynamic PET time series were processed and quantified using MR‐guided imaging pipelines optimized for DVR (Logan, k2’ = 0.149 min−1, cerebellum gray matter (CBLM‐GM) reference region) and SUVR (50‐70 min frames) quantification with CBLM‐GM, whole cerebellum (WHOLE‐CBLM), or subcortical white matter (WM) as a reference region. Mean cortical PiB DVR and SUVR were each calculated by averaging across eight bilateral ROIs. Group‐based trajectory modeling was applied to longitudinal DVR data to establish an amyloid positivity (A+) threshold (DVR>1.14; equivalent to 14 centiloids). LME was used to define the relationship between DVR and SUVR and translate the DVR threshold to SUVR (SUVRCBLM GM>1.28; SUVRWHOLE CBLM>1.07; SUVRWM>0.68). Within‐person paired differences between EAOADVR and EAOASUVR_REFERENCE_REGION are reported as (mean; [95% CI]).ResultEAOA derived from DVR and SUVR were compared for n = 198 (34%) DVR A+ participants. Amyloid vs. age data are shown for each quantification approach in Figure 1. EAOASUVR‐WHOLE‐CBLM (‐1.29 years; [‐1.51, ‐1.08]) had the highest agreement with EAOADVR compared to EAOASUVR‐CBLM‐GM (‐1.44 years; [‐1.75, ‐1.12]) and EAOASUVR‐WM (‐0.67 years; [‐2.13, 0.79]; Figure 2). EAOA variability was highest for DVR>1.8 where fewer longitudinal observations were available for model training.ConclusionSUVR with whole cerebellum as a reference region resulted in the smallest difference in EAOA compared to DVR from the SILA model. Future work is ongoing to define SUVR thresholds that will optimize agreement with DVR‐trained EAOA results for harmonization of temporal estimates.

Effects of de‐facing T1 MRI, FLAIR MRI, Amyloid PET, and Tau PET scans on imaging‐clinical correlations analyses

AbstractBackgroundAutomated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.MethodWe sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.ResultAnalyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).ConclusionDe‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

Performance on complex memory tests is associated with β-amyloid in individuals at risk of developing Alzheimer's disease.

The pathophysiological development of Alzheimer's disease (AD) begins in the brain years before the onset of clinical symptoms. The accumulation of beta-amyloid (Aβ) is thought to be the first cortical pathology to occur. Carrying one apolipoprotein E (APOE) ε4 allele increases the risk of developing AD at least 2-3 times and is associated with earlier Aβ accumulation. Although it is difficult to identify Aβ-related cognitive impairment in early AD with standard cognitive tests, more sensitive memory tests may be able to do this. We sought to examine associations between Aβ and performance on three tests within three subdomains of memory, verbal, visual, and associative memory, to elucidate which of these tests were sensitive to Aβ-related cognitive impairment in at-risk subjects. 55 APOE ε4 carriers underwent MRI, 11 C-Pittsburgh Compound B (PiB) PET, and cognitive testing. A composite cortical PiB SUVR cut-off score of 1.5 was used to categorise subjects as either APOE ε4 Aβ+ or APOE ε4 Aβ-. Correlations were carried out using cortical surface analysis. In the whole APOE ε4 group, we found significant correlations between Aβ load and performance on verbal, visual, and associative memory tests in widespread cortical areas, the strongest association being with performance on associative memory tests. In the APOE ε4 Aβ+ group, we found significant correlations between Aβ load and performance of verbal and associative, but not visual, memory in localised cortical areas. Performance on verbal and associative memory tests provides sensitive markers of early Aβ-related cognitive impairment in at-risk subjects.

Beta‐amyloid plaque accumulation comparison in down syndrome and non‐down syndrome cohorts

AbstractBackgroundDown 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.MethodsIndividuals 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+.ResultsPlotting 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.ConclusionsThe 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.

