Jack, Clifford R.

AbstractBackgroundData heterogeneity due to cross‐scanner variations is a major challenge in multi‐center aging and dementia studies. Brain structural measures for the same participant can vary due to scanner differences (manufacturer, scanner age/technology, signal‐to‐noise ratio, pulse sequence design, contrast, resolution) and result in biomarker variability. In this work, we applied basic image processing and deep learning methods (style transfer (ST) and generative adversarial networks (GANs)) for harmonizing T1‐weighted MRI scans across change in vendors (GE to Siemens) and resolution (1x1x1.2mm to 0.8x0.8x0.8mm) and test whether this harmonization improved compatibility of AD biomarker measures.MethodWe utilized training (n=814) and test (n=113 same participants scanned on two scanners close‐in‐time, typically on the same day, with different resolution) data from cognitively impaired and unimpaired participants scanned on GE and Siemens scanners from Mayo Clinic Study of Aging and Mayo ADRC. The harmonization was conducted to translate GE to Siemens. All scans were affine transformed to the same template space, resampled to 1.5 mm3, and bias corrected. Histogram matching was performed using cumulative density functions. A ResNet18 was trained for the ST’s content and style layers. Two types of GANs, conditional GANs (cGAN) and cycle‐consistent GANs (cycleGAN), were constructed. We then measured regional cortical thickness from each image using FreeSurfer‐7.1.1 and compared the thickness measurements from the original and translated scans using intraclass‐correlation (ICC) and Deming regression.ResultIn the test dataset, ST and cycleGAN showed the best performances when comparing the translated GE to Siemens. With ST, the ICC increased (vs. the original scans) slightly for the temporal meta‐ROI (0.956 from 0.954) and more substantially for the frontal pole ROI (0.706 from 0.594). The cycleGAN showed an increase in ICC for the frontal pole ROI (0.610 from 0.594) but not the meta‐ROI (Fig. 1).ConclusionEach method leveraged a different strength. Histogram matching captures contrast, ST extracts textural information, and GANs learn combination of contrast, edge and texture properties. We found that deep learning is a promising approach to cross‐scanner variability. Our comparisons revealed anatomical differences across methods; thus, disease specific measure should be considered for selection of the scanner harmonization method.

AbstractBackgroundTau PET imaging is an established tool for studying Alzheimer Disease but its sensitivity to primary age‐related tauopathy (PART) as well as sex effects in individuals without abnormal levels of amyloid are unknown.MethodsCross‐sectional data were collected from 2014 to 2019 in 144 cognitively normal younger, middle‐aged, and older adults free from amyloid pathology as quantified using PET. Regional values of tau PET binding were measured using flortaucipir. Statistical models examined the main effects of increasing age, sex, race, and sub‐threshold levels of amyloid. Secondary analyses also examined the relationship between tau PET binding and iron as measured using T2* weighted imaging and calcification as measured using CT.ResultsThere were significant positive associations between tau binding and age in 19 regions, with the largest effects seen in the inferior temporal cortex (t=4.59), caudate (t=9.58), and putamen (t=12.57). Iron as measured using T2* imaging mediated only a modest (11.9%) amount of the association between age and tau binding. Elevated tracer uptake in females was present in 23 regions in frontal, lateral parietal, and temporal regions including, most prominently the rostral middle frontal gyrus (t=7.48), superior temporal gyrus (t=5.12) and the inferior temporal gyrus (t=4.22). There were no significant effects of race or sub‐threshold levels of amyloid.ConclusionsTau PET is sensitive to primary effects of age in regions consistent with the neuropathological definition of PART although strong age effects in regions of off‐target binding were also present. There was a robust effect of sex, suggesting prior observations of elevated binding in women is not solely a potentiation of Alzheimer pathology but instead represents an ubiquitous phenomenon. Understand how both sex and age impact tracer binding is critical to understanding the utility of PET tracers as well as interpreting tracer values in the context of disease.

