Journal of Alzheimer's disease : JAD
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Significant variability exists in the trajectories of late-life cognitive decline; however, their associated lifestyle factors remain less studied. We examined these trajectories among elderly participants from the recent five waves (at three-year intervals) of the Chinese Longitudinal Healthy Longevity Study (CLHLS) from 2002 to 2014. Participants from this cohort were included if they completed at least four waves of measurements. ⋯ The inclusion of lifestyle factors significantly improved the prediction of the different trajectories, above and beyond demographics and medical variables; the 'no decline' class was significantly more likely to report exercising regularly. Changes in cognitive functioning across the late-life period are characterized by multiple trajectories. Cognitive decline is not inevitable across the late-life period; the absence of such cognitive decline is partly explained by certain lifestyle factors.
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Patients with dementia are particularly vulnerable during the COVID-19 pandemic. The initial response to COVID-19 promoted behavioral changes in both society and healthcare, while a long-term solution is sought by prioritizing societal values. In addition, there has been disruption to clinical care and clinical research. This pandemic might have significantly changed the care for our patients with dementia toward increased acceptance of telemedicine by the patients and providers, and its utilization in both clinical care and research.
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A digital version of the clock drawing test (dCDT) provides new latency and graphomotor behavioral measurements. These variables have yet to be validated with external neuropsychological domains in non-demented adults. ⋯ Command dCDT variables of interest were primarily processing speed and working memory dependent. MCI participants showed dCDT differences relative to non-MCI peers, suggesting the dCDT may assist with classification. Results document cognitive construct validation to digital metrics of clock drawing.
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Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches. ⋯ Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.
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Both pain interference and depressive symptoms have certain effects on dementia, and these are reciprocally related. However, comorbid effects of pain interference and depressive symptoms on dementia have not been examined in detail. ⋯ The coexistence of pain interference and depressive symptoms had a greater effect on the incidence of dementia than either condition alone in community-dwelling elderly individuals. Pain interference and depressive symptoms are known as common comorbid conditions and often form a negative cycle that accelerates the worsening of the individual symptoms of both. Thus, the comorbidity of these conditions may require monitoring for the prevention of dementia.