Evaluating Dementia Neuroanatomic Risk of a Clinical Cohort Using MRI

AbstractBackgroundMRI is a widely used modality to evaluate dementia risk. Here, we extend previous work by estimating the Alzheimer’s disease pattern similarity (AD‐PS) scores for participants of a community‐dwelling older adult cohort to evaluate neuroanatomic risk of AD using MRI.MethodBaseline MRI data were available from 522 participants from the Wake Forest Alzheimer’s Disease Research Center’s (ADRC) Clinical Core with mean age 70.2, 81.2% White and 17.6% African American. Of those, 253 were cognitively impaired (dementia and mild cognitive impairment (MCI)) and 138 underwent [11C]PiB PET (MCI = 48 and dementia = 19). Global PiB SUVR was averaged from a cortical region of interest sensitive to early AD. Participants were classified as amyloid positivity using a previously defined threshold (≥1.21 SUVR). AD‐PS scores were estimated based on GM probability maps according to previously reported machine learning methodology[1]. High‐dimensional classifiers were trained using ADNI MRI data. To generate the AD‐PS scores, MRI data from the ADRC cohort was provided as input to the classifiers.We investigated associations that AD‐PS scores [range 0‐1] had with amyloid positivity and cognitive impairment. All analyses were adjusted for age, sex, race and education. AD‐PS scores discrimination of participants with dementia versus normal cognition (CN) was compared to hippocampal volume and cortical thickness in temporal regions based meta‐ROI which are sensitive to AD.ResultAD‐PS scores were strongly associated with cognitive impairment: OR = 4.1, CI: 95% [2.9‐5.9], p<0.0001. In addition, they were associated with amyloid positivity with OR = 2.8,CI: 95% [1.7‐4.7], p<0.001, N = 138. These amyloid positivity results did not hold in analyses limited to CN participants only(N = 71). Performance when discriminating patients with dementia from CN individuals was highest for AD‐PS score (AUC = 92.2, CI: 95% [86.9‐97.5]), followed by hippocampal volume (AUC = 86.5, CI: 95% [81.1‐91.2]) and cortical thickness in temporal regions based meta‐ROI (AUC = 81.5 CI: 95% [74.4‐88.6], See‐Figure).ConclusionThis work highlights further the generalization performance of our machine learning algorithm. AD‐PS scores, a measure of dementia neuroanatomic risk, strongly discriminated individuals with dementia from CN individuals in the Wake Forest ADRC.

Impaired glucose tolerance in Mild Cognitive Impairment is associated with lower brain amyloid on PET

AbstractBackgroundAmong community‐dwelling older adults with mild cognitive impairment (MCI), there exists little knowledge regarding how metabolic function impacts brain amyloid pathology (measured using PET) and cognitive status.MethodThis study explored, in participants diagnosed with MCI enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core, markers of baseline metabolic risk (Oral Glucose Tolerance Testing of blood glucose 120‐minute post challenge; OGTT‐2h), amyloid deposition by global PiB PET SUVr and disease subtype (MCI with and without amnestic symptoms) at a subsequent visit, as well as genetic (APOE‐ε4 carrier status) and demographic variables (age, sex, education). Consensus diagnosis of MCI was adjudicated by a panel of experts close in time to PET. OGTT‐2h>140 mg/dL indicates impaired glucose tolerance (IGT).Result59 MCI participants underwent OGTT at initial visit (39 normoglycemic, 20 IGT), and amyloid PET at a later visit (mean delay=362days). N=55 also had APOE genotype determined. PiB SUVR was not statistically different (p=.15) between MCI‐NG (1.51) and MCI‐IGT (1.33). However, in models controlling for age, sex, years education, and time between OGTT and PET, glycemic status was significantly associated (p=.007) with amyloid, such that MCI‐IGT had lower SUVR. APOE‐ε4 status did not attenuate this effect (glycemic status p=.011). A similar significant association was present for continuous OGTT‐2h values before (p=.006) and after (p=.02) controlling for APOE‐ε4 status, with higher OGTT‐2h associated with lower SUVR. Scatterplots of the negative association between OGTT‐2h and PiB SUVR are presented in Figure 1 (before [a] and after stratifying by APOE‐ε4 status [b]). Associations between glycemic status and PiB SUVR were similar when restricting to participants with amnestic symptoms (i.e., MCI‐A or MCI‐MDA; n=52), before (p=.008) and after controlling for APOE‐ε4 status (p=.012). In none of these analyses was an interaction term of APOE‐ε4 status and the glycemic variable significant (p’s>.23).ConclusionAmong community‐dwelling participants with MCI, elevated OGTT‐2h and IGT are associated with lower PiB SUVR. Participants with IGT, at the same level of cognition (MCI), have lower brain amyloid. In these MCI‐IGT participants, factors other than amyloid may be driving MCI status.

