A macaque connectome for large-scale network simulations in TheVirtualBrain

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.Design Type(s)data integration objective • source-based data transformation objective • modeling and simulation objectiveMeasurement Type(s)brain measurementTechnology Type(s)functional magnetic resonance imaging • Diffusion Weighted ImagingFactor Type(s)Sample Characteristic(s)Macaca • brainMachine-accessible metadata file describing the reported data (ISA-Tab format)

[1]  Stefan Everling,et al.  Network Structure Shapes Spontaneous Functional Connectivity Dynamics , 2015, The Journal of Neuroscience.

[2]  Viktor K. Jirsa,et al.  How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models? , 2016, NeuroImage.

[3]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

[4]  Nikos K. Logothetis,et al.  Validation of High-Resolution Tractography Against In Vivo Tracing in the Macaque Visual Cortex , 2015, Cerebral cortex.

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

[6]  R. Caminiti,et al.  Diameter, Length, Speed, and Conduction Delay of Callosal Axons in Macaque Monkeys and Humans: Comparing Data from Histology and Magnetic Resonance Imaging Diffusion Tractography , 2013, The Journal of Neuroscience.

[7]  D. V. van Essen,et al.  Surface-based approaches to spatial localization and registration in primate cerebral cortex. , 2004, NeuroImage.

[8]  Ben Jeurissen,et al.  Modeling brain dynamics in brain tumor patients using The Virtual Brain , 2018 .

[9]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[10]  J. Capitanio,et al.  Contributions of non-human primates to neuroscience research , 2008, The Lancet.

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

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

[13]  Jude F. Mitchell,et al.  Differential Attention-Dependent Response Modulation across Cell Classes in Macaque Visual Area V4 , 2007, Neuron.

[14]  O. Sporns,et al.  Functional connectivity between anatomically unconnected areas is shaped by collective network-level effects in the macaque cortex. , 2012, Cerebral cortex.

[15]  Justin L. Vincent,et al.  Intrinsic functional architecture in the anaesthetized monkey brain , 2007, Nature.

[16]  Kotagiri Ramamohanarao,et al.  Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? , 2018, Magnetic resonance in medicine.

[17]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[18]  S. Treue,et al.  Basic Neuroscience Research with Nonhuman Primates: A Small but Indispensable Component of Biomedical Research , 2014, Neuron.

[19]  Joseph S. Gati,et al.  Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex , 2018, NeuroImage.

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

[21]  P S Goldman-Rakic,et al.  Functional synergism between putative gamma-aminobutyrate-containing neurons and pyramidal neurons in prefrontal cortex. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[22]  William D Hopkins,et al.  Why primate models matter , 2014, American journal of primatology.

[23]  Joseph S. Gati,et al.  Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex , 2015, NeuroImage.

[24]  J. Rilling,et al.  Comparison of diffusion tractography and tract‐tracing measures of connectivity strength in rhesus macaque connectome , 2015, Human brain mapping.

[25]  J. Zimmermann,et al.  Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models , 2018, NeuroImage: Clinical.

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

[27]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[28]  Gustavo Deco,et al.  Inferring multi-scale neural mechanisms with brain network modelling , 2017, bioRxiv.

[29]  Olaf Sporns,et al.  Comparative Connectomics , 2016, Trends in Cognitive Sciences.

[30]  Daniel S. Margulies,et al.  An Open Resource for Non-human Primate Imaging , 2018, Neuron.

[31]  Henry Kennedy,et al.  A Predictive Network Model of Cerebral Cortical Connectivity Based on a Distance Rule , 2013, Neuron.

[32]  D. V. Essen,et al.  Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex , 2007, Neuron.

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

[34]  R. Buxton,et al.  Dynamics of blood flow and oxygenation changes during brain activation: The balloon model , 1998, Magnetic resonance in medicine.

[35]  Christophe Bernard,et al.  The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics , 2017, eNeuro.

[36]  D. Leopold,et al.  Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited , 2014, Proceedings of the National Academy of Sciences.

[37]  Joseph S. Gati,et al.  Resting-state networks in the macaque at 7T , 2011, NeuroImage.

[38]  Ravi S. Menon,et al.  Information Processing Architecture of Functionally Defined Clusters in the Macaque Cortex , 2012, The Journal of Neuroscience.

[39]  J. Bullier,et al.  Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. , 2001, Journal of neurophysiology.

[40]  Wei Zhang,et al.  Connectome-scale functional intrinsic connectivity networks in macaques , 2017, Neuroscience.

[41]  Stefan Everling,et al.  A macaque connectome for large-scale network simulations in TheVirtualBrain , 2018 .

[42]  Anthony Randal McIntosh,et al.  Hundreds of brain maps in one atlas: Registering coordinate-independent primate neuro-anatomical data to a standard brain , 2012, NeuroImage.

[43]  M. Young,et al.  Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[44]  Petra Ritter,et al.  Mapping complementary features of cross‐species structural connectivity to construct realistic “Virtual Brains” , 2017, Human brain mapping.

[45]  M. Breakspear.  Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[46]  Stefan Everling,et al.  Monkey Prefrontal Cortical Pyramidal and Putative Interneurons Exhibit Differential Patterns of Activity Between Prosaccade and Antisaccade Tasks , 2009, The Journal of Neuroscience.

[47]  A. Nieder,et al.  Complementary Contributions of Prefrontal Neuron Classes in Abstract Numerical Categorization , 2008, The Journal of Neuroscience.

[48]  Nikola T. Markov,et al.  A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex , 2012, Cerebral cortex.

[49]  Carl-Fredrik Westin,et al.  Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain , 2007, NeuroImage.

[50]  Fernando Pérez,et al.  CoCoTools: Open-source Software for Building Connectomes Using the CoCoMac Anatomical Database , 2014, Journal of Cognitive Neuroscience.

[51]  Chad J. Donahue,et al.  Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey , 2016, The Journal of Neuroscience.

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

[53]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[54]  J. Zimmermann,et al.  Subject specificity of the correlation between large-scale structural and functional connectivity , 2018, Network Neuroscience.

[55]  M. Corbetta,et al.  How Local Excitation–Inhibition Ratio Impacts the Whole Brain Dynamics , 2014, The Journal of Neuroscience.

[56]  Joseph S. Gati,et al.  Optimized parallel transmit and receive radiofrequency coil for ultrahigh-field MRI of monkeys , 2016, NeuroImage.

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

[58]  D. Leopold,et al.  Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest , 2008, Human brain mapping.

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

[60]  Leonardo L. Gollo,et al.  Connectome sensitivity or specificity: which is more important? , 2016, NeuroImage.

[61]  Ravi S. Menon,et al.  Frontoparietal Functional Connectivity in the Common Marmoset , 2016, Cerebral cortex.

[62]  S. Everling,et al.  Monkey in the middle: why non-human primates are needed to bridge the gap in resting-state investigations , 2012, Front. Neuroanat..

[63]  Nikola T. Markov,et al.  Weight Consistency Specifies Regularities of Macaque Cortical Networks , 2010, Cerebral cortex.

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

[65]  Egon Wanke,et al.  Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty , 2008, Neural Networks.

[66]  A. Toga,et al.  The Rhesus Monkey Brain in Stereotaxic Coordinates , 1999 .