1
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Bardella G, Giuffrida V, Giarrocco F, Brunamonti E, Pani P, Ferraina S. Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network. Netw Neurosci 2024; 8:597-622. [PMID: 38952814 PMCID: PMC11168728 DOI: 10.1162/netn_a_00365] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/18/2024] [Indexed: 07/03/2024] Open
Abstract
Recent studies have explored functional and effective neural networks in animal models; however, the dynamics of information propagation among functional modules under cognitive control remain largely unknown. Here, we addressed the issue using transfer entropy and graph theory methods on mesoscopic neural activities recorded in the dorsal premotor cortex of rhesus monkeys. We focused our study on the decision time of a Stop-signal task, looking for patterns in the network configuration that could influence motor plan maturation when the Stop signal is provided. When comparing trials with successful inhibition to those with generated movement, the nodes of the network resulted organized into four clusters, hierarchically arranged, and distinctly involved in information transfer. Interestingly, the hierarchies and the strength of information transmission between clusters varied throughout the task, distinguishing between generated movements and canceled ones and corresponding to measurable levels of network complexity. Our results suggest a putative mechanism for motor inhibition in premotor cortex: a topological reshuffle of the information exchanged among ensembles of neurons.
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Affiliation(s)
- Giampiero Bardella
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Valentina Giuffrida
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Franco Giarrocco
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Pierpaolo Pani
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferraina
- Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy
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2
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Zamora-López G, Gilson M. An integrative dynamical perspective for graph theory and the analysis of complex networks. CHAOS (WOODBURY, N.Y.) 2024; 34:041501. [PMID: 38625080 DOI: 10.1063/5.0202241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/25/2024] [Indexed: 04/17/2024]
Abstract
Built upon the shoulders of graph theory, the field of complex networks has become a central tool for studying real systems across various fields of research. Represented as graphs, different systems can be studied using the same analysis methods, which allows for their comparison. Here, we challenge the widespread idea that graph theory is a universal analysis tool, uniformly applicable to any kind of network data. Instead, we show that many classical graph metrics-including degree, clustering coefficient, and geodesic distance-arise from a common hidden propagation model: the discrete cascade. From this perspective, graph metrics are no longer regarded as combinatorial measures of the graph but as spatiotemporal properties of the network dynamics unfolded at different temporal scales. Once graph theory is seen as a model-based (and not a purely data-driven) analysis tool, we can freely or intentionally replace the discrete cascade by other canonical propagation models and define new network metrics. This opens the opportunity to design-explicitly and transparently-dedicated analyses for different types of real networks by choosing a propagation model that matches their individual constraints. In this way, we take stand that network topology cannot always be abstracted independently from network dynamics but shall be jointly studied, which is key for the interpretability of the analyses. The model-based perspective here proposed serves to integrate into a common context both the classical graph analysis and the more recent network metrics defined in the literature which were, directly or indirectly, inspired by propagation phenomena on networks.
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Affiliation(s)
- Gorka Zamora-López
- Center for Brain and Cognition, Pompeu Fabra University, 08005 Barcelona, Spain
- Department of Information and Communication Technologies, Pompeu Fabra University, 08018 Barcelona, Spain
| | - Matthieu Gilson
- Institut des Neurosciences de la Timone, CNRS-AMU, 13005 Marseille, France
- Institut des Neurosciences des Systemes, INSERM-AMU, 13005 Marseille, France
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3
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Mecklenbrauck F, Gruber M, Siestrup S, Zahedi A, Grotegerd D, Mauritz M, Trempler I, Dannlowski U, Schubotz RI. The significance of structural rich club hubs for the processing of hierarchical stimuli. Hum Brain Mapp 2024; 45:e26543. [PMID: 38069537 PMCID: PMC10915744 DOI: 10.1002/hbm.26543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/17/2023] [Accepted: 11/09/2023] [Indexed: 03/07/2024] Open
Abstract
The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.
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Affiliation(s)
- Falko Mecklenbrauck
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Marius Gruber
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Department for Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital Frankfurt, Goethe UniversityFrankfurtGermany
| | - Sophie Siestrup
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Anoushiravan Zahedi
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Dominik Grotegerd
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Marco Mauritz
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Ima Trempler
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
| | - Udo Dannlowski
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
- Institute for Translational PsychiatryUniversity of MünsterMünsterGermany
| | - Ricarda I. Schubotz
- Department of Psychology, Biological PsychologyUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral NeuroscienceUniversity of MünsterMünsterGermany
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4
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Suárez LE, Mihalik A, Milisav F, Marshall K, Li M, Vértes PE, Lajoie G, Misic B. Connectome-based reservoir computing with the conn2res toolbox. Nat Commun 2024; 15:656. [PMID: 38253577 PMCID: PMC10803782 DOI: 10.1038/s41467-024-44900-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.
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Affiliation(s)
- Laura E Suárez
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Agoston Mihalik
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Kenji Marshall
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mingze Li
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Guillaume Lajoie
- Mila, Quebec Artificial Intelligence Institute, Montreal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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5
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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6
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Rubinov M. Circular and unified analysis in network neuroscience. eLife 2023; 12:e79559. [PMID: 38014843 PMCID: PMC10684154 DOI: 10.7554/elife.79559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 10/18/2023] [Indexed: 11/29/2023] Open
Abstract
Genuinely new discovery transcends existing knowledge. Despite this, many analyses in systems neuroscience neglect to test new speculative hypotheses against benchmark empirical facts. Some of these analyses inadvertently use circular reasoning to present existