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© 2016 Wolters Kluwer Health, Inc. All rights reserved. Introduction: Racial and ethnic groups are under-represented among research subjects who assent to brain donation in Alzheimer disease research studies. There has been little research on this important topic. Although there are some studies that have investigated the barriers to brain donation among African American study volunteers, there is no known research on the factors that influence whether or not Asians or Latinos are willing to donate their brains for research. Methods: African American, Caucasian, Asian, and Latino research volunteers were surveyed at 15 Alzheimer Disease Centers to identify predictors of willingness to assent to brain donation. Results: Positive predictors included older age, Latino ethnicity, understanding of how the brain is used by researchers, and understanding of what participants need to do to ensure that their brain will be donated. Negative predictors included African/African American race, belief that the body should remain whole at burial, and concern that researchers might not be respectful of the body during autopsy. Discussion: The predictive factors identified in this study may be useful for researchers seeking to increase participation of diverse ethnic groups in brain donation.

Objectives: To ascertain the association between self-report of low mood and unobtrusively measured behaviors (walking speed, time out of residence, frequency of room transitions, and computer use) in community-dwelling older adults using novel monitoring technologies. Design: Longitudinal cohort study of older adults whose homes were outfitted with activity sensors. Participants completed Internet-based weekly health questionnaires with questions about mood. Setting: Apartments and homes of older adults living in the Portland, Oregon, metropolitan area. Participants: Adults, average age 84, followed for an average of 3.7 years (n = 157). Measurements: Mood was assessed according to self-report each week. Walking speed, time spent out of residence, and room transitions were estimated using data from sensors; computer use was measured by timing actual use. The association between global or weekly low mood and the four behavior measures was ascertained, adjusting for baseline characteristics. Results: Eighteen thousand nine hundred sixty weekly observations of mood were analyzed; 2.6% involved low mood. Individuals who reported low mood more often had no average differences in any behavior parameters from those who reported low mood less often. During weeks when they reported low mood, participants spent significantly less time out of residence and on the computer but showed no change in walking speed or room transitions. Conclusion: Low mood in these community-dwelling older adults involved going out of the house less and using the computer less but no consistent changes in movements. Technologies to monitor in-home behavior may have potential for research and clinical care.

Background: Adults 80 years and older are the fastest-growing segment of the Japanese population and face a high risk of cognitive decline. There are some evidences connecting hypertension to cognitive decline. In mid-life hypertension is known to have influence the cognitive decline in older age. However, a few study have examined the association between hypertension or vascular stiffness and cognitive function among elderly over 80 years old. We analyzed the associations between vascular stiffness and cognitive function among relatively healthy community-dwelling non-demented oldest old. Method: Data came from the Keys to Optimal Cognitive Aging (KOCOA) study; an ongoing cohort of relatively healthy volunteers aged over 80 years old, living in Okinawa, Japan. In 2017, 105 non-demented (Clinical Dementia Rating < 1) subjects completed three kinds of examination for vascular function (75 % female, mean age (SD) 84.0 (3.0)). We categorized subjects into low and high cognitive function groups using Montreal Cognitive Assessment (MoCA) (25/26 as a cutpoint). Logistic regression models were used to examine the association between cognitive and vascular functions. Results: Narrower pulse pressure, an indicator of lower arterial stiffness, was associated with better cognitive function among subjects, after adjusting for gender, age, and education (p≦0.05), although systolic and diastolic blood pressure were not. Conclusion: Our findings suggest that narrower pulse pressure is related with cognitive preservation. The present study supports the hypothesis that lower arterial stiffness is related with better cognitive function even among the oldest old.

In our previous NIH-funded randomized controlled behavioral clinical trial, we developed a conversation-based social interaction cognitive stimulation protocol delivered by trained interviewers through webcams and a user-friendly interactive Internet interface. Daily 30 minute face-to-face video-chats were conducted for 6 weeks. Despite a short duration, this proof of concept study demonstrated high adherence among older adults (mean age 80 years) and showed improvement in cognitive domains which tap language-based executive functions and semantic memory among the experimental group compared to the control group who did not engage in any video-chats. Building on these results, we are now conducting two NIH-funded projects (https://www.i-conect.org), targeting socially isolated older adults who are less likely to participate in clinical trials despite their high risk of cognitive decline. In this presentation, we introduce a series of projects outlined above and share the challenges and opportunities identified in our behavioral intervention trials focused on social interaction.

