The Virtual Brain: a simulator of primate brain network dynamics

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.

[1]  Stephen Coombes,et al.  Large-scale neural dynamics: Simple and complex , 2010, NeuroImage.

[2]  B. A. Conway,et al.  The effects of laforin, malin, Stbd1, and Ptg deficiencies on heart glycogen levels in Pompe disease mouse models , 2015 .

[3]  M L Hines,et al.  Neuron: A Tool for Neuroscientists , 2001, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[4]  P A Robinson,et al.  Neural field theory of synaptic plasticity. , 2011, Journal of theoretical biology.

[5]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[6]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[7]  Hamid Soltanian-Zadeh,et al.  Multi-area neural mass modeling of EEG and MEG signals , 2010, NeuroImage.

[8]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[9]  Upinder S. Bhalla,et al.  PyMOOSE: Interoperable Scripting in Python for MOOSE , 2008, Frontiers in neuroinformatics.

[10]  T. Faniran Numerical Solution of Stochastic Differential Equations , 2015 .

[11]  G. Ermentrout Dynamic patterns: The self-organization of brain and behavior , 1997 .

[12]  S. Amari Homogeneous nets of neuron-like elements , 1975, Biological Cybernetics.

[13]  Xiao-Jing Wang,et al.  A Recurrent Network Mechanism of Time Integration in Perceptual Decisions , 2006, The Journal of Neuroscience.

[14]  Donald L Rowe,et al.  Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics. , 2004, Journal of theoretical biology.

[15]  W. J. Nowack Neocortical Dynamics and Human EEG Rhythms , 1995, Neurology.

[16]  John R. Terry,et al.  A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. , 2006, Cerebral cortex.

[17]  J Riera,et al.  Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[19]  Axel Hutt,et al.  Neural Fields with Distributed Transmission Speeds and Long-Range Feedback Delays , 2006, SIAM J. Appl. Dyn. Syst..

[20]  Klaus Schuch,et al.  PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python , 2008, Frontiers Neuroinformatics.

[21]  Roy,et al.  Fast, accurate algorithm for numerical simulation of exponentially correlated colored noise. , 1988, Physical review. A, General physics.

[22]  Marc-Oliver Gewaltig,et al.  NEST (NEural Simulation Tool) , 2007, Scholarpedia.

[23]  Scott Chacon,et al.  Pro Git , 2009, Apress.

[24]  K. Kaplan H. Haken, Synergetics. An Introduction. Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology (2nd Edition). XI + 355 S., 152 Abb. Berlin—Heidelberg—New York 1978. Springer-Verlag. DM 66,00 , 1980 .

[25]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

[26]  A. Pérez-Villalba Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

[27]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[28]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[29]  James J. Wright,et al.  Propagation and stability of waves of electrical activity in the cerebral cortex , 1997 .

[30]  Egon Wanke,et al.  Mapping brains without coordinates , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  Viktor K. Jirsa,et al.  The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging , 2013, Brain Connect..

[32]  H Haken Delay, noise and phase locking in pulse coupled neural networks. , 2001, Bio Systems.

[33]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[34]  S. Amari Dynamics of pattern formation in lateral-inhibition type neural fields , 1977, Biological Cybernetics.

[35]  G. Schneider Two visual systems. , 1969, Science.

[36]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[37]  Donald O. Walter,et al.  Mass action in the nervous system , 1975 .

[38]  Jose M. Sanchez-Bornot,et al.  Model driven EEG/fMRI fusion of brain oscillations , 2009, Human brain mapping.

[39]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[40]  Ravi S. Menon,et al.  On the characteristics of functional magnetic resonance imaging of the brain. , 1998, Annual review of biophysics and biomolecular structure.

[41]  Ingo Bojak,et al.  Axonal Velocity Distributions in Neural Field Equations , 2010, PLoS Comput. Biol..

[42]  Nelson J. Trujillo-Barreto,et al.  Biophysical model for integrating neuronal activity, EEG, fMRI and metabolism , 2008, NeuroImage.

[43]  S. Yoshizawa,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.

[44]  R. Whitehouse,et al.  Neurophysical Modeling of Brain Dynamics , 2003, Neuropsychopharmacology.

