Aerospace medicine and biology: A continuing bibliography with indexes, supplement 130, July 1974

This special bibliography lists 291 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1974

Genetics of human sleep EEG

Sleep characteristics are candidates for predictive biological markers in patients with severe psychiatric diseases, in particular affective disorder and schizophrenia. The genetic components of sleep determination in humans remain, to a large degree, unelucidated. In particular, the heritability of rapid eye movement (REM) sleep and EEG bursts of oscillatory brain activity in Non-REM sleep, i.e. sleep spindles, are of interest. In addition, recent findings suggest a strong role of distinct sleep spindle types in memory consolidation, making it important to identify sleep spindles in slow wave sleep (SWS) and to separate slow and fast spindle localization in the frequency range. However, predictive sleep biomarker research requires large sample sizes of healthy and affected human individuals. Therefore, the present work addressed two questions. The first aim was to optimize data analysis by developing algorithms that allow an efficient and reliable identification of rapid eye movements (REMs) and sleep EEG spindles. In the second part, developed methods were applied to sleep EEG data from a classical twin study to identify genetic effects on tonic and phasic REM sleep parameters, sleep spindles, and their trait-like characteristics. The algorithm for REM detection was developed for standard clinical two channel electrooculographic montage. The goal was to detect REMs visible above the background noise, and in the case of REM saccades to classify each movement separately. In order to achieve a high level of sensitivity, detection was based on a first derivative of electrooculogram (EOG) potentials and two detection thresholds. The developed REM detector was then validated in n=12 polysomnographic recordings from n=7 healthy subjects who had been previously scored visually by two human experts according to standard guidelines. Comparison of automatic REM detection with human scorers revealed mean correlations of 0.94 and 0.90, respectively (mean correlation between experts was 0.91). The developed automatic sleep spindle detector assessed individualized signal amplitude for each channel as well as slow and fast spindle frequency peaks based on the spectral analysis of the EEG signal. The spindle detection was based on Continuous Wavelet Transform (CWT); it localized the exact length of sleep spindles and was sensitive also for detection of sleep spindles intermingled in high amplitude slow wave EEG activity. The automatic spindle detector was validated in n=18 naps from n=10 subjects, where EEG data were scored both visually and by a commercial automatic algorithm (SIESTA). Comparison of our own spindle detector with results from the SIESTA algorithm and visual scoring revealed the correlations of 0.97 and 0.92, respectively (correlation between SIESTA algorithm and visual scoring was 0.90). In the second part of the work, the similarity of given sleep EEG parameters in n=32 healthy monozygotic (MZ) twins was compared with the similarity in n=14 healthy same-gender dizygotic (DZ) twins. The author of the current work did not participate in acquisition of twin study sample. EEG sleep recordings used for the heritability study were collected and already described by Ambrosius et al. (2008). Investigation of REM sleep included the absolute EEG spectral power, the shape of REM power spectrum, the amount and the structural organization of REMs; parameters of Non-REM sleep included slow and fast sleep spindle characteristics as well as the shape of the Non-REM power spectrum in general. In addition to estimating genetic effects, differences in within-pair similarity and night-to-night stability of given parameters were illustrated by intraclass correlation coefficients (ICC) and cluster analysis. A substantial genetic influence on both spectral composition and phasic parameters of REM sleep was observed. A significant genetic variance in spectral power affected delta to high sigma and high beta to gamma EEG frequency bands, as well as all phasic REM parameters with the exception of the REMs occurring outside REM bursts. Furthermore, MZ and DZ twins differed significantly in their within-pair similarity of non-REM and REM EEG spectra morphology. Regarding sleep spindles, statistical analysis revealed a significant genetic influence on localization in frequency range as well as on basic spindle characteristics (amplitude, length, quantity), except in the quantity of fast spindles in stage 2 and whole Non-REM sleep. Basic spindle parameters showed trait-like characteristics and significant differences in within-pair similarity between the twin groups. In summary, the developed algorithms for automatic REM and sleep spindle detection provide several advantages: the elimination of human scorer biases and intra-rater variability, investigation of structural organization of REMs, exact determination of fast and slow spindle frequency for each individual. Algorithms are fully automated and therefore well suited to score REM density and sleep spindles in large patient samples. In the second part of the study, sleep EEG analysis in MZ and DZ twins revealed a substantial genetic determination of both tonic and phasic REM sleep parameters. This complements previous findings of a high genetic determination of the Non-REM sleep power spectrum. Interestingly, smaller genetic effects and lower night-to-night stability were observed for fast spindles, especially in SWS. This is in line with recent hypotheses on the differential function of sleep spindle types for memory consolidation. The results from the presented studies strongly support the application of sleep EEG to identify clinically relevant biomarkers for psychiatric disorders

Eye-tracking the time‐course of novel word learning and lexical competition in adults and children

Lexical competition is a hallmark of proficient, automatic word recognition. Previous research suggests that there is a delay before a new spoken word becomes engaged in this process, with sleep playing an important role. However, data from one method--the visual world paradigm--consistently show competition without a delay. We trained 42 adults and 40 children (aged 7-8) on novel word-object pairings, and employed this paradigm to measure the time-course of lexical competition. Fixations to novel objects upon hearing existing words (e.g., looks to the novel object biscal upon hearing “click on the biscuit”) were compared to fixations on untrained objects. Novel word-object pairings learned immediately before testing and those learned the previous day exhibited significant competition effects, with stronger competition for the previous day pairings for children but not adults. Crucially, this competition effect was significantly smaller for novel than existing competitors (e.g., looks to candy upon hearing “click on the candle”), suggesting that novel items may not compete for recognition like fully-fledged lexical items, even after 24 hours. Explicit memory (cued recall) was superior for words learned the day before testing, particularly for children; this effect (but not the lexical competition effects) correlated with sleep-spindle density. Together, the results suggest that different aspects of new word learning follow different time courses: visual world competition effects can emerge swiftly, but are qualitatively different from those observed with established words, and are less reliant upon sleep. Furthermore, the findings fit with the view that word learning earlier in development is boosted by sleep to a greater degree

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

Sleep, a critical necessity for health, cognition, and well-being, consumes 25 years of the average human’s life. Alterations in sleep’s macro and microstate have been observed in multiple illnesses, including depression and Alzheimer’s disease, and sleep may represent a biomarker of these difficult to diagnose pathologies. Despite this, adequate models of the dynamics of sleep able to capture the fine-grained minutia of sleep’s many states and processes are lacking. The current work leverages over 1000 nights of healthy sleep polysomnography recordings from multiple open-source datasets. Data were automatically analyzed through a machine learning and data engineering pipeline to extract important features of the electroencephalogram, such as sleep spindles, slow oscillations (SO), and Rapid Eye Movement (REM) events. The counts of these sleep features and transition probabilities of sleep stages were modeled across the night in 30-second epoch intervals, using a flexible yet principled Bayesian cognitive modeling approach. The model, christened P(Sleep) 2.0, defines “typical” patterns of healthy sleep, and how they are affected by time, circadian processes and individual differences (e.g., age, sex). Transition probability dynamics were found to best be predicted by the previous 2 stages, time spent in stage, time across the night, age and sex, but not an age*sex interaction. Spindles, SO and REM events were also affected by the same features, but an age*sex interaction was additionally present. N2 spindles and both N2 and N3 slow oscillations were found to increase with time spent in a stage, whereas N3 spindles and REM decreased. Spindles and SO were more present in the evening compared to the morning; in contrast, REM increased over the night. REM counts and N3 spindles increased with age, while N2 spindles and SO reduced. Females had more N2 spindles, and SO in both N2 and N3 than men. The current work replicates a body of previous literature based on small sample sizes, and the increased power in this study provides firm conclusions to arguments on the patterns of healthy sleep across the night, both at the macroscale (stages) and microscale (feature events). The proposed work has broader impacts on the detection of abnormal (and potentially pathologic) sleep patterns

