Global PiB Research Articles

Characterizing brain tau and cognitive decline along the amyloid timeline in Alzheimer's disease.

Recent longitudinal PET imaging studies have established methods to estimate the age at which amyloid becomes abnormal at the level of the individual. Here we recontextualized amyloid levels into the temporal domain to better understand the downstream Alzheimer's disease processes of tau neurofibrillary tangle (NFT) accumulation and cognitive decline. This cohort study included a total of 601 individuals from the Wisconsin Registry for Alzheimer's Prevention and Wisconsin Alzheimer's Disease Research Center that underwent amyloid and tau PET, longitudinal neuropsychological assessments and met clinical criteria for three clinical diagnosis groups: cognitively unimpaired (n = 537); mild cognitive impairment (n = 48); or dementia (n = 16). Cortical 11C-Pittsburgh compound B (PiB) distribution volume ratio (DVR) and sampled iterative local approximation were used to estimate amyloid positive (A+; global PiB DVR > 1.16 equivalent to 17.1 centiloids) onset age and years of A+ duration at tau PET (i.e. amyloid chronicity). Tau PET burden was quantified using 18F-MK-6240 standardized uptake value ratios (70-90 min, inferior cerebellar grey matter reference region). Whole-brain and region-specific approaches were used to examine tau PET binding along the amyloid timeline and across the Alzheimer's disease clinical continuum. Voxel-wise 18F-MK-6240 analyses revealed that with each decade of A+, the spatial extent of measurable tau spread (i.e. progressed) from regions associated with early to late NFT tau stages. Regional analyses indicated that tau burden in the entorhinal cortex was detectable, on average, within 10 years of A+ onset. Additionally, the entorhinal cortex was the region most sensitive to early amyloid pathology and clinical impairment in this predominantly preclinical sample. Among initially cognitively unimpaired (n = 472) individuals with longitudinal cognitive follow-up, mixed effects models showed significant linear and non-linear interactions of A+ duration and entorhinal tau on cognitive decline, suggesting a synergistic effect whereby greater A+ duration, together with a higher entorhinal tau burden, increases the likelihood of cognitive decline beyond their separable effects. Overall, the amyloid time framework enabled a spatiotemporal characterization of tau deposition patterns across the Alzheimer's disease continuum. This approach, which examined cross-sectional tau PET data along the amyloid timeline to make longitudinal disease course inferences, demonstrated that A+ duration explains a considerable amount of variability in the magnitude and topography of tau spread, which largely recapitulated NFT staging observed in human neuropathological studies. By anchoring disease progression to the onset of amyloid, this study provides a temporal disease context, which may help inform disease prognosis and timing windows for anti-amyloid therapies.

Tau accumulation and atrophy predict amyloid independent cognitive decline in aging.

Amyloid beta (Aβ) and tau pathology are cross-sectionally associated with atrophy and cognitive decline in aging and Alzheimer's disease (AD). We investigated relationships between concurrent longitudinal measures of Aβ (Pittsburgh compound B [PiB] positron emission tomography [PET]), tau (flortaucipir [FTP] PET), atrophy (structural magnetic resonance imaging), episodic memory (EM), and non-memory (NM) in 78 cognitively healthy older adults (OA). Entorhinal FTP change was correlated with EM decline regardless of Aβ, but meta-temporal FTP and global PiB change were only associated with EM and NM decline in Aβ+ OA. Voxel-wise analyses revealed significant associations between temporal lobe FTP change and EM decline in all groups. PiB and FTP change were not associated with structural change, suggesting a functional or microstructural mechanism linking these measures to cognitive decline. Our results show that longitudinal Aβ is linked to cognitive decline only in the presence of elevated Aβ, but longitudinal temporal lobe tau is associated with memory decline regardless of Aβ status. Entorhinal tau change was associated with memory decline in older adults (OA), regardless of amyloid beta (Aβ). Greater meta-region of interest (ROI) tau change correlated with memory decline in Aβ+ OA. Voxel-wise temporal tau change correlated with memory decline, regardless of Aβ. Meta-ROI tau and global amyloid change correlated with non-memory change in Aβ+ OA. Tau and amyloid accumulation were not associated with structural change in OA.

