The Virtual Mouse Brain: a computational neuroinformatics platform to study whole mouse brain dynamics

Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain’s structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain to whole mouse brain network modeling based on individual diffusion Magnetic Resonance Imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. The Virtual Mouse Brain enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.

[1]  Christophe Bernard,et al.  Newly formed excitatory pathways provide a substrate for hyperexcitability in experimental temporal lobe epilepsy , 1999, The Journal of comparative neurology.

[2]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[3]  S. Cash,et al.  Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling , 2016, Brain : a journal of neurology.

[4]  M. Newman Mathematics of networks , 2018, Oxford Scholarship Online.

[5]  Andreas Spiegler,et al.  Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain , 2016, eNeuro.

[6]  Jürgen Hennig,et al.  Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI , 2014, NeuroImage.

[7]  E. D’Angelo The human brain project. , 2012, Functional neurology.

[8]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[9]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[10]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[11]  Brian D. Mills,et al.  Large-scale topology and the default mode network in the mouse connectome , 2014, Proceedings of the National Academy of Sciences.

[12]  Maurizio Corbetta,et al.  Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations , 2013, The Journal of Neuroscience.

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

[14]  Viktor K. Jirsa,et al.  Individual brain structure and modelling predict seizure propagation , 2017, Brain : a journal of neurology.

[15]  Shaoyun Chen,et al.  Stereological analysis of forebrain regions in kainate-treated epileptic rats , 2005, Brain Research.

[16]  Viktor K. Jirsa,et al.  The Virtual Brain: a simulator of primate brain network dynamics , 2013, Front. Neuroinform..

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

[18]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[19]  Wulfram Gerstner,et al.  Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. , 2005, Journal of neurophysiology.

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

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

[22]  Alessandro Gozzi,et al.  Functional connectivity hubs of the mouse brain , 2015, NeuroImage.

[23]  Shella D. Keilholz,et al.  Dynamic Properties of Functional Connectivity in the Rodent , 2013, Brain Connect..

[24]  Viktor Jirsa Neural field dynamics with local and global connectivity and time delay , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Viktor K. Jirsa,et al.  The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread , 2017, NeuroImage.

[26]  G. Johnson,et al.  A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data , 2015, Cerebral cortex.

[27]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[28]  D. Carmichael,et al.  Network Connectivity in Epilepsy: Resting State fMRI and EEG–fMRI Contributions , 2014, Front. Neurol..

[29]  Christophe Bernard,et al.  Permittivity Coupling across Brain Regions Determines Seizure Recruitment in Partial Epilepsy , 2014, The Journal of Neuroscience.

[30]  Xiao Liu,et al.  Dynamic resting state functional connectivity in awake and anesthetized rodents , 2015, NeuroImage.

[31]  W. Stacey,et al.  On the nature of seizure dynamics. , 2014, Brain : a journal of neurology.

[32]  Allan R. Jones,et al.  A mesoscale connectome of the mouse brain , 2014, Nature.

[33]  Viktor K. Jirsa,et al.  Mathematical framework for large-scale brain network modeling in The Virtual Brain , 2015, NeuroImage.

[34]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

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