Lowe, Val J.

Hormone therapy improves sleep in menopausal women and recent data suggest that transdermal 17β-estradiol may reduce the accumulation of cortical amyloid-β. However, how menopausal hormone therapies modify the associations of amyloid-β accumulation with sleep quality is not known. In this study, associations of sleep quality with cortical amyloid-β deposition and cognitive function were assessed in a subset of women who had participated in the Kronos early estrogen prevention study. It was a randomized, placebo-controlled trial in which recently menopausal women (age, 42–58; 5–36 months past menopause) were randomized to (1) oral conjugated equine estrogen (n = 19); (2) transdermal 17β-estradiol (tE2, n = 21); (3) placebo pills and patch (n = 32) for 4 years. Global sleep quality score was calculated using Pittsburgh sleep quality index, cortical amyloid-β deposition was measured with Pittsburgh compound-B positron emission tomography standard uptake value ratio and cognitive function was assessed in four cognitive domains 3 years after completion of trial treatments. Lower global sleep quality score (i.e., better sleep quality) correlated with lower cortical Pittsburgh compound-B standard uptake value ratio only in the tE2 group (r = 0.45, P = 0.047). Better global sleep quality also correlated with higher visual attention and executive function scores in the tE2 group (r = −0.54, P = 0.02) and in the oral conjugated equine estrogen group (r = −0.65, P = 0.005). Menopausal hormone therapies may influence the effects of sleep on cognitive function, specifically, visual attention and executive function. There also appears to be a complex relationship between sleep, menopausal hormone therapies, cortical amyloid-β accumulation and cognitive function, and tE2 formulation may modify the relationship between sleep and amyloid-β accumulation.

AbstractBackgroundAlthough semantic dementia (SMD) presents with language decline, patients often develop behavioral changes. Behavioral symptoms in SMD are attributed to both cortical (orbitofrontal cortex atrophy) and subcortical (nucleus accumbens) degeneration in imaging and neuropathological studies. The caudate nucleus has been implicated in early behavioral changes in some phenotypes of frontotemporal dementia. The objective of this study was to determine whether asymmetric ipsilateral caudate nucleus hypometabolism on [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is associated with early behavioral changes in the absence of frontal hypometabolism in SMD.MethodFifteen patients with a diagnosis of SMD evaluated by the Neurodegenerative Research Group (NRG) at the Mayo Clinic, Rochester from 2006‐2016, who had FDG‐PET, were included in this study. Clinical data was extracted from the medical records including age at presentation, sex, behavioral symptoms and duration of symptoms. Automated average Z‐scores generated from CortexID (GE Healthcare, WI, USA) using 3D stereotactic surface projections were obtained for the following regions bilaterally: temporal lobes, frontal lobes and caudate nucleus. Patients were categorized as left or right hemisphere lateralized based on the higher temporal lobe Z‐scores. Caudate nucleus and frontal lobe Z‐scores were then compared between the two sides for each patient. Hypometabolism was considered present if Z‐scores were greater than 2.ResultOf our cohort, 9/15 (60%) were females with a median age of 68 at presentation and had 3.5 years symptom duration. 13/15 had at least 1 behavioral symptom at presentation. Eleven patients were considered to have left‐lateralized whereas 4 were right‐lateralized in their temporal lobe hypometabolism. Median Z‐scores were significantly different for ipsilateral and contralateral caudate nuclei at 2.5 and 1.6, respectively (p=0.02, Wilcoxon nonparametric test). Frontal lobe median Z‐scores were 1.2 (ipsilateral) and 0.59 (contralateral) with no statistical difference between two sides (p=0.13).ConclusionIn SMD patients who exhibit behavioral symptoms with no frontal hypometabolism on FDG‐PET, asymmetric caudate nuclei hypometabolism should be explored as a possible imaging marker. This implicates abnormal frontal‐striatal circuitry in the development of behavioral symptoms even in the absence of frontal hypometabolism in early behavioral changes in SMD.

