Targeted Assessment of Enlargement of the Perivascular Space in Alzheimer's Disease and Vascular Dementia Subtypes Implicates Astroglial Involvement Specific to Alzheimer's Disease.

Waste clearance from the brain parenchyma occurs along perivascular pathways. Enlargement of the perivascular space (ePVS) is associated with pathologic features of Alzheimer's disease (AD), although the mechanisms and implications of this dilation are unclear. Fluid exchange along the cerebral vasculature is dependent on the perivascular astrocytic water channel aquaporin-4 (AQP4) and loss of perivascular AQP4 localization is found in AD. We directly measured ePVS in postmortem samples of pathologically characterized tissue from participants who were cognitively intact or had AD or mixed dementia (vascular lesions with AD). We found that both AD and mixed dementia groups had significantly increased ePVS compared to cognitively intact subjects. In addition, we found increased global AQP4 expression of the AD group over both control and mixed dementia groups and a qualitative reduction in perivascular localization of AQP4 in the AD group. Among these cases, increasing ePVS burden was associated with the presence of tau and amyloid-ß pathology. These findings are consistent with the existing evidence of ePVS in AD and provide novel information regarding differences in AD and vascular dementia and the potential role of astroglial pathology in ePVS.

Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. DISCUSSION: 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.

Estimation of rest-activity patterns using motion sensors.

Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency, and nap time from passive infrared sensors distributed around the home. We evaluated this algorithm using 404 days of data collected in the homes of 8 elderly community-dwelling elders. Data from this algorithm were highly correlated to ground truth measures (bed mats) and were surprisingly robust to variability in sensor layout and sleep habits.

Unobtrusive assessment of activity patterns associated with mild cognitive impairment.

BACKGROUND: Timely detection of early cognitive impairment is difficult. Measures taken in the clinic reflect a single snapshot of performance that might be confounded by the increased variability typical in aging and disease. We evaluated the use of continuous, long-term, and unobtrusive in-home monitoring to assess neurologic function in healthy and cognitively impaired elders. METHODS: Fourteen older adults 65 years and older living independently in the community were monitored in their homes by using an unobtrusive sensor system. Measures of walking speed and amount of activity in the home were obtained. Wavelet analysis was used to examine variance in activity at multiple time scales. RESULTS: More than 108,000 person-hours of continuous activity data were collected during periods as long as 418 days (mean, 315 +/- 82 days). The coefficient of variation in the median walking speed was twice as high in the mild cognitive impairment (MCI) group (0.147 +/- 0.074) as compared with the healthy group (0.079 +/- 0.027; t(11) = 2.266, P < .03). Furthermore, the 24-hour wavelet variance was greater in the MCI group (MCI, 4.07 +/- 0.14; healthy elderly, 3.79 +/- 0.23; F = 7.58, P