BACKGROUND AND OBJECTIVES Social isolation is associated with a higher risk of dementia. We previously conducted and showed the efficacy of an intervention which uses conversation (the core component of social interactions) as a tool to enhance cognitive function. We now explore whether cognitive improvements through conversation-based intervention depend on an individual's personality. RESEARCH DESIGN AND METHODS We reexamined data from a 6-week randomized controlled trial (ClinicalTrials.gov Number: NCT01571427) to determine whether conversation-based intervention effects were moderated by personality traits in 83 older adults (mean age = 80.51 years, 49 cognitively intact, 34 individuals with mild cognitive impairment). The intervention group participated in daily 30-min face-to-face semi-structured conversations with trained interviewers through a web-enabled system for 6 weeks. At baseline, psychosocial questionnaires and a neuropsychological battery were completed. RESULTS Intervention group participants with high agreeableness, conscientiousness, and extraversion exhibited significant improvements in language-based executive function tasks beyond changes in the control group (ps < .05). An opposite pattern for delayed recall memory and working memory tasks emerged among highly extraverted participants (ps < .05). DISCUSSION AND IMPLICATIONS Our exploratory findings suggest the adaptive role of personality traits in conversation-based cognitive interventions may be limited to tasks incorporating a language component, and offer initial evidence for personalized approaches to cognitive health in late life.

Epidemiological studies have demonstrated that larger social networks or more frequent social interactions may have protective effects against cognitive decline and the incidence of dementia. Therefore, increasing social interaction could be a promising intervention for improving cognitive well-being in socially isolated older adults. We have conducted multiple NIH-funded randomized controlled trials (RCT) over 10 years, examining whether conversational interactions through webcam and internet can improve cognitive functions and enhance cognitive reserve. In this symposium, we will introduce this series of behavioral intervention trials through 4 presentations. First, Dodge will provide background and results of their previous RCT where they showed efficacy of conversational intervention on domain-specific cognitive functions and introduce the ongoing larger project called I-CONECT (https://www.i-conect.org). Second, Lindsey will introduce technological innovations used in the I-CONECT project including development of user-friendly video-chat devices, recording of audio and video data and innovative recruitment efforts. Third, Asgari will share results on how speech utterance and characteristics collected through the project could distinguish those with mild cognitive impairment from those with normal cognition using machine learning modelling approaches. Finally, Cerino and his team will show results of the study which examined whether cognitive improvements through conversation-based intervention depend on an individual’s personality, laying the groundwork for a personalized intervention trial in the future. The symposium is of interest for those who study social isolation and its prevention, the link among cognition, social isolation and personality, as well as those who focus on technology as a tool for improving well-being of older adults.

Many older adults express a lack of confidence in using technology and this can become a barrier to participating in technology heavy research. This presentation will introduce the face-to-face digital communication, electronic medication adherence tracking, and online recruitment technology used in the I-CONECT study (ClinicalTrials.gov: NCT02871921). In particular, we will demonstrate how the project has simplified enterprise video conferencing for in-home use, removed obstacles related to remote hardware troubleshooting, and further discuss how it has been received by older adults who have participated thus far. Finally we will cover particular hurdles related to I-CONECT (e.g., targeting social isolated older adults, aiming to recruit 50% of participants being African American older subjects living in the Detroit Metropolitan area). Our experience indicates that such high tech gerontological research is feasible given a creative and solution-focused research approaches and multi-disciplinary team.

