Brain simulation as a cloud service: The Virtual Brain on EBRAINS

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation

Large scale modeling of the mouse brain dynamics

International audienceModeling the mouse whole-brain dynamics is viable using the paradigm of brain network models based on the structural connectivity of the mouse brain. This approach is implemented in the neuroinformatic platform The VirtualBrain (TVB) [1], where the tracing data from Allen Institute [2] can be combined with a range of neural mass models [3]. Another common strategy is based on spiking neural networks and uses experimental data at the cellular level for the parameters, leading to bottom-up models such as the Blue Brain project. The former are better suited for study from dynamical systems viewpoint, while the complexity of the latter makes them more physiologicalplausible.In this work we bridge the two approaches, by building a connectome based large-scale brain network model, where each region contains a population or a surface of spiking neurons, thus allowing a direct link to neuroimaging data, while increasing the biological realism. As a first step we use our modeling paradigm to analyze the impact of heterogeneous connectivity on the network synchronisation. For this, we reproduce earlier analysis of FitzHugh-Nagumo neurons on a torus[4], using adaptive exponential integrate and fire neurons.This is a more complex and realistic neuron model [5] and it is implemented with the Nest simulator [6] After this, we analyze the dynamics of the whole-brain model, and we compare the simulated activity with experimental results, with a focus on different metrics of functional connectivity. This allows us to link the results at the brain activity levels to the spiking neural networks, and to validate the model by using functional data. Hence, the new modelling approach allows bridging from neural mass model to spiking neural networks and using the advantages of the macro- and meso-scopic scales.Reference:[1]P. Sanz Leon et al., “The Virtual Brain: a simulator of primate brain network dynamics,” Front. Neuroinform., vol. 7, 2013.[2]S. W. Oh et al., “A mesoscale connectome of the mouse brain,” Nature, vol. 508, no. 7495, pp. 207-214, Apr. 2014.[3]F. Melozzi, M. M. Woodman, V. K. Jirsa, and C. Bernard, “The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics,” eNeuro, vol. 4, no. 3, p. ENEURO.0111-17.2017, May 2017.[4]V. K. Jirsa and R. A. Stefanescu, “Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics,” Bulletin of Mathematical Biology, vol. 73, no. 2, pp. 325-343, Feb. 2011.[5]J. Touboul and R. Brette, “Dynamics and bifurcations of the adaptive exponential integrate-and-fire model,” Biol Cybern, vol. 99, no. 4-5, p. 319, Nov. 2008.[6]Peyser, Alexander et al. (2017). NEST 2.14.0. Zenodo

Large scale modeling of the mouse brain dynamics

International audienceModeling the mouse whole-brain dynamics with spiking neural networks can be performed using bottom-up models such as the Blue Brain project [1]. Another strategy is using the paradigm of brain network models based on the structural connectivity of the mouse brain, i.e. its connectome. This approach is implemented in the neuroinformatic platform The Virtual Brain (TVB) [2], where the tracing data from Allen Institute [3] can be combined with a range of neural mass models [4]. The latter are better suited for study from dynamical systems viewpoint, while the complexity of the former makes them more physiological plausible.In this work we bridge the two approaches, by building a connectome based large-scale brain network model, where each region contains a population or surface of spiking neurons, thus allowing direct link to neuroimaging data, while increasing the biological realism.As a first step, we use our modeling paradigm to analyze the impact of heterogeneous connectivity on the network synchronisation. Similar analysis has already been performed using FitzHugh–Nagumo network on a torus [5]. We reproduce the results using adaptive exponential integrate and fire neurons, which is a more complex and realistic neuron model [6]. After this, we analyze the dynamics of the whole-brain model, and we compare the simulated activity with experimental results, with a focus on different metrics of functional connectivity. This allows us to link the results at the brain activity levels to the spiking neural networks, and to validate the model by using functional data. Hence, the new modelling approach allows bridging from neural mass model to spiking neural networks.References1. Blue brain [https://www.epfl.ch/research/domains/bluebrain/]2. Sanz Leon, Paula, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony R. McIntosh, and Viktor Jirsa. The Virtual Brain: A Simulator of Primate Brain Network Dynamics. Frontiers in Neuroinformatics 7 (2013). https://doi.org/10.3389/fninf.2013.00010.3. Oh, Seung Wook, Julie A. Harris, Lydia Ng, Brent Winslow, Nicholas Cain, Stefan Mihalas, Quanxin Wang, et al. A Mesoscale Connectome of the Mouse Brain Nature 508, no. 7495 (April 2014): 207–14. https://doi.org/10.1038/nature13186.4. Melozzi, Francesca, Marmaduke M. Woodman, Viktor K. Jirsa, and Christophe Bernard The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics ENEURO.0111-17.2017.https://doi.org/10.1523/ENEURO.0111-17.2017.5. Viktor K. Jirsa and Roxana A. Stefanescu, Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics Bulletin of Mathematical Biology 73, no. 2 (February 2011): 325–43, 2011.http://doi.org/10.1007/s11538-010-9573-96. Touboul, Jonathan, and Romain Brette. Dynamics and Bifurcations of the Adaptive Exponential Integrate-and-Fire Model Biological Cybernetics 99, no. 4–5 (November 1, 2008): 319. https://doi.org/10.1007/s00422-008-0267-4

