Citations

Abstract Background Hospitalization can impair physical and functional status of older adults, but it is unclear whether these deficits are transient or chronic. This study determined the association between hospitalization of older adults and changes in long-term longitudinal trajectories of 2 measures of physical and functional status: gait speed (GS) and instrumental activities of daily living measured with Functional Activities Questionnaire (FAQ). Method Linear mixed-effects models assessed the association between hospitalization (nonelective vs elective, and surgical vs medical) and outcomes of GS and FAQ score in participants (older than 60 years) enrolled in the Mayo Clinic Study of Aging who had longitudinal assessments. Results Of 4902 participants, 1879 had ≥1 hospital admission. Median GS at enrollment was 1.1 m/s. The slope of the annual decline in GS before hospitalization was −0.015 m/s. The parameter estimate (95% CI) for additional annual change in GS trajectory after hospitalization was −0.009 (−0.011 to −0.006) m/s, p < .001. The accelerated GS decline was greater for medical versus surgical hospitalizations (−0.010 vs −0.003 m/s, p = .005), and nonelective versus elective hospitalizations (−0.011 vs −0.006 m/s, p = .067). The odds of a worsening FAQ score increased on average by 4% per year. Following hospitalization, odds of FAQ score worsening further increased (multiplicative annual increase in odds ratio per year [95% CI] following hospitalization was 1.05 [1.03, 1.07], p < .001). Conclusions Hospitalization of older adults is associated with accelerated long-term decline in GS and functional limitations, especially after nonelective admissions and those for medical indications. However, for most well-functioning participants, these changes have little clinical significance.

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.

AbstractBackgroundPrevious research has suggested that, compared to males, females are at greater risk for and have greater pathology burden in late onset. However, sex differences in early onset AD (EOAD) have not yet been studied.MethodWe included 167 participants [28 cognitively normal (CN, 68% females), 98 early onset AD (EOAD, 55% females), and 41 cognitively impaired amyloid‐negative (EOnonAD, 31% females] subjects from the Longitudinal Early‐Onset AD Study (LEADS) with available Flortaucipir PET, Florbetaben PET, and MRI data. Multiple linear regression (MLR) models including age and MMSE as covariates were used in the pooled sample to examine the effects of sex on hippocampal and white matter hyperintensity volume, mean cortical thickness, mean tau distribution by Braak regions and mean cortical amyloid SUVR. We also ran voxelwise MLR with sex as the predictor and cortical thickness, amyloid SUVR normalized to whole cerebellum, tau SUVR normalized to cerebellar crus, respectively, as the outcome measures while controlling for age, MMSE, and total intracranial volume (MRI only). Results are displayed at a cluster‐level FWE correction of p<0.05.ResultThere were no significant demographic differences between males and females in any diagnostic group. Across the pooled sample females showed significantly greater atrophy of the hippocampus (p=0.0001), greater tau SUVR in Braak regions 3&4 (p=0.05) and 5&6 (p=0.04) and trend for greater global amyloid uptake (p=0.074) (Table 2). The analyses in imaging space confirmed these findings and showed that the effects are driven by the EOAD group. Females showed greater amyloid deposition globally and greater tau deposition in the frontal, inferior parietal and temporal lobes (Figure 3).ConclusionFemale sex is associated with greater pathology burden in EOAD. Longitudinal studies will be needed to establish whether such difference translates in faster rates of progression in women relative to men.

AbstractBackgroundApproximately 5% of the 5.6 million (∼280,000) Americans with Alzheimer’s disease (AD) develop symptoms at age 65 or younger and are classified as having early‐onset AD (EOAD). Although EOAD and late‐onset AD (LOAD) share the same pathologic substrate, there are notable differences in their clinical and biological phenotypes. The Longitudinal Early‐onset AD study (LEADS) is a multi‐site, observational clinical and biomarker study of EOAD. In the present study we utilized the currently available LEADS imaging data to explore the spatial extent and magnitude of neurodegeneration EOADMethod95 amyloid positive patients with EOAD, 37 amyloid negative cognitively impaired patients (EOnonAD) , and 55 cognitively unimpaired controls (CNs) were included in the analysis. The magnitude of cortical thinning in the EOAD and EOnonAD groups was estimated using whole‐brain GLMs compared to the CN group. In addition, w‐scores were derived for each EOAD patient for these ROIs: average cortical thickness, hippocampal volume, AD‐signature cortical thickness (typically used in LOAD), and precuneus cortical thickness controlling for age, sex, and acquisition site. The utility of each ROI to differentiate EOAD patients from CNs was assessed by calculating the percentage of patients falling below a W‐threshold of ‐1.5.ResultIn EOAD, a distributed pattern of cortical thinning was observed in lateral temporal, parietal, and frontal cortex (p<.01), and was most prominent in the precuneus (Cohen’s d’ = 1.5). In the EOnonAD group, cortical thinning was observed but was highly variable across individuals. The w‐score analysis in the EOAD group yielded the following results: Mean Cortical thickness: Mean = ‐3.34, SD = 2.97, Classification = 72%.; Hippocampal volume: Mean = ‐2.96, SD = 2.39, Classification = 74%; AD Signature: Mean = ‐4.63, SD = 3.07, Classification = 80%; Precuneus: Mean = ‐5.98, SD = 4.1, Classification = 90%.ConclusionOur results are consistent with previous findings demonstrating cortical thinning in lateral parietal, precuneus, and lateral temporal lobes in EOAD with relatively less involvement of the medial temporal lobes. Our results also suggest that the precuneus may be especially vulnerable to neurodegeneration in patients with EOAD and may serve as a useful biomarker for patient classification.

