Citations

AbstractBackgroundMayo Test Drive (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based platform for remote self‐administered assessment for novel (Stricker Learning Span; SLS) and open access (Symbols Test) cognitive measures. The SLS is a computer adaptive word‐list memory test that matches test difficulty to user performance. The SLS visually presents words for learning exposures and 4‐choice recognition to test encoding across 5 trials. Symbols is a measure of processing speed being used in many remote assessment studies and typically employs a burst design; in MTD participants complete 4 trials (12‐items each). We examine validity and reliability of the SLS and Symbols via MTD.MethodForty‐five women enrolled in an ongoing study of the Sex‐Specific Effects of Endocrine Disruption on Aging and Alzheimer’s Disease were recruited via email or phone. The Auditory Verbal Learning Test (AVLT) and Digit Symbol Coding (Coding), administered in person within the past 2 years (Mean=11 months), were used to establish construct validity. Participants (91.3% White, Mean age=66.2 years, Mean education=15.3 years) were emailed a link to complete the SLS and Symbols remotely at their convenience on their own device and emailed to repeat testing after approximately 1 week. Pearson correlations (n=45) and 2‐way mixed intraclass correlation coefficients (ICC; n=42) determined construct validity and test‐retest reliability, respectively. Significance was set at p<.05.ResultAcross all test sessions initiated, 97% were successfully completed. Most were completed at home (92.6%; vs. at work or in a public space), and device type varied (48% smartphone, 2% tablet, 50% PC). Median session completion time was 15 minutes. SLS learning scores (max span, total correct trials 1‐5) significantly correlated with AVLT 1‐5 total (r’s .37‐.39) and AVLT Trial 5 (r’s .51‐.52). SLS delay significantly correlated with AVLT delay (r=0.45) and recognition (r=0.61). Symbols time‐based scores averaged across trials were significantly correlated with Coding (r’s=‐.48‐(‐).50). Test‐retest reliabilities across 2 sessions were high for SLS (ICC’s=.80‐.88) and Symbols (ICC’s=.81‐.82).ConclusionSLS and Symbols are valid and reliable self‐administered remote tests in older females. Future work will examine effects of Alzheimer’s disease neuroimaging biomarkers and risk factors on these tests.

AbstractBackgroundThere have been no standard definitions of successful cognitive aging (SA). Our goal was to apply different SA definitions to the 80+ population in the Mayo Clinic Study of Aging (MCSA) on the basis of (i) cross‐sectional vs longitudinal cognition (“Exceptionality” vs “Maintenance”) and (ii) memory vs global cognitive scores and explore the participant characteristics for each SA definition on the basis of baseline imaging markers and mid/late‐life health.MethodWe included 2157 participants from the MCSA, all ≥80 years at baseline and classified them into two SA definitions using memory z‐scores or global z‐scores: “Exceptionality” when high cognitive performance was observed (z‐scores ≥ 1 SD), “Maintenance” when cognitive function was preserved over time (Slope ≥ ‐0.1/year using mixed‐effect models). Our imaging markers were: PET‐based amyloid positivity (A+), MRI‐based neurodegeneration positivity (N+), WMH, FDG‐PET SUVR markers of resilience (anterior cingulate, ACC) and AD‐related areas (posterior cingulate). We also examined APOE4 status and other health characteristics. Group comparison was performed within each definition (e.g. exceptional vs non‐exceptional memory) using ANCOVA and binary logistic regressions with adjustments for age, sex, and education.ResultA lower number of participants met the “Exceptionality” criteria (10%: memory and 4%: global). A greater number met the “Maintenance” criteria (53%: memory and 32%: global). Less than 7% showed both “Exceptional” cognition and “Maintenance” over time. Participants across SA definitions were in general younger, more often women, with a higher education/occupation and less often APOE4 carriers. All SA groups showed higher ACC metabolism and lower N+ compared to the corresponding non‐SA group. More than 50% of the participants in the “Exceptional” groups were A+ (57%: memory, 50%: global). A+ frequency was significantly lower in the “Maintainers” (<42%). Better mid/late‐life health indicators were observed in the SA groups, including lower cardio‐metabolic conditions, higher mid‐life cognitive activities, lower depressive symptomatology, lower hypersomnolence and better self‐perception of health.ConclusionDifferent SA definitions identified mainly non‐overlapping 80+ groups that showed several common features including higher education/occupation, percent of women, better lifestyles, lower N+ frequency, and higher ACC metabolism. Lower A+ frequency was a key distinct feature of “Maintenance”.

