A hybrid method for modelling two dimensional non-breaking and breaking waves

This is the first paper to present a hybrid method coupling a Improved Meshless Local Petrov Galerkin method with Rankine source solution (IMLPG_R) based on the Navier Stokes (NS) equations, with a finite element method (FEM) based on the fully nonlinear potential flow theory (FNPT) in order to efficiently simulate the violent waves and their interaction with marine structures. The two models are strongly coupled in space and time domains using a moving overlapping zone, wherein the information from both the solvers is exchanged. In the time domain, the Runge-Kutta 2nd order method is nested with a predictor-corrector scheme. In the space domain, numerical techniques including ‘Feeding Particles’ and two-layer particle interpolation with relaxation coefficients are introduced to achieve the robust coupling of the two models. The properties and behaviours of the new hybrid model are tested by modelling a regular wave, solitary wave and Cnoidal wave including breaking and overtopping. It is validated by comparing the results of the method with analytical solutions, results from other methods and experimental data. The paper demonstrates that the method can produce satisfactory results but uses much less computational time compared with a method based on the full NS model

The impact of sleep loss on performance monitoring and error-monitoring: a systematic review and meta-analysis

Awareness of performance deficits and errors during sleep loss could be protective against the consequences of sleep deprivation, however, it is unclear whether sleep deprived individuals have insight into their performance. We conducted a systematic review and meta-analysis of the impact of sleep loss (sleep duration <6 h) on monitoring of performance and errors using Embase, MEDLINE, PsycINFO & Cochrane Central. We identified 28 studies, 11 of which were appropriate for meta-analysis. The systematic review indicated limited consensus regarding sleep loss impacts on performance monitoring, due to substantial differences in study methodology. However, participants typically demonstrated more conservative estimates of performance during sleep loss. Error-monitoring literature was more consistent, indicating an impairment in error-monitoring following sleep loss. Meta-analyses supported the findings of the systematic review. In terms of methodology, we found the performance monitoring literature is limited by an overreliance on correlational designs, which are likely confounded by response bias. The error-monitoring literature is limited by very few studies utilising behavioural measures to directly measure error-awareness. Future performance monitoring studies must employ methods which control for confounds such as bias, and error-monitoring studies must incorporate combined behavioural and ERP measures to better understand the impact of sleep loss on error-monitoring

Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin

What is the level of consciousness of the psychedelic state? Empirically, measures of neural signal diversity such as entropy and Lempel-Ziv (LZ) complexity score higher for wakeful rest than for states with lower conscious level like propofol-induced anesthesia. Here we compute these measures for spontaneous magnetoencephalographic (MEG) signals from humans during altered states of consciousness induced by three psychedelic substances: psilocybin, ketamine and LSD. For all three, we find reliably higher spontaneous signal diversity, even when controlling for spectral changes. This increase is most pronounced for the single-channel LZ complexity measure, and hence for temporal, as opposed to spatial, signal diversity. We also uncover selective correlations between changes in signal diversity and phenomenological reports of the intensity of psychedelic experience. This is the first time that these measures have been applied to the psychedelic state and, crucially, that they have yielded values exceeding those of normal waking consciousness. These findings suggest that the sustained occurrence of psychedelic phenomenology constitutes an elevated level of consciousness - as measured by neural signal diversity

Hymn to America

80.7568.522 – “Hymn to America”: Fernando Andrillon: Theo. T. Barker: C. H. Ditson & Co.: 1880: Vocal Solo

The neural correlates of dreaming.

Consciousness never fades during waking. However, when awakened from sleep, we sometimes recall dreams and sometimes recall no experiences. Traditionally, dreaming has been identified with rapid eye-movement (REM) sleep, characterized by wake-like, globally 'activated', high-frequency electroencephalographic activity. However, dreaming also occurs in non-REM (NREM) sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density electroencephalography, we contrasted the presence and absence of dreaming in NREM and REM sleep. In both NREM and REM sleep, reports of dream experience were associated with local decreases in low-frequency activity in posterior cortical regions. High-frequency activity in these regions correlated with specific dream contents. Monitoring this posterior 'hot zone' in real time predicted whether an individual reported dreaming or the absence of dream experiences during NREM sleep, suggesting that it may constitute a core correlate of conscious experiences in sleep