A profusion of technologies and protocols have been developed to more effectively assess and deliver care to older adults with cognitive impairment, challenged health and declining function. These technologies take advantage of important developments in sensing and pervasive computing, wearable technologies, mobile and wireless communications, and “big data” analytics. Despite great promise challenges remain to realizing their full potential and achieving wider uptake and dissemination in research and practice. This presentation will review and provide an overview of major technologies, their integration, and their use-cases, as well as key challenges present in the current landscape. The presentation will highlight ongoing developments addressing these challenges with particular attention to the Collaborative Aging Research using Technology (CART) initiative supported by the NIH and VA, an initiative directed toward providing an open technology research platform to be used by diverse investigators across the U.S. to facilitate and improve aging research using technology.

OBJECTIVES: Nutritional metabolomics to objectively assess dietary intake in aging permit the opportunity to circumvent measurement errors that accompany subjective means of dietary assessment. At the same time, they may offer insights into mechanisms of action and metabolic disturbances that are actionable targets for modulation through diet in hopes of disease prevention and treatment. However, prior to more broad deployment the pre-analytical and temporal variation over time should be documented in order to design and interpret epidemiological studies properly. We quantified and examined 155 nutrient biomarkers and metabolites selected for their potential relevance to dementia. METHODS: Blood samples from three time points, spanning a 2-year period, were obtained from older adults participating in the NIA-Layton Oregon Alzheimer's Disease Center's Nutrition and Brain Aging Study (NBAS). Blood samples were batched randomly across three time points for quantification of blood amino acids, minerals, water and fat-soluble micronutrients, lipids, one carbon, and kynurenine pathway metabolites using a variety of methods including, tandem mass spectrometry. Pre-analytical coefficients of variation (CV) were calculated for all the biomarkers and intraclass correlation coefficients (ICC) were calculated to evaluate the within-person reproducibility in a subset of 137 participants. RESULTS: The mean baseline age of the analytic sample (n = 137) was 85.6 (± 8.3, 57 - 101 years), 70% are female, 21% carry the ApoEe4 allele and MMSE was 28.3 (± 1.78). The pre-analytical CVs ranged from 0.9% to 55.0% and the ICC ranged from 0 to 0.87 (25%-tile/median/75%-tile 0.41/0.54/0.66). Twenty four % had ICC 0.75. CONCLUSIONS: The pre-analytical and within-person reproducibility of nutritional metabolomics in aging ranges widely. The majority can reliably estimate average concentrations over a 2 year period from a single time point and the biomarkers with ICC's above 0.40 can be used for correction of measurement error and those below 0.40 should consider multiple samples per subject and exploring the methodological and biological explanation for the variation over time. FUNDING SOURCES: Nestle Institute of Health Sciences, Hinda and Arthur Marcus Institute for Aging Research, NIA-Layton Aging & Alzheimer's Disease Center (P30AGO8017).

People are good for your brain. Decades of research have shown that individuals who have a larger number of people in their social network or higher quality ties with individuals within their network have lower rates of morbidity and mortality across a wide range of health outcomes. Among these outcomes, cognitive function, especially in the context of brain aging, has been one area of particular interest with regard to social engagement, or more broadly, socially integrated lifestyles. Many studies have observed an association between the size of a person’s social network or levels of social engagement and the risk for cognitive decline or dementia (e.g. see review by Fratiglioni et al., 2004). The dementia risk reduction associated with a larger social network or social engagement shown by some epidemiological studies is fairly large. The population effect size of increasing social engagement on delaying dementia disease progression could exceed that of current FDA approved medications for Alzheimer’s disease. The positive effects of social interactions and engagement on cognitive function have been demonstrated even at the level of biomarkers. For example, recent MRI studies found associations between the size and complexity of real-world social networks and the density of gray matter (Kanai et al., 2012) and amygdala volume (Bickart et al., 2011). Possible modifiable effects of larger social networks on symptomatic outcomes of Alzheimer’s disease pathologies have also been shown (Bennett et al., 2006). Human epidemiological studies are not free from possible reverse causations such as lower levels of social engagement being the result of presymptomatic dementia, not the cause of dementia. However, non-human research suggests that social network size could actually contribute to changes both in brain structure and function, providing further support for causal links (Sallet et al., 2011).