Huomaamattomat mittausmenetelmät unen laadun tarkkailussa

Sleep is an important part of health and well-being. While sleep quantity is directly measurable, sleep quality has traditionally been assessed with subjective methods such as questionnaires. The study of sleep disorders has for a long time been confined to clinical environments, and patients have had to endure cumbersome procedures involving multiple electrodes placed on the body. Recent developments in sensor technology as well as data analysis methods have enabled continuous, unobtrusive sleep data recording in the home environment. This has opened new possibilities for studying various sleep parameters and their effect on the quality of sleep. This thesis consists of two parts. The first part is a literature review examining the field of sleep quality research with focus on the application of intelligent methods and signal processing. The second part is a descriptive data analysis look at sleep data obtained with non-invasive sensors.Uni on terveyden ja hyvinvoinnin keskeinen tekijä. Unen määrä on helposti mitattavissa, mutta unen laatua on perinteisesti seurattu kyselylomakkeiden kaltaisin subjektiivisin menetelmin. Unihäiriöiden tutkiminen on pitkään rajoittunut kliinisiin ympäristöihin, ja potilaiden on täytynyt sietää hankalia tutkimusmenetelmiä useine kehoon kiinnitettävine elektrodeineen. Anturiteknologian ja data-analyysimenetelmien kehittyminen on mahdollistanut unidatan jatkuvan ja huomaamattoman tallentamisen kotiympäristössä. Tämä on avannut uusia mahdollisuuksia sekä unen ominaisuuksien että niiden unen laatuun vaikuttavien tekijöiden tutkimiselle. Tämä tutkimus jakautuu kahteen osaan. Ensimmäinen osa on kirjallisuuskatsaus unen laadun tutkimukseen, painopisteenä älykkäiden menetelmien ja signaalinkäsittelyn soveltaminen. Toisessa osassa esitellään huomaamattomilla sensoreilla kerättävän unidatan tutkimista ja sen deskriptiivistä data-analyysiä, esimerkkinä ballistokardiografia

Enhanced Memory Consolidation Via Automatic Sound Stimulation During Non-REM Sleep

Introduction: Slow-wave sleep (SWS) slow waves and sleep spindle activity have been shown to be crucial for memory consolidation. Recently, memory consolidation has been causally facilitated in human participants via auditory stimuli phase-locked to SWS slow waves. Aims: Here, we aimed to develop a new acoustic stimulus protocol to facilitate learning and to validate it using different memory tasks. Most importantly, the stimulation setup was automated to be applicable for ambulatory home use. Methods: Fifteen healthy participants slept 3 nights in the laboratory. Learning was tested with 4 memory tasks (word pairs, serial finger tapping, picture recognition, and face-name association). Additional questionnaires addressed subjective sleep quality and overnight changes in mood. During the stimulus night, auditory stimuli were adjusted and targeted by an unsupervised algorithm to be phase-locked to the negative peak of slow waves in SWS. During the control night no sounds were presented. Results: Results showed that the sound stimulation increased both slow wave (p =.002) and sleep spindle activity (p Conclusions: We showed that the memory effect of the SWS-targeted individually triggered single-sound stimulation is specific to verbal associative memory. Moreover, the ambulatory and automated sound stimulus setup was promising and allows for a broad range of potential follow-up studies in the future.Peer reviewe

SEMIAUTOMATIC ANALYSIS OF SLEEP MICROSTRUCTURE PARAMETERS: AROUSAL, CYCLIC ALTERNATING PATTERN AND REM MUSCLE ATONIA.

