Petersen, Ronald C.

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.

AbstractBackgroundDisproportionately enlarged subarachnoid‐space hydrocephalus (DESH), characterized by tight high convexity CSF spaces, ventriculomegaly, and enlarged Sylvian fissures, is thought to be an indirect marker of a CSF dynamics disorder. Although this imaging phenotype was originally described in normal pressure hydrocephalus (NPH), it has since been more widely recognized in the aging population with a prevalence of 6‐7% in the Mayo Clinic Study of Aging (MCSA) 1. The clinical significance of DESH with regards to cognitive decline outside of NPH is not yet well‐defined. The goal of this work is to determine if DESH is associated with risk of cognitive impairment.MethodParticipants in the population‐based MCSA who met the following criteria were included: age ≥ 65 years, 3T MRI, cognitively unimpaired at enrollment, and at least one follow‐up visit. A support vector machine based method to detect the DESH imaging features on T1‐weighted MRI was used to calculate “computational DESH” or CDESH scores2 (Figure 1). Cox proportional hazard models were fit with events defined as progression from cognitively unimpaired to mild cognitive impairment or dementia. Time was defined as years from MCSA enrollment to the first visit with cognitive impairment with censoring at last follow‐up. CDESH was modelled as a spline to allow for non‐linear effects. Covariates included age, sex, education, APOE status, cortical thickness, white matter hyperintensity (WMH) volume, and total intracranial volume.ResultParticipant demographics are summarized in Table 1. The model including the above co‐variates showed a significant CDESH effect (p=0.02) with an approximately 20% greater hazard of progression for a CDESH score of +1 versus −1 (HR 1.2, 95% CI 1.0‐1.4) (Figure 2).ConclusionImaging features of disordered CSF dynamics are an independent predictor of subsequent cognitive decline in the MCSA, among other well‐known factors including age, cortical thickness, and APOE status. Therefore, since DESH contributes to cognitive decline and is present in the general population, identifying individuals with DESH features may be important clinically and also used as a selection stratification variable for clinical trials. References: (1) Graff‐Radford et al doi:10.1212/WNL.0000000000008616; (2) N. Gunter et al doi:10.1016/j.nicl.2018.11.015.

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.

AbstractBackgroundA previous study demonstrated that cerebrospinal fluid (CSF) phosphorylated tau 217 (pTau217), compared to phosphorylated tau 181 (pTau181), correlated more strongly with Tau PET and was better able to distinguish between AD and non‐AD neurodegenerative diseases. The aim of this study was to compare these two CSF tau markers for cognitive progression.MethodWe included 666 cognitively unimpaired (CU) and 89 mild cognitive impairment (MCI) Mayo Clinic Study on Aging community‐based participants (mean age of 71.6 years) with CSF amyloid and tau measurements. CSF amyloid‐beta 42 (Aβ42) was measured with automated electrochemiluminescence Elecsys immunoassays (Roche Diagnostics); a cut‐off of <1026 pg/ml was considered amyloid positive (A+). CSF pTau181 and pTau217 were measured with the Mesoscale discovery platform (MSD). Cutoffs of pTau181≥217 pg/ml and pTau217≥288 pg/ml were considered tau positive (T+). Cox proportional hazards models with time as the time scale were used to examine the relationship between the CSF markers and progression from CU to MCI and MCI to dementia. Covariates included age, sex, education, and APOE.ResultTau positivity (T+) based on pTau217 (HR = 2.45, 95% CI: 1.45, 4.11) was a greater risk factor for incident MCI compared to pTau181 (HR = 2.07, 95% CI: 1.19, 3.61). Among individuals who were A+T+, T+ based on pTau217 (HR = 3.01, 95% CI: 1.66‐5.45) was a greater risk factor of incident MCI than pTau181 (2.80, 95% CI: 1.48, 5.31). Among the 89 MCI participants, T+ based on pTau217 was a much greater risk factor for dementia (HR = 4.92, 95% CI: 1.37, 17.61) than pTau181 (HR = 1.55, 95% CI: 0.43‐5.55).ConclusionCSF pTau217 is a better marker of cognitive progression, especially from MCI to dementia, compared to CSF pTau181.

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.