Background Mild disturbances of higher order activities of daily living are present in people diagnosed with mild cognitive impairment (MCI). These deficits may be difficult to detect among those still living independently. Unobtrusive continuous assessment of a complex activity such as home computer use may detect mild functional changes and identify MCI. We sought to determine whether long-term changes in remotely monitored computer use differ in persons with MCI in comparison with cognitively intact volunteers. Methods Participants enrolled in a longitudinal cohort study of unobtrusive in-home technologies to detect cognitive and motor decline in independently living seniors were assessed for computer use (number of days with use, mean daily use, and coefficient of variation of use) measured by remotely monitoring computer session start and end times. Results More than 230,000 computer sessions from 113 computer users (mean age, 85 years; 38 with MCI) were acquired during a mean of 36 months. In mixed-effects models, there was no difference in computer use at baseline between MCI and intact participants controlling for age, sex, education, race, and computer experience. However, over time, between MCI and intact participants, there was a significant decrease in number of days with use ( P = .01), mean daily use (∼1% greater decrease/month; P = .009), and an increase in day-to-day use variability ( P = .002). Conclusions Computer use change can be monitored unobtrusively and indicates individuals with MCI. With 79% of those 55 to 64 years old now online, this may be an ecologically valid and efficient approach to track subtle, clinically meaningful change with aging.

We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

Background: Cerebral vascular pathology may contribute to cognitive decline experienced by some elderly near death. Given evidence for mixed neuropathologies in advanced age, preventing or reducing cerebrovascular burden in late life may be beneficial. Objective: To correlate measures of cerebral vascular pathology with cognitive trajectories. Setting: Observational study. Participants: A cohort of 2,274 individuals who came to autopsy at a mean age of 89.3 years and 82 percent of whom had at least two cognitive assessments within the last six years of life was compiled from six centers conducting longitudinal studies. Measurements: For each cognitive domain: immediate and delayed memory, language, and naming, three trajectories were examined: good, intermediate, and poor cognition. The probability of a participant belonging to each trajectory was associated with measures of cerebral vascular pathology after adjustment for demographics, APOE, and Alzheimer neuropathology. Results: A large proportion of the cohort (72-94%) experienced good or intermediate cognition in the four domains examined. The presence of arteriolosclerosis and the presence of lacunar infarcts doubled the odds of belonging to the poor cognitive trajectory for language when compared to the good trajectory. The presence of lacunar infarcts increased the odds of an intermediate or poor trajectory for immediate and delayed recall while the presence of large artery infarcts increased the odds of poor trajectories for all four cognitive domains examined. Microinfarcts and cerebral amyloid angiopathy had little effect on the trajectories. Conclusion: Indicators of cerebral vascular pathology act differently on late life cognition.

Loneliness is a common condition in older adults and is associated with increased morbidity and mortality, decreased sleep quality, and increased risk of cognitive decline. Assessing loneliness in older adults is challenging due to the negative desirability biases associated with being lonely. Thus, it is necessary to develop more objective techniques to assess loneliness in older adults. In this paper, we describe a system to measure loneliness by assessing in-home behavior using wireless motion and contact sensors, phone monitors, and computer software as well as algorithms developed to assess key behaviors of interest. We then present results showing the accuracy of the system in detecting loneliness in a longitudinal study of 16 older adults who agreed to have the sensor platform installed in their own homes for up to 8 months. We show that loneliness is significantly associated with both time out-of-home (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ {\beta } = -0.88$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p, In this paper, we describe an in-home sensor platform that detects daily behaviors such as detailed computer use, total hours spent outside the home, in-home walking speed, and total sleep time. We use this system to relate in-home behavior to loneliness, and show that loneliness (as measured using the 20-item UCLA loneliness scale) is closely related to in-home behavior (R₂

In neuroimaging research, brain networks derived from different tractography methods may lead to different results and perform differently when used in classification tasks. As there is no ground truth to determine which brain network models are most accurate or most sensitive to group differences, we developed a new sparse learning method that combines information from multiple network models. We used it to learn a convex combination of brain connectivity matrices from 9 different tractography methods, to optimally distinguish people with early mild cognitive impairment from healthy control subjects, based on the structural connectivity patterns. Our fused networks outperformed the best single network model, Probtrackx (0.89 versus 0.77 cross-validated AUC), suggesting its potential for numerous connectivity analysis.