Impaired glucose tolerance in Mild Cognitive Impairment is associated with lower brain amyloid on PET

AbstractBackgroundAmong community‐dwelling older adults with mild cognitive impairment (MCI), there exists little knowledge regarding how metabolic function impacts brain amyloid pathology (measured using PET) and cognitive status.MethodThis study explored, in participants diagnosed with MCI enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core, markers of baseline metabolic risk (Oral Glucose Tolerance Testing of blood glucose 120‐minute post challenge; OGTT‐2h), amyloid deposition by global PiB PET SUVr and disease subtype (MCI with and without amnestic symptoms) at a subsequent visit, as well as genetic (APOE‐ε4 carrier status) and demographic variables (age, sex, education). Consensus diagnosis of MCI was adjudicated by a panel of experts close in time to PET. OGTT‐2h>140 mg/dL indicates impaired glucose tolerance (IGT).Result59 MCI participants underwent OGTT at initial visit (39 normoglycemic, 20 IGT), and amyloid PET at a later visit (mean delay=362days). N=55 also had APOE genotype determined. PiB SUVR was not statistically different (p=.15) between MCI‐NG (1.51) and MCI‐IGT (1.33). However, in models controlling for age, sex, years education, and time between OGTT and PET, glycemic status was significantly associated (p=.007) with amyloid, such that MCI‐IGT had lower SUVR. APOE‐ε4 status did not attenuate this effect (glycemic status p=.011). A similar significant association was present for continuous OGTT‐2h values before (p=.006) and after (p=.02) controlling for APOE‐ε4 status, with higher OGTT‐2h associated with lower SUVR. Scatterplots of the negative association between OGTT‐2h and PiB SUVR are presented in Figure 1 (before [a] and after stratifying by APOE‐ε4 status [b]). Associations between glycemic status and PiB SUVR were similar when restricting to participants with amnestic symptoms (i.e., MCI‐A or MCI‐MDA; n=52), before (p=.008) and after controlling for APOE‐ε4 status (p=.012). In none of these analyses was an interaction term of APOE‐ε4 status and the glycemic variable significant (p’s>.23).ConclusionAmong community‐dwelling participants with MCI, elevated OGTT‐2h and IGT are associated with lower PiB SUVR. Participants with IGT, at the same level of cognition (MCI), have lower brain amyloid. In these MCI‐IGT participants, factors other than amyloid may be driving MCI status.

Racial differences in dementia‐related pathology underlying cognitive decline: The Multi‐Ethnic Study of Atherosclerosis (MESA)

AbstractBackgroundRacial differences in dementia‐related pathology are purported to underlie health disparities in Alzheimer’s disease and related disorders. We examined the prevalence and relevance of neuroimaging biomarkers of cerebral small vessel disease (cSVD), and β‐amyloid (Aβ) deposition in cognitive decline and impairment among a diverse cohort of older adults.MethodParticipants at the Wake Forest MESA site were recruited at Exam 6 (2016‐19) to receive additional detailed cognitive testing, cognitive adjudication, and multimodal neuroimaging. Brain MRI on 3T Siemens Skyra, included T1, SWI, FLAIR and NODDI sequences. Total gray matter and white matter hyperintensities (WMH) volumes were adjusted for intracranial volume. A single neuroradiologist graded vascular lesions, including cerebral microbleeds, large and lacunar infarcts. Cognitive decline was measured by the Cognitive Abilities Screening Instrument (CASI) at Exam 5 (2010‐12) and Exam 6 (2016‐19). A subset of 157 participants completed Aβ‐PET at Exam 6 to quantify Aβ burden and positivity (PiB SUVR ≥1.21). Multivariable linear and logistic regression modeled neuroimaging parameters with change in CASI and cognitive status adjusted for age, gender, education, and APOE genotype, considering potential interactions by race.ResultThe imaging sample included 252 participants (53% African‐American and 47% White; aged 72±7 years; 58% women, Table 1). 27% of participants were adjudicated to have mild cognitive impairment (MCI) and 4% had dementia. Aβ positivity (36%) and cSVD (microbleeds in 37% and lacunes in 32%) were common and did not differ by race. Further, temporal lobe cortical thickness and WMH volumes did not differ by race. Lower total gray matter volume, higher WMH volumes, and lower temporal lobe cortical thickness were consistently associated with greater cognitive decline and impairment across racial groups (Table 2). Significant interactions between race and imaging biomarkers with cognitive decline and impairment observed for: greater NODDI Free Water only in White participants and microbleeds and Aβ only in African‐American participants.ConclusionImaging biomarkers indicative of cSVD and Aβ are common in older adults. These biomarkers were not more prevalent in African‐American than White older adults as previously reported; however, they appear to be more strongly related to cognitive decline and impairment among African‐Americans.