AbstractBackgroundThe Mayo Clinic Study of Aging and Alzheimer’s Disease Research Center used GE scanners exclusively for MRI from 2005‐2017, employing consistent imaging parameters throughout. In 2017 the MRI protocol was updated and migrated to Siemens Prisma scanners; 113 participants were scanned with each configuration. The GE protocol included a 2D FLAIR. The Siemens protocol includes 3D FLAIR and T2‐weighted images. Both vendor protocols included a 3D T1. Experience with multi‐center trials suggests such changes may introduce systematic variations.MethodsGE scanning was carried out on 3T scanners with 12 channel phased array head coils. Siemens Prisma scanning was carried out with 64 channel head/neck coils. Imaging sequences and parameters are given in Table 1. We used the MCALT template and priors to perform standard six‐class segmentation on T1‐weighted (T1w) images. SPM12 unified segmentation supports the simultaneous use of multiple input contrasts; segmentation of multi‐contrast inputs was done using eight tissue classes including deep grey and white matter hyperintensity (WMH) priors. For a given MRI study (either GE or Siemens) all images were registered and resampled to the study’s T1w image. Two‐channel (T1+FLAIR) segmentation was performed on GE scans. Two and three‐channel segmentations were performed on Siemens scans. Comparisons included total intracranial volume, total tissue volume and total grey matter volumes, as well as hippocampal volumes and grey matter volume in a temporal lobe meta‐ROI.ResultsFour participants were excluded for poor image quality: one poor GE T1w quality, one poor Siemens T1, T2 and FLAIR quality, and two poor Siemens T2 quality. Comparisons were made among 109 participants aged 34–97 (Table 2). By visual assessment, adding T2‐weighted images always improved intracranial volume correctness. Using the combination of single‐channel GE and three‐channel Siemens generally improved cross‐vendor agreement on grey matter volumes with the exceptions of hippocampal and intracranial volume estimates.ConclusionRequiring multiple input channels increases the likelihood that the result of a study being excluded. Combining T1w, FLAIR and T2w images allows the delineation of WMH and deep grey matter probability outputs but may introduce differences in GM volumes.

AbstractBackgroundEarly changes in white matter (WM) measured using diffusion tensor imaging (DTI) are sensitive markers of WM degeneration and predict future cognitive decline in mild cognitive impairment (MCI). We sought to examine whether changes in WM integrity differed among empirically derived subtypes of incident MCI.MethodParticipants included 66 individuals with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) previously identified via cluster analysis as having one of four MCI subtypes: subtle cognitive impairment (SCI; n = 13, mean age 78; 15% women), amnestic (n = 24; mean age 79; 54% women), dysnomic (n = 8; mean age 79; 38% women), and dysexecutive (n = 21; mean age 82; 33% women). All participants completed 3T MRI at the time of the MCI diagnosis. We compared the MCI subtypes to 100 cognitively unimpaired (CU) participants (mean age 77; 49% women). We used tract‐based spatial statistics to examine voxel‐level differences in fractional anisotropy (FA). We also fit a linear regression model to regional FA data from the corpus callosum (CC), the major interhemispheric WM connection, to quantify the relative differences in WM damage.ResultAll MCI subtypes except the subtle cognitive impairment group had widespread decreased FA. Relative to CU, the amnestic cluster showed decreased FA in the CC (genu), cingulum, fornix and uncinate fasciculus (UF); the dysnomic cluster showed decreased FA in the CC (genu, splenium), fornix, UF, anterior thalamic radiation (ATR), and corticospinal tract (CST); and the dysexecutive group showed decreased FA in the CC (genu, body, splenium), cingulum, UF, ATR, CST, and superior longitudinal fasciculus. The dysexecutive cluster had the most severe bilateral involvement among all MCI subtypes. The regional analysis showed that the amnestic, dysnomic, and dysexecutive groups (but not the subtle cognitive impairment group) had significantly lower FA of the CC compared to CU (p<0.001; Figure 1).ConclusionWe found widespread WM degeneration in the commissural and long association fibers in amnestic, dysnomic, and dysexecutive incident MCI subtypes. The extent of CC damage seen in Figure 1. confirmed the voxel‐level results and suggests that interhemispheric WM disconnection is relevant to the development of MCI.

