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AbstractBackgroundOur objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired.MethodAll 337 participants in this study were enrolled in one of the three longitudinal cohort studies at the Mayo Clinic that include serial tau PET scanning. For inclusion a person must have had a baseline study visit with MRI, amyloid PET, and tau PET exams, had at least one follow‐up tau PET exam, and have met clinical criteria for membership in one of two clinical diagnostic groups ‐ cognitively unimpaired (n=203) or cognitively impaired (n=134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer clinical syndrome). A temporal lobe tau PET meta‐region of interest (ROI) was used in our primary analysis but we also examined four additional tau PET ROIs. Linear mixed effects models where used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET SUVR, cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log‐transformed tau PET SUVR was used as the response and rates were summarized as annual percent change.ResultIn the cognitively unimpaired group only higher baseline amyloid PET was a significant independent predictor (i.e. in a multivariable model) of higher tau accumulation rates in the temporal meta‐ROI (p<0.001). In the cognitively impaired group, younger age (p=0.02), higher baseline amyloid PET (p=0.05), APOE ε4 (p=0.05), and better cognitive performance (p=0.05) were significant independent predictors of higher tau accumulation rates in the temporal meta‐ROI.ConclusionWhile we examined many possible predictor variables, the main independent predictor of high tau PET accumulation rates in cognitively unimpaired persons was amyloidosis. In contrast, in cognitively impaired individuals imaging and clinical variables that are consistent with early onset Alzheimer’s disease phenotype (younger age, amyloidosis, higher tau, APOE, less severe cognitive impairment) were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct clinical trials that target tau‐related mechanisms.

AbstractBackgroundTau deposition is a key biological feature of Alzheimer’s disease (AD) that is closely related to cognitive impairment. However, it remains poorly understood why certain individuals may be more susceptible to tau accumulation while others are more resistant. The recent availability of tau positron emission tomography (tau‐PET) provides an opportunity to test the hypothesis that genetic factors are associated with differential tau deposition.MethodsWe performed a genome‐wide association study (GWAS) of tau‐PET on a sample of 754 individuals over age 50 (mean age 72.4 years, 54.6% men, 87.4% cognitively unimpaired) from the population‐based Mayo Clinic Study of Aging. Following genotyping with the Illumina GSA array and standard quality control, 515,206 single nucleotide polymorphisms (SNPs) were tested for association with entorhinal cortex 18F‐flortaucipir tau‐PET burden using linear regression under an additive genetic model and covarying for age and sex.ResultA genome‐wide significant association was identified for rs75546066, an intergenic SNP on chromosome 9 between MED27 (mediator complex subunit 27) and NTNG2 (netrin G2), with the minor (A) allele associated with lower tau burden (p=2.85 x 10‐8, β=‐0.48). Linkage disequilibrium and public expression quantitative trait loci (eQTL) data suggested a functional relationship between rs75546066 and NTNG2, the latter of which is highly expressed in the brain. In addition to this novel finding, three SNPs within MAPT (microtubule‐associated protein tau) displayed nominal associations to tau‐PET burden, including rs3785883 (p=0.04, β=0.07) which was previously associated with higher cerebrospinal fluid tau in an independent cohort. No associations with tau‐PET burden were identified for the APOE (apolipoprotein E) ɛ4 or ɛ2 alleles or for genotyped SNPs previously associated with AD in large case‐control studies.ConclusionTo our knowledge, this study represents the first reported neuroimaging GWAS of tau pathology. Our findings confirm the association of MAPT with tau burden and nominate the rs75546066‐NTNG2 locus as a novel potential protective influence against tau pathology which warrants further functional characterization. Our data also supports the inference that tau accumulation may have a genetic architecture distinct from APOE and other known AD susceptibility genes, which may have implications for improved risk stratification and therapeutic targeting models.