Abstract 150: Regional Molecular Alterations in Cerebral Amyloid Angiopathy-Related Hemorrhagic Lesions

Background: Previous case reports suggested co-localization of tau but not β-Amyloid with cortical superficial siderosis (cSS) in cerebral amyloid angiopathy (CAA). We thus hypothesized that the tau load represents a continuum with amyloid accumulation in the disease cascade and that the tau load would be higher in lobes with cSS and intracerebral hemorrhages (ICH) when compared to the contralateral lobes without hemorrhages in CAA patients. Methods: The study included 26 cognitively healthy probable CAA patients (mean age 71±7.5, 53.8% male) who underwent multimodal MRI, Pittsburgh compound B (PiB, for amyloid), and 18 F-flortaucipir (FTP, for tau) PET imaging. Mean global cortical PiB uptake and FTP uptake were calculated in each lobar region. The presence of ICH and cSS were identified in each lobar region independent of molecular imaging. A pairwise comparison of PiB and FTP uptake was done between lobes with any hemorrhagic lesion (ICH/cSS) and their corresponding lobes without hemorrhages. Results: The FTP uptake was significantly higher in 8 frontal, 16 temporal, and 11 occipital lobes with hemorrhages compared to their corresponding contralateral lobes without hemorrhages (1.18±0.1 vs. 1.13±0.1; p=0.002, 1.28±0.1 vs. 1.20±0.1; p=0.004, 1.29±0.1 vs. 1.10±0.1; p=0.002, respectively) but not in parietal lobes (p=0.107). The PiB uptake was significantly higher in occipital lobes with hemorrhages compared to their contralateral lobes without hemorrhages (1.41±0.3 vs. 1.35±0.3, p=0.004) but there was no difference in the comparison of amyloid burden for the remaining lobar regions with or without hemorrhage (p>0.2 for all comparisons). Conclusions: Reduced PiB uptake at sites of larger hemorrhagic lesions (cSS/ICH) has been observed in the past so the lack of differences in amyloid between most lobes with and without hemorrhage was not surprising in our study. Our results otherwise show significantly increased tau load at most brain lobes with hemorrhagic lesions among CAA patients. Potential clinical effects of the tau burden related to cSS/ICH should be studied in larger CAA cohorts using appropriate clinical testing.

Developing Topics.

Both 11C-Pittsburgh compound-B (PiB) and 18F-Flutemetamol (FMT) have off-target white matter (WM) signal that increases with age, but WM uptake is stronger with FMT than PiB. Although their WM patterns are comparably associated with age, the difference between them may impact visual and quantitative assessments of cortical amyloid-β uptake. Our objective was to determine whether PiB PET and FMT PET visual assessments agree with quantitative cortical amyloid-β evaluations. A cohort of cognitively unimpaired (CU) younger adults with median (range) of 39 (30, 48) years (N = 30), CU older adults with age of 67 (61, 83) years (N = 30) and older adults with AD dementia with age of 67 (54-84) years (N = 23) underwent MRI, PiB PET and FMT PET. PiB-PET and FMT PET scans were evaluated visually by two nuclear medicine specialists blinded to participant information. Disagreements were reconciled with a second, joint round of visual reads. Readers applied standard visual assessment criteria using the absence of regional gray matter (GM) and WM contrast as an indication of a positive scan. The global cortical PiB standard uptake value ratio (SUVr) was obtained referencing to cerebellar crus uptake and converted to centiloid values. Participants were classified as PiB positive/negative using a centiloid cut-off of 22. PiB and FMT visual positivity agreed with quantitative positivity on PiB-based centiloid values in 69/83 for PiB and 78/83 for FMT. There were 10 disagreements with PiB, 1 disagreement with FMT and 4 disagreements with both. Few disagreements occurred near the centiloid threshold. Interestingly, all disagreements between visual reads (amyloid-β positive) and centiloid (amyloid-β negative) in the CU younger were with PiB. FMT disagreements were always in the CU older and AD groups. Disagreements between amyloid-β positivity on visual reads and quantitative measurements (centiloid) may depend on age and tracer. PiB WM uptake is lower than FMT WM uptake, which may lead to reduced contrast between GM and WM (part of the standard criteria for visual reads), especially in younger ages. In contrast, WM uptake increases at older ages, and increased WM signal may bleed more into cortical regions, hiding subtle, near-threshold cortical uptake, especially with FMT.

Using neuroimaging biomarkers to define plasma thresholds in a community dwelling cohort