AbstractBackgroundNeurodegeneration of the substantia nigra in dementia with Lewy bodies is associated with iron deposition, which increases the magnetic susceptibility of the substantia nigra on MRI. The objective of this study was to measure the magnetic susceptibility of the substantia nigra in patients with dementia with Lewy bodies (DLB) and patients with mild cognitive impairment with at least one core feature of DLB (MCI‐DLB) compared to cognitively unimpaired controls (CU) by quantitative susceptibility mapping (QSM).MethodWe studied clinically diagnosed patients with DLB (n=36), patients with MCI‐DLB (n=16), and age‐, gender‐matched CU (n=104) in a case‐control study. MR images used for QSM were performed on 3T MRI with a multi‐echo (5 echoes) 3D gradient recalled echo sequence with a bipolar‐readout. Three‐dimensional regions of interest (ROIs) of bilateral substantia nigra, based on the DISTAL atlas, were propagated from the Mayo Clinic Adult Lifespan Template (MCALT) to each subject’s T1 image using ANTs nonlinear registration. The mean susceptibility values of the substantia nigra were extracted by aligning each subjects QSM image to their T1 image and corresponding ROIs with a 6 DOF rigid registration.ResultThe mean susceptibility values of the substantia nigra bilaterally was 0.110 ± 0.026 for DLB, 0.099 ± 0.028 for MCI‐DLB and 0.091 ± 0.025 for controls (Figure 1). No difference was found across the mean susceptibility from left, right and combined bilateral substantia nigra. Compared to controls, patients with DLB had higher susceptibility in the substantia nigra (p<0.001). The susceptibility of substantia nigra showed an increasing trend from controls to MCI‐DLB and to DLB (p<0.001). There was no correlation between the Unified Parkinson's Disease Rating Scale (UPDRS) and the mean susceptibility in the substantia nigra in patients with MCI‐DLB and DLB (r=0.03; p=0.82).ConclusionQuantitative susceptibility mapping is sensitive to the increased magnetic susceptibility due to higher iron content in the substantia nigra in DLB. The trend of increasing susceptibility from CU to MCI‐DLB and to DLB suggests that iron deposition in the substantia nigra starts to increase as early as the prodromal stage in DLB and continues to increase as the disease progresses independent of the severity of Parkinsonism.

AbstractBackgroundData shared for research use must be de‐identified to protect participant confidentiality. A recent study has shown that face imagery from brain imaging studies could be used to re‐identify anonymous participants via automatic face recognition, linking them with all study‐collected protected health information. Here we measure how well existing software for removing MRI face imagery protects against this privacy threat.MethodWe recruited 153 participants with existing 3D FLAIR MRI and photographed their face from 5 varying angles. We used surf_ice to reconstruct each participant’s face from their MRI and tested whether free, publicly‐available face recognition software (Microsoft Azure) could match their photos to their correct MRI. We tested two existing programs for removing face voxels in MRI: mri_deface (FreeSurfer) and pydeface (python package), and one in‐house implementation. We observed that existing methods often left participants’ eyes intact while removing most of their nose/mouth/chin. This prevented Azure from detecting the reconstructions as faces, therefore preventing its attempt of face recognition. We hypothesized that the retained eye imagery alone could be sufficient for face recognition, if a face were detected. Therefore, we also reconstructed each de‐faced MRI after applying novel pre‐processing that replaced any removed/missing face voxels with those from our population‐average template, e.g. replacing missing noses/mouths/chins with population‐average noses/mouths/chins, before testing face recognition.ResultsWithout any MRI de‐facing, Microsoft Azure correctly matched 78% of participants’ photos to their MRI. After mri_deface and pydeface, 8% and 6% were still identified, respectively. After preprocessing the de‐faced MRI to replace missing areas with those from a population‐average template prior to face reconstruction, the face imagery remaining in the de‐faced images was enough to allow re‐identification of 23% and 20% of participants, respectively. With an in‐house face removal implementation that removes the whole face from every image, 0% of faces were re‐identified from the standard reconstructions, and 5% were identified from the “missing‐parts‐replacement” reconstructions.ConclusionsEven after applying popular MRI de‐facing software, Microsoft Azure face recognition automatically re‐identified up to 23% of participants. Better de‐facing software is needed to remove more of the face more reliably, without affecting brain measurements from de‐faced images.

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.