AbstractBackgroundThe Network Failure Quotient (NFQ) metric summarizes functional MRI network integrity. We wanted to investigate the stability of the NFQ results across time, hardware, software and protocol changes during the last decade in ADNI Resting State (RS‐) fMRI.MethodIn ADNI‐2 (2009‐2016) RS‐fMRI was acquired on Philips scanners. In ADNI‐3 (2017‐present) RS‐fMRI is acquired on GE, Philips, and Siemens systems with “Basic” and “Advanced” variants based on the availability of simultaneous multi‐slice (SMS, multi‐band) imaging. (Table 1.) Resting state images were preprocessed by performing slice timing correction, trimming volumes from the first 10 seconds of acquired data, despiking and within‐series alignment. Simultaneous bandpass and regression was performed using spatial registration parameters and their first temporal derivatives, mean WM signal, mean CSF signal and global signal. Dual regression via GIFT was applied creating regional time courses. The NFQ is calculated as NFQ=(pDMN_to_vDMN + pDMN_to_adDMN) / (pDMN +vDMN) where pDMN_to_vDMN and pDMN_to_adDMN are correlations between time courses in the posterior DMN, ventral DMN and anterior‐dorsal DMN. pDMN and vDMN are the within‐region correlations. NFQ values from 1447 scans of 739 individual study participants (Table 1) were analyzed using linear mixed effects models to account for age, clinical diagnosis, vendor, and time trends.ResultBox plots of NFQ values by diagnosis within ADNI‐phase and protocol are shown in Figure 2 and suggest important differences among clinical diagnoses and between ADNI‐2 and ADNI‐3. The mixed model analysis indicates that a 10‐year increase in age is associated with a 0.05±0.01 (mean±SD) increase in NFQ; individuals with MCI average roughly 0.05±0.01 higher than unimpaired and individuals with dementia average 0.14±0.02 higher. Differences by vendor for ADNI‐2 and ADNI‐3 are summarized in Figures 2 and 3 with effect sizes on par with these clinical factors. Mean NFQ for Phillips ADNI‐2 were highest (0.54±0.02); somewhat lower were Philips ADNI‐3 (0.51±0.02) and Siemens ADNI‐3 advanced (0.49±0.03); Siemens ADNI‐3 basic (0.40±0.02) and GE ADNI‐3 were the lowest (0.38±0.02).ConclusionNFQ values consistently increase with impairment. Vendor‐specific differences are observed and vendor changes over the life of a study introduce variability on the scale of disease‐related changes in NFQ.

AbstractBackgroundPrevious studies have reported that age modifies the distribution and burden of tau (and, to a lesser extent, amyloid) pathology in sporadic Alzheimer’s disease (AD). Here we present preliminary baseline amyloid and tau PET results from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), a multi‐site longitudinal study of sporadic early‐onset AD.Method135 patients meeting clinical criteria for MCI or probable AD and 50 cognitively normal controls (all age<65 at enrollment) were enrolled at 12 US centers between August 2018 and December 2019 (Table 1). 18F‐Florbetaben amyloid‐PET (FBB) was used to assign patients to EOAD (amyloid‐positive) or EOnonAD (amyloid‐negative) subgroups based on visual rating and semi‐quantification. 130 patients and all controls had 18F‐Flortaucipir tau‐PET (FTP). Regional Standardized Uptake Value Ratios (SUVR) for FBB (whole cerebellum reference) and FTP (inferior cerebellar gray reference) were extracted using co‐registered 3T‐MRI.Result98 patients (72.6%) were amyloid PET‐positive (EOAD) and 37 (27.4%) were amyloid PET‐negative (EOnonAD). Compared to EOAD, EOnonAD patients had higher MMSE, MOCA and CDR sum‐of‐boxes (CDR‐SB) and were more frequently male (Table 1). Patients with EOAD showed elevated FBB and FTP SUVR in temporoparietal and frontal cortex compared to CN and EOnonAD (Figures 1‐2). In EOAD, MMSE, MOCA and CDR‐SB were significantly correlated with FTP SUVR (Figure 3), while no significant correlations were found with FBB SUVR. EOnonAD patients showed variable FTP binding ranging from negative to mildly elevated binding in anterior temporal and frontal cortex and underlying white matter. Two EOnonAD cases showed intense FTP binding comparable to typical EOAD cases, despite visually and quantitatively negative FBB scans.ConclusionPatients with clinically mild, sporadic EOAD typically show an extensive distribution and burden of tau pathology in the setting of positive amyloid PET. Global clinical measures correlate with tau but not amyloid PET. Over 25% of patients meeting clinical criteria for early‐onset MCI/probable AD have negative amyloid PET, suggesting alternative etiologies for cognitive decline. These findings will inform future design of drug trials in this important and under‐studied population.

