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Seguin C, Sporns O, Zalesky A. Brain network communication: concepts, models and applications. Nat Rev Neurosci 2023; 24:557-574. [PMID: 37438433 DOI: 10.1038/s41583-023-00718-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/08/2023] [Indexed: 07/14/2023]
Abstract
Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Program in Cognitive Science, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia
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Varga B, Soós B, Jákli B, Bálint E, Somogyvári Z, Négyessy L. Network Path Convergence Shapes Low-Level Processing in the Visual Cortex. Front Syst Neurosci 2021; 15:645709. [PMID: 34108867 PMCID: PMC8181740 DOI: 10.3389/fnsys.2021.645709] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/21/2021] [Indexed: 11/13/2022] Open
Abstract
Hierarchical counterstream via feedforward and feedback interactions is a major organizing principle of the cerebral cortex. The counterstream, as a topological feature of the network of cortical areas, is captured by the convergence and divergence of paths through directed links. So defined, the convergence degree (CD) reveals the reciprocal nature of forward and backward connections, and also hierarchically relevant integrative properties of areas through their inward and outward connections. We asked if topology shapes large-scale cortical functioning by studying the role of CD in network resilience and Granger causal coupling in a model of hierarchical network dynamics. Our results indicate that topological synchronizability is highly vulnerable to attacking edges based on CD, while global network efficiency depends mostly on edge betweenness, a measure of the connectedness of a link. Furthermore, similar to anatomical hierarchy determined by the laminar distribution of connections, CD highly correlated with causal coupling in feedforward gamma, and feedback alpha-beta band synchronizations in a well-studied subnetwork, including low-level visual cortical areas. In contrast, causal coupling did not correlate with edge betweenness. Considering the entire network, the CD-based hierarchy correlated well with both the anatomical and functional hierarchy for low-level areas that are far apart in the hierarchy. Conversely, in a large part of the anatomical network where hierarchical distances are small between the areas, the correlations were not significant. These findings suggest that CD-based and functional hierarchies are interrelated in low-level processing in the visual cortex. Our results are consistent with the idea that the interplay of multiple hierarchical features forms the basis of flexible functional cortical interactions.
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Affiliation(s)
- Bálint Varga
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Bettina Soós
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary.,Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands
| | - Balázs Jákli
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Eszter Bálint
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Zoltán Somogyvári
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
| | - László Négyessy
- Computational Neuroscience and Complex Systems Research Group, Department of Computational Sciences, Wigner Research Centre for Physics, Budapest, Hungary
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A systematic evaluation of assumptions in centrality measures by empirical flow data. SOCIAL NETWORK ANALYSIS AND MINING 2021. [DOI: 10.1007/s13278-021-00725-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractWhen considering complex systems, identifying the most important actors is often of relevance. When the system is modeled as a network, centrality measures are used which assign each node a value due to its position in the network. It is often disregarded that they implicitly assume a network process flowing through a network, and also make assumptions of how the network process flows through the network. A node is then central with respect to this network process (Borgatti in Soc Netw 27(1):55–71, 2005, 10.1016/j.socnet.2004.11.008). It has been shown that real-world processes often do not fulfill these assumptions (Bockholt and Zweig, in Complex networks and their applications VIII, Springer, Cham, 2019, 10.1007/978-3-030-36683-4_7). In this work, we systematically investigate the impact of the measures’ assumptions by using four datasets of real-world processes. In order to do so, we introduce several variants of the betweenness and closeness centrality which, for each assumption, use either the assumed process model or the behavior of the real-world process. The results are twofold: on the one hand, for all measure variants and almost all datasets, we find that, in general, the standard centrality measures are quite robust against deviations in their process model. On the other hand, we observe a large variation of ranking positions of single nodes, even among the nodes ranked high by the standard measures. This has implications for the interpretability of results of those centrality measures. Since a mismatch of the behaviour of the real network process and the assumed process model does even affect the highly-ranked nodes, resulting rankings need to be interpreted with care.
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Sabrin KM, Wei Y, van den Heuvel MP, Dovrolis C. The hourglass organization of the Caenorhabditis elegans connectome. PLoS Comput Biol 2020; 16:e1007526. [PMID: 32027645 PMCID: PMC7029875 DOI: 10.1371/journal.pcbi.1007526] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 02/19/2020] [Accepted: 11/01/2019] [Indexed: 11/18/2022] Open
Abstract
We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode's muscles. We focus on the feedforward flow of information from sensory neurons to motor neurons, and apply a recently developed network analysis framework referred to as the "hourglass effect". The analysis reveals that this feedforward flow traverses a small core ("hourglass waist") that consists of 10-15 interneurons. These are mostly the same interneurons that were previously shown (using a different analytical approach) to constitute the "rich-club" of the C. elegans connectome. This result is robust to the methodology that separates the feedforward from the feedback flow of information. The set of core interneurons remains mostly the same when we consider only chemical synapses or the combination of chemical synapses and gap junctions. The hourglass organization of the connectome suggests that C. elegans has some similarities with encoder-decoder artificial neural networks in which the input is first compressed and integrated in a low-dimensional latent space that encodes the given data in a more efficient manner, followed by a decoding network through which intermediate-level sub-functions are combined in different ways to compute the correlated outputs of the network. The core neurons at the hourglass waist represent the information bottleneck of the system, balancing the representation accuracy and compactness (complexity) of the given sensory information.
