Brain connectomes come of age

Databases of consistent, directed- and weighted inter-areal connectivity for mouse, macaque and marmoset monkeys have recently become available and begun to be used to build structural and dynamical models. A structural hierarchy can be defined based by laminar patterns of cortical connections. A large-scale dynamical model of the macaque cortex endowed with a laminar structure accounts for empirically observed frequency-modulated interplay between bottom-up and top-down processes. Signal propagation in the model with spiking neurons displays a threshold of stimulus amplitude for the activity to gain access to the prefrontal cortex, reminiscent of the ignition phenomenon associated with conscious perception. These two examples illustrate how connectomics inform structurally based dynamic models of multi-regional brain systems. Theory raises novel questions for future anatomical and physiological empirical research, in a back-and-forth collaboration between experimentalists and theorists. Directed- and weighted inter-areal cortical connectivity matrices of macaque, marmoset and mouse exhibit similarities as well as marked differences. The new connectomic data provide quantitative information for structural and dynamical modeling of multi-regional cortical circuit providing insight to the global cortical function. Quantification of cortical hierarchy guides investigations of interplay between bottom-up and top-down information processes.

[1]  C. Honey,et al.  Hierarchical process memory: memory as an integral component of information processing , 2015, Trends in Cognitive Sciences.

[2]  H. Kennedy,et al.  A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex , 2015, Neuron.

[3]  S Dehaene,et al.  A neuronal model of a global workspace in effortful cognitive tasks. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Henry Kennedy,et al.  Brain structure and dynamics across scales: in search of rules , 2016, Current Opinion in Neurobiology.

[5]  J L Ringo,et al.  Neuronal interconnection as a function of brain size. , 1991, Brain, behavior and evolution.

[6]  H. F. Song,et al.  Spatial embedding of structural similarity in the cerebral cortex , 2014, Proceedings of the National Academy of Sciences.

[7]  Miao He,et al.  Brain-wide Maps Reveal Stereotyped Cell-Type-Based Cortical Architecture and Subcortical Sexual Dimorphism , 2017, Cell.

[8]  Kevin L. Briggman,et al.  3D structural imaging of the brain with photons and electrons , 2008, Current Opinion in Neurobiology.

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

[10]  P. Roelfsema,et al.  The threshold for conscious report: Signal loss and response bias in visual and frontal cortex , 2018, Science.

[11]  David A. Leopold,et al.  A resource for detailed 3D mapping of white matter pathways in the marmoset brain , 2019, Nature Neuroscience.

[12]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[13]  A. Bernacchia,et al.  Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography , 2018, Nature Neuroscience.

[14]  Karl J. Friston,et al.  Computational psychiatry , 2012, Trends in Cognitive Sciences.

[15]  Partha P. Mitra,et al.  Open access resource for cellular-resolution analyses of corticocortical connectivity in the marmoset monkey , 2020, Nature Communications.

[16]  J. Livet,et al.  A technicolour approach to the connectome , 2008, Nature Reviews Neuroscience.

[17]  Xiao-Jing Wang,et al.  A disinhibitory circuit motif and flexible information routing in the brain , 2018, Current Opinion in Neurobiology.

[18]  D. V. van Essen,et al.  Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates , 2016, PLoS biology.

[19]  H. Kennedy,et al.  Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas , 2016, Neuron.

[20]  J. Lichtman,et al.  From Cajal to Connectome and Beyond. , 2016, Annual review of neuroscience.

[21]  Tristan A. Chaplin,et al.  Cortical Afferents of Area 10 in Cebus Monkeys: Implications for the Evolution of the Frontal Pole , 2019, Cerebral cortex.

[22]  Bruce R. Rosen,et al.  The Mind of a Mouse , 2020, Cell.

[23]  Rodney J. Douglas,et al.  Behavioral architecture of the cortical sheet , 2012, Current Biology.

[24]  Zoltán Toroczkai,et al.  The Brain in Space , 2016 .

