Paving the Way for New Clinical Trials for Alzheimer's Disease

Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools

As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach-digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults.

Remind Me To Remember: A pilot study of a novel smartphone reminder application for older adults with dementia and mild cognitive impairment

The SmartPrompt is a smartphone-based reminder application informed by a neuropsychological model of functional disability. This laboratory-based pilot study examined the SmartPrompt feasibility, efficacy, and subjective usability using a within-participant, counterbalanced, cross-over design. Ten participants (M age = 80.3 + 8.2; M education = 15.7 + 2.5; 60% female) with mild cognitive impairment or mild dementia completed the Remember to Drink Test, which required preparing a glass of water at four predetermined times, in a SmartPrompt (SP) and Unprompted condition (UP). Written cues and a clock were available in both conditions; however, in the SP, the smartphone presented auditory alarms and visual reminders to obtain the water at specified times and required photo logging. In a separate session, caregivers were trained and tested on configuring the SmartPrompt. Overall, caregivers and participants learned to effectively use the SmartPrompt. Caregivers achieved near-perfect scores on the configuration quiz and responded well to training. Participants completed significantly more Remember to Drink tasks in the SP (93%) than UP (56%); checking the cues/clock decreased by 87% in the SP. Usability ratings were excellent among caregivers and fair among participants. Results indicate that the SmartPrompt holds promise for reducing functional disability in older adults with cognitive difficulties in at-home contexts.

Analysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster

Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.