AbstractBackgroundAD Imaging biomarkers (IBM) include measures of amyloid (A) on PET, and neurodegeneration (N) on MRI, with thresholds for classification of imaging A/N positivity commonly used at research centers. Novel AD plasma biomarkers (PBM) can potentially enhance neuropathological characterization; however, knowledge regarding which thresholds to use to characterize PBM abnormalities is limited.MethodWe evaluated a range of PBM thresholds based on (1) plasma data alone (gaussian mixture models [GMM]), and (2) when classifying A/N IBM using both existing and data‐driven cutoffs (ROC analyses). Baseline plasma and imaging data were obtained in a community‐dwelling cohort enrolled in the Wake Forest ADRC, including cognitively normal participants (N = 300) and individuals with consensus diagnosis of mild cognitive impairment (N = 192) or dementia (N = 64; Table 1). We examined PBMs (Quanterix SIMOA HD‐X from NCRAD: Aß42/40, GFAP, NfL, p‐tau181) and IBM measures of A (global PiB PET; A+>1.21 SUVR) and N (FreeSurfer temporal cortical thickness; MAYOTHCK), N+ = 2.68mm; FreeSurfer hippocampal volume, N+ = 0.454% head size; HPC‐VOL).ResultOptimal/low/high GMM‐derived thresholds based on PBM distributions (Figure 1a) are provided in Figure 1b. ROC analysis using existing IBM cutoffs are in Figure 1c, with details (sensitivity/specificity/AUC) in Figure 1d. GMM‐ and ROC‐derived p‐tau181 thresholds were highly aligned (range 3.698‐4.071 pg/ml). For NfL and GFAP, imaging ROC‐derived thresholds were lower than GMM‐derived thresholds. AUCs were highest when PBM classification was used to predict A‐PET using existing PET‐positivity cutoffs, with PBM performing more poorly for MRI‐based cutoffs. A/N IBM histograms and current/optimal/low/high GMM‐derived thresholds for each IBM are in Figure 2a. Figure 2b represents how different data‐driven IBM cutoffs impact PBM thresholds in ROC analyses, with AUC statistics for these ROC analyses in Figures 2c‐e. Notably, GMM‐optimal IBM cutoffs performed similarly to current cutoffs for defining PBM thresholds, especially when using A‐PET (AUCs for current and GMM‐optimal >0.747).ConclusionWhile use of existing and data‐driven IBM cutoffs to define PBM thresholds in a community‐dwelling cohort is feasible, some plasma assays (e.g., p‐tau181) are more consistent than others, and caution is warranted against selecting a single threshold. To facilitate clinical use, future work will focus on establishing confidence intervals for PBM abnormalities.

Evaluating cognitive phenotypes and associations with neuroimaging biomarkers of dementia in a sample of aging individuals using unsupervised machine learning

AbstractBackgroundOtherwise cognitively normal individuals can experience age‐related cognitive dysfunction and may present with heterogenous patterns of cognitive impairment. There is limited knowledge regarding the presence and stability of cognitive subtypes observed in individuals with mild cognitive impairment and dementia (e.g., dysexecutive, non‐amnestic). Further, the extent to which cognitive subtypes related to age‐ and dementia‐related neuroimaging biomarkers is uncertain.MethodThis study sought to evaluate the presence and composition of cognitive subtypes in a large diverse community sample of aging individuals without dementia (NC; Baseline N = 337; Table 1). Participants enrolled in the Wake Forest ADRC and completed an annual battery of cognitive assessments (Uniform Data Set; NACC), the Mini‐Mental State Exam (MMSE), and Clinical Diagnostic Rating Scale (CDR). Unsupervised machine learning (e.g., clustering: hierarchical, k‐means, and k‐medoids algorithms; 1000 bootstrap iterations; majority voting rule) was applied to the battery of cognitive assessments. Demographic characteristics and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aβ‐PET), total brain volume (BVOL), diffusion‐weighted fractional anisotropy (FA), and NODDI freewater (FW) were compared across prospective clusters using general linear models. Highly correlated cognitive metrics (r>.85) were removed prior to analysis.ResultCluster analyses using baseline cognitive scores suggested NC participants could be partitioned into two to eight clusters; two primary clusters representing one large homogenous group of relatively unimpaired individuals and another heterogenous cluster composed of 4‐7 possible cognitive subgroups (Figure 1). The two‐cluster solution partitioned NC participants into higher and lower cognitive performers. Across cluster solutions, lower performing individuals were older, had higher Aβ‐PET, and lower total brain volume on average (p<.05; Figure 2).ConclusionWe did not observe well‐defined clusters representing distinct cognitive subtypes reported in previous studies (e.g., dysexecutive, amnestic, visuospatial). However, a heterogenous subset of NC participants exhibited substantial variability across assessments. Three‐ and four‐cluster solutions identified individuals with greater impairment in executive functioning, and verbal learning and memory respectively. Findings highlight the need to model complex patterns of cognitive abilities in otherwise healthy older adults. Future longitudinal work will assess how health information and neuroimaging biomarkers of dementia contribute to the composition and stability of cognitively defined subgroups.