AbstractBackgroundWhite matter (WM) changes are widely seen in the elderly due to aging and pathological processes and have significant impact on cognitive aging. It is critical to understand the heterogeneity and drivers (causes) of the longitudinal deterioration of WM microstructural integrity, especially in a population‐based sample, to provide mechanistic insights into cognitive and brain aging.MethodLeveraging longitudinal diffusion tensor imaging (DTI) data from Mayo Clinic Study of Aging, we assessed whether individuals clustered based on distinct longitudinal fractional anisotropy (FA) trajectories. A total of 533 subjects (487 cognitively unimpaired, 62 with mild cognitive impairment and 4 with dementia diagnosis) aged ≥ 60 years and with at least two DTI scans (125 subjects had at least 3 scans) were included. To identify clusters of individuals with common FA profiles over time, we applied an in‐house longitudinal Bayesian mixed‐effects clustering analysis framework to the imaging data. Age at MRI accounted for the longitudinal component of the model, while sex and APOE4 carrier status were used as fixed effects.ResultWe identified four distinct clusters (Figure 1). We found that most WM tracts decline with age across all clusters but brainstem and association fibers were least likely to decline with aging. APOE4 carriership and amyloid deposition did not differ between the clusters. We found a resilient cluster of individuals (34%) (cluster 1) with better baseline WM and systemic vascular health who had a slower rate of cognitive and WM integrity decline over time. These individuals had highest baseline commissural and association fibers. The resilient group was in contrast to an “at risk” cluster of individuals (16%) (cluster 2) who had lower baseline WM integrity and systemic health and declined the fastest in both WM health and cognition over timeConclusionLongitudinal clustering of WM tracts provided insights into the differential aging of WM in the population which may be unrelated to Alzheimer’s disease pathophysiology. Measuring overall WM health and vulnerable tracts in the elderly can be useful as biomarkers and aid in the identification of “at risk” groups for prevention of future cognitive decline.

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.

AbstractBackgroundWhile β‐amyloid and tau PET imaging are widely studied in cognitive aging and Alzheimer’s disease (AD), little is known about findings in aging multiple sclerosis (MS) patients. As aging is an essential factor in developing neurodegeneration in MS, β‐amyloid and tau biomarkers may be utilized to understand the influence of AD pathophysiology on cognitive aging in MS.MethodWe studied patients with MS (n=16) and controls (n=80) matched for age, sex and APOE ε4 status from the population‐based Mayo Clinic Study of Aging who underwent Pittsburgh compound‐B (PiB) PET imaging. Twelve of the 16 patients with MS and 60 matching controls also underwent flortaucipir (AV1451) tau PET. Cortical PiB and AV1451 standard uptake value ratios (SUVr) from previously determined AD signature regions and from the entire cortex were calculated for group comparisons and testing for associations with age.ResultAD signature PiB SUVr (p=0.044), total cortical PiB SUVr (p=0.048) and the frequency of abnormal PiB SUVrs (p=0.040) were lower in patients with MS than controls. Although AD signature and total cortical AV1451 SUVrs were not different between the groups, the frequency of abnormal AV1451 SUVrs was higher (p=0.041) in patients with MS than controls. The association of AD signature PiB SUVr with age was steeper in the controls compared to patients with MS (p=0.002). Similarly, the association of total cortical PiB SUVr with age was steeper in the controls compared to patients with MS (p=0.002). There was no difference in the association of AV1451 SUVr findings with age between the MS patients and controls (Figure 1). The AD signature PiB SUVr increased more with increasing AD signature AV1451 SUVr in controls than in patients with MS (p=0.03) (Figure 2).ConclusionIn patients with MS, cortical β‐amyloid deposition was lower than age‐matched controls, while cortical tau deposition was not different between the groups. This suggests that some aspect of MS pathobiology retards the accumulation of β‐amyloid, but not the accumulation of tau.

AbstractBackgroundAlzheimer’s disease (AD) is marked by selective disruptions to anatomically‐distinct brain regions and their connectivity which collectively support cognition. Based on a variation of pattern‐recognition analysis of FDG‐PET images, we previously developed a framework, the global functional state space (GFSS), which captures this macroscale architecture by assigning each individual a series of weightings on 10 independent patterns (eigenbrains=EBs). In this study, we applied the GFSS as a quantitative endophenotype to test whether known AD genetic risk factors were associated with variability in large‐scale neurodynamics represented by the GFSS.MethodThe study sample included 410 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with evidence of cognitive impairment (CDR‐global>0) and amyloidosis (positive amyloid‐PET). The GFSS was estimated for each individual using the set of 10 EBs to describe global patterns of variation in glucose metabolism. AD genetic risk variants identified in prior large consortium case‐control studies were included for analysis if the variant (or a proxy by linkage disequilibrium r 2>0.7) was assayed on both genotyping platforms used in the sample and if the minor allele frequency exceeded 1%. Genetic associations with the collective set of EBs were tested using multivariate linear regression under a dominant genetic model.ResultSignificant multivariate associations were identified for CD33 (p=7.04x10‐4), MEF2C (p=0.001), and SORL1 (p=0.02). These findings remained significant after covarying for age and sex. The strongest component loadings for CD33 (p=9.09x10‐4; std. β=‐0.16) and MEF2C (p=1.59x10‐4; std. β=‐0.18) were for EB1, with the minor allele in each case associated with a negative axis deviation in the direction of the GFSS area representing episodic memory dysfunction. SORL1 displayed its strongest loading onto EB4 (p=0.03, std. β=‐0.11). The association of APOE ɛ4 with the GFSS was marginally nonsignificant (p=0.09).ConclusionWe identified associations of risk variants in CD33, MEF2C, and SORL1 with differences in patterns of FDG hypometabolism in persons with symptomatic AD. These findings establish the GFSS as a useful endophenotype for genetic analyses, particularly as an avenue toward a mechanistic understanding of how heritable factors may influence inter‐individual variability and syndromic diversity in AD. Further investigation in larger and independent cohorts is warranted.

