Shift work, long work hours, and operational tasks contribute to sleep and circadian disruption in defence personnel, with profound impacts on cognition. To address this, a digital technology, the SleepSync app, was designed for use in defence. A pre-post design study was undertaken to examine whether four weeks app use improved sleep and cognitive fitness (high performance neurocognition) in a cohort of shift workers from the Royal Australian Air Force. In total, 13 of approximately 20 shift-working personnel from one base volunteered for the study. Sleep outcomes were assessed using the Insomnia Severity Index (ISI), the Patient-Reported Outcomes Measurement Information System (PROMIS), Sleep Disturbance and Sleep-Related Impairment Scales, the Glasgow Sleep Effort Scale, the Sleep Hygiene Index, and mental health was assessed using the Depression, Anxiety, and Stress Scale-21. Sustained attention was measured using the 3-min Psychomotor Vigilance Task (PVT) and controlled response using the NBack. Results showed significant improvements in insomnia (ISI scores 10.31 at baseline and 7.50 after app use), sleep-related impairments (SRI T-scores 53.03 at baseline to 46.75 post-app use), and healthy sleep practices (SHI scores 21.61 at baseline to 18.83 post-app use; all p < 0.001). Trends for improvement were recorded for depression. NBack incorrect responses reduced significantly (9.36 at baseline; reduced by −3.87 at last week of app use, p < 0.001), but no other objective measures improved. These findings suggest that SleepSync may improve sleep and positively enhance cognitive fitness but warrants further investigation in large samples. Randomised control trials with other cohorts of defence personnel are needed to confirm the utility of this intervention in defence settings.

Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts.

Abstract Study Objectives The study aimed to, for the first time, (1) compare sleep, circadian phase, and alertness of intensive care unit (ICU) nurses working rotating shifts with those predicted by a model of arousal dynamics; and (2) investigate how different environmental constraints affect predictions and agreement with data. Methods The model was used to simulate individual sleep-wake cycles, urinary 6-sulphatoxymelatonin (aMT6s) profiles, subjective sleepiness on the Karolinska Sleepiness Scale (KSS), and performance on a Psychomotor Vigilance Task (PVT) of 21 ICU nurses working day, evening, and night shifts. Combinations of individual shift schedules, forced wake time before/after work and lighting, were used as inputs to the model. Predictions were compared to empirical data. Simulations with self-reported sleep as an input were performed for comparison. Results All input constraints produced similar prediction for KSS, with 56%–60% of KSS scores predicted within ±1 on a day and 48%–52% on a night shift. Accurate prediction of an individual’s circadian phase required individualized light input. Combinations including light information predicted aMT6s acrophase within ±1 h of the study data for 65% and 35%–47% of nurses on diurnal and nocturnal schedules. Minute-by-minute sleep-wake state overlap between the model and the data was between 81 ± 6% and 87 ± 5% depending on choice of input constraint. Conclusions The use of individualized environmental constraints in the model of arousal dynamics allowed for accurate prediction of alertness, circadian phase, and sleep for more than half of the nurses. Individual differences in physiological parameters will need to be accounted for in the future to further improve predictions.

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

Event Abstract Back to Event Abstract Field: A Python Module For Neural Field Modelling Paula Sanz-Leon1, 2* and Stuart A. Knock3 1 The University Of Sydney, School of Physics, Australia 2 The University Of Sydney, Centre of Integrative Brain Function, Australia 3 QIMR Berghofer, Systems Neuroscience Group, Australia In this work we present two main contributions: the first one is a Python implementation of the discrete approximation of the Laplace-Beltrami operator (LBO) (Belkin et al., 2008) allowing us to solve Robinson's neural field model (Robinson et al., 2002) on a curved (cortical) surface; the second contribution is a generalised implementation of the aforementioned neural field model which we call Abstract Field Representation (AFR). With this simplified dynamical model one can also express the spatially homogeneous case presented in previous work (Robinson et al., 2003; Breakspear et al., 2006) where various brain states and diseases are studied (e. g., rest, sleep and epilepsy). Many other variants may be defined with the AFR thanks to vector operations and spatialization of parameters. These two components constitute an open-source module for neural field modelling that can be seamlessly integrated in the multiscale hierarchy of The Virtual Brain (TVB) simulator (Sanz-Leon et al., 2013; Sanz-Leon et al., 2015), which is an established user-ready software (Gewaltig and Cannon, 2014). From a systems neuroscience point of view, our contribution favours the integration of multiple approaches to large-scale brain modelling that may lead to more biophysically realistic explanations of how our brains work and interact with the world. From a neuroinformatics perspective, we think this is a valuable addition to the ecosystem of software for meso- and macroscopic brain modelling. Indeed, having different computational models under the same numerical framework is, mildly put, desirable. In simple words, this module represents a way to compare and validate a range of computational models for large scale brain modelling in a systematic way. Lastly, we hope it will assist in the creation of very much needed standard tests in computational neuroscience (Gewaltig and Cannon, 2014). References Belkin, M. and Wang, J. S. (2008). Discrete Laplace operator on meshed surfaces. Proc. 24th Ann Symp On Comp Geom. 278–287 Robinson, P.A, Rennie, C.J and Rowe, D.L. (2002). Dynamics of large-scale brain activity in normal arousal states and epileptic seizures, Phys Revi E. 65:4. 041924 (1-9) Breakspear, M., Roberts, J.A, Terry, J.T, Rodrigues, S., Mahant, N. and Robinson, P.A. (2006). A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cereb Cor. 16:9. 1296-1313 Robinson, P., Rennie, C., Rowe, D., O'Connor, S., Wright, J., Gordon, E., Whitehouse, R. (2003). Neurophysical Modeling of Brain Dynamics. Neuropsychopharmacology. 28. S74-S79. Sanz-Leon, P., Knock, S. A, Spiegler, A. and Jirsa, V. K. (2015). Mathematical framework for large-scale brain network modeling in The Virtual Brain. NeuroIm. 111. 385-430. Sanz-Leon, P., Knock, S.A, Woodman, M.M, Domide, L., Mersmann, J., McIntosh, A.R, Jirsa, V.K. (2013). The Virtual Brain: a simulator of primate brain network dynamics. Frontiers in Neuroinformatics. 7:10. doi: 10.3389/fninf.2013.00010 Gewaltig, M. O and Cannon, R. (2014). Current Practice in Software Development for Computational Neuroscience and How to Improve It. Plos Comp Bio. doi: 10.1371/journal.pcbi.1003376 Keywords: neural field model, large-scale modeling, python, virtual brain, Brain Network Models Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015. Presentation Type: Poster, to be considered for oral presentation Topic: Large-scale modeling Citation: Sanz-Leon P and Knock SA (2015). Abstract Field: A Python Module For Neural Field Modelling. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00039 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 31 May 2015; Published Online: 05 Aug 2015. * Correspondence: Dr. Paula Sanz-Leon, The University Of Sydney, School of Physics, Sydney, NSW, 2006, Australia, paula.sanz-leon@sydney.edu.au Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. 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In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.

Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals.

TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting.

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

Event Abstract Back to Event What does the brain do when it does nothing? Stuart Knock1* 1 Hospital Carlos Haya, Avenida Carlos Haya 82, Laboratorio de Medicina Regenerativa, Fundación IMABIS, Spain Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor and cognitive paradigms. However, even at rest, in absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anaesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100Hz, commonly observed in electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1Hz, observed in functional magnetic resonance imaging (fMRI). The combination of anatomical structure and time delays creates a space-time structure, in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire. Conference: 3rd Mediterranean Conference of Neuroscience , Alexandria, Egypt, 13 Dec - 16 Dec, 2009. Presentation Type: Oral Presentation Topic: Symposium 01 – Neural dynamics, learning and functional recovery Citation: Knock S (2009). What does the brain do when it does nothing?. Front. Neurosci. Conference Abstract: 3rd Mediterranean Conference of Neuroscience . doi: 10.3389/conf.neuro.01.2009.16.008 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Nov 2009; Published Online: 18 Nov 2009. * Correspondence: Stuart Knock, Hospital Carlos Haya, Avenida Carlos Haya 82, Laboratorio de Medicina Regenerativa, Fundación IMABIS, 13331 Marseille, Spain, stuart.knock@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Stuart Knock Google Stuart Knock Google Scholar Stuart Knock PubMed Stuart Knock Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

A disturbance in the interactions between distributed cortical regions may underlie the cognitive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia "disconnection hypothesis." This is achieved by analyzing the spatiotemporal structure of resting state scalp EEG data previously acquired from 40 young subjects with a recent first episode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the topography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of "cliquishness"), and hub structure of these graphs were used as metrics of the underlying brain network activity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths--features consistent with a "small-world" topology--as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still "small-world," these effects are consistent with a subtle randomization in the underlying network architecture--likely associated with a greater number of links connecting disparate clusters. This randomization may underlie the cognitive disturbances characteristic of schizophrenia.

Brain imaging sciences, like neurosciences in general, have predominantly been an empirical endeavour. This paper argues that the maturation of "kinetic models" of large-scale neuronal activity will provide a unifying theory to underpin brain imaging sciences. In particular, this framework will provide a means of unifying data from different imaging modalities, afford a direct link with cognitive theories of brain function, equip researchers with novel data analysis methodologies and underpin a dialogue in which theoretical formalisms are iteratively refined or refuted through empirical studies. Three steps are crucial to this endeavour: (1) The extension of models of spiking neural ensembles (where the states of all neurons are specified) to statistical models of neural "masses" (where only a few moments of the distribution of states are specified); (2) The refinement of "forward models", such as neurovascular coupling, which map neuronal states to observables; and (3) A theory which links the distribution of neuronal states to cognitive operations, hence informing cognitive neuroscience experiments. We provide illustrative examples of kinetic models of neuronal dynamics at the mesoscopic scale, focusing on the manner by which sensory inputs modify the expression of ongoing "background" activity. The paper concludes with an overview of some of the cutting edge developments in kinetic models of brain activity.

Type II solar radio bursts are produced by shock waves moving through the corona and solar wind. An existing theory predicts Type II dynamic spectra by considering electron acceleration at the shock, growth of Langmuir waves, and their conversion into radiation. Two contributions are presented, both relevant to space weather research and missions like STEREO. First, a more realistic 2-dimensional model for the solar wind is developed, based on spacecraft data at 1 AU, and illustrated for a specific period. It relates time to longitude and extrapolates to different heliocentric distances using MHD style equations and power-law temperature models. Second, the type II theory is combined with the data-driven solar wind model to predict the dynamic spectrum of a specific Type II burst. The results show reasonable semiquantitative agreement with observations. Issues and possible improvements are outlined.