Normal-appearing white matter (NAWM) surrounding WMHs is associated with decreased structural integrity and perfusion, increased risk of WMH growth, and is referred to as the WMH penumbra. Studies comparing structural and cerebral blood flow (CBF) penumbras within the same individuals are lacking, however, and would facilitate our understanding of mechanisms resulting in WM damage. This study aimed to compare both CBF and structural WMH penumbras in non-demented aging. Eighty-two elderly volunteers underwent 3T-MRI including fluid attenuated inversion recovery (FLAIR), pulsed arterial spin labeling and diffusion tensor imaging (DTI). A NAWM layer mask was generated for periventricular and deep WMHs. Mean CBF, DTI-fractional anisotropy (DTI-FA), DTI-mean diffusivity (DTI-MD) and FLAIR intensity for WMHs and its corresponding NAWM layer masks were computed and compared against its mean within total brain NAWM using mixed effects models. For both periventricular and deep WMHs, DTI-FA, DTI-MD and FLAIR intensity changes extended 2-9 mm surrounding WMHs (p ≤ 0.05), while CBF changes extended 13-14 mm (p ≤ 0.05). The CBF penumbra is more extensive than structural penumbras in relation to WMHs and includes WM tissue both with and without microstructural changes. Findings implicate CBF as a potential target for the prevention of both micro and macro structural WM damage.

BACKGROUND/AIMS Early changes in cognitively demanding daily activities occur between normal cognition and the development of mild cognitive impairment (MCI). These real-world functional changes as early signals of cognitive change form a prime target for meaningful early detection of dementia. We examined whether passive aspects of responding to a remotely monitored weekly online questionnaire discriminated between older adults with and without MCI. METHODS Participants were 83 independent, community-dwelling older adults enrolled in a longitudinal study of in-home monitoring technologies, which included completion of a short weekly online questionnaire of health and life events. RESULTS In longitudinal analyses, time to complete the online questionnaire decreased over 1 year in both MCI and cognitively intact participants (P

Background This study examines differences in computer-related self-efficacy and anxiety in subgroups of older adults, and changes in those measures after exposure to a systematic training program and subsequent computer use. Methods Participants were volunteers in the Intelligent Systems for Assessment of Aging Changes study (ISAAC) carried out by the Oregon Center for Aging and Technology. Participants were administered two questionnaires before training and again 1 year later, which were related to computer self-efficacy and anxiety. Continuous recording of computer use was also assessed for a subset of participants. Results Baseline comparisons by sex, age, education, living arrangement, and computer proficiency, but not cognitive status, yielded significant differences in confidence and anxiety related to specific aspects of computer use. At 1-year follow-up, participants reported less anxiety and greater confidence. However, the benefits of training and exposure varied by group and task. Comparisons based on cognitive status showed that the cognitively intact participants benefited more from training and/or experience with computers than did participants with mild cognitive impairment (MCI), who after 1 year continued to report less confidence and more anxiety regarding certain aspects of computer use. Conclusion After 1 year of consistent computer use, cognitively intact participants in this study reported reduced levels of anxiety and increased self-confidence in their ability to perform specific computer tasks. Participants with MCI at baseline were less likely to demonstrate increased efficacy or confidence than their cognitively intact counterparts.

One of the recommendations of the 2010 Leon Thal Symposium, organized to develop strategies to prevent Alzheimer's disease, was to build a global database of longitudinal aging studies. Although several databases of longitudinal aging studies exist, none of these are comprehensive or complete. In this article, we review selected databases of longitudinal aging studies. We also make recommendations on future steps to create a comprehensive database. Additionally, we discuss issues related to data harmonization.