AbstractBackgroundGiven that a proportion of mild cognitive impaired (MCI) individuals are amyloid negative and have cerebrovascular disease (CVD), there is a need for biomarkers that can complement AD biomarkers (amyloid and neurodegeneration) for the prediction of future cognitive decline in MCI. The aim of our study was to determine the utility of diffusion tensor imaging (DTI) as a CVD marker for prediction of future cognitive decline in individuals with MCI in conjunction with amyloidosis and neurodegeneration.MethodWe identified 132 MCI individuals ≥60 years old (55 % were amyloid positive and 59 % were male) from the population based sample of Mayo Clinic Study of Aging (MCSA) who had structural MRI, DTI, amyloid PET, and at least one clinical follow‐up after the imaging at the time of their clinical visit as MCI. All imaging scans were acquired and processed using in house developed protocols and pipelines. We used fractional anisotropy in the genu of corpus callosum (FAGCC), as a CVD marker, education/occupation score as a resilience variable, and global cognitive z‐score as the cognitive outcome. Using mixed effect models for cognitive decline, we evaluated three models to test the prognostic ability of (i) DTI marker alone, (ii) DTI marker and amyloid PET, (iii) DTI marker, amyloid PET, and neurodegeneration (cortical thickness). The final parsimonious models included the significant interactions of potential predictors with time, main effects nested within those interactions, and other significant main effects. Since the adjustment for conventional CVD measures like WMH was important to determine the utility DTI over traditional measures, we further assessed the value of FAGCC in conjunction with white matter hyper intensity (WMH).ResultWe found: i) FAGCC was associated with cognitive decline after adjusting for amyloid PET neurodegeneration (Figure 1 and Table 1) and ii) FAGCC was associated with cognitive decline even after controlling for WMH and WMH was no longer significant after accounting for FAGCC (Table 2).ConclusionDTI has significant utility for prediction of cognitive decline in MCI from population‐based samples and will provide complementary information to AD biomarkers.

AbstractBackgroundRecent studies suggest that lifestyle changes (physical exercise, Mediterranean diet adherence, cognitive stimulation, vascular risk management) may protect against cognitive decline and reduce AD pathophysiology. The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a 2‐year randomized controlled trial to evaluate whether lifestyle interventions can protect cognitive function in older adults (60‐79 years) who are at increased risk for cognitive impairment and dementia. The POINTER Imaging ancillary study will assess effects of the POINTER lifestyle interventions on neuroimaging biomarkers of AD and cerebrovascular pathophysiology, and whether baseline neuroimaging measures predict cognitive responses to the interventions.MethodThe POINTER Imaging ancillary study aims to enroll 1250 participants from the five U.S. POINTER sites. Ancillary study participants will undergo PET imaging (Baseline, Month 24) to measure Aβ ([18F]Florbetaben) and tau ([18F]MK‐6240) burden, and MRI (Baseline, Months 12 & 24) to measure brain morphometry, white matter (WM) hyperintensities and microstructure, and cerebral blood flow. U.S. POINTER provides standardized cognitive, clinical, genetic (e.g. APOE), and lifestyle activity assessments, and demographics for integration with neuroimaging data.ResultThe study design and methods for the POINTER Imaging ancillary study will be presented. The study will examine whether: (1) neuroimaging biomarkers at baseline (Aβ, tau, hippocampal volume, WM hyperintensities) predict intervention‐related cognitive changes and (2) biomarker outcomes are differentially modified by participation in lifestyle interventions. It will also examine changes in exploratory biomarkers (tensor‐based morphometry, microstructural integrity, microbleeds, cerebral blood flow).ConclusionThe POINTER Imaging study aims to address gaps in our understanding of how a variety of lifestyle practices (diet, exercise, cognitive stimulation and vascular risk management) influence brain health. In particular, the study will determine whether imaging measurements of AD and cerebrovascular pathophysiology are amenable to improvement with the POINTER lifestyle interventions. It also aims to determine pathological profiles of individuals who benefit most from such interventions.