Abstract Study Objectives To analyze cognitive deficits leading to unsafe driving in patients with REM Sleep Behavior Disorder (RBD), strongly associated with cognitive impairment and synucleinopathy-related neurodegeneration. Methods Twenty isolated RBD (iRBD), 10 symptomatic RBD (sRBD), and 20 age- and education-matched controls participated in a prospective case-control driving simulation study. Group mean differences were compared with correlations between cognitive and driving safety measures. Results iRBD and sRBD patients were more cognitively impaired than controls in global neurocognitive functioning, processing speeds, visuospatial attention, and distractibility (p &lt; .05). sRBD patients drove slower with more collisions than iRBD patients and controls (p &lt; .05), required more warnings, and had greater difficulty following and matching speed of a lead car during simulated car-following tasks (p &lt; .05). Driving safety measures were similar between iRBD patients and controls. Slower psychomotor speed correlated with more off-road accidents (r = 0.65) while processing speed (−0.88), executive function (−0.90), and visuospatial impairment (0.74) correlated with safety warnings in sRBD patients. Slower stimulus recognition was associated with more signal-light (0.64) and stop-sign (0.56) infractions in iRBD patients. Conclusions iRBD and sRBD patients have greater selective cognitive impairments than controls, particularly visuospatial abilities and processing speed. sRBD patients exhibited unsafe driving behaviors, associated with processing speed, visuospatial awareness, and attentional impairments. Our results suggest that iRBD patients have similar driving-simulator performance as healthy controls but that driving capabilities regress as RBD progresses to symptomatic RBD with overt signs of cognitive, autonomic, and motor impairment. Longitudinal studies with serial driving simulator evaluations and objective on-road driving performance are needed.

Abstract Women with premenopausal bilateral oophorectomy (PBO) are at increased risk for physical function (PF) declines. This study investigated the relationships of field-based physical activity measures with clinical PF and strength parameters in post-menopausal women with and without PBO. Women with (n=21; age=64±4 years; BMI=32±8 kg.m-2) and without (n=15; age=67±6 years; BMI=28±6 kg.m-2) PBO performed PF and strength tests (walking speed, distance walked, short physical performance battery (SPBB), leg and chest strength), and wore ankle accelerometers for 7 days (daily step count and loading index [the cumulative sum of each step’s skeletal loading]). Age, BMI, step count and loading index were entered into stepwise multiple regression to identify significant predictors of PF and strength parameters. Step count was a predictor of SPBB score in both groups. In women without PBO, step count was a predictor of walking speed; loading index was a predictor of leg strength; step count and loading index were predictors of distance; and step count and age were predictors of chest strength. For PBO women, loading index and BMI were predictors of walking speed and distance; BMI was a predictor of leg strength; and there were no predictors of chest strength. These data suggest while field-based physical activity was strongly and positively associated with clinical PF and strength measures for women without PBO, BMI was a dominant negative factor for PF in women with PBO. Future work will include a larger sample size and additional confounders to further elucidate underlying factors of reduced PF and mobility after PBO.

AbstractIntroductionBlood‐based biomarkers of amyloid pathology and neurodegeneration are entering clinical use. It is critical to understand what factors affect the levels of these markers.MethodsPlasma markers (Aβ42, Aβ40, NfL, T‐tau, Aβ42/40 ratio) were measured on the Quanterix Simoa HD‐1 analyzer for 996 Mayo Clinic Study of Aging (MCSA) participants, aged 51 to 95 years. All other data were collected during in‐person MCSA visits or abstracted from the medical record.ResultsAmong cognitively unimpaired (CU) participants, all plasma markers correlated with age. Linear regression models revealed multiple relationships. For example, higher Charlson Comorbidity Index and chronic kidney disease were associated with higher levels of all biomarkers. Some relationships differed between mild cognitive impairment and dementia participants.DiscussionMultiple variables affect plasma biomarkers of amyloid pathology and neurodegeneration among CU in the general population. Incorporating this information is critical for accurate interpretation of the biomarker levels and for the development of reference ranges.

AbstractIntroductionEfforts to model the cost‐effectiveness of managing/modifying cognitive impairment lack reliable, objective, baseline medical, and nursing‐home (NH) costs.MethodsA stratified‐random sample of Olmsted County, MN, residents ages 70–89 years (N = 3545), well‐characterized as cognitively unimpaired, mild cognitive impairment (MCI), or dementia, were followed forward ≤1 year in provider‐linked billing data and the Centers for Medicare & Medicaid Services NH assessments. Direct medical/nursing home/medical + NH costs were estimated. Costs were stratified by vital status and NH‐use intensity (NH days/follow‐up days [0%, 1% to 24%, 25% to 99%, and 100%]). Between‐category mean‐annual cost differences were adjusted for patient characteristics and follow‐up days.ResultsCosts/follow‐up day distributions differed significantly across cognitive categories. Mean costs/follow‐up days were 2.5 to 18 times higher for decedents versus survivors. Among all persons with MCI, <9% with any NH use accounted for 18% of all total annual medical + NH costs. Adjusted‐between‐category comparisons revealed significantly higher medical and medical + NH costs for MCI versus cognitively unimpaired.DiscussionCost‐effectiveness for managing/modifying both MCI and dementia should consider end‐of‐life costs and NH‐use intensity. Results can help inform cost‐effectiveness models, predict future‐care needs, and aid decision‐making by individuals/providers/payers/policymakers.