Objective: To determine the time of acceleration in white matter hyperintensity (WMH) burden, a common indicator of cerebrovascular pathology, in relation to conversion to mild cognitive impairment (MCI) in the elderly. Methods: A total of 181 cognitively intact elderly volunteers from the longitudinal, prospective, Oregon Brain Aging Study underwent yearly evaluations, including brain MRI, and cognitive testing. MRIs were analyzed for imaging markers of neurodegeneration: WMH and ventricular CSF (vCSF) volumes. The time before MCI, when the changes in WMH and vCSF burden accelerate, was assessed using a mixed-effects model with a change point for subjects who developed MCI during follow-up. Results: During a follow-up duration of up to 19.6 years, 134 subjects converted to MCI. Acceleration in %WMH volume increase occurred 10.6 years before MCI onset. On average, the annual rate of change in %WMH increased an additional 3.3% after the change point. Acceleration in %vCSF volume increase occurred 3.7 years before the onset of MCI. Out of 63 subjects who converted to MCI and had autopsy, only 28.5% had Alzheimer disease (AD) as the sole etiology of their dementia, while almost just as many (24%) had both AD and significant ischemic cerebrovascular disease present. Conclusions: Acceleration in WMH burden, a common indicator of cerebrovascular disease in the elderly, is a pathologic change that emerges early in the presymptomatic phase leading to MCI. Longitudinal changes in WMH may thus be useful in determining those at risk for cognitive impairment and for planning strategies for introducing disease-modifying therapies prior to dementia onset.

Objective: To determine whether unobtrusive long-term in-home assessment of walking speed and its variability can distinguish those with mild cognitive impairment (MCI) from those with intact cognition. Methods: Walking speed was assessed using passive infrared sensors fixed in series on the ceiling of the homes of elderly individuals participating in the Intelligent Systems for Assessing Aging Change (ISAAC) cohort study. Latent trajectory models were used to analyze weekly mean speed and walking speed variability (coefficient of variation [COV]). Results: ISAAC participants living alone included 54 participants with intact cognition, 31 participants with nonamnestic MCI (naMCI), and 8 participants with amnestic MCI at baseline, with a mean follow-up of 2.6 ± 1.0 years. Trajectory models identified 3 distinct trajectories (fast, moderate, and slow) of mean weekly walking speed. Participants with naMCI were more likely to be in the slow speed group than in the fast ( p = 0.01) or moderate ( p = 0.04) speed groups. For COV, 4 distinct trajectories were identified: group 1, the highest baseline and increasing COV followed by a sharply declining COV; groups 2 and 3, relatively stable COV; and group 4, the lowest baseline and decreasing COV. Participants with naMCI were more likely to be members of either highest or lowest baseline COV groups (groups 1 or 4), possibly representing the trajectory of walking speed variability for early- and late-stage MCI, respectively. Conclusion: Walking speed and its daily variability may be an early marker of the development of MCI. These and other real-time measures of function may offer novel ways of detecting transition phases leading to dementia.

Alzheimer's disease (AD) is associated with a cascade of pathological events involving formation of amyloid-based neuritic plaques and tau-based neurofibrillary tangles, changes in brain structure and function, and eventually, cognitive impairment and functional disability. The precise sequence of when each of these disease markers becomes abnormal is not yet clearly understood. The present study systematically tested the relationship between classes of biomarkers according to a proposed model of temporal sequence by Jack et al. (Lancet Neurology 9:119-128, 2010). We examined temporal relations among four classes of biomarkers: CSF Aβ, CSF tau, neuroimaging variables (hippocampal volume, ventricular volume, FDG PET), and cognitive variables (memory and executive function). Random effects modeling of longitudinal data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to test hypotheses that putative earlier markers of AD predicted change in later markers, and that intervening markers reduced effects of earlier on later markers. Specifically, we hypothesized that CSF tau would explain CSF Aβ's relation to neuroimaging and cognitive variables, and neuroimaging variables would explain tau's relation to cognitive variables. Consistent with hypotheses, results indicated that CSF Aβ effects on cognition change were substantially attenuated by CSF tau and measures of brain structure and function, and CSF tau effects on cognitive change were attenuated by neuroimaging variables. Contrary to hypotheses, CSF Aβ and CSF tau were observed to have independent effects on neuroimaging and CSF tau had a direct effect on baseline cognition independent of brain structure and function. These results have implications for clarifying the temporal sequence of AD changes and corresponding biomarkers.

