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Dreaming
Dreams, conceived as conscious experience or phenomenal states during sleep, offer an important contrast condition for theories of consciousness and the self. Yet, although there is a wealth of empirical research on sleep and dreaming, its potential contribution to consciousness research and philosophy of mind is largely overlooked. This might be due, in part, to a lack of conceptual clarity and an underlying disagreement about the nature of the phenomenon of dreaming itself. InDreaming, Jennifer Windt lays the groundwork for solving this problem. She develops a conceptual framework describing not only what it means to say that dreams are conscious experiences but also how to locate dreams relative to such concepts as perception, hallucination, and imagination, as well as thinking, knowledge, belief, deception, and self-consciousness.Arguing that a conceptual framework must be not only conceptually sound but also phenomenologically plausible and carefully informed by neuroscientific research, Windt integrates her review of philosophical work on dreaming, both historical and contemporary, with a survey of the most important empirical findings. This allows her to work toward a systematic and comprehensive new theoretical understanding of dreaming informed by a critical reading of contemporary research findings. Windt's account demonstrates that a philosophical analysis of the concept of dreaming can provide an important enrichment and extension to the conceptual repertoire of discussions of consciousness and the self and raises new questions for future research.
eBook
Sleep medicine, sleep research, and sleep education: a whole life devoted to sleep
This article describes my participation in sleep medicine, sleep research, and sleep education, mainly in Europe, between the years 1970 and 2000.This article describes my participation in sleep medicine, sleep research, and sleep education, mainly in Europe, between the years 1970 and 2000.
Journal Article
Mapping the darkness : the visionary scientists who unlocked the mysteries of sleep
Journalist Kenneth Miller weaves science with history to tell the story of four outsider academics who carried the study of sleep from fringe discipline to mainstream obsession. In the 1920s Nathaniel Kleitman founded the world's first dedicated sleep lab, with breakthrough experiments in 1938. Kleitman mentored Eugene Aserinsky who discovered REM sleep, and William Dement, who became known as the father of sleep medicine. Dement, in turn, mentored Mary Carskadon, who uncovered an epidemic of sleep deprivation among teenagers.-- Adapted from book jacket flap.
Book
Sleep-Deep-Learner is taught sleep–wake scoring by the end-user to complete each record in their style
Abstract
Sleep–wake scoring is a time-consuming, tedious but essential component of clinical and preclinical sleep research. Sleep scoring is even more laborious and challenging in rodents due to the smaller EEG amplitude differences between states and the rapid state transitions which necessitate scoring in shorter epochs. Although many automated rodent sleep scoring methods exist, they do not perform as well when scoring new datasets, especially those which involve changes in the EEG/EMG profile. Thus, manual scoring by expert scorers remains the gold standard. Here we take a different approach to this problem by using a neural network to accelerate the scoring of expert scorers. Sleep-Deep-Learner creates a bespoke deep convolution neural network model for individual electroencephalographic or local-field-potential (LFP) records via transfer learning of GoogLeNet, by learning from a small subset of manual scores of each EEG/LFP record as provided by the end-user. Sleep-Deep-Learner then automates scoring of the remainder of the EEG/LFP record. A novel REM sleep scoring correction procedure further enhanced accuracy. Sleep-Deep-Learner reliably scores EEG and LFP data and retains sleep–wake architecture in wild-type mice, in sleep induced by the hypnotic zolpidem, in a mouse model of Alzheimer’s disease and in a genetic knock-down study, when compared to manual scoring. Sleep-Deep-Learner reduced manual scoring time to 1/12. Since Sleep-Deep-Learner uses transfer learning on each independent recording, it is not biased by previously scored existing datasets. Thus, we find Sleep-Deep-Learner performs well when used on signals altered by a drug, disease model, or genetic modification.
Journal Article
Multiethnic Meta-Analysis Identifies RAI1 as a Possible Obstructive Sleep Apnea–related Quantitative Trait Locus in Men
Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea–hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 × 10−8) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki–Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.