Using neuroimaging biomarkers to define plasma thresholds in a community dwelling cohort

AbstractBackgroundAD Imaging biomarkers (IBM) include measures of amyloid (A) on PET, and neurodegeneration (N) on MRI, with thresholds for classification of imaging A/N positivity commonly used at research centers. Novel AD plasma biomarkers (PBM) can potentially enhance neuropathological characterization; however, knowledge regarding which thresholds to use to characterize PBM abnormalities is limited.MethodWe evaluated a range of PBM thresholds based on (1) plasma data alone (gaussian mixture models [GMM]), and (2) when classifying A/N IBM using both existing and data‐driven cutoffs (ROC analyses). Baseline plasma and imaging data were obtained in a community‐dwelling cohort enrolled in the Wake Forest ADRC, including cognitively normal participants (N = 300) and individuals with consensus diagnosis of mild cognitive impairment (N = 192) or dementia (N = 64; Table 1). We examined PBMs (Quanterix SIMOA HD‐X from NCRAD: Aβ42/40, GFAP, NfL, p‐tau181) and IBM measures of A (global PiB PET; A+>1.21 SUVR) and N (FreeSurfer temporal cortical thickness; MAYOTHCK), N+≤2.68mm; FreeSurfer hippocampal volume, N+≤0.454% head size; HPC‐VOL).ResultsOptimal/low/high GMM‐derived thresholds based on PBM distributions (Figure 1a) are provided in Figure 1b. ROC analysis using existing IBM cutoffs are in Figure 1c, with details (sensitivity/specificity/AUC) in Figure 1d. GMM‐ and ROC‐derived p‐tau181 thresholds were highly aligned (range 3.698‐4.071 pg/ml). For NfL and GFAP, imaging ROC‐derived thresholds were lower than GMM‐derived thresholds. AUCs were highest when PBM classification was used to predict A‐PET using existing PET‐positivity cutoffs, with PBM performing more poorly for MRI‐based cutoffs. A/N IBM histograms and current/optimal/low/high GMM‐derived thresholds for each IBM are in Figure 2a. Figure 2b represents how different data‐driven IBM cutoffs impact PBM thresholds in ROC analyses, with AUC statistics for these ROC analyses in Figures 2c‐e. Notably, GMM‐optimal IBM cutoffs performed similarly to current cutoffs for defining PBM thresholds, especially when using A‐PET (AUCs for current and GMM‐optimal >0.747).ConclusionWhile use of existing and data‐driven IBM cutoffs to define PBM thresholds in a community‐dwelling cohort is feasible, some plasma assays (e.g., p‐tau181) are more consistent than others, and caution is warranted against selecting a single threshold. To facilitate clinical use, future work will focus on establishing confidence intervals for PBM abnormalities.

Relationship between APOE risk score and myelin in Alzheimer’s disease

AbstractBackgroundPostmortem brain tissue analysis suggests that apolipoprotein E (APOE) ε4 is associated with lower brain myelination (Blanchard et al. 2022). However, in vivo evidence for this association is lacking. Here, we examined the relationship between APOE genotype and myelin content in the brain using multicomponent relaxometry (mcDESPOT) imaging and an APOE neuropathologic risk score (Deming et al, 2023).Methods121 cognitively unimpaired participants from the Wisconsin Alzheimer’s Disease Research Center and the Wisconsin Registry for Alzheimer’s Prevention completed at least one mcDESPOT MRI scan and were APOE genotyped. Myelin content was quantified using mcDESPOT‐derived myelin water fraction (MWF) maps. Amyloid positivity was determined using CSF Aβ42/Aβ40 or a [C‐11]PiB PET scan. Cutoffs for amyloid positivity was a CSF Aβ42/Aβ40 ratio of <0.046 (Van Hulle et al, 2020) or a global PiB DVR threshold >1.19 (Betthauser et al, 2020). MWF maps were registered to MNI space and smoothed with an 8mm FWHM kernel. We conducted a voxel‐wise regression analysis to determine the relationship between APOE risk and myelin content. Covariates included age, age difference between MRI and amyloid acquisition, gender, race, amyloid status, APOE by amyloid status interaction, and APOE by gender interaction. We followed up these analyses examining specific brain regions implicated in AD. All analyses were corrected for multiple comparisons.ResultsParticipant characteristics are shown in Table 1. Voxel‐wise analysis indicated that MWF was negatively associated with age in the superior longitudinal fasciculus and small regions of the brainstem (Figure 1). MWF was not associated with APOE risk, gender, or race. ROI analysis showed a significant APOE by amyloid status interaction in the left (β = 0.006, p‐value = 0.035) and right cingulum (β = ‐0.013, p‐value = 0.037; Table 2). However, these interactions did not survive FDR correction.ConclusionThese findings suggest that myelin content decreases with age. Although APOE ε4 increases risk for developing AD dementia, it was not associated with brain myelin content in this cohort. In preclinical participants harboring amyloid pathology, APOE risk may impact myelin content although our findings did not survive FDR correction. More research is needed to fully elucidate this relationship.

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.