Neurofibrillary tangles (NFT) and amyloid plaques are hallmark neuropathological features of Alzheimer's disease (AD). There is some debate as to which neuropathological feature comes first in the disease process, with early autopsy studies suggesting that NFT develop first, and more recent neuroimaging studies supporting the early role of amyloid beta (Aβ) deposition. Cerebrospinal fluid (CSF) biomarkers of Aβ₄₂ and hyperphosphorylated tau (p-tau) have been shown to serve as in vivo proxy measures of amyloid plaques and NFT, respectively. The aim of this study was to examine the association between CSF biomarkers and rate of atrophy in the precuneus and hippocampus. These regions were selected because the precuneus appears to be affected early and severely by Aβ deposition, and the hippocampus similarly by NFT pathology. We predicted (1) baseline Aβ₄₂ would be related to accelerated rate of cortical thinning in the precuneus and volume loss in the hippocampus, with the latter relationship expected to be weaker, (2) baseline p-tau(181p) would be related to accelerated rate of hippocampal atrophy and cortical thinning in the precuneus, with the latter relationship expected to be weaker. Using all ADNI cohorts, we fitted separate linear mixed-effects models for changes in hippocampus and precuneus longitudinal outcome measures with baseline CSF biomarkers modeled as predictors. Results partially supported our hypotheses: Both baseline p-tau(181p) and Aβ₄₂ were associated with hippocampal atrophy over time. Neither p-tau(181p) nor Aβ₄₂ were significantly related to cortical thinning in the precuneus over time. However, follow-up analyses demonstrated that having abnormal levels of both Aβ₄₂ and p-tau(181p) was associated with an accelerated rate of atrophy in both the hippocampus and precuneus. Results support early effects of Aβ in the Alzheimer's disease process, which are less apparent than and perhaps dependent on p-tau effects as the disease progresses. However, amyloid deposition alone may be insufficient for emergence of significant morphometric changes and clinical symptoms.

Objective: To examine the cross-sectional relationship between nutrient status and psychometric and imaging indices of brain health in dementia-free elders. Methods: Thirty plasma biomarkers of diet were assayed in the Oregon Brain Aging Study cohort (n = 104). Principal component analysis constructed nutrient biomarker patterns (NBPs) and regression models assessed the relationship of these with cognitive and MRI outcomes. Results: Mean age was 87 ± 10 years and 62% of subjects were female. Two NBPs associated with more favorable cognitive and MRI measures: one high in plasma vitamins B (B1, B2, B6, folate, and B12), C, D, and E, and another high in plasma marine ω-3 fatty acids. A third pattern characterized by high trans fat was associated with less favorable cognitive function and less total cerebral brain volume. Depression attenuated the relationship between the marine ω-3 pattern and white matter hyperintensity volume. Conclusion: Distinct nutrient biomarker patterns detected in plasma are interpretable and account for a significant degree of variance in both cognitive function and brain volume. Objective and multivariate approaches to the study of nutrition in brain health warrant further study. These findings should be confirmed in a separate population. Neurology ® 2012;78:241–249

To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community.Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sensors include total daily activity, time out of home, and walking speed. Participants were given a computer as well as training, and computer usage was monitored. Participants are assessed annually with health and function questionnaires, physical examinations, and neuropsychological testing.Mean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day.These results demonstrate for the first time the feasibility of engaging seniors in a large-scale deployment of in-home activity assessment technology and the successful collection of these activity metrics. We plan to use this platform to determine if continuous unobtrusive monitoring may detect incident cognitive decline.