Association between cerebral small‐vessel disease and Alzheimer’s disease pathologies

AbstractBackgroundControversy exists regarding the etiological role of cerebral small vessel disease (CSVD) in Alzheimer's disease (AD). We thus evaluated the association between brain Aβ/tau burden and white matter hyperintensity (WMH) volume among subjects with varying severity levels of cognitive impairment.MethodA total of 84 stroke‐free subjects (healthy controls [HC]=10; subjective cognitive decline [SCD]=32; mild cognitive impairment [MCI]=26; dementia =16) were included. All patients underwent structural MRI, 11C‐ PIB, and 18F‐T807 PET to measure CSVD makers, Aβ deposition (A+) and pathologic tau (T+), respectively. WMH volume was quantified by automatic segmentation using AccuBrain® IV 1.1 (BrainNow Medical Technology Company Ltd.).ResultNo significant association was found between A+T+ and WMH volume in the overall population (β =‐0.03 ;95% CI, ‐0.04‐0.11; p=0.10). There was also no significant association found within different cognitive diagnostic group except in those with dementia, where we found that increasing global 11C‐ PIB SUVR correlated with lower WMH volume (B=‐1.76 ;95% CI, ‐2.92‐ ‐0.59; p=0.007). No significant association between 18F‐T807 SUVR and WMH volume was observedConclusionWe found no association between brain Aβ/tau burden and WMH volume except an inverse association was noted between Aβ and WMH in dementia patients. PiB retention might be restrained by WMH in those patients with higher CSVD burden.

Multimodal neuroimaging of sex differences in cognitively impaired patients on the Alzheimer's continuum: greater tau-PET retention in females

We assessed sex differences in amyloid- and tau-PET retention in 119 amyloid positive patients with mild cognitive impairment or Alzheimer's disease (AD) dementia. Patients underwent 3T-MRI, 11C-PIB amyloid-PET and 18F-Flortaucipir tau-PET. Linear ordinary least squares regression models tested sex differences in Flortaucipir-PET SUVR in a summary temporal region of interest as well as global PIB-PET. No sex differences were observed in demographics, Clinical Dementia Rating Sum of Boxes (CDR-SoB), Mini-Mental State Exam (MMSE), raw episodic memory scores, or cortical thickness. Females had higher global PIB SUVR (ηp²=.043, p=.025) and temporal Flortaucipir SUVR (ηp²=.070, p=.004), adjusting for age and CDR-SoB. Sex differences in temporal Flortaucipir-PET remained significant when controlling additionally for PIB SUVR and APOE4 status (ηp²=.055, p=.013), or when using partial volume-corrected data. No sex differences were present in areas of known Flortaucipir off-target binding. Overall, females demonstrated greater AD regional tau-PET burden than males despite clinical comparability. Further characterization of sex differences will provide insight into AD pathogenesis and support development of personalized therapeutic strategies.