Neurodegenerative biomarkers of A‐T+ compared to A+T+ and A‐T‐ older adults

AbstractBackgroundPrimary age‐related tauopathy (PART) is a neuropathological entity with neurofibrillary tangles in the absence of beta‐amyloid plaques in the brain. Recent advances in neuroimaging made it possible to identify amyloid PET‐negative (A‐) tau PET‐positive (T+) condition as a possible candidate for PART in living human brains. We tried to investigate neurodegenerative biomarkers of A‐T+ older adults compared to those of A+T+ and A‐T‐ [corresponding to Alzheimer’s disease (AD) and no AD pathology, respectively] individuals.MethodA total of 156 older participants (64 cognitively normal, 43 mild cognitive impairment (MCI), and 49 dementia), were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE). All participants underwent comprehensive clinical evaluation and multimodal brain imaging including amyloid and tau PET, and MRI. Global PiB retention measured by 11C‐PiB‐PET and tau deposition in the regions corresponding to Braak stages measured by 18F‐AV‐1451 PET were used in this study. A+ was defined as global mean Aß deposition standardized uptake value ratio (SUVR) > 1.4 and T+ was defined as tau burden corresponding to Braak stages I/II, III/IV, or V/VI. Hippocampal volume, AD‐signature cortical thickness (AD‐CT), AD‐signature cerebral glucose metabolism (AD‐CM) and white matter hyperintensity (WMH) volume were also measured as neurodegenerative biomarkers. Multiple linear regression analysis controlling for age, gender, and APOE e4 positivity was used to investigate the group differences of neurodegenerative biomarkers.ResultOf the 156 participants, 31 were defined as A‐T‐, 53 as A+T‐, and 72 as A+T+. A‐T+ group had higher levels of hippocampal volume (B = 683.448, p<0.001), AD‐CT (B = 0.267, p<0.001) and AD‐CM (B = 0.174, p<0.001), but not WMH volume (B = ‐1.188, p = 0.651), compared to A+T+ group. When compared to A‐T‐ group, A‐T+ group did not show significant difference in any of the neurodegenerative biomarkers.ConclusionOur findings suggest that, in regard of neurodegeneration, older adults with A‐T+ condition, close to pathological PART, are less severe than those with A+T+, while they appear similar to those with A‐T‐.

Associations among plasma, MRI and amyloid PET biomarkers of dementia and the impact of health‐related comorbidities

AbstractBackgroundKnowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.MethodWe examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aβ42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aβ‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐ε4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).ResultsPlasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aβ42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aβ‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aβ42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐ε4 status exclusively impacted associations with Aβ‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aβ42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aβ‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.ConclusionAmong aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aβ‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.

Neurodegenerative biomarkers of A‐T+ compared to A+T+ and A‐T‐ older adults

AbstractBackgroundPrimary age‐related tauopathy (PART) is a neuropathological entity with neurofibrillary tangles in the absence of beta‐amyloid plaques in the brain. Recent advances in neuroimaging made it possible to identify amyloid PET‐negative (A‐) tau PET‐positive (T+) condition as a possible candidate for PART in living human brains. We tried to investigate neurodegenerative biomarkers of A‐T+ older adults compared to those of A+T+ and A‐T‐ [corresponding to Alzheimer’s disease (AD) and no AD pathology, respectively] individuals.MethodA total of 156 older participants (64 cognitively normal, 43 mild cognitive impairment (MCI), and 49 dementia), were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE). All participants underwent comprehensive clinical evaluation and multimodal brain imaging including amyloid and tau PET, and MRI. Global PiB retention measured by 11C‐PiB‐PET and tau deposition in the regions corresponding to Braak stages measured by 18F‐AV‐1451 PET were used in this study. A+ was defined as global mean Aβ deposition standardized uptake value ratio (SUVR) > 1.4 and T+ was defined as tau burden corresponding to Braak stages I/II, III/IV, or V/VI. Hippocampal volume, AD‐signature cortical thickness (AD‐CT), AD‐signature cerebral glucose metabolism (AD‐CM) and white matter hyperintensity (WMH) volume were also measured as neurodegenerative biomarkers. Multiple linear regression analysis controlling for age, gender, and APOE ε4 positivity was used to investigate the group differences of neurodegenerative biomarkers.ResultOf the 156 participants, 31 were defined as A‐T‐, 53 as A+T‐, and 72 as A+T+. A‐T+ group had higher levels of hippocampal volume (B = 683.448, p<0.001), AD‐CT (B = 0.267, p<0.001) and AD‐CM (B = 0.174, p<0.001), but not WMH volume (B = ‐1.188, p = 0.651), compared to A+T+ group. When compared to A‐T‐ group, A‐T+ group did not show significant difference in any of the neurodegenerative biomarkers.ConclusionOur findings suggest that, in regard of neurodegeneration, older adults with A‐T+ condition, close to pathological PART, are less severe than those with A+T+, while they appear similar to those with A‐T‐.