[45]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[46]  Romain Brette,et al.  The Brian Simulator , 2009, Front. Neurosci..

[47]  H. Haken,et al.  A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics , 1997 .

[48]  H. Haken Synergetics: an Introduction, Nonequilibrium Phase Transitions and Self-organization in Physics, Chemistry, and Biology , 1977 .

[49]  Viktor K Jirsa,et al.  Reduced representations of heterogeneous mixed neural networks with synaptic coupling. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[50]  David C. Van Essen,et al.  The future of the human connectome , 2012, NeuroImage.

[51]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[52]  Andreas Spiegler,et al.  Bifurcation Analysis of Neural Mass Models , 2010 .

[53]  J A Kelso,et al.  Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[54]  James A. Roberts,et al.  Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms , 2011, The Journal of Neuroscience.

[55]  A R McIntosh,et al.  The development of a noisy brain. , 2010, Archives italiennes de biologie.

[56]  K. Burrage,et al.  Numerical methods for strong solutions of stochastic differential equations: an overview , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[57]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[58]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[59]  Gustavo Deco,et al.  Structural connectivity allows for multi-threading during rest: The structure of the cortex leads to efficient alternation between resting state exploratory behavior and default mode processing , 2012, NeuroImage.

[60]  A. R. McIntosh,et al.  The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models , 2009, Journal of Neuroscience Methods.

[61]  Tom Davis,et al.  Opengl programming guide: the official guide to learning opengl , 1993 .

[62]  Timothy J. Blanche,et al.  Python for Large-Scale Electrophysiology , 2009, Front. Neuroinform..

[63]  Markus Diesmann,et al.  CoCoMac 2.0 and the future of tract-tracing databases , 2012, Front. Neuroinform..

[64]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[65]  R. Jindra Mass action in the nervous system W. J. Freeman, Academic Press, New York (1975), 489 pp., (hard covers). $34.50 , 1976, Neuroscience.

[66]  F. H. Lopes da Silva,et al.  Model of brain rhythmic activity , 1974, Kybernetik.

[67]  Romain Brette,et al.  Vectorized Algorithms for Spiking Neural Network Simulation , 2011, Neural Computation.

[68]  Walter J. Freeman,et al.  TUTORIAL ON NEUROBIOLOGY: FROM SINGLE NEURONS TO BRAIN CHAOS , 1992 .

[69]  D R Freestone,et al.  A data-driven framework for neural field modeling , 2011, NeuroImage.

[70]  Ben H. Jansen,et al.  A neurophysiologically-based mathematical model of flash visual evoked potentials , 2004, Biological Cybernetics.

[71]  J. R. Moorman,et al.  Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. , 2011, American journal of physiology. Heart and circulatory physiology.

[72]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[73]  Carlos H. Muravchik,et al.  Electrode and brain modeling in stereo-EEG , 2012, Clinical Neurophysiology.

[74]  R. Kötter,et al.  Connecting Mean Field Models of Neural Activity to EEG and fMRI Data , 2010, Brain Topography.

[75]  Karl J. Friston,et al.  Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics , 2003, Network.

[76]  Trevor Bekolay,et al.  A Large-Scale Model of the Functioning Brain , 2012, Science.

[77]  Thom F. Oostendorp,et al.  Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[78]  P. Nunez The brain wave equation: a model for the EEG , 1974 .

[79]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[80]  James M. Bower,et al.  Python as a Federation Tool for GENESIS 3.0 , 2012, PloS one.

[81]  James J. Wright,et al.  Effects of local feedback on dispersion of electrical waves in the cerebral cortex , 1999 .

[82]  P. Bressloff From invasion to extinction in heterogeneous neural fields , 2012, Journal of mathematical neuroscience.

[83]  Jean-Philippe Thiran,et al.  The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes , 2011, Front. Neuroinform..

[84]  Nelson J. Trujillo-Barreto,et al.  Realistically Coupled Neural Mass Models Can Generate EEG Rhythms , 2007, Neural Computation.

[85]  Viktor K. Jirsa,et al.  A Low Dimensional Description of Globally Coupled Heterogeneous Neural Networks of Excitatory and Inhibitory Neurons , 2008, PLoS Comput. Biol..

