Citations

AbstractIntroductionThis study evaluated the diagnostic accuracy of the Cogstate Brief Battery (CBB) for mild cognitive impairment (MCI) and prodromal Alzheimer's disease (AD) in a population‐based sample.MethodsParticipants included adults ages 50+ classified as cognitively unimpaired (CU, n = 2866) or MCI (n = 226), and a subset with amyloid (A) and tau (T) positron emission tomography who were AD biomarker negative (A–T–) or had prodromal AD (A+T+).ResultsDiagnostic accuracy of the Learning/Working Memory Composite (Lrn/WM) for discriminating all CU and MCI was moderate (area under the curve [AUC] = 0.75), but improved when discriminating CU A–T– and MCI A+T+ (AUC = 0.93) and when differentiating MCI participants without AD biomarkers from those with prodromal AD (AUC = 0.86). Conventional cut‐offs yielded lower than expected sensitivity for both MCI (38%) and prodromal AD (73%).DiscussionClinical utility of the CBB for detecting MCI in a population‐based sample is lower than expected. Caution is needed when using currently available CBB normative data for clinical interpretation.

Background: Transactive response DNA-binding protein of 43 kDa (TDP-43) is associated with memory impairment and overall cognitive decline. It is unclear how TDP-43 contributes to the rate of clinical decline. Objective: To determine whether cross-sectional and longitudinal cognitive and functional decline are associated with anatomical distribution of TDP-43 in the brain. Methods: Longitudinal clinical-neuropathologic autopsy cohort study of 385 initially cognitively normal/mildly impaired older adults prospectively followed until death. We investigated how TDP-43, amyloid-β (Aβ), tau neurofibrillary tangles (NFT), Lewy body disease (LBD), age, sex, and genetics are associated with clinical scores and rates of their longitudinal decline. Results: Of 385 participants, 260 (68%) had no TDP-43, 32 (8%) had TDP-43 limited to amygdala, and 93 (24%) had TDP-43 in the hippocampus and beyond. Higher TDP-43 and Braak NFT stages independently were associated with faster decline in global cognition, functional performance measured by Clinical Dementia Rating scale, and naming and episodic memory, whereas older age was associated with slower rate of cognitive, psychiatric, and functional decline. Cross-sectionally the following associations were found: higher TDP-43 and Braak NFT - worse performance; higher Aβ burden - worse global cognition, more behavioral changes, the latter also with higher LBD; older age - worse naming, lower frequency of behavioral changes; female sex - more impaired naming and better preserved episodic memory. There were no genetic associations. Conclusion: The association of TDP-43 distribution with decline in cognitive and functional performance suggests that TDP-43 is playing a role in the clinical progression to dementia. Further characterization of clinical features associated with TDP-43 can facilitate establishment of antemortem diagnosis.

Abstract Deterioration in white-matter health plays a role in cognitive ageing. Our goal was to discern heterogeneity of white-matter tract vulnerability in ageing using longitudinal imaging data (two to five imaging and cognitive assessments per participant) from a population-based sample of 553 elderly participants (age ≥60 years). We found that different clusters (healthy white matter, fast white-matter decliners and intermediate white-matter group) were heterogeneous in the spatial distribution of white-matter integrity, systemic health and cognitive trajectories. White-matter health of specific tracts (genu of corpus callosum, posterior corona radiata and anterior internal capsule) informed about cluster assignments. Not surprisingly, brain amyloidosis was not significantly different between clusters. Clusters had differential white-matter tract vulnerability to ageing (commissural fibres > association/brainstem fibres). Identification of vulnerable white-matter tracts is a valuable approach to assessing risk for cognitive decline.

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.

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.

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.

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.