Inter‐network connectivity of early Aβ and tau‐seeded functional networks is associated with longitudinal tau accumulation in at‐risk cognitively normal older adults

AbstractBackgroundFunctional connectivity (FC) between networks may be related to Aβ and tau interactions in preclinical Alzheimer’s disease (AD). Here, we investigated associations between inter‐network FC of early Aβ and tau‐seeded networks, APOEe4, global Aβ, and longitudinal tau accumulation in cognitively healthy older adults (OA).MethodVoxel‐wise PiB‐PET and FTP‐PET OA images were ordered by increasing pathology burden and iteratively compared to a reference set of young adult (YA) PET images. Peak voxels of the earliest significant differences were matched to Brainnetome ROIs and used to construct separate Aβ and tau‐seeded networks within a group average YA FC matrix (Figure 1). Next, the average inter‐network FC between all node pairs between the networks was calculated in OA (n = 60; aged 77.6±5.5). Longitudinal FTP slopes of the entorhinal cortex (EC) and inferior temporal gyrus (ITG) were estimated using linear mixed effects models. Linear regression was used to assess relationships between baseline inter‐network FC, APOEe4 status, baseline global PiB, and both a prior ROI and voxel‐wise FTP slopes.ResultIn all OA, inter‐network FC was not associated with baseline global PIB (t(58) = ‐0.23, p = 0.82), EC FTP slope (t(58) = 0.49, p = 0.63), or ITG FTP slope (t(58) = ‐1.45, p = 0.15). There was a significant interaction between baseline global PiB status and inter‐network FC where FC was negatively associated with ITG FTP slope in PiB+ participants (t(56) = ‐2.01, p = 0.04; Figure 2A). There was also a significant interaction between APOEe4 status and inter‐network FC in predicting ITG FTP slope with a negative relationship only in e4 carriers (t(56) = ‐3.19, p = 0.003; Figure 2B). Voxel‐wise analyses revealed significant negative relationships between inter‐network FC and FTP slope in the left fusiform gyrus and ITG in PiB+ participants and e4 carriers, respectively (Figure 2C‐D).ConclusionLower baseline inter‐network FC of early Aβ and tau‐seeded networks was associated with greater longitudinal tau accumulation outside of the medial temporal lobe (MTL) in cognitively normal OA with higher AD risk (i.e., elevated Aβ or APOEe4). Our results suggest that this network disruption is associated with faster rates of tau accumulation outside of the MTL, possibly reflecting a later stage in the preclinical progression to AD.

Associations among plasma, MRI and amyloid PET biomarkers of dementia and the impact of health‐related comorbidities

AbstractBackgroundKnowledge regarding associations between plasma and neuroimaging biomarkers indexing neurodegeneration and neuropathology observed in dementia is limited. Further, it is uncertain how comorbid health complications (e.g., kidney function) may alter plasma levels and impact associations with neuroimaging biomarkers.MethodWe examined associations between plasma and neuroimaging biomarkers in cognitively normal participants (NC; N = 300) and individuals with consensus diagnosis (Dx) of mild cognitive impairment (MCI; N = 192) or dementia (DEM; N = 64) enrolled in the Wake Forest ADRC (Table 1). We examined plasma biomarkers (Quanterix SIMOA HD‐X: Aß42/40, GFAP, NfL, p‐tau181) and neuroimaging measures of amyloid deposition (global PiB PET SUVr; Aß‐PET), total brain volume (BVOL), global white matter hyperintensity volume (WMH), diffusion‐weighted fractional anisotropy (FA) and NODDI freewater (FW; white matter). Linear models adjusted for APOE‐e4 carrier status, demographics (age, sex, race, education), and cardiometabolic factors (estimated glomerular filtration rate (eGFR); BMI).ResultPlasma biomarkers were moderately correlated with each other (absolute r = .22‐.64; all p<.001) and significantly elevated (Aß42/40 lower) in DEM and MCI (Figure 1) versus NC, and Aß‐PET‐positive (SUVr >1.21) versus negative individuals (all p<.001). Plasma and neuroimaging markers were significantly associated in both unadjusted models and models including eGFR and BMI (all p<.05; attenuation effect <10% with and without Dx; Figure 2a & 2b). In fully adjusted models (Figure 2b: Dx and all covariates), age, sex, and race differentially impacted associations of Aß42/40, p‐tau181, and NfL with neuroimaging biomarkers (coefficients p <.05). APOE‐e4 status exclusively impacted associations with Aß‐PET SUVr/status. GFAP remained significantly associated with all neuroimaging biomarkers after covariate adjustment; no Aß42/40 associations survived adjustment. P‐tau181 remained significantly associated with Aß‐PET and BVOL, while associations with NfL were reduced in models stratified by Dx.ConclusionAmong aging community‐dwelling participants, plasma biomarkers significantly differed between diagnostic groups (DEM>MCI>NC), were elevated in Aß‐PET positive individuals, and associated with poorer brain health. Except for GFAP, and to an extent p‐tau181, associations between plasma and neuroimaging biomarkers were differentially impacted by inclusion of comorbidities and covariates when stratified by diagnosis. Future work will examine high‐dimensional interactions among comorbidities, demographic information, and plasma and neuroimaging biomarkers in individuals with or at‐risk of dementia.