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Affiliation(s)
- Kaeser M. Sabrin
- School of Computer Science, Georgia Institute of Technology, Atlanta, Geogria, United States of America
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn Pieter van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Constantine Dovrolis
- School of Computer Science, Georgia Institute of Technology, Atlanta, Geogria, United States of America
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Gulyás A, Bíró J, Rétvári G, Novák M, Kőrösi A, Slíz M, Heszberger Z. The role of detours in individual human navigation patterns of complex networks. Sci Rep 2020; 10:1098. [PMID: 31980682 PMCID: PMC6981150 DOI: 10.1038/s41598-020-57856-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 12/29/2019] [Indexed: 11/20/2022] Open
Abstract
Despite its importance for public transportation, communication within organizations or the general understanding of organized knowledge, our understanding of how human individuals navigate complex networked systems is still limited owing to the lack of datasets recording a sufficient amount of navigation paths of individual humans. Here, we analyse 10587 paths recorded from 259 human subjects when navigating between nodes of a complex word-morph network. We find a clear presence of systematic detours organized around individual hierarchical scaffolds guiding navigation. Our dataset is the first enabling the visualization and analysis of scaffold hierarchies whose presence and role in supporting human navigation is assumed in existing navigational models. By using an information-theoretic argumentation, we argue that taking short detours following the hierarchical scaffolds is a clear sign of human subjects simplifying the interpretation of the complex networked system by an order of magnitude. We also discuss the role of these scaffolds in the phases of learning to navigate a network from scratch.
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Affiliation(s)
- András Gulyás
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary.
| | - József Bíró
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary
| | - Gábor Rétvári
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary
| | - Márton Novák
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary
| | - Attila Kőrösi
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary
| | - Mariann Slíz
- Eöotvös Loránd University, Institute of Hungarian Linguistics and Finno-Ugric Studies, Múzeum krt. 4/A, H-1088, Budapest, Hungary
| | - Zalán Heszberger
- MTA-BME Information Systems Research Group, Budapest University of Technology and Economics, Magyar tudósok krt. 2, H-1117, Budapest, Hungary
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Avena-Koenigsberger A, Yan X, Kolchinsky A, van den Heuvel MP, Hagmann P, Sporns O. A spectrum of routing strategies for brain networks. PLoS Comput Biol 2019; 15:e1006833. [PMID: 30849087 PMCID: PMC6426276 DOI: 10.1371/journal.pcbi.1006833] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 03/20/2019] [Accepted: 01/30/2019] [Indexed: 11/18/2022] Open
Abstract
Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally “cheap” but inefficient. We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bias on the network’s communication cost. We identify routing strategies that approach a (highly efficient) shortest-path communication process with a relatively small global information bias on the system’s dynamics. Moreover, we show that the cost of routing messages from and to hub nodes varies as a function of the global information bias driving the system’s dynamics. Finally, we implement the model to identify individual subject differences from a communication dynamics point of view. The present framework departs from the classical shortest paths vs. diffusion dichotomy, unifying both models under a single family of dynamical processes that differ by the extent to which global information about the network topology influences the routing patterns of neural signals traversing the network. Brain network communication is typically approached from the perspective of the length of inferred paths and the cost of building and maintaining network connections. However, these analyses often disregard the dynamical processes taking place on the network and the additional costs that these processes incur. Here, we introduce a framework to study communication-cost trade-offs on a broad range of communication processes modeled as biased random walks. We control the system’s dynamics that dictates the flow of messages traversing a network by biasing node’s routing strategies with different degrees of “knowledge” about the topology of the network. On the human connectome, this framework uncovers a spectrum of dynamic communication processes, some of which can achieve efficient routing strategies at low informational cost.
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Affiliation(s)
- Andrea Avena-Koenigsberger
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- * E-mail:
| | - Xiaoran Yan
- IU Network Institute, Indiana University, Bloomington, IN, United States of America
| | | | - Martijn P. van den Heuvel
- Connectome Lab, Complex Trait Genetics, Department of Neuroscience, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Amsterdam
- Department of Clinical Genetics, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States of America
- IU Network Institute, Indiana University, Bloomington, IN, United States of America
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Abstract
Humans are involved in various real-life networked systems. The most obvious examples are social and collaboration networks but the language and the related mental lexicon they use, or the physical map of their territory can also be interpreted as networks. How do they find paths between endpoints in these networks? How do they obtain information about a foreign networked world they find themselves in, how they build mental model for it and how well they succeed in using it? Large, open datasets allowing the exploration of such questions are hard to find. Here we report a dataset collected by a smartphone application, in which players navigate between fixed length source and destination English words step-by-step by changing only one letter at a time. The paths reflect how the players master their navigation skills in such a foreign networked world. The dataset can be used in the study of human mental models for the world around us, or in a broader scope to investigate the navigation strategies in complex networked systems.
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