Strategies to reduce sample sizes in Alzheimer\u2019s disease primary and secondary prevention trials using longitudinal amyloid PET imaging

BackgroundDetecting subtle-to-moderate biomarker changes such as those in amyloid PET imaging becomes increasingly relevant in the context of primary and secondary prevention of Alzheimer’s disease (AD). This work aimed to determine if and when distribution volume ratio (DVR; derived from dynamic imaging) and regional quantitative values could improve statistical power in AD prevention trials.MethodsBaseline and annualized % change in [11C]PIB SUVR and DVR were computed for a global (cortical) and regional (early) composite from scans of 237 cognitively unimpaired subjects from the OASIS-3 database (www.oasis-brains.org). Bland-Altman and correlation analyses were used to assess the relationship between SUVR and DVR. General linear models and linear mixed effects models were used to determine effects of age, sex, and APOE-ε4 carriership on baseline and longitudinal amyloid burden. Finally, differences in statistical power of SUVR and DVR (cortical or early composite) were assessed considering three anti-amyloid trial scenarios: secondary prevention trials including subjects with (1) intermediate-to-high (Centiloid > 20.1), or (2) intermediate (20.1 < Centiloid ≤ 49.4) amyloid burden, and (3) a primary prevention trial focusing on subjects with low amyloid burden (Centiloid ≤ 20.1). Trial scenarios were set to detect 20% reduction in accumulation rates across the whole population and in APOE-ε4 carriers only.ResultsAlthough highly correlated to DVR (ρ = .96), cortical SUVR overestimated DVR cross-sectionally and in annual % change. In secondary prevention trials, DVR required 143 subjects per arm, compared with 176 for SUVR. Both restricting inclusion to individuals with intermediate amyloid burden levels or to APOE-ε4 carriers alone further reduced sample sizes. For primary prevention, SUVR required less subjects per arm (n = 855) compared with DVR (n = 1508) and the early composite also provided considerable sample size reductions (n = 855 to n = 509 for SUVR, n = 1508 to n = 734 for DVR).ConclusionSample sizes in AD secondary prevention trials can be reduced by the acquisition of dynamic PET scans and/or by restricting inclusion to subjects with intermediate amyloid burden or to APOE-ε4 carriers only. Using a targeted early composite only leads to reductions of sample size requirements in primary prevention trials. These findings support strategies to enable smaller Proof-of-Concept Phase II clinical trials to better streamline drug development.

β-Amyloid PET and 123I-FP-CIT SPECT in Mild Cognitive Impairment at Risk for Lewy Body Dementia.

ObjectiveTo determine the clinical phenotypes associated with the β-amyloid PET and dopamine transporter imaging (123I-FP-CIT SPECT) findings in mild cognitive impairment (MCI) with the core clinical features of dementia with Lewy bodies (DLB; MCI-LB).MethodsPatients with MCI who had at least 1 core clinical feature of DLB (n = 34) were grouped into β-amyloid A+ or A− and 123I-FP-CIT SPECT D+ or D− groups based on previously established abnormality cut points for A+ with Pittsburgh compound B PET standardized uptake value ratio (PiB SUVR) ≥1.48 and D+ with putamen z score with DaTQUANT <−0.82 on 123I-FP-CIT SPECT. Individual patients with MCI-LB fell into 1 of 4 groups: A+D+, A+D−, A−D+, or A−D−. Log-transformed PiB SUVR and putamen z score were tested for associations with patient characteristics.ResultsThe A−D+ biomarker profile was most common (38.2%), followed by A+D+ (26.5%) and A−D− (26.5%). The least common was the A+D- biomarker profile (8.8%). The A+ group was older, had a higher frequency of APOE ε4 carriers, and had a lower Mini-Mental State Examination score than the A− group. The D+ group was more likely to have probable REM sleep behavior disorder. Lower putamen DaTQUANT z scores and lower PiB SUVRs were independently associated with higher Unified Parkinson’s Disease Rating Scale-III scores.ConclusionsA majority of patients with MCI-LB are characterized by low β-amyloid deposition and reduced dopaminergic activity. β-Amyloid PET and 123I-FP-CIT SPECT are complementary in characterizing clinical phenotypes of patients with MCI-LB.