Amyloid and Tau PET Positivity in Progressive Agrammatic Aphasia and Apraxia of Speech.

The agrammatic variant of primary progressive aphasia (PAA), primary progressive apraxia of speech (PPAOS), or a combination of both (AOS-PAA) are neurodegenerative disorders characterized by speech-language impairments and together compose the AOS-PAA spectrum disorders. These patients typically have an underlying 4-repeat tauopathy, although they sometimes show evidence of amyloid-β and tau deposition on PET, suggesting Alzheimer's disease (AD). Given the growing number of pharmacologic treatment options for AD, it is important to better understand the incidence of AD pathology in these patients. This study aimed to evaluate the frequency of amyloid-β and tau positivity in AOS-PAA spectrum disorders. Sixty-five patients with AOS-PAA underwent a clinical speech-language battery and PiB PET and flortaucipir PET imaging. Global PiB PET standardized uptake value ratios (SUVRs) and flortaucipir PET SUVRs from the temporal meta region of interest were compared between patient groups. For 19 patients who had died and undergone autopsy, their PET and pathology findings were also compared. The results showed that although roughly half of the patients are positive for at least one biomarker, their clinical symptoms and biomarker status were not related, suggesting that AD is not the primary cause of their neurodegeneration. All but one patient in the autopsy subset had a Braak stage of IV or less, despite four being positive on tau PET imaging. Inclusion criteria for clinical trials should specify clinical presentation or adjust the evaluation of such treatments to be specific to disease diagnosis beyond the presence of certain imaging biomarkers.

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.

Optimizing early detection of beta‐amyloid accumulation with PET using spatial extent

AbstractBackgroundAs clinical trials shift towards prevention, there has been increasing interest in improving early detection of beta‐amyloid (Aβ), below standard global PET positivity thresholds. While many studies have demonstrated that some regions appear more vulnerable to the earliest deposits, those regions have varied widely across studies. We sought to develop more flexible metrics for identifying subthreshold individuals at risk for progression to global Aβ positivity by evaluating a wide range of early regional targets and expanding our measurement beyond tracer retention (DVR/SUVR) to include spatial extent.Method269 clinically‐normal older adults were included from the Harvard Aging Brain Study, focusing on 150 initially globally PIB‐ adults with longitudinal PET. Region‐specific positivity thresholds were computed using iterative longitudinal two‐level linear mixed effect models that identified the cutoff beyond which local accumulation significantly increases over time. Next, we identified all regions significantly predictive of future global accumulation in initially PIB‐ adults and generated 3 aggregate masks at liberal (p<.05), moderate (p<.001) and conservative a‐levels (Bonferroni p<.0007). DVR and spatial extent (number of elevated ROIs) were computed in each mask. Receiver operator characteristic (ROC) curve analysis determined sensitivity/specificity of each metric to predict future global positivity. External validation was performed using globally florbetapir‐negative (FBP‐) healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=211).ResultRegional variations in measurement reliability were detected (Fig1) and accounted for by employing accumulation‐based regional positivity thresholds. A broad set of ROIs were identified as potential early targets based on prediction of global accumulation, encompassing bilateral cingulate, medial frontal/parietal cortices and left‐lateralized lateral frontal/parietal/temporal cortices (Fig2). ROC analyses demonstrated that spatial extent outperformed DVR in predicting progression to global PIB+ within 3 years (Fig3), with 82% sensitivity and 97% and 98.5% specificity, respectively, for the two larger aggregate masks. External validation in ADNI (Fig4) again demonstrated the superior performance of spatial extent (SE=100%, SP=91.6%) in predicting progression to global positivity within 3 years.ConclusionOur findings demonstrate that due to a high level of heterogeneity in early Aβ deposition, measures of spatial extent across a broad set of neocortical regions are more sensitive to detect early Aβ than traditional DVR/SUVR.