Multiscale cosimulation design template for neuroscience applications

Integration of information across heterogeneous sources creates added scientific value. It is, however, a challenge to progress, often a barrier, to interoperate data, tools and models across spatial and temporal scales. Here we present a design template for coupling simulators operating at different scales and enabling co-simulation. We illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level to address mechanistic questions, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same Multiscale cosimulation design template for neuroscience applications simulation framework and validate them against multiscale experiments, thereby largely widening the explanatory power of computational models

Co-simulation framework for brain simulations: a multi-scale mouse brain model with TVB 1 and NEST

International audienceIntegration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models

The Virtual Epileptic Patient Workflow

We developed a workflow of Virtual Epileptic Patient (VEP) brain model to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients with drug-resistant epilepsy (Jirsa et al., 2017

A comprehensive neural simulation of slow-wave sleep and highly responsive wakefulness dynamics

Hallmarks of neural dynamics during healthy human brain states span spatial scales from neuromodulators acting on microscopic ion channels to macroscopic changes in communication between brain regions. Developing a scale-integrated understanding of neural dynamics has therefore remained challenging. Here, we perform the integration across scales using mean-field modeling of Adaptive Exponential (AdEx) neurons, explicitly incorporating intrinsic properties of excitatory and inhibitory neurons. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. We report that when AdEx mean-field neural populations are connected via structural tracts defined by the human connectome, macroscopic dynamics resembling human brain activity emerge. Importantly, the model can qualitatively and quantitatively account for properties of empirically observed spontaneous and stimulus-evoked dynamics in space, time, phase, and frequency domains. Large-scale properties of cortical dynamics are shown to emerge from both microscopic-scale adaptation that control transitions between wake-like to sleep-like activity, and the organization of the human structural connectome; together, they shape the spatial extent of synchrony and phase coherence across brain regions consistent with the propagation of sleep-like spontaneous traveling waves at intermediate scales. Remarkably, the model also reproduces brain-wide, enhanced responsiveness and capacity to encode information particularly during wake-like states, as quantified using the perturbational complexity index. The model was run using The Virtual Brain (TVB) simulator, and is open-access in EBRAINS. This approach not only provides a scale-integrated understanding of brain states and their underlying mechanisms, but also open access tools to investigate brain responsiveness, toward producing a more unified, formal understanding of experimental data from conscious and unconscious states, as well as their associated pathologie

Data_Sheet_1_Multiscale co-simulation design pattern for neuroscience applications.pdf

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.</p

Brain simulation as a cloud service: the Virtual Brain on EBRAINS

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation.We acknowledge support by H2020 Research and Innovation Action grants Human Brain Project SGA2 785907, SGA3 945539, ICEI 800858, VirtualBrainCloud 826421 and ERC 683049; Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative. Several computations have also been performed on the HPC for Research cluster of the Berlin Institute of Health. We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858. German Research Foundation SFB 1436 (project ID 425899996); SFB 1315 (project ID 327654276); SFB 936 (project ID 178316478); SFB-TRR 295 (project ID 424778381); SPP Computational Connectomics RI 2073/6–1, RI 2073/10–2, RI 2073/9–1

Brain Modelling as a Service: The Virtual Brain on EBRAINS

The Virtual Brain (TVB) is now available as open-source cloud ecosystem on EBRAINS, a shared digital research platform for brain science. It offers services for constructing, simulating and analysing brain network models (BNMs) including the TVB network simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional connectomes; multiscale co-simulation of spiking and large-scale networks; a domain specific language for automatic high-performance code generation from user-specified models; simulation-ready BNMs of patients and healthy volunteers; Bayesian inference of epilepsy spread; data and code for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability and clinical translation