AbstractBackgroundPhysical and cognitive activities are associated with decreased risk of mild cognitive impairment and dementia. However, the longitudinal associations between physical and cognitive activities with trajectories of Alzheimer’s disease (AD) neuroimaging biomarkers among older adults free of dementia remain unclear.MethodWe conducted a longitudinal study derived from the population‐based Mayo Clinic Study of Aging, including individuals aged ≥ 50 years who were free of dementia. Participants had information on physical and cognitive activity engagement in midlife (ages 30‐45 for participants aged 50‐69; ages 50‐65 for participants aged ≥ 70) and late‐life (12 months prior to baseline assessment), and AD biomarker assessments. We calculated linear mixed‐effect models to examine the association between baseline physical and cognitive activity composite scores for mid‐ and late‐life, and trajectories for individual yearly change in amyloid deposition (measured by PiB‐PET), tau burden (measured by Tau‐PET), regional glucose hypometabolism (measured by FDG‐PET), cortical thickness (measured by MRI), and white matter hyper intensities (WMH, measured by FLAIR‐MRI). The models were adjusted for age, sex, and APOE ɛ4 carrier status.ResultThe sample included 2755 persons (52% males; 2462 cognitively unimpaired, 293 with MCI). The mean [SD] age was 72.7 [9.8] years; 774 participants were APOE ɛ4 carriers. Although, overall, participants showed an increase in amyloid deposition, tau burden, WMH, and a decrease in glucose metabolism and cortical thickness over time, those with higher late‐life physical activity experienced less pronounced increase in Tau‐PET SUVR over time [estimate for late‐life physical activity with time interaction: ‐0.002; 95% CI ‐0.004, ‐0.000; p = 0.034]. In addition, participants with higher cognitive activities in midlife [est. for midlife cognitive activity with time interaction: 0.001; 95% CI 0.000, 0.003; p = 0.026] and late‐life [est. for late‐life cognitive activity with time interaction 0.002; 95% CI 0.001, 0.003; p = 0.005] had less pronounced decrease in FDG‐PET SUVR over time. There were no further significant interactions.ConclusionPreliminary findings suggest significant associations between 1) late‐life physical activity and less tauopathy, and 2) cognitive activities in mid‐ and late‐life with less synaptic dysfunction over time. Further research is needed to validate study findings.

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform optimized for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). Study aims were to determine 1) convergent validity of MTD through correlations with in‐person neuropsychological measures and 2) criterion validity of MTD by qualitatively comparing the ability of Mayo Test Drive and in‐person administered neuropsychological measures to differentiate biomarker‐defined groups, independent of clinical diagnosis.MethodsMayo Clinic Study of Aging participants (N = 282) completed a brief remote cognitive assessment (mean age = 74, SD = 12; 262 CU and 20 MCI per consensus conference diagnosis; see Table 1). A subset of participants had brain amyloid and/or brain tau PET scans available within 3 years; overlapping groups were formed for 1) those on the Alzheimer’s disease (AD) continuum (A+, n = 31) or not (A‐, n = 57) and for 2) those with biological AD (A+T+, n = 19) or without any AD biomarkers (A‐T‐, n = 47). Primary outcome variables were SLS sum of trials, AVLT sum of trials, SYM Correct Trials response time, Digit Symbol Coding, MTD composite and Mayo‐PACC (AVLT sum of trials, Trails B, animals). Bivariate correlations were performed across all participants. Linear model ANOVAs were used to investigate biomarker subgroup differences; Hedge’s g effect sizes are reported.ResultsConvergent validity was supported through significant correlation (p’s < .001) of SLS and AVLT (r = .63), SYM and Coding (r = ‐.64), and MTD composite with Mayo‐PACC (r = .67); see Table 2. MTD subtests showed expected relationships with age and education (p’s < .05; Table 3). All MTD performances were significantly lower for A+ and A+T+ participants relative to A‐ or A‐T‐ (p’s<.001; Table 4). All MTD outcome measures showed a similar or greater group difference effect size relative to traditional in‐person administered neuropsychological measures for separating A+ vs. A‐ and A+T+ vs. A‐T‐ groups. See Figure 1 and Table 4.ConclusionsMTD subtests show evidence of convergent validity through strong correlations with traditional in‐person cognitive measures. Preliminary analyses suggest MTD has promise for differentiating PET biomarker groups.

AbstractBackgroundRemote assessment tools offer significant promise for aiding early detection of cognitive impairment. Mayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment that includes a computer adaptive word list memory test (Stricker Learning Span; SLS) and a measure of processing speed (Symbols Test). We examined the diagnostic accuracy of the SLS and a MTD composite (SLS max span, SLS trials 1–5 total correct, SLS delay correct, [Symbols correct item response time*‐1]) for amnestic mild cognitive impairment (aMCI). We also explored diagnostic accuracy for a broader group that included individuals with possible MCI (pMCI; see Figure 1).MethodsParticipants were recruited from the Mayo Clinic Study of Aging for this ancillary remote study. 226 were cognitively unimpaired (CU; concordant CU diagnosis by 3 independent raters). Fifty‐six participants had possible MCI (at least 1 of 3 raters indicated MCI) and 16 had a consensus diagnosis of aMCI. Primary outcome variables were SLS sum of trials, AVLT sum of trials, Symbols correct items response time, and MTD composite. Mean difference analyses used linear model ANOVAs (alpha = .05). Receiver operating characteristic (ROC) curves were applied; we derived optimal cutoff scores based on the Youden index method.ResultsBoth aMCI and possible MCI groups showed significantly lower performance than the CU group on SLS (Hedge’s g aMCI = ‐1.72, pMCI = ‐1.04), Symbols (Hedge’s g aMCI = 1.38, pMCI = 0.64), and MTD composite (Hedge’s g aMCI = ‐1.89, pMCI = ‐1.09); see Tables 1 and 2 (all p‘s<.01 even when additionally covarying age, education, sex). Total area under the curve was high for differentiating of CU vs. aMCI (MTD Composite = 0.91, SLS = 0.91) and acceptable for CU vs. pMCI (MTD Composite = 0.77, SLS = 0.77); see Table 2. Consistent with our flexible platform, a variety of device types were used to complete remote testing (32% smartphone, 12% tablet, 56% PC).ConclusionsMTD and the SLS show high diagnostic accuracy for aMCI. MTD is a flexible, brief, and easy‐to‐use remote cognitive assessment tool with great potential for scalable use in future studies seeking to maximize inclusion of individuals with aMCI in clinical trials or for cognitive screening in clinical settings.

