The nexus of Aβ, aging, and sleep

Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Science translational medicine (Impact Factor: 15.84). 09/2012; 4(150):150fs34. DOI: 10.1126/scitranslmed.3004815
Source: PubMed


Roh et al. report a positive feedback loop between sleep-wake irregularities and aggregation of β-amyloid peptide, suggesting that sleep alterations could be an early event in Alzheimer's disease.

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Available from: Jason R Gerstner, Oct 09, 2015
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    ABSTRACT: SUMMARY Sleep-wake disturbances are a highly prevalent and often disabling feature of Alzheimer's disease (AD). A cardinal feature of AD includes the formation of amyloid plaques, associated with the extracellular accumulation of the amyloid-β (Aβ) peptide. Evidence from animal and human studies suggests that Aβ pathology may disrupt the sleep-wake cycle, in that as Aβ accumulates, more sleep-wake fragmentation develops. Furthermore, recent research in animal and human studies suggests that the sleep-wake cycle itself may influence Alzheimer's disease onset and progression. Chronic sleep deprivation increases amyloid plaque deposition, and sleep extension results in fewer plaques in experimental models. In this review geared towards the practicing clinician, we discuss possible mechanisms underlying the reciprocal relationship between the sleep-wake cycle and AD pathology and behavior, and present current approaches to therapy for sleep disorders in AD.
    10/2014; 4(5):351-62. DOI:10.2217/nmt.14.33
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    ABSTRACT: The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18 to 79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models—specifically Gaussian mixtures and hidden Markov models—are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations.
    Computers in Biology and Medicine 01/2015; 59(1):54-63. DOI:10.1016/j.compbiomed.2015.01.012 · 1.24 Impact Factor