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.

AbstractBackgroundWe report the associations between change in renal biomarkers in CKD and non‐ CKD participants and change in structural MRI outcomes over 3 years in the BRain IN Kidney disease (BRINK) study MRI cohort. The imaging outcomes were cortical thickness in Alzheimer’s disease signature regions, white matter hyperintensities (WMH), and fractional anisotropy in the corpus callosum (FA CC), an early marker of cerebrovascular disease.MethodsParticipants were ≥ 45 years with CKD (estimated glomerular filtration rate (eGFR) in mL/min/1.73 m2< 60; non‐ dialysis) or non – CKD (eGFR ≥ 60) in Minneapolis/St. Paul. CKD severity was measured by the eGFR and urine albumin creatinine ratio (UACR‐albuminuria, a measure of microvascular disease). Brain images were obtained on a 1.5 T Phillips Ingenia machine at baseline and 3 years. Linear regression models measured the associations between 3 year change in eGFR and UACR, as predictors of change in MRI measures: AD cortical thickness, WMH, and FA CC over 3 years. Adjusted models included age, gender, AA race, education years, cerebrovascular disease risk factor score (CVD RF), smoker/alcohol use, and pulse pressure as covariates, and TIV for change in WMH models.ResultsAt baseline, mean age of the 97 participants was 67 years, and mean eGFR was 37 for the CKD group vs. 83.2 for non‐ CKD (Table 1). Mean change in eGFR and UACR in the CKD group were ‐3.6 and +212, respectively compared with ‐ 8.5 and +1.7 in the non‐ CKD group. Mean WMH volume increase (+5.8 cc) and mean FA CC decrease (‐0.012) were significant in CKD (p < 0.001) for both (Table 2). In adjusted models both a 1% decrease and a ≥ 3 unit/year decline in eGFR were associated with a decline in FA CC over 3 years in the CKD group (β (SE) =‐0.020 (0.007); p = 0.004) and ‐0.0097 (0.0042); p = 0.025, respectively). Cortical thickness models were not significant. Unexpectedly, change in UACR was not associated with change in any MRI outcomes.ConclusionsDecreased eGFR was associated with decreased FA CC in CKD, suggesting that CKD progression exerts its influence largely through cerebrovascular mechanisms.

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.

AbstractBackgroundWhite matter (WM) hypointensity volume in T1 MRI correlates strongly with T2 hyperintensities and both are typically associated with advancing age and vascular brain injury (VBI). Recent research has suggested that Alzheimer’s Disease (AD) neuropathologic changes (ADNC) also increases WM hypointensity volume. Sporadic early onset AD (EOAD) patients provide a unique model to examine the relationship between ADNC biomarkers and WM hypointensity volume in the absence of advanced age, cardiovascular injury, or autosomal dominant genetic mutations.MethodIncluded in the analysis were sporadic EOAD patients (n = 75) and cognitively normal controls (n = 39) from the Longitudinal Alzheimer’s Disease Study (LEADS) for whom full demographic and health information were available. WM hypointensity volume in T1 MRI was segmented by Freesurfer and corrected for total white matter volume. Multiple‐factor ANOVA models with logarithmic transformations were used to determine the effects of age, cohort, and presence of cardiovascular conditions on WM hypointensity volume. Further multiple‐factor ANOVA models assessed whether the addition of covariates improved the model fit.ResultIn a reproduction of prior research, WM hypointensity volume was correlated with age for controls (p < 0.05), but not for EOAD patients. We additionally found an effect of cohort, such that EOAD patients had a higher mean WM hypointensity volume than controls when accounting for age (p < 0.05). No difference in the presence or number of cardiovascular conditions was found between groups. Additionally, no correlation was found between WM hypointensity volume and the presence or number of cardiovascular conditions. Including cardiovascular condition as a covariate failed to improve the model fit, even when accounting for interaction effects.ConclusionThese results support the hypothesis that patients with ADNC have higher WM hypointensity volume beyond what would be expected based on aging or cardiovascular risk factors. Further research is needed to assess the co‐localization of WM hypointensities with cortical degradation, amyloid pathology, and tau pathology.

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.