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for self‐administered assessment for novel (Stricker Learning Span) and open access (Symbols Test) cognitive measures with previously demonstrated validity and reliability. Our study aim was to examine the usability of MTD, defined as the extent to which MTD was used to achieve the desired study goal of completing a testing session among participants willing to engage in an all‐remote study.Methods248 Mayo Clinic Study of Aging and Mayo Alzheimer’s Disease Research Center participants were invited to participate in this ancillary, uncompensated remote pilot study via email. To examine 1) willingness to participate in a remote cognitive assessment study and 2) usability of MTD, the initiation and completion rates of MTD were examined by cognitively unimpaired (CU; n = 142) and cognitively impaired (CI; n = 106) groups (see Figure 1). These rates and other factors associated with MTD feasibility were further explored across age in CU (i.e., 50–69, 70–79, and 80+ years) and specific diagnostic groups in CI (i.e., individuals with dementia, mild cognitive impairment (MCI), and possible MCI).ResultsSession completion rate (usability) was 98% across all participants who chose to participate, and, surprisingly, completion rates did not vary by age or diagnostic groups (p = .69; see Table 1). As expected, more individuals chose to participate in the CU (77%) versus CI (52%) group (p < .001). In terms of feasibility, individuals over 80 and with CI were more likely to have some contact with the study coordinator. Use of smartphones decreased with age, with most CU individuals over 70 using a personal computer (69‐73%; Table 2).ConclusionsThough the CI group was less likely to choose to participate than the CU group, out of those that did choose to participate the rate of completion was 98%. This demonstrates high usability of MTD. Study coordinator support and a flexible testing platform is critical for facilitating participation in remote testing across the aging population, and more supports and incentives may be needed for individuals with cognitive impairment to improve participation rates.

AbstractBackgroundSlow walking speed is associated with numerous negative health outcomes including global cognitive decline.1 More detailed gait analysis can differentiate among different dementia subtypes,10 but the predictive power of baseline gait metrics for domain‐specific cognitive decline needs further characterization.MethodWe conducted a follow up study on 534 participants of the Mayo Clinic Study of Aging who underwent evaluations every 15 months (median follow‐up = 5.4 years). We analyzed quantitative gait metrics, acquired with a pressure‐sensing walkway (GAITRite® instrument), and cognitive scores using a linear mixed effects model to identify correlations between z‐scored baseline gait metrics (cadence, swing time, stance time, velocity, double support time (DST), stride length, stride length standard deviation (SD), stride time SD, swing time SD, stance time SD, DST SD) and cognitive domain z‐scores (memory, attention, language, visuospatial, global) at baseline and over time. Models controlled for age, sex, and probable REM sleep behavior disorder at baseline.ResultThe table includes both baseline associations and difference in slopes (Δ), relative to the average person, in cognitive domains. These slopes ranged from about ‐0.05 for visuospatial to ‐0.12 for attention.ConclusionIncreased gait velocity and stride length are associated with less severe domain‐specific and global cognitive decline while increased swing time SD and DST are associated with more severe rates of decline in some domains. These findings suggest that baseline gait characteristics could be a helpful predictor of future cognitive changes.References:1. Savica R, Wennberg AMV, Hagen C, et al. Comparison of Gait Parameters for Predicting Cognitive Decline: The Mayo Clinic Study of Aging. Journal of Alzheimer's disease : JAD. 2017;55(2):559. doi:10.3233/JAD‐1606972. Mc Ardle R, del Din S, Galna B, Thomas A, Rochester L. Differentiating dementia disease subtypes with gait analysis: feasibility of wearable sensors? Gait & Posture. 2020;76:372–376. doi:10.1016/j.gaitpost.2019.12.028

