Study Objectives: Emerging evidence suggests a role for sleep in contributing to the progression of Alzheimer disease (AD). Slow wave sleep (SWS) is the stage during which synaptic activity is minimal and clearance of neuronal metabolites is high, making it an ideal state to regulate levels of amyloid beta (Aβ). We thus aimed to examine relationships between concentrations of Aβ42 in the cerebrospinal fluid (CSF) and measures of SWS in cognitively normal elderly subjects. Methods: Thirty-six subjects underwent a clinical and cognitive assessment, a structural MRI, a morning to early afternoon lumbar puncture, and nocturnal polysomnography. Correlations and linear regression analyses were used to assess for associations between CSF Aβ42 levels and measures of SWS controlling for potential confounders. Resulting models were compared to each other using ordinary least squared linear regression analysis. Additionally, the participant sample was dichotomized into "high" and "low" Aβ42 groups to compare SWS bout length using survival analyses. Results: A significant inverse correlation was found between CSF Aβ42 levels, SWS duration and other SWS characteristics. Collectively, total SWA in the frontal lead was the best predictor of reduced CSF Aβ42 levels when controlling for age and ApoE status. Total sleep time, time spent in NREM1, NREM2, or REM sleep were not correlated with CSF Aβ42. Conclusions: In cognitively normal elderly, reduced and fragmented SWS is associated with increases in CSF Aβ42, suggesting that disturbed sleep might drive an increase in soluble brain Aβ levels prior to amyloid deposition.

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Total variation (TV) denoising is an effective noise suppression method when the derivative of the underlying signal is known to be sparse. TV denoising is defined in terms of a convex optimization problem involving a quadratic data fidelity term and a convex regularization term. A non-convex regularizer can promote sparsity more strongly, but generally leads to a non-convex optimization problem with non-optimal local minima. This letter proposes the use of a non-convex regularizer constrained so that the total objective function to be minimized maintains its convexity. Conditions for a non-convex regularizer are given that ensure the total TV denoising objective function is convex. An efficient algorithm is given for the resulting problem.

Comparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.

The consolidation of spatial navigational memory during sleep is supported by electrophysiological and behavioral evidence. The features of sleep that mediate this ability may change with aging, as percentage of slow wave sleep is canonically thought to decrease with age, and slow waves are thought to help orchestrate hippocampal-neocortical dialogue that supports systems level consolidation. In this study, groups of younger and older subjects performed timed trials before and after polysomnographically recorded sleep on a 3D spatial maze navigational task. Although younger subjects performed better than older subjects at baseline, both groups showed similar improvement across pre-sleep trials. However, younger subjects experienced significant improvement in maze performance during sleep that was not observed in older subjects, without differences in morning psychomotor vigilance between groups. Older subjects had sleep quality marked by decreased amount of slow wave sleep and increased fragmentation of slow wave sleep, resulting in decreased slow wave activity. Across all subjects, frontal slow wave activity was positively correlated with both overnight change in maze performance and medial prefrontal cortical volume, illuminating a potential neuroanatomical substrate for slow wave activity changes with aging and underscoring the importance of slow wave activity in sleep-dependent spatial navigational memory consolidation.