This thesis project is focused on systems of automatic analysis of sleep parameters and it is composed by two main parts: the first is focused on the process of creation of a software for the analysis of Cyclic Alternating Pattern (CAP) a particular parameter of sleep microstructure and the second part is focused on the use of automatic analysis of muscle activity during sleep. CAP is defined as periodic EEG activity of NREM sleep characterized by sequences of transient electrocortical events, that are distinct from the background electroencephalogram (EEG) activity and occurs at up to 1-minute intervals. CAP represents the microstructure of sleep, and its analysis gives fundamental information that are otherwise neglected with the analysis of sleep macrostructure (sleep staging) alone. CAP is considered a marker for the evaluation of sleep stability and its oscillatory presence is fundamental preservation of sleep stability through the night and in response to arousal stimuli. Analysis of CAP is a very time consuming procedure and it is still used mainly for research purpose rather than in the clinical practice. The development of a software for the analysis of CAP was the main focus of the work in collaboration with Micromed® (an international company for the manufacturing of hardware and software for neurophysiology based in Mogliano Veneto (TV)). During the months spent at Micromed® the PhD student worked with the software programmers and engineers for the creation and validation of the software, individuating all the clinical parameters from guidelines and verifying their correct application and the validity of the results. In the first part of this thesis all the creation process is described in detail. The second part of this thesis is focused on the automatic analysis of muscle EMG tone during both REM and NREM sleep. Muscle tone during sleep gradually diminishes throughout the different sleep stages reaching its minimum with REM muscle atonia. Evaluation of muscle tone during REM sleep is fundamental for the diagnosis of REM sleep Behavior Disorder (RBD) in which there is loss of muscle atonia during REM associated to dream enacting behavior. Muscle activity is measured in polysomnography (PSG) through the recording of different EMG channels. This activity is evaluated almost exclusively during REM sleep using a manual method of visual scoring that require high expertise is highly time consuming. A validated method developed by R. Ferri and co. allows automatic analysis of chin EMG activity through the calculation of Atonia index. Few studies evaluated muscle tone during NREM sleep, and little is known about the neurophysiology of muscle control. Manual methods would be difficult to apply to NREM sleep; the method developed by Ferri is capable to perform an analysis of muscle tone for all sleep stages. RBD is associated to neurodegenerative disorders, synucleinopathies such as Parkinson disease (PD), Multiple System Atrophy (MSA). MSA patients have a more severe loss of atonia during REM sleep compared to PD with RBD. Starting from the fortuitous observation of a prominent facial activity during NREM sleep, we decided to evaluate the facial activity recorded in vPSG in patients with PD, MSA and controls and to evaluate the muscle tone in both REM and NREM sleep using the automatic method for the calculation of atonia index. Our results showed that MSA have a more sustained muscle tone compared to healthy controls in all sleep stages and compared to PD in all NREM stages. Moreover a particular facial expression was noted to be significantly more frequent in MSA compared to PD. This results may help the differential diagnosis between PD and MSA. This is the first study to evaluate muscle tone during all sleep stages using Atonia index and this analysis may open to different perspectives for the understanding of REM behavior disorder and the mechanism underlying the control of muscle tone in NREM slee

Artifact Rejection Methodology Enables Continuous, Noninvasive Measurement of Gastric Myoelectric Activity in Ambulatory Subjects.

The increasing prevalence of functional and motility gastrointestinal (GI) disorders is at odds with bottlenecks in their diagnosis, treatment, and follow-up. Lack of noninvasive approaches means that only specialized centers can perform objective assessment procedures. Abnormal GI muscular activity, which is coordinated by electrical slow-waves, may play a key role in symptoms. As such, the electrogastrogram (EGG), a noninvasive means to continuously monitor gastric electrical activity, can be used to inform diagnoses over broader populations. However, it is seldom used due to technical issues: inconsistent results from single-channel measurements and signal artifacts that make interpretation difficult and limit prolonged monitoring. Here, we overcome these limitations with a wearable multi-channel system and artifact removal signal processing methods. Our approach yields an increase of 0.56 in the mean correlation coefficient between EGG and the clinical "gold standard", gastric manometry, across 11 subjects (p < 0.001). We also demonstrate this system's usage for ambulatory monitoring, which reveals myoelectric dynamics in response to meals akin to gastric emptying patterns and circadian-related oscillations. Our approach is noninvasive, easy to administer, and has promise to widen the scope of populations with GI disorders for which clinicians can screen patients, diagnose disorders, and refine treatments objectively