AbstractBackgroundWe previously demonstrated that Alzheimer’s disease (AD) patients ≥70 y/o tend to have smaller medial temporal volumes in the presence of relatively less medial temporal 18F‐flortaucipir‐PET uptake compared to young AD patients. TAR DNA‐binding protein 43 (TDP‐43) is frequently found in the brains of AD patients and is associated with old age and medial temporal atrophy. In this study, we aimed to determine whether TDP‐43 could be contributing to the mismatch between medial temporal volumes and flortaucipir‐PET uptake in older AD patients.MethodWe identified 77 participants (median age at death 79 years [range: 51‐99]) who had been enrolled into the Mayo Clinic Alzheimer’s Disease Research Center or Neurodegenerative Research Group (NRG), had undergone flortaucipir‐PET and volumetric MRI scans close to death, and autopsy confirmed AD neuropathological changes, as well as TDP‐43 assessments. Grey matter volumes and flortaucipir standard uptake value ratios (SUVRs) were calculated for hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices. We used bivariate‐response linear regression modelling, adjusting for Braak neurofibrillary tangle stage, to estimate the effect of TDP‐43 and age on gray matter volume and flortaucipir uptake, and tested whether TDP‐43 effect sizes varied according to age.ResultAfter accounting for Braak stage, older individuals tended to have significantly lower flortaucipir uptake across regions and lower hippocampal and entorhinal cortex (ERC) volumes. TDP‐43 was associated with reduced hippocampus and ERC volumes but was unrelated to flortaucipir SUVRs (Figure 1). Notably, the TDP‐43 effect size for the hippocampus and ERC was largely consistent across age, i.e., at any age, TDP‐43‐positive participants had significantly smaller hippocampal (∼12% decrease) and ERC (13% decrease) volumes than TDP‐43‐negative participants. However, at older ages, the mean volumes in the whole cohort and, more evidently, in those with dementia moved closer towards the mean of those with TDP‐43 (Figure 1), likely attributable to the increasing frequency of TDP‐43 with age.ConclusionTDP‐43 is a major contributor to the mismatch between flortaucipir uptake and medial temporal lobe volumes in older AD patients where lower medial temporal regional volumes are observed in the presence of less flortaucipir uptake.

AbstractBackgroundCognitive resilience, the discrepancy observed in individuals with high Alzheimer’s disease (AD) neuropathology and near‐normal cognition, is a growing area of study, wherein multiple factors are responsible. Currently, the role of the microglial activation in cognitive resilience is unknown. Herein described is a case report characterizing the amyloid responsive microglia (ARM) in an 88‐year‐old male decedent homozygous for APOE E4, clinically cognitively intact, and found to have AD neuropathologic change (ADNC) upon autopsy.MethodThe participant was enrolled in the Mayo Clinic Study of Aging (MCSA) and continuously followed for 14 years from 73‐ to 87‐years‐old, wherein serial MRI and clinical assessments were obtained. An autopsy was performed upon death, and routine neuropathologic sections were taken. Formalin‐fixed, paraffin‐embedded sections were stained with H&E, and single immunohistochemistry was performed using antibodies against tau (AT8), beta‐amyloid (6F3D), alpha‐synuclein (LB509), TDP‐43 (pS409/410), and CD163 (10D6). Digital image analysis was performed on dorsolateral prefrontal cortex sections scanned at 40x (Leica GT450). ARM (CD163% area) and beta‐amyloid (% area) measurements were obtained using QuPath threshold classifiers. Descriptive statistics were performed (Graphpad).ResultThe decedent was an 88‐year‐old male, homozygous for APOE E4, with a history of three cardiovascular and metabolic conditions, and lifelong smoking. His MMSE (average 28/30) and global cognition (average ‐0.51) were relatively stable (Figure 1), with no clinical concerns for cognitive impairment. Cortical thickness was normal (temporal meta‐ROI>3 mm) on four serial MRIs. The participant reported high cognitive, physical activities, and education‐occupation scores (upper quartile for the 70+ population). Neuropathologic evaluation was notable for severe cerebral amyloid angiopathy, high ADNC (A3B3C3), and remote microinfarcts. CD163‐positive ARM were observed in the neocortices and perivascular spaces, and the ARM:beta‐amyloid ratio was 0.26 (Figure 1). This ARM:beta‐amyloid ratio trended upwards (>90th percentile) compared to APOE E4/E4‐matched‐ and age‐matched controls provided from the University of Pennsylvania.ConclusionThis case report demonstrates a cognitively resilient individual with an elevated ARM:amyloid ratio. Conversely, lower ARM ratios were observed in a cognitively impaired cohort, raising the possibility ARM may associate with cognition. ARM may represent a protective and phagocytic population, and further studies correlating microglia, cognition, and putative mechanism are required.