Lives saved by public health restrictions over the Victorian COVID-19 Delta variant epidemic wave, Aug-Nov 2021.

BACKGROUND: Prior to mid-2021, Australia's approach to COVID-19 was to eliminate community transmission. However, between August-November 2021, the state of Victoria, Australia, experienced an outbreak of the Delta variant that continued to grow despite extensive lockdowns and public health measures in place. While these public health restrictions were ultimately unable to stop community transmission, they likely had a major impact reducing transmission and adverse health outcomes relative to voluntary risk-mitigation only (e.g., in response to rising cases and deaths, some people may avoid crowded settings, hospitality, retail, social occasions, or indoor settings). This study aims to estimate the impact of the August-November 2021 enforced public health restrictions in Victoria, compared to voluntary risk-mitigation only. METHODS: An agent-based model was calibrated to Victorian epidemiological, health and behavioural data from 1 August to 30 November 2021, as well as policies that were implemented over that period. Two counter-factual scenarios were run for the same period with (a) no restrictions in place; or (b) voluntary risk-mitigation only, based on behaviour measured over the December-January Omicron BA.1 epidemic wave when restrictions were not in place. RESULTS: Over August-November 2021, the baseline model scenario resulted in 97,000 (91,000-102,000) diagnoses, 9100 (8500-9700) hospital admissions, and 480 (430-530) deaths. Without any restrictions in place, there were 3,228,000 (3,200,000-3,253,000) diagnoses, 375,100 (370,200-380,900) hospital admissions, and 16,700 (16,000-17,500) deaths. With voluntary risk-mitigation equal to those observed during the Omicron BA.1 epidemic wave, there were 1,507,000 (1,469,000-1,549,000) diagnoses, 130,300 (124,500-136,000) hospital admissions, and 5500 (5000-6100) deaths. CONCLUSION: Public health restrictions implemented in Victoria over August-November 2021 are likely to have averted more than 120,000 hospitalizations and 5000 deaths relative to voluntary risk-mitigation only. During a COVID-19 epidemic wave voluntary behaviour change can reduce transmission substantially, but not to the same extent as enforced restrictions.

Transient spectral events in resting state MEG predict individual task responses.

Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N â€‹= â€‹89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.

Bayesian Optimisation of Large-Scale Biophysical Networks.

The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties (e.g. band-specific functional connectivity); and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. Using five parameters controlling key aspects of our model, we find that this method is able to converge to regions of high functional similarity with real MEG data, with very few samples given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.

Real-time automated EEG tracking of brain states using neural field theory.

A real-time fitting system is developed and used to fit the predictions of an established physiologically-based neural field model to electroencephalographic spectra, yielding a trajectory in a physiological parameter space that parametrizes intracortical, intrathalamic, and corticothalamic feedbacks as the arousal state evolves continuously over time. This avoids traditional sleep/wake staging (e.g., using Rechtschaffen-Kales stages), which is fundamentally limited because it forces classification of continuous dynamics into a few discrete categories that are neither physiologically informative nor individualized. The classification is also subject to substantial interobserver disagreement because traditional staging relies in part on subjective evaluations. The fitting routine objectively and robustly tracks arousal parameters over the course of a full night of sleep, and runs in real-time on a desktop computer. The system developed here supersedes discrete staging systems by representing arousal states in terms of physiology, and provides an objective measure of arousal state which solves the problem of interobserver disagreement. Discrete stages from traditional schemes can be expressed in terms of model parameters for backward compatibility with prior studies.

Physiologically based arousal state estimation and dynamics.