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols

Capturing the emergent dynamical structure in biophysical neural models

Complex neural systems can display structured emergent dynamics. Capturing this structure remains a significant scientific challenge. Using information theory, we apply Dynamical Independence (DI) to uncover the emergent dynamical structure in a minimal 5-node biophysical neural model, shaped by the interplay of two key aspects of brain organisation: integration and segregation. In our study, functional integration within the biophysical neural model is modulated by a global coupling parameter, while functional segregation is influenced by adding dynamical noise, which counteracts global coupling. Leveraging transfer entropy, DI defines a dimensionally-reduced macroscopic variable (e.g., a coarse-graining) as emergent to the extent that it behaves as an independent dynamical process, distinct from the micro-level dynamics. Dynamical dependence (a departure from dynamical independence) is measured by minimising the transfer entropy from microlevel variables to macroscopic variables across spatial scales. Our results indicate that the degree of emergence of macroscopic variables is relatively minimised at balanced points of integration and segregation and maximised at the extremes. Additionally, our method identifies to which degree the macroscopic dynamics are localised across microlevel nodes, thereby elucidating the emergent dynamical structure through the relationship between microscopic and macroscopic processes. We find that deviation from a balanced point between integration and segregation results in a less localised, more distributed emergent dynamical structure as identified by DI. This finding suggests that a balance of functional integration and segregation is associated with lower levels of emergence (higher dynamical dependence), which may be crucial for sustaining coherent, localised emergent macroscopic dynamical structures. This work also provides a complete computational implementation for the identification of emergent neural dynamics that could be applied both in silico and in vivo

O039 Differential effects of sleep deprivation and sleep restriction on error awareness

Abstract Introduction The ability to detect and subsequently correct errors is important in preventing the detrimental consequences of sleep loss. We report the first study to compare the effects of total sleep deprivation (TSD) and sleep restriction (SR) on error awareness. Methods Thirteen healthy adults (11F, age=26.8±3.4y) underwent a 34h TSD protocol, completing the Error Awareness Task (EAT: a combined Stroop/1-back/GoNogo task) at 4h and 27h post-wake. Twenty healthy adults (11F, age=27.4±5.3y) were studied both well-rested (WR: 9h sleep) and following SR (3 nights of 3h sleep), completing the EAT once/day (8-9h post-habitual wake). The EAT required participants to withhold responding to “nogo” stimuli and signal, via a button press, whenever they realised they made an error on these nogo trials. Results TSD did not significantly affect error rate (p=.712) or error awareness rate (p=.517), however, participants were slower to recognise errors after TSD (p=.004). In contrast, SR increased error rate (p&amp;lt;.001), decreased error awareness (p&amp;lt;.001), and slowed recognition of errors (p&amp;lt;.01). Discussion Three nights SR impaired the ability to recognise errors in real-time, despite a greater number of errors being made. Thus, impaired error awareness may be one mechanism underlying increased sleep loss-related accidents and errors in occupational settings, as well as at home. Interestingly, 1-night TSD did not lead to more, or impaired recognition of errors. TSD participants were slower to recognise errors, which may be problematic in safety critical settings. Technological and/or operational solutions may be needed to reduce the risk of errors going unrecognised. </jats:sec

0293 Sleep Deprivation Affects the Acoustic Properties of Human Speech

Abstract Introduction Lack of sleep drastically affects many aspects of human behavior. The early detection of sleepiness is thus a major challenge for health and security reasons. Here we investigated the effect of sleep deprivation on the acoustic properties of human speech. Methods Twenty-four participants were sleep deprived for two days (two successive nights with only 3 hours of sleep). They were recorded reading a short text aloud before and after sleep deprivation. An auditory model, based on spectro-temporal modulations, was used to analyse the acoustic properties of their speech and served as a front-end to machine-learning classifiers. Results Results showed that sleepiness could be accurately detected with individually-trained classifiers. However,we were not able to fit a generic classifier for all participants. As we relied on an auditory-inspired model,we could identify and interpret the acoustic features impacted by sleep deprivation. Again,no simple diagnostic feature could be easily identified in the group- level analyses of the speech signals. We therefore developed a novel probing method, combining signal detection theory and noise activation of the classifier, to understand what made the classifier successful for each participant. This led to a diagnostic map for each participant, specifying which frequency region and modulation rates were impacted by sleep deprivation for this particular individual Conclusion In addition to suggesting a practical machine learning algorithm to detect sleep deprivation, combining our probing method with considerations about voice production could help uncover the physiological impact of sleep deprivation at the level of each individual. Support   </jats:sec