Journal Article
Objectively measured sleep characteristics and prevalence of coronary artery calcification: the Multi-Ethnic Study of Atherosclerosis Sleep study
BackgroundWe tested whether objectively measured indices of obstructive sleep apnoea (OSA) and sleep quality are associated with coronary artery calcification (CAC) prevalence independent of obesity, a classic confounder.Methods1465 Multi-Ethnic Study of Atherosclerosis participants (mean age 68 years), who were free of clinical cardiovascular disease, had both coronary CT and in-home polysomnography and actigraphy performed. OSA categories were defined by the Apnea-Hypopnea Index (AHI). Prevalence ratios (PRs) for CAC >0 and >400 (high burden) were calculated.ResultsParticipants with severe OSA (AHI ≥30; 14.6%) were more likely to have prevalent CAC, relative to those with no evidence of OSA, after adjustment for demographics and smoking status (PR 1.16; 95% CI 1.06 to 1.26), body mass index (1.11; 1.02 to 1.21) and traditional cardiovascular risk factors (1.10; 1.01 to 1.19). Other markers of hypoxaemia tended to be associated with a higher prevalence of CAC >0. For CAC >400, a higher prevalence was observed with both a higher arousal index and less slow-wave sleep. Overall, associations were somewhat stronger among younger participants, but did not vary by sex or race/ethnicity.ConclusionsIn this population-based multi-ethnic sample, severe OSA was associated with subclinical coronary artery disease (CAC >0), independent of obesity and traditional cardiovascular risk factors. Furthermore, the associations of the arousal index and slow-wave sleep with high CAC burden suggest that higher nightly sympathetic nervous system activation is also a risk factor. These findings highlight the potential importance of measuring disturbances in OSA as well as sleep fragmentation as possible risk factors for coronary artery disease.
Journal Article
Sleep and performance
Abstract
I review the path of my career in sleep. My focus has been on the need for sleep and the relationship between sleep and performance. I have done sleep research in the sleep lab setting and have also taken unique opportunities to measure sleep loss effects on real-world performance. My studies have included long and short sleeper studies, evaluations of various sleep aids, sleep loss effects, jet lag effects, naps, and the consequences of being a poor sleeper. Over the course of my career in sleep, I have also taught about sleep in university and professional educational settings. I am a Board Certified Sleep Medicine Specialist with a private practice, providing diagnosis and treatment of sleep disorders in children and adults.
Journal Article
Sleep Parameter Assessment Accuracy of a Consumer Home Sleep Monitoring Ballistocardiograph Beddit Sleep Tracker: A Validation Study
Study Objectives:
Growing interest in monitoring sleep and well-being has created a market for consumer home sleep monitoring devices. Additionally, sleep disorder diagnostics, and sleep and dream research would benefit from reliable and valid home sleep monitoring devices. Yet, majority of currently available home sleep monitoring devices lack validation. In this study, the sleep parameter assessment accuracy of Beddit Sleep Tracker (BST), an unobtrusive and non-wearable sleep monitoring device based on ballistocardiography, was evaluated by comparing it with polysomnography (PSG) measures. We measured total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE). Additionally, we examined whether BST can differentiate sleep stages.
Methods:
We performed sleep studies simultaneously with PSG and BST in ten healthy young adults (5 female/5 male) during two non-consecutive nights in a sleep laboratory.
Results:
BST was able to distinguish SOL with some accuracy. However, it underestimated WASO and thus overestimated TST and SE. Also, it failed to discriminate between non-rapid eye movement sleep stages and did not detect the rapid eye movement sleep stage.
Conclusions:
These findings indicate that BST is not a valid device to monitor sleep. Consumers should be careful in interpreting the conclusions on sleep quality and efficiency provided by the device.
Citation:
Tuominen J, Peltola K, Saaresranta T, Valli K. Sleep parameter assessment accuracy of a consumer home sleep monitoring ballistocardiograph beddit sleep tracker: a validation study.
J Clin Sleep Med
2019;15(3):483–487.
Journal Article