Dysfunctional Overnight Memory Consolidation in Patients with Schizophrenia in Comparison to Healthy Controls: Disturbed Slow-Wave Sleep as Contributing Factor?

<b><i>Introduction:</i></b> Memory deficiency has been shown in schizophrenia patients, but results on the role of sleep parameters in overnight consolidation of associative verbal memory are still missing. Therefore, the aim of our study was to elucidate underlying processes of impaired sleep-related consolidation of associative word pairs in schizophrenia including standard sleep parameters as well as sleep spindle counts and spectral analysis. <b><i>Methods:</i></b> Eighteen stably medicated schizophrenia patients and 24 healthy age-matched controls performed an associative declarative memory task before and after polysomnographic recordings. Part of the participants expected verbal associative memory testing in the morning, while the others did not. Furthermore, participants filled in self-rating questionnaires of schizophrenia-typical experiences (Eppendorf Schizophrenia Inventory [ESI] and Psychotic Symptom Rating Scale). <b><i>Results:</i></b> Schizophrenia patients performed worse in verbal declarative memory in the evening as well as in overnight consolidation (morning compared to evening performance). While duration of slow-wave sleep was nearly comparable between groups, schizophrenia patients showed lower sleep spindle count, reduced delta power during slow-wave sleep, and reduced spindle power during the slow oscillation (SO) up-state. In healthy but not in schizophrenia patients, a linear relationship between overnight memory consolidation and slow-wave sleep duration as well as delta power was evident. No significant effect with respect to the expectation of memory retrieval was evident in our data. Additionally, we observed a negative linear relationship between total number of sleep spindles and ESI score in healthy participants. <b><i>Discussion/Conclusion:</i></b> As expected, schizophrenia patients showed deficient overnight verbal declarative memory consolidation as compared to healthy controls. Reduced sleep spindles, delta power, and spindle power during the SO up-state may link sleep and memory deficiency in schizophrenia. Additionally, the absence of a linear relationship between sleep-related memory consolidation and slow-wave sleep as well as delta power suggests further functional impairments in schizophrenia. Note that this conclusion is based on observational data. Future studies should investigate if stimulation of delta waves during sleep could improve memory performance and thereby quality of life in schizophrenia.

151 Sex Differences in the Maturational Trajectories of Sleep Spindles in the Transition from Childhood to Adolescence

Introduction Sleep spindles occur as bursts of EEG activity in the sigma (11-16 Hz) frequency range and are purported biomarkers of cortical development. However, the few studies examining maturational changes in sleep spindles are limited by small samples and/or short follow-up periods. Thus, large longitudinal population-based studies are needed to determine their developmental trajectories as the child transitions to adolescence. Methods We analyzed the sleep EEG of 572 un-medicated subjects aged 6-21 (47.6% female, 25.9% racial/ethnic minority), of whom 332 were 5-12 years at baseline and followed-up at ages 12-22. Multivariable-adjusted models tested the cross-sectional and longitudinal trajectories of sleep spindle density, frequency, and power. Results From age 6 to 21, the trajectory of sleep spindle density was best fit by a quadratic model (p=0.02), particularly in males (p-quadratic=0.05). Females maintained more stable levels of sleep spindle density (p-linear=0.26), as shown by a longitudinal increase 37.6% lower than males by age 14 (p=0.01). Sleep spindle frequency increased (p-linear&lt;0.01), while sleep spindle power decreased (p-linear&lt;0.01), from age 6 to 21. The trajectory of sleep spindle frequency diverged between females (p-linear&lt;0.01) and males (p-quadratic=0.02), in whom it plateaued by age 15 onwards. Females had experienced a longitudinal increase in sleep spindle frequency 2.4% higher than males by age 20-22 (p=0.05). Males had experienced a steeper decreasing slope in sleep spindle power (p-linear&lt;0.01) than females (p-linear=0.12), as confirmed by a longitudinal decline 25.4% greater than females by age 19 (p=0.02). Conclusion Sleep spindle metrics follow distinct maturational trajectories from each other and from other EEG oscillations (e.g., slow wave activity). The increase in sleep spindle density from childhood to early adolescence coupled with the linear increase in sleep spindle frequency from childhood to young adulthood may represent the emergence of fast sleep spindles, which appears to occur earlier in females. Overall, males experience greater maturational changes in all sleep spindle metrics and sex differences become prominent in young adulthood, when males show lower sleep spindle density and sleep spindle frequency, indicative of less fast sleep spindles. Support (if any) NIH Awards Number R01MH118308, R01HL136587, R01HL97165, R01HL63772, UL1TR000127

Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

Remaining useful tool life predictions in turning using Bayesian inference

Tool wear is an important factor in determining machining productivity. In this paper, tool wear is characterized by remaining useful tool life in a turning operation and is predicted using spindle power and a random sample path method of Bayesian inference. Turning tests are performed at different speeds and feed rates using a carbide tool and MS309 steel work material. The spindle power and the tool flank wear are monitored during cutting; the root mean square of the time domain power is found to be sensitive to tool wear. Sample root mean square power growth curves are generated and the probability of each curve being the true growth curve is updated using Bayes’ rule. The updated probabilities are used to determine the remaining useful tool life. Results show good agreement between the predicted tool life and the empirically-determined true remaining life. The proposed method takes into account the uncertainty in tool life and the growth of the root mean square power at the end of tool life and is, therefore, robust and reliable.

Study of Double-Edged Gun Drills with Different Edge Types in Drilling Compacted Graphite Iron

Deep-hole machining is an important part in the field of mechanical processing of diesel engine. Gun drill has been widely used in deep-hole machining because of its high dimensional accuracy, high efficiency and good straightness. Through experiments on drilling compacted graphite iron with two different edge types of double-edged gun drills, the spindle power, axial force and tool wear were analyzed and found out one edge type which is more suitable for processing compacted graphite iron. This paper presents a simulation of deep hole drilling to validate the analysis. The research results have important guiding significance for deep hole processing of compacted graphite iron.

Sleep spindles mediate hippocampal-neocortical coupling during long-duration ripples

Sleep is pivotal for memory consolidation. According to two-stage accounts, memory traces are gradually translocated from hippocampus to neocortex during non-rapid-eye-movement (NREM) sleep. Mechanistically, this information transfer is thought to rely on interactions between thalamocortical spindles and hippocampal ripples. To test this hypothesis, we analyzed intracranial and scalp Electroencephalography sleep recordings from pre-surgical epilepsy patients. We first observed a concurrent spindle power increase in hippocampus (HIPP) and neocortex (NC) time-locked to individual hippocampal ripple events. Coherence analysis confirmed elevated levels of hippocampal-neocortical spindle coupling around ripples, with directionality analyses indicating an influence from NC to HIPP. Importantly, these hippocampal-neocortical dynamics were particularly pronounced during long-duration compared to short-duration ripples. Together, our findings reveal a potential mechanism underlying active consolidation, comprising a neocortical-hippocampal-neocortical reactivation loop initiated by the neocortex. This hippocampal-cortical dialogue is mediated by sleep spindles and is enhanced during long-duration hippocampal ripples.

Sleep spindles mediate hippocampal-neocortical coupling during sharp-wave ripples

Sleep is pivotal for the consolidation of memories [1]. According to two-stage accounts, experiences are temporarily stored in the hippocampus and gradually translocated to neocortical sites during non-rapid-eye-movement (NREM) sleep [2,3]. Mechanistically, information transfer is thought to rely on interactions between thalamocortical spindles and hippocampal ripples. In particular, spindles may open precisely-timed communication channels, across which reactivation patterns may travel between the hippocampus and cortical target sites when ripples occur. To test this hypothesis, we first derived time-frequency representations (TFRs) in hippocampus (HIPP) and at scalp electrode Cz (neocortex, NC) time-locked to individual hippocampal ripple events. Compared to matched ripple-free intervals, results revealed a concurrent increase in spindle power both in HIPP and NC. As revealed by coherence analysis, hippocampal-neocortical coupling was indeed enhanced in the spindle band around ripples. Finally, we examined the directionality of spindle coupling and observed a strong driving effect from NC to HIPP. Specifically, ∼250 ms prior to the HIPP ripple, NC spindles emerge and entrain HIPP spindles. Both regions then remain synchronised until ∼500 ms after the ripple. Consistent with recent rodent work, these findings suggest that active consolidation is initiated by neocortex and draws on neocortical-hippocampal-neocortical reactivation loops [4], with a role of sleep spindles in mediating this process.