White matter hypertensity burden varies in empirically derived incident MCI subtypes

AbstractBackgroundWhite matter changes and Alzheimer’s disease pathology frequently co‐exist in the population. Recently we empirically derived subtypes of incident mild cognitive impairment (MCI) and found that there was significant heterogeneity not only in cognitive impairment profiles but also gray matter atrophy suggesting that multiple etiologies may contribute to cognitive impairment in these subtypes. The aim of this study was to examine differences in white matter hyperintensity (WMH) volume in these same MCI subtypes and investigate amyloid deposition in a smaller subset.MethodWe analyzed data from 188 participants with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) identified as having one of four MCI subtypes: subtle cognitive impairment (SCI), amnestic, dysnomic, and dysexecutive. All participants completed 3T imaging at the time of the MCI diagnosis. Using WMH volume scaled by TIV and PIB SUVR, we compared each MCI subtype to 344 cognitively impaired (CU) participants (mean age 78; 46% women) and each other.ResultThe amnestic (n = 83; mean age 80; 43% women), dysnomic (n = 30; mean age 84; 47% women) and dysexecutive subtypes (n = 49; mean age 83; 35% women) had significantly higher WMH volumes compared to CU (p &lt; .02) while the SCI subtype (n = 26; mean age 80; 23% women) did not differ from CU (p = .76). Amongst the MCI subtypes, the amnestic (p = .04) and dysexecutive subtypes (p = .01) had significantly higher WMH volumes than the SCI subtype [Figure 1]. In a smaller subset of individuals who also completed PiB‐PET (17/26 SCI; 39/83 amnestic; 10/30 dysnomic, 28/49 dysexecutive), we found evidence for higher amyloid load in all MCI subgroups except the SCI group (p&lt;0.05) in comparison to CU.ConclusionWe found significantly elevated levels of WMH and amyloid burden in amnestic, dysnomic, and dysexecutive subtypes of incident MCI supporting the notion that both vascular changes and amyloid contribute to the development of clinical symptoms. Surprisingly, WMH and amyloid positivity were not high in SCI suggesting that the pathophysiology in this group needs further investigation.

Predicting the time to tau onset in Down syndrome using amyloid chronicity

AbstractBackgroundAdults with Down syndrome (DS) are predisposed to Alzheimer’s disease (AD) with the majority developing clinical dementia by their 60s. This study aims to estimate the time to tau onset in DS using the metric of Aβ chronicity (estimated duration of Aβ(+)) as a predictor.MethodN=140 adults with DS (age=39.3±8.4 years) were evaluated for Aβ and tau using [C‐11]PiB and [F‐18]AV‐1451 PET, respectively. 50‐70 minute PiB SUVr and 80‐100 minute AV‐1451 SUVr images were created (cerebellar gray matter reference region). Longitudinal PiB scans were conducted for n=68 (3.0±0.7 scans; 2.4±0.6 years apart). The Amyloid Load (AβL), calculated from PiB SUVr images, indicates global Aβ burden. Group‐based trajectory modeling (GBTM) was applied across longitudinal AβL data to identify Aβ accumulation groups. The trajectories with the lowest BIC were solved to determine the estimated age of Aβ(+) onset (AβL cutoff: 20.0% (Zammit, DADM 2020)) and Aβ chronicity was calculated as chronological age minus estimated Aβ(+) age (Koscik and Betthauser, DADM 2020). Regional AV‐1451 SUVr values were extracted based on ROIs encompassing the Braak staging of NFT pathology. Preliminary thresholds for elevated tau (tau(+)) were determined for each Braak composite region using k‐means clustering with resampling. Linear regression models were performed between Aβ chronicity and AV‐1451 SUVr. The functional form of the regression fit was solved to estimate average Aβ(+) chronicity at tau(+) in each Braak region.ResultGBTM identified two Aβ accumulator groups in our DS cohort with estimated ages of Aβ(+) onset of 32 and 49 years (Figure 1). Of the early accumulators, only n=2 carried the APOE E4 allele. Aβ chronicity and AV‐1451 SUVr displayed a linear relationship for Braak regions 1‐2, and a quadratic relationship for Braak regions 3‐6 (Figure 2). The pattern of tau deposition in DS followed the conventional Braak NFT staging observed in late‐onset AD. Estimated Aβ(+) chronicity at tau(+) onset increased as tau pathology progressed through each Braak stage (Table 1).ConclusionThese preliminary findings reveal insight on the time course of tau deposition relative to Aβ in DS. Longitudinal AV‐1451 scans are currently underway to further refine the model and characterize tau progression in this population.