[25]  J. Changeux,et al.  Conscious Processing and the Global Neuronal Workspace Hypothesis , 2020, Neuron.

[26]  Nicholas A. Steinmetz,et al.  Distributed coding of choice, action, and engagement across the mouse brain , 2019, Nature.

[27]  Stéphane Lafon,et al.  Diffusion maps , 2006 .

[28]  Henry Kennedy,et al.  Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks , 2020, NeuroImage.

[29]  Sergey L. Gratiy,et al.  Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.

[30]  Jorge F. Mejias,et al.  Mechanisms of distributed working memory in a large-scale model of the macaque neocortex , 2019, bioRxiv.

[31]  George A. Mashour,et al.  The controversial correlates of consciousness , 2018, Science.

[32]  H. Barbas General cortical and special prefrontal connections: principles from structure to function. , 2015, Annual review of neuroscience.

[33]  Henry Kennedy,et al.  Cortical High-Density Counterstream Architectures , 2013, Science.

[34]  Xiao-Jing Wang,et al.  Feedforward and feedback frequency-dependent interactions in a large-scale laminar network of the primate cortex , 2016, Science Advances.

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

[36]  R. Douglas,et al.  Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.

[37]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[38]  Sen Song,et al.  Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits , 2005, PLoS biology.

[39]  Arthur W. Toga,et al.  Neural Networks of the Mouse Neocortex , 2014, Cell.

[40]  H. Kennedy,et al.  Laminar Distribution of Neurons in Extrastriate Areas Projecting to Visual Areas V1 and V4 Correlates with the Hierarchical Rank and Indicates the Operation of a Distance Rule , 2000, The Journal of Neuroscience.

[41]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[42]  Ben D. Fulcher,et al.  Multimodal gradients across mouse cortex , 2018, Proceedings of the National Academy of Sciences.

[43]  D. Leopold,et al.  Layer-Specific Entrainment of Gamma-Band Neural Activity by the Alpha Rhythm in Monkey Visual Cortex , 2012, Current Biology.

[44]  Fuhui Long,et al.  A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas , 2019, bioRxiv.

[45]  D. V. van Essen,et al.  Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI , 2011, The Journal of Neuroscience.

[46]  G. Elston Specialization of the Neocortical Pyramidal Cell during Primate Evolution , 2007 .

[47]  H. Kennedy,et al.  Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels , 2014, Neuron.

[48]  Mikko Pohja,et al.  On the human sensorimotor-cortex beta rhythm: Sources and modeling , 2005, NeuroImage.

[49]  Piotr Majka,et al.  Structural Attributes and Principles of the Neocortical Connectome in the Marmoset Monkey , 2020, bioRxiv.

[50]  Marcello G P Rosa,et al.  Quantitative analysis of the corticocortical projections to the middle temporal area in the marmoset monkey: evolutionary and functional implications. , 2006, Cerebral cortex.

[51]  Tristan A. Chaplin,et al.  A Conserved Pattern of Differential Expansion of Cortical Areas in Simian Primates , 2013, The Journal of Neuroscience.

[52]  Henry Kennedy,et al.  Micro-, Meso- and Macro-Connectomics of the Brain , 2016, Research and Perspectives in Neurosciences.

[53]  M G Rosa,et al.  Cellular heterogeneity in cerebral cortex: A study of the morphology of pyramidal neurones in visual areas of the marmoset monkey , 1999, The Journal of comparative neurology.

[54]  Piotr Majka,et al.  Neuronal distribution across the cerebral cortex of the marmoset monkey (Callithrix jacchus) , 2018, bioRxiv.

[55]  Miles A. Whittington,et al.  Top-Down Beta Rhythms Support Selective Attention via Interlaminar Interaction: A Model , 2013, PLoS Comput. Biol..

[56]  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.

[57]  Nicholas A. Steinmetz,et al.  High-dimensional geometry of population responses in visual cortex , 2018, Nature.

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

[59]  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.