AbstractBackgroundThe critical‐period theory of menopausal hormone therapy (mHT) suggests that if menopausal hormone therapy begins shortly after menopause, it may have beneficial effects on brain structure and cognitive health. We investigated white matter integrity with diffusion tensor MRI (DTI) 3 years after the end of mHT trial in recently menopausal women who were randomized shortly after menopause.MethodParticipants of this study were enrolled in the Kronos Early Estrogen Prevention Study (aged 42–56 years; 5–36 months past menopause). Participants received the following treatments: 0.45 mg/d oral conjugated equine estrogen (oCEE, n=19); 50 μg/d transdermal 17β‐estradiol (tE2, n=22); or placebo (n=33) for 4 years. Oral progesterone (200 mg/d) was given to hormone therapy groups for 12 days each month. DTI was performed 3 years after the end of 4 years of hormone therapy (n=75), which corresponded to 7 years after randomization. Voxel‐based analysis was performed on DTI to evaluate fractional anisotropy (FA) and mean diffusivity (MD) differences between treatment groups (uncorrected p<0.001).ResultVoxel based analysis showed higher FA, a measure of white matter integrity, in the superior corona radiata, anterior limb of the internal capsule and splenium of the corpus callosum of participants treated with oCEE compared to placebo (p <0.001, Figure 1A). Furthermore, higher FA was observed in the superior corona radiata, anterior limb of internal capsule and thalamus in participants treated with tE2 compared to placebo (p <0.001, Figure 1B). There were no differences in FA findings between oCEE and tE2 groups, and no MD differences between treatments groups.ConclusionA relatively higher FA in a variety of white matter regions in the oCEE and tE2 groups compared to placebo suggest that mHT may have long‐term protective effects on white matter integrity. Previously, we had shown that women in the tE2 group preserved their dorsolateral prefrontal cortex volumes better than the placebo group over 7 years. Our findings suggest that the tracts that connect the dorsolateral prefrontal cortex and the thalamus such as the superior corona radiata and the anterior limb of internal capsule may particularly benefit from mHT.

AbstractBackgroundCharacterization of the changes that occur throughout the transition from asymptomatic to symptomatic for those with familial frontotemporal dementia is essential for planning future disease‐modifying trials.MethodWe identified four related microtubule associated protein tau (MAPT) N279K mutation carriers who have phenoconverted from the asymptomatic to the symptomatic state. Key features were as follows: Case 1 (female) initial exam age 36, onset age 44, current age 49; Case 2 (male) initial exam age 39, onset age 43, current age 48; Case 3 (female) initial exam age 41, onset age 47, current age 53; Case 4 (male) initial exam age 36, onset age 37, current age 46. MRI and neuropsychological data were analyzed.ResultStructural MRI was analyzed using Tensor Based Morphometry Symmetric Diffeomorphic Image Normalization (TBM‐SyN) methodology to quantify lobar volumes, which was then analyzed using a Mixed Effects Model to determine the annual change in lobar volumes. The phenoconverters had a greater estimated annual rate of cortical atrophy in temporal (p <0.001), frontal (p <0.01) and parietal (p <0.05) lobes compared to non‐carriers, with the rate of annual change in symptomatic individuals double that of asymptomatic individuals. Annual change in the parietal and occipital lobes was also analyzed and found to be similar to healthy controls of a similar age. Raw scores on neuropsychological measures were converted to z‐scores for analysis. Category fluency, letter fluency, and Trails A and B were all impaired (z‐score <‐1.5) following phenoconversion, but performance on these and other measures was variable across these four in the late asymptomatic state.ConclusionFor each of the four phenoconverters, structural MRI changes preceded symptom onset, whereas neuropsychological performance was more variable and occurred in closer proximity to the time of phenoconversion. Decrements in verbal fluency and cognitive flexibility were observed earlier than other cognitive domains. These findings suggest that changes in frontal and temporal volume on MRI precede symptom onset, with regional brain volumes most consistently tracking the evolution from the asymptomatic to symptomatic state in N279K MAPT mutation carriers. Supported by: AG063911, AG045390, NS092089, AG016976, AG016574, AG062677.