Correspondence between blood‐based amyloid‐β by immuno‐precipitation mass spectrometry and PIB‐PET imaging in a population cohort

AbstractBackgroundRecently developed immuno‐precipitation mass spectrometry (IP‐MS) assays to measure blood‐based amyloid‐β with high accuracy holds exciting potential. Adoption of amyloid‐β measurement will be especially valuable in large population studies where not previously feasible. We evaluated the correspondence between a new IP‐MS assay developed on a commercially available platform with brain amyloid PET imaging as the gold standard.MethodN=99 older participants without dementia, active in the population‐based Monongahela‐Youghiogheny Healthy Aging Team (MYHAT) study, underwent blood draws at the time of brain amyloid imaging with PiB‐PET (50‐70 minutes post‐injection, SUVR with cerebellar reference). Adapting methods of Nakamura et al. (2018), immunoprecipitation from plasma was followed by time‐of‐flight MS, using a benchtop mass spectrometer, to measure relative amounts of endogenous peptides Aβ1‐40, Aβ1‐42, APP669‐711, and a stable‐isotope‐labeled internal standard. A composite plasma biomarker was calculated as the mean of the normalized ratios APP669711/Aβ142 and Aβ140/Aβ142.Resultn= 24 (24%) of participants were PiB‐positive by a global cutoff, 7 (7%) were CDR 0.5, and 13 (13%) were APOE*4 carriers. Higher global PiB SUVR was associated with older age, higher CDR (0.5 vs. 0) and APOE*4 genotype (p’s &lt; .05), while higher plasma composite biomarker was similarly associated with CDR and APOE*4 but not age. ROC analysis yielded an AUC of 0.78 (SE 0.05) for the composite plasma biomarker predicting PiB‐PET status, with an optimized cut‐point corresponding to sensitivity of 0.75 and specificity of 0.69. Including age and APOE*4 into the model yielded an overall AUC of 0.89, and plasma biomarker sensitivity of 0.91 and specificity of 0.77.ConclusionThis IP‐MS assay was successfully implemented using a commercially‐available, benchtop MALDI‐TOF instrument that is easy to operate and already used in clinic settings. Concurrent prediction of amyloid status from plasma amyloid alone was significant, with moderately high accuracy. Combining plasma amyloid with age and APOE genotype improved prediction to high accuracy. Planned follow‐up of the sample with repeat PiB scans will yield yet further information about prospective prediction of brain amyloid status. This non‐invasive approach using a peripheral biomarker could be highly useful and feasible to apply in large population studies of AD risk.

Staging of tau distribution by positron emission tomography may be useful in clinical staging of Alzheimer disease

AbstractBackgroundClinical staging of Alzheimer disease (AD) may help develop novel treatment in the early stage.AimWe measured the accumulation of amyloid beta (Aβ) and tau with positron emission tomography (PET) in patients with AD to explore its utility for clinical staging.MethodsSix patients with AD, two patients with mild cognitive impairment (MCI), and 12 healthy controls (HC) were studied. Aβ and tau accumulation was evaluated with [11C]PiB and [11C]PBB3, respectively.ResultsPBB3‐PET showed that two cases were in stage I‐II (Braak stage), one case was in stage III‐IV, and three cases were in stage V‐VI. PiB‐PET showed that all cases were in stage C. The duration of the disease correlated positively with PBB3 staging but not with PiB staging. The performance on the Mini‐Mental State Examination (MMSE) tended to decrease with advancing of PBB3 stage but not with PiB stage. PBB3 SUVR and PiB SUVR in all AD signature areas including the parahippocampal gyrus were significantly higher in AD than in HC. A decrease in MMSE is correlated with increases in PBB3 and PiB. Increase in PBB3 started with decrease in MMSE whereas increase in PiB was already observed in the point of highest MMSE, indicating amyloid is already accumulated in the earliest stage.ConclusionsThe expansion of tau distribution from the parahippocampal gyrus to the cerebral cortex was observed with advancing AD, whereas Aβ distribution was already advanced in the earliest stage. PBB3 may be useful in determining stages in AD based on tau distribution.