Amyloid duration and entorhinal tau interact to influence cognition in preclinical Alzheimer’s disease

AbstractBackgroundThe degree of involvement of amyloid and tau on cognitive decline in the pre‐symptomatic phase of AD remains unresolved. This study examines whether duration of amyloid positivity and entorhinal tau individually contribute or interact to influence longitudinal cognitive decline in initially unimpaired older individuals.MethodData were from 358 initially unimpaired individuals from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center who underwent amyloid and tau PET imaging and longitudinal cognitive assessment (Baseline age: 59.7 ±6.7 years; 67% female; follow‐up years, Median(IQR)=8.0 (6.1‐9.8); Table 1). Amyloid burden was quantified from cortical 11C‐PiB DVR, and amyloid duration, quantified as years from PiB positivity (A+/‐; global PiB DVR1.16), was estimated using a sampled iterative local approximation algorithm. Tau burden was quantified using 18F‐MK‐6240 standard uptake value ratios (SUVRs; 70‐90min, inferior cerebellar grey matter reference region). Linear mixed effects (LME) models (random intercept and age‐related slope; unstructured covariance) investigated contributions of amyloid duration (at MK‐6240 scan) and entorhinal cortex (EC) MK‐6240 uptake to retrospective cognitive decline represented by a modified three‐test Preclinical Alzheimer’s Cognitive Composite (PACC‐3).ResultAmong initially unimpaired individuals, faster PACC‐3 decline was associated with both longer amyloid duration and higher EC tau. LME models showed significant linear and non‐linear interactions of amyloid duration and EC tau on cognitive decline, suggesting a synergistic effect whereby amyloid duration together with a higher tau burden increases the likelihood of cognitive decline beyond their separable effects (Table 2). To further characterize these interactions, we compared cognitive trajectories of groups based on amyloid duration and EC tau positivity (Figure 1A‐C). Post‐hoc analyses revealed significantly greater decline in the group with 10+ years of amyloid duration and high EC tau compared with all other groups over the study duration (Figure 1C).ConclusionThese results suggest that in this pre‐symptomatic sample, amyloid duration and EC tau were not redundant contributors to decline, but rather interacted to influence decline during the retrospective period of observation. Prospective follow‐up and mediation analyses will be essential to fully elucidate the influence of amyloid duration and early tau on cognitive decline in preclinical Alzheimer’s disease.

Examining differences across cognitive trajectory profiles in late middle‐aged adults: Results from the Wisconsin Registry for Alzheimer’s Prevention

AbstractBackgroundCognitive decline in Alzheimer’s disease (AD) and other dementias may accelerate in preclinical phases as brain pathology increases. Here we: 1) developed cognitive trajectory profiles using longitudinally‐based random slope and change point (CP) parameter estimates from a cognitive composite; and 2) examined how AD‐related biomarkers varied across these cognitive profiles.MethodWRAP participants with > = 3 Preclinical Alzheimer’s Cognitive Composite (PACC3) scores, dementia‐free at baseline, were included (n = 1068). Amyloid measures from positron emission tomography (PET) [C‐11]Pittsburgh Compound B (PiB; n = 361) scans included Global PiB DVR and proportion PiB+ (Global PiB DVR>1.16). Tau measures included PET MK‐6240 regional entorhinal cortex and hippocampal SUVR (n = 321). Neurodegeneration measures included MRI hippocampal volume and global brain atrophy (GBA; n = 581). Plasma measures included pTau217 (n = 166). Posterior median estimate person‐level CPs, slopes pre‐ and post‐CP, and intercepts at CP for PACC3 were extracted from Bayesian random CP mixed models (BRCPMM; age = time scale) and used to characterize cognitive trajectory profiles (K‐means clustering). We compared demographic, last visit cognitive statuses (cognitively unimpaired‐stable (CU‐S), CU‐declining (CU‐D), and MCI/Dementia), amyloid, tau, neurodegenerations and plasma measures across the cognitive trajectory profiles using analysis of variance, chi‐square and Fisher’s exact tests. Significant omnibus tests (p<.05) were followed with pairwise comparisons.ResultMean(sd) last cognitive assessment age was 66.6(6.6) years; PiB, MK, MRI scans and plasma occurred within mean(sd) 1.1(2.7), 1.6(1.4), .6(3) and .09(.7) years of cognitive assessment. Cluster analysis identified 3 groups of performance patterns representing highest to lowest risk of cognitive decline (high: n = 77(7.2%); intermediate: n = 446(41.8%); and low: n = 545(51.0%); Figure 1 & 2). The high risk group was older, had more females, APOE e4 carriers, and MCI/Dementia at last visit (Table 1). The high risk group also had worse PiB‐amyloid, MK‐tau, pTau217, and MRI‐neurogeneration levels than the lower risk groups (Table2; Figure 3).ConclusionIn this initially non‐demented sample, differences between cognitive clusters across multiple biomarkers and AD risk factors indicate that within‐person PACC3 performance patterns are sensitive to preclinical change. Identifying cognitive trajectory profiles may provide an opportunity for early intervention for high‐risk subjects at the right time for treatment and/or enrollment in a clinical trial.

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.