AbstractBackgroundPractice effects can obscure interpretation of longitudinal cognitive performance. Reliable change indices (RCIs) provide data for determining a significant change, typically for a single repeat assessment, but multiple follow‐up visits are common clinically and in research cohorts. Further, regression‐based or linear mixed effects models improve prediction of future cognitive decline and have higher correlations with neuroimaging biomarkers than RCI approaches. We present norms for change for the Auditory Verbal Learning Tests (AVLT) with conditional normative z‐scores for up to 7 repeat assessments, and demonstrate application of this method to individuals with preclinical Alzheimer’s disease (AD).MethodNormative Sample: Robust conditional norms were derived from 1001 adults ages 50‐90 from the Mayo Clinic Study of Aging (MCSA) who completed the AVLT across 4‐8 visits approximately 15 months apart and remained cognitively unimpaired (CU) across all available visits. Linear mixed effects models were used to compute a conditional normative z‐score for AVLT 30‐minute recall and sum of trials. Models included adjustment for baseline performance, number of prior test exposures, demographic factors (age, sex, education), and interactions (baseline*time; sex*time). Test Sample: We used a preclinical AD sample with positive amyloid and tau imaging (n= 27 CU A+T+; Jack et al. 2017) expected to have a higher rate of intra‐individual cognitive decline relative to CU A‐T‐ individuals (n = 268) to compare mean performances of conditional normative z‐scores to typical age‐corrected Mayo’s Older Americans Normative Studies (MOANS) norms. Participants had at least 1 and up to 4 follow‐up visits, with their most recent AT data used for classification.ResultAVLT performance based on typical norms did not differ across A+T+ and A‐T‐ groups. Specifically, across 2‐4 follow‐up visits, the typical norms mean z‐score was similar across groups (p=0.945; see Table 1). However, the conditional norms mean z‐score that accounts for practice effects and typical trajectories of AVLT performance was lower in the A+T+ relative to the A‐T‐ group for 30‐minute recall (p=0.055) and sum of trials (p=0.060).ConclusionConditional normative methods that account for practice effects are more sensitive to preclinical AD than typical norms, and show promise for identifying transitional cognitive decline.

AbstractBackgroundThe numeric clinical staging scheme described by the NIA‐AA workgroup proposes evidence of subtle decline on longitudinal cognitive testing as one option to define transitional cognitive decline for individuals in the Alzheimer’s continuum. Few studies have compared longitudinal and cross‐sectional definitions of subtle objective cognitive impairment (sOBJ). The study aim was to compare the diagnostic accuracy of cross‐sectional sOBJ and longitudinal objective cognitive decline (ΔOBJ) for preclinical Alzheimer’s disease (AD) based on Cogstate One Card Learning (OCL) performance.MethodWe included Mayo Clinic Study of Aging cognitively unimpaired (CU) participants who were A+T+ by amyloid and tau PET assessment (n=32) and A‐T‐ (n=211); all had at least two follow‐up visits. sOBJ was defined as performance ≤ ‐1SD on OCL accuracy using age‐corrected normative data. ΔOBJ was measured using two different methods: (1) within subjects’ standard deviation (WSD) using a ≤ ‐1 z‐score for change to define decline based on Cogstate’s normative change data, and (2) extracted subject‐specific slopes from a linear mixed effects model (LME) which can be interpreted as approximate annual change; decline was defined as slope < 10%ile among a reference sample of CU participants aged 50‐65 at baseline (N=732).ResultssOBJ and ΔOBJ‐WSD both showed low sensitivity in detection of A+T+ (9.4% and 12.5%, respectively). Total AUC values did not differ significantly (p=.31) across sOBJ (AUC = .64, CI = .53‐.75) and ΔOBJ‐WSD (AUC = .53, CI = .42‐.65) methods. Sensitivity of the ΔOBJ‐LME method to predict A+T+ was improved (37.5%) when using a conventional cutoff of <10%ile slope and was further improved (56%) when using an optimal derived cutoff equivalent to <17%ile slope within the reference group. Like the sOBJ method, the ΔOBJ‐LME method differentiated groups better than chance (AUC = .69, CI = .58‐.80). Despite increased sensitivity, total AUC values were not significantly different across sOBJ and ΔOBJ‐LME methods (p=.34). However, total AUC of ΔOBJ‐LME was significantly better than ΔOBJ‐WSD (p=.03).ConclusionsΔOBJ may be more sensitive to preclinical AD than sOBJ and may serve as a helpful method for identifying at risk CU individuals if advanced statistical methods are used for defining change.