[86]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[87]  J. Cowan,et al.  A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.

[88]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[89]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[90]  Viktor K. Jirsa,et al.  Connectivity and dynamics of neural information processing , 2007, Neuroinformatics.

[91]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[92]  J. Hindmarsh,et al.  A model of neuronal bursting using three coupled first order differential equations , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[93]  Karl J. Friston,et al.  Dynamic causal modeling , 2010, Scholarpedia.

[94]  H. Haken,et al.  Field Theory of Electromagnetic Brain Activity. , 1996, Physical review letters.

[95]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[96]  Nadim Joni Shah,et al.  Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm , 2012, NeuroImage.

[97]  R. Leahy,et al.  EEG and MEG: forward solutions for inverse methods , 1999, IEEE Transactions on Biomedical Engineering.

[98]  P. Robinson,et al.  Prediction of electroencephalographic spectra from neurophysiology. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[99]  O. Sporns,et al.  Towards the virtual brain: network modeling of the intact and the damaged brain. , 2010, Archives italiennes de biologie.

[100]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[101]  Viktor K. Jirsa,et al.  Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..

[102]  Travis E. Oliphant,et al.  Guide to NumPy , 2015 .

[103]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[104]  David T. J. Liley,et al.  Simulation of electrocortical waves , 1995, Biological Cybernetics.

[105]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[106]  Gaël Varoquaux,et al.  Mayavi: 3D Visualization of Scientific Data , 2010, Computing in Science & Engineering.

[107]  David L Donoho,et al.  An invitation to reproducible computational research. , 2010, Biostatistics.

[108]  Viktor K. Jirsa,et al.  Spatiotemporal forward solution of the EEG and MEG using network modeling , 2002, IEEE Transactions on Medical Imaging.

[109]  Peter A. Robinson,et al.  Unified neurophysical model of EEG spectra and evoked potentials , 2002, Biological Cybernetics.

[110]  M. Hämäläinen Magnetoencephalography: A tool for functional brain imaging , 2005, Brain Topography.

[111]  R. Buxton,et al.  A Model for the Coupling between Cerebral Blood Flow and Oxygen Metabolism during Neural Stimulation , 1997, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[112]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[113]  Rolf Kötter,et al.  Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac Database , 2007, Neuroinformatics.

[114]  Ravi S. Menon,et al.  Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. , 1993, Biophysical journal.

[115]  M. Goodale,et al.  Separate visual pathways for perception and action , 1992, Trends in Neurosciences.

[116]  H. Dinse,et al.  Repetitive tactile stimulation changes resting-state functional connectivity—implications for treatment of sensorimotor decline , 2012, Front. Hum. Neurosci..

[117]  Karl J. Friston,et al.  Mechanisms of evoked and induced responses in MEG/EEG , 2006, NeuroImage.

[118]  R. L. Beurle Properties of a mass of cells capable of regenerating pulses , 1956, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[119]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[120]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[121]  Romain Brette,et al.  Neuroinformatics Original Research Article Brian: a Simulator for Spiking Neural Networks in Python , 2022 .

[122]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[123]  Viktor K Jirsa,et al.  Neural Population Modes Capture Biologically Realistic Large Scale Network Dynamics , 2011, Bulletin of mathematical biology.

[124]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[125]  D. Liley,et al.  Understanding the Transition to Seizure by Modeling the Epileptiform Activity of General Anesthetic Agents , 2005, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[126]  P. Kloeden,et al.  Numerical Solution of Stochastic Differential Equations , 1992 .

[127]  J A Scott Kelso,et al.  Synchrony and clustering in heterogeneous networks with global coupling and parameter dispersion. , 2004, Physical review letters.

[128]  Xiao-Jing Wang,et al.  What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance. , 2003, Journal of neurophysiology.

[129]  Karl J. Friston,et al.  Dynamic causal modeling with neural fields , 2012, NeuroImage.

[130]  Karl J. Friston,et al.  Neural fields, spectral responses and lateral connections , 2011, NeuroImage.

[131]  Katrin Amunts,et al.  Locating the functional and anatomical boundaries of human primary visual cortex , 2009, NeuroImage.