A neural field model of the brain is used to represent brain states using physiologically based parameters rather than arbitrary, discrete sleep stages. Each brain state is represented as a point in a physiologically parametrized space. Over time, changes in brain state cause these points to trace continuous trajectories, unlike the artificial discrete jumps in sleep stage that occur with traditional sleep staging. The discrete Rechtschaffen and Kales sleep stages are associated with regions in the physiological parameter space based on their electroencephalographic features, which enables interpretation of traditional sleep stages in terms of physiological trajectories. Wake states are found to be associated with strong positive corticothalamic feedback compared to sleep. The existence of physiologically valid trajectories between brain states in the model is demonstrated. Actual trajectories for an individual can be determined by fitting the model using EEG alone, and enable analysis of the physiological differences between subjects.

Experimental observation of a theoretically predicted nonlinear sleep spindle harmonic in human EEG.

OBJECTIVE: To investigate the properties of a sleep spindle harmonic oscillation previously predicted by a theoretical neural field model of the brain. METHODS: Spindle oscillations were extracted from EEG data from nine subjects using an automated algorithm. The power and frequency of the spindle oscillation and the harmonic oscillation were compared across subjects. The bicoherence of the EEG was calculated to identify nonlinear coupling. RESULTS: All subjects displayed a spindle harmonic at almost exactly twice the frequency of the spindle. The power of the harmonic scaled nonlinearly with that of the spindle peak, consistent with model predictions. Bicoherence was observed at the spindle frequency, confirming the nonlinear origin of the harmonic oscillation. CONCLUSIONS: The properties of the sleep spindle harmonic were consistent with the theoretical modeling of the sleep spindle harmonic as a nonlinear phenomenon. SIGNIFICANCE: Most models of sleep spindle generation are unable to produce a spindle harmonic oscillation, so the observation and theoretical explanation of the harmonic is a significant step in understanding the mechanisms of sleep spindle generation. Unlike seizures, sleep spindles produce nonlinear effects that can be observed in healthy controls, and unlike the alpha oscillation, there is no linearly generated harmonic that can obscure nonlinear effects. This makes the spindle harmonic a good candidate for future investigation of nonlinearity in the brain.

Prediction and verification of nonlinear sleep spindle harmonic oscillations.

This paper examines nonlinear effects in a neural field model of the corticothalamic system to predict the EEG power spectrum of sleep spindles. Nonlinearity in the thalamic relay nuclei gives rise to a spindle harmonic visible in the cortical EEG. By deriving an analytic expression for nonlinear spectrum, the power in the spindle harmonic is predicted to scale quadratically with the power in the spindle oscillation. By isolating sleep spindles from background sleep in experimental EEG data, the spindle harmonic is directly observed.

Physiologically based quantitative modeling of unihemispheric sleep.

Unihemispheric sleep has been observed in numerous species, including birds and aquatic mammals. While knowledge of its functional role has been improved in recent years, the physiological mechanisms that generate this behavior remain poorly understood. Here, unihemispheric sleep is simulated using a physiologically based quantitative model of the mammalian ascending arousal system. The model includes mutual inhibition between wake-promoting monoaminergic nuclei (MA) and sleep-promoting ventrolateral preoptic nuclei (VLPO), driven by circadian and homeostatic drives as well as cholinergic and orexinergic input to MA. The model is extended here to incorporate two distinct hemispheres and their interconnections. It is postulated that inhibitory connections between VLPO nuclei in opposite hemispheres are responsible for unihemispheric sleep, and it is shown that contralateral inhibitory connections promote unihemispheric sleep while ipsilateral inhibitory connections promote bihemispheric sleep. The frequency of alternating unihemispheric sleep bouts is chiefly determined by sleep homeostasis and its corresponding time constant. It is shown that the model reproduces dolphin sleep, and that the sleep regimes of humans, cetaceans, and fur seals, the latter both terrestrially and in a marine environment, require only modest changes in contralateral connection strength and homeostatic time constant. It is further demonstrated that fur seals can potentially switch between their terrestrial bihemispheric and aquatic unihemispheric sleep patterns by varying just the contralateral connection strength. These results provide experimentally testable predictions regarding the differences between species that sleep bihemispherically and unihemispherically.