[60]  Cecil Chern-Chyi Yen,et al.  Anatomical and functional investigation of the marmoset default mode network , 2019, Nature Communications.

[61]  G. Buzsáki,et al.  Mechanisms of gamma oscillations. , 2012, Annual review of neuroscience.

[62]  Sacha Jennifer van Albada,et al.  An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain , 2019, Network Neuroscience.

[63]  Eric T. Trautman,et al.  A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster , 2017, Cell.

[64]  Larry W Swanson,et al.  From gene networks to brain networks , 2003, Nature Neuroscience.

[65]  Elizabeth Jefferies,et al.  Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.

[66]  Kei Ito,et al.  A Connectome of the Adult Drosophila Central Brain , 2020, bioRxiv.

[67]  Nikola T. Markov,et al.  Anatomy of hierarchy: Feedforward and feedback pathways in macaque visual cortex , 2013, The Journal of comparative neurology.

[68]  Xiao-Jing Wang,et al.  Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex , 2017, Neuron.

[69]  Xiao-Jing Wang Neurophysiological and computational principles of cortical rhythms in cognition. , 2010, Physiological reviews.

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

[71]  D. C. Essen,et al.  The Mouse Cortical Connectome, Characterized by an Ultra-Dense Cortical Graph, Maintains Specificity by Distinct Connectivity Profiles , 2018, Neuron.

[72]  Yun Wang,et al.  Hierarchical organization of cortical and thalamic connectivity , 2019, Nature.

[73]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.

[74]  J. Changeux,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.5 May 2006 Conscious, preconscious, and subliminal processing: a testable taxonomy , 2022 .

[75]  Joseph S. Gati,et al.  Comparison of resting-state functional connectivity in marmosets with tracer-based cellular connectivity , 2020, NeuroImage.

[76]  Karl J. Friston,et al.  Canonical Microcircuits for Predictive Coding , 2012, Neuron.

[77]  G. Elston,et al.  Morphological variation of layer III pyramidal neurones in the occipitotemporal pathway of the macaque monkey visual cortex. , 1998, Cerebral cortex.

[78]  Ad Aertsen,et al.  Stable propagation of synchronous spiking in cortical neural networks , 1999, Nature.

[79]  M P Young,et al.  Indeterminate Organization of the Visual System , 1996, Science.

[80]  Zoltán Toroczkai,et al.  Why data coherence and quality is critical for understanding interareal cortical networks , 2013, NeuroImage.

[81]  Henry Kennedy,et al.  Canonical and noncanonical features of the mouse visual cortical hierarchy , 2020, bioRxiv.

[82]  Xiao-Jing Wang Macroscopic gradients of synaptic excitation and inhibition in the neocortex , 2020, Nature Reviews Neuroscience.

[83]  Jeremy D. Schmahmann,et al.  A Proposal for a Coordinated Effort for the Determination of Brainwide Neuroanatomical Connectivity in Model Organisms at a Mesoscopic Scale , 2009, PLoS Comput. Biol..

[84]  P. Roelfsema,et al.  Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex , 2014, Proceedings of the National Academy of Sciences.

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

[86]  A. Angelucci,et al.  A direct interareal feedback-to-feedforward circuit in primate visual cortex , 2020, Nature Communications.

[87]  Andrew R. McKinstry-Wu,et al.  Connectome: How the Brain’s Wiring Makes Us Who We Are , 2013 .

[88]  O. Sporns Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[89]  Eric Shea-Brown,et al.  High-resolution data-driven model of the mouse connectome , 2018, bioRxiv.

[90]  G. Elston,et al.  Complex dendritic fields of pyramidal cells in the frontal eye field of the macaque monkey: comparison with parietal areas 7a and LIP , 1998, Neuroreport.

[91]  Daniel S. Margulies,et al.  Macroscale cortical organization and a default-like apex transmodal network in the marmoset monkey , 2019, Nature Communications.

[92]  David J. Freedman,et al.  A hierarchy of intrinsic timescales across primate cortex , 2014, Nature Neuroscience.