Certain micronutrients are protective against cognitive decline. We examined whether there is any uniform pattern of circulating micronutrients cross-culturally that are associated with successful cognitive aging. For the U.S. sample, we used the stored serum/plasma of 115 participants, collected in Oregon, USA. The Okinawa sample consisted of 49 participants selected using similar inclusion criteria as the Oregon sample, from the Keys to Optimal Cognitive Aging Project. All participants were aged 85 years and older without cognitive impairment. We found that the Okinawan elders used fewer vitamin supplements but had similar levels of vitamin B(12) and α-tocopherol, lower folate and γ-tocopherol, compared with Oregonian elders. That is, we did not find a uniform pattern of circulating micronutrients, suggesting that micronutrients other than those examined here or other lifestyle factors than nutrition could play an important role in achieving successful cognitive aging.

OBJECTIVES: To compare, in a longitudinal cohort study, declines in specific cognitive domains on their ability to predict time to death, in the presence and absence of dementia, and to explore an explanatory role for vascular disease. DESIGN: Prospective population-based epidemiological study. SETTING: The mid-Monongahela valley of southwestern Pennsylvania from 1987 to 2002. PARTICIPANTS: Nine hundred eighty-nine community-dwelling adults aged 65 and older enrolled in the Monongahela Valley Independent Elders Survey. MEASUREMENTS: Biennial assessments of a range of cognitive domains for up to 12 years. Mortality was modeled as a function of decline in each domain, adjusting for vascular diseases and stratified according to age (≤75 (younger-old) and >75 (older-old)) using Cox proportional hazards modeling. RESULTS: Average annual declines in almost all cognitive domains were significant predictors of mortality in the cohort as a whole. However, after adjustment for dementia, only general cognition, processing speed, the language composite, and the executive function composite remained significant. Adjustment for vascular diseases did not alter the results. In the younger-old group, decline in memory (hazard ratio (HR)=21.4) and executive function (HR=25.5) remained strong predictors after adjustment for dementia and vascular disease. In the older-old group, decline in processing speed was a strong predictor of mortality before (HR=7.4) and after (HR=5.3) controlling for dementia and vascular diseases. CONCLUSION: Decline in most cognitive domains predicted mortality across the cohort, but declines in memory and learning were not independent of dementia. Different domains predicted mortality in the younger and older subgroups.

Introduction: Observational studies show reduced incidence of Alzheimer dementia (AD) in users of nonsteroidal anti-inflammatory drugs (NSAIDs). One hypothesis holds that the subset of NSAIDs known as selective Aβ 42 -lowering agents (SALAs) is responsible for this apparent reduction in AD risk. Methods: We pooled individual-level data from six prospective studies to obtain a sufficient sample to examine AD risk in users of SALA vs non-SALA NSAIDs. Results: Of 13,499 initially dementia-free participants (70,863 person-years), 820 developed incident AD. Users of NSAIDs (29.6%) showed reduced risk of AD (adjusted hazard ratio [aHR] 0.77, 95% CI 0.65–0.91). The point estimates were similar for SALAs (aHR 0.87, CI 0.72–1.04) and non-SALAs (aHR 0.75, CI 0.56–1.01). Because 573 NSAID users (14.5%) reported taking both a SALA and non-SALA, we examined their use alone and in combination. Resulting aHRs were 0.82 (CI 0.67–0.99) for SALA only, 0.60 (CI 0.40–0.90) for non-SALA only, and 0.87 (CI 0.57–1.33) for both NSAIDs (Wald test for differences, p = 0.32). The 40.7% of participants who used aspirin also showed reduced risk of AD, even when they used no other NSAIDs (aHR 0.78, CI 0.66–0.92). By contrast, there was no association with use of acetaminophen (aHR 0.93, CI 0.76–1.13). Conclusions: In this pooled dataset, nonsteroidal anti-inflammatory drug (NSAID) use reduced the risk of Alzheimer dementia (AD). However, there was no apparent advantage in AD risk reduction for the subset of NSAIDs shown to selectively lower Aβ 42 , suggesting that all conventional NSAIDs including aspirin have a similar protective effect in humans.