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

open access articleThe 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 con- version 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 collabo- ration 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

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

TVB-EduPack: An interactive learning and scripting platform for The Virtual Brain

The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

AbstractLarge amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface

Collective Neurofeedback in an Immersive Art Environment

While human brains are specialized for complex and variable real world tasks, most neuroscience studies reduce environmental complexity, which limits the range of behaviours that can be explored. Motivated to overcome this limitation, we conducted a large-scale experiment with electroencephalography (EEG) based brain-computer interface (BCI) technology as part of an immersive multi-media science-art installation. Data from 523 participants were collected in a single night. The exploratory experiment was designed as a collective computer game where players manipulated mental states of relaxation and concentration with neurofeedback targeting modulation of relative spectral power in alpha and beta frequency ranges. Besides validating robust time-of- night effects, gender differences and distinct spectral power patterns for the two mental states, our results also show differences in neurofeedback learning outcome. The unusually large sample size allowed us to detect unprecedented speed of learning changes in the power spectrum (~ 1 min). Moreover, we found that participants' baseline brain activity predicted subsequent neurofeedback beta training, indicating state-dependent learning. Besides revealing these training effects, which are relevant for BCI applications, our results validate a novel platform engaging art and science and fostering the understanding of brains under natural conditions

Brain network, modelling and corresponding EEG patterns for health and disease states

EEG is a significant tool used to capture normal and abnormal cerebral electrical activities in human brain. To understand and test complex hypotheses about the mechanisms of their generation, various model and modelling approaches have been proposed and developed. Among these models and approaches, a new type of network model has emerged known as large-scale brain network model (LSBNM). LSBNM is becoming increasingly important in understanding, studying and testing the mechanisms of the generation of normal and abnormal oscillatory activities of the human brain. It also offers unique predictive tools for studying disease states and brain abnormalities. However, there are still many limitations in the existing LSBNM approaches. Hence, developing novel methods for LSBNM leads to the exploration, generation and prediction of a new and rich repertoire of healthy and disease rhythmic activities in the human brain. The aim of this project is to develop LSBNM to include new versions of network models comprising various human cerebral areas in the left and right hemispheres. First, two network models at multi scale are developed to generate EEG patterns for health states: alpha rhythms with a low frequency at 7Hz and, and the alpha band of EEG rhythms at different ranges of frequencies 7–8 Hz, 8 9 Hz and 10–11 Hz. Second, a new network model for simulating multi-bands of EEG patterns: delta–range frequency of (1-4 Hz), theta at a frequency of (4-7Hz) and diverse narrowband oscillations ranging from delta to theta (0-5Hz) is introduced. Third, novel brain network models are simulated and used to predict the abnormal electrical activity such as oscillations observed in the epileptic brain. The design and simulation of each of the network models are implemented using the unique neuro informatics platform: The Virtual Brain (TVB). This project made significant contributions to brain modelling, in particularly to the understanding of neural activity in the human brain at multi levels of scale. Further, it emphasises the role of structural connectivity of the connectome on emerging normal and abnormal dynamics of brain oscillations, as well as affirming that modelling with TVB can provide reliable neuroimaging data such as EEGS for the healthy and diseased brain. In particular, the results of this study help researchers and physicians studying large-scale brain activity associated with lower and higher alpha oscillations and the delta waves of Stages 3 and 4 of the sleep and theta waves of Stages 1 and 2 of sleep. Moreover, they will be able to assist researchers and clinical doctors in the field of epilepsy to understand the complex neural mechanisms generating abnormal oscillatory activities and, thus, may open up new avenues towards the discovery of new clinical interventions related to these types of activities

Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging

The value of in vivo preclinical diffusion MRI (dMRI) is substantial. Small-animal dMRI has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. Many of the influential works in this field were first performed in small animals or ex vivo samples. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the data. This work aims to serve as a reference, presenting selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. In each section, we also highlight areas for which no guidelines exist (and why), and where future work should focus. We first describe the value that small animal imaging adds to the field of dMRI, followed by general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss how they are appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, imaging sequences and data processing, including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl

Simuladores cerebrales: revisión de modelos micro- y macroescala

Las simulaciones de redes cerebrales pretenden comprender las funciones del cerebro tanto en condiciones normales como patológicas. A tal fin, existen en la actualidad múltiples simuladores y paquetes software pertenecientes al ámbito de la neurociencia computacional. El objetivo final de los modelos computacionales consiste en tratar de explicar la relación entre la estructura, la función y la dinámica cerebrales. Los modelos multiescala trabajan con datos biológicos de distinto tipo y granularidad, en un rango que va desde modelos de neuronas, sinapsis y microcircuitos -microescala- hasta modelos a macroescala con cerebros virtuales. En el presente trabajo se pretende realizar una revisión de los modelos microescala y macroescala, resaltando sus principales características y funcionalidades y su orientación para investigación. Finalmente, se trabajará experimentalmente con un modelo macroescala, para el cual se elaborará una guía de usuario.Grado en Ingeniería Biomédic

Simulation and Theory of Large-Scale Cortical Networks

Cerebral cortex is composed of intricate networks of neurons. These neuronal networks are strongly interconnected: every neuron receives, on average, input from thousands or more presynaptic neurons. In fact, to support such a number of connections, a majority of the volume in the cortical gray matter is filled by axons and dendrites. Besides the networks, neurons themselves are also highly complex. They possess an elaborate spatial structure and support various types of active processes and nonlinearities. In the face of such complexity, it seems necessary to abstract away some of the details and to investigate simplified models. In this thesis, such simplified models of neuronal networks are examined on varying levels of abstraction. Neurons are modeled as point neurons, both rate-based and spike-based, and networks are modeled as block-structured random networks. Crucially, on this level of abstraction, the models are still amenable to analytical treatment using the framework of dynamical mean-field theory. The main focus of this thesis is to leverage the analytical tractability of random networks of point neurons in order to relate the network structure, and the neuron parameters, to the dynamics of the neurons—in physics parlance, to bridge across the scales from neurons to networks. More concretely, four different models are investigated: 1) fully connected feedforward networks and vanilla recurrent networks of rate neurons; 2) block-structured networks of rate neurons in continuous time; 3) block-structured networks of spiking neurons; and 4) a multi-scale, data-based network of spiking neurons. We consider the first class of models in the light of Bayesian supervised learning and compute their kernel in the infinite-size limit. In the second class of models, we connect dynamical mean-field theory with large-deviation theory, calculate beyond mean-field fluctuations, and perform parameter inference. For the third class of models, we develop a theory for the autocorrelation time of the neurons. Lastly, we consolidate data across multiple modalities into a layer- and population-resolved model of human cortex and compare its activity with cortical recordings. In two detours from the investigation of these four network models, we examine the distribution of neuron densities in cerebral cortex and present a software toolbox for mean-field analyses of spiking networks

The role of network directionality in the brain

Network science is a vast interdisciplinary area which connects disparate subjects such as mathematics, the natural sciences, sociology, information technology and more. Network neuroscience, in particular, is a thriving and rapidly expanding field in which graph theory techniques have been deployed to better understand structure-function relations in the brain across multiple temporal and spatial scales. In this thesis, we use large-scale brain network models for a range of different species (cat, Macaque monkey and C.elegans) to simulate important aspects of brain function, such as associative memory and synchrony related activities. Network directionality is a fundamental feature of such models, yet it is typically ignored due to limitations of non-invasive imaging techniques. Here, we explore the role that directionality plays in determining neural activity in the brain. We start by considering a system of Hopfield neural elements with heterogeneous structural connectivity given by range of species and parcellations for which network directionality information is present. We investigate the effect of removing directionality of connections on brain capacity, which we quantify via its ability to store attractor states. In addition to determining large numbers of fixed-point attractor sets, we deploy the recently developed basin stability technique in order to assess the global stability of such brain states as well as their robustness to non-small perturbations. By comparison with standard network models with the same coarse statistics, we find that directionality effects not only the number of fixed-point attractors but also the likelihood that neural systems remain in their most 'desirable' states. These findings suggest that directionality plays an important role in shaping transition routes between different brain networks states. We then go onto consider the impact that network directionality has on the synchrony properties of the brain. We simulate neural dynamics on the aforementioned connectome-based networks deploying a phase delayed Kuramoto Model, which is perhaps the simplest example of a delay coupled oscillatory network and is well-suited to assessing how directed connectomes govern synchronisation properties of the brain. In particular, we find that network directionality profoundly impacts both the time-scale at which coordinated rhythmic activity occurs across large-scale brain networks as well as the stability properties of these synchronised states. We also find that recently observed relations between network structure and directed functional connectivity, as quantified using the directed phase lag index, appear far less conclusive when network directionality is accounted for. This study thereby emphasizes the substantial role network directionality plays in shaping the brain’s ability to both store and process information