AbstractBackgroundMultiple imaging modalities have been individually shown to be useful in diagnosing Dementia with Lewy Bodies (DLB). Reduced striatonigral uptake on ioflupane‐SPECT reflects striatonigral dopamine dysfunction observed in DLB, while other imaging modalities assess different aspects of DLB, which potentially work complementarily when used together. We assessed how well ioflupane‐SPECT differentiates DLB from Alzheimer’s Disease Dementia (ADem) and whether adding another imaging modality to ioflupane‐SPECT would provide additional value.MethodIoflupane‐SPECT, MRI, FDG‐PET, and PiB‐PET were assessed on 35 DLB and 14 ADem patients (including 12 patients with eventual autopsy confirmation). Striatonigral dopamine transporter uptake was evaluated semi‐quantitatively with ioflupane‐SPECT using DaTQUANT software (GE Healthcare) to calculate z‐scores of putamen uptake. Hippocampal volume was calculated with structural MRI, cingulate island sign (CIS) ratio with FDG‐PET, and global cortical PiB retention with PiB‐PET.ResultLower DaTQUANT z‐scores of putamen were observed in DLB patients compared to ADem patients (c‐statistic 0.896, p<0.001). Ioflupane‐SPECT showed higher c‐statistic in differentiating DLB from ADem than hippocampal volume on MRI (c‐statistic 0.718, p=0.034), CIS on FDG‐PET (c‐statistic 0.869, p=0.003), or global cortical PiB retention on PiB‐PET (c‐statistic 0.869, p=0.002), but some overlap between two diagnostic groups was still observed with the single imaging modality. Among these imaging modalities, ioflupane‐SPECT was the only modality correlated with the Unified Parkinson Disease Rating Scale (UPDRS) part III, while only PiB‐PET correlated with Montreal Cognitive Assessment (MoCA) test score. Adding another imaging modality to ioflupane‐SPECT enhanced c‐statistics (+MRI c‐statistic 0.931, p<0.001; +FDG‐PET c‐statistic 0.957, p=0.005; +PiB‐PET c‐statistic 0.955, p=0.007), and ioflupane‐SPECT in combination with both FDG‐PET and PiB‐PET showed the highest c‐statistic of 0.974 (p=0.043).ConclusionIoflupane‐SPECT is an excellent imaging modality to identify DLB by detecting striatonigral dopamine dysfunction which is strongly associated with motor impairment in DLB. Correlative neuroimaging with MRI, FDG‐PET, or PiB‐PET may add small incremental value in differentiation of DLB and ADem. Supported by NIH grants (AG016574, AG006786, AG015866, NS100620, AG062677), grant from GE Healthcare, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program, the Deal Family Foundation, and the Little Family Foundation.

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.

AbstractBackgroundTranslating recent advances in the diagnosis of Alzheimer’s disease (AD) and other dementias to clinical practice using biomarker‐based approaches requires accessible, affordable biomarkers. This protocol tested the performance of a Retinal Deep Phenotyping platform for the detection of likely positron‐emission tomography (PET) amyloid status (negative or positive) in human subjects. This platform generates data rich retinal reflectance images captured non‐invasively with a Metabolic Hyperspectral Retinal Camera (MHRC) yielding specific spatial‐spectral features which are analyzed by a machine learning algorithm relatively to a recognized gold standard biomarker (in this case PET amyloid imaging).Methods134 subjects (50 years and older) from 3 clinical sites were included, 94 with normal, 40 with abnormal cognition, imaged with the MHRC and amyloid PET (unanimous visual reads from a panel of 3 expert reviewers; 42 amyloid positive, 92 negative). Over 600 spatial‐spectral features were extracted from hyperspectral retinal images obtained at 450‐900 nm. After a feature selection based on F‐score ranking, a classifier was trained on the datasets from 87 patients (2/3 of the cohort) using the most discriminating features. The classification performance was then evaluated with the datasets from the remaining 47 patients.ResultsResubstituting the training set of 87 patients into the classifier yielded sensitivity and specificity values of 88% and 91%, respectively. The training misclassification rate was predicted using the k‐fold loss metric, with a predicted error rate of 19%. To verify this performance, the test set of 47 patients was classified by the model resulting in sensitivity and specificity values of 80% and 81%, respectively.ConclusionsThe Retinal Deep Phenotyping platform shows promise for detecting the likely cerebral amyloid PET status in human subjects and could serve as a screening tool to identify subjects in the AD continuum, for instance in a clinical or drug development context. The platform could also be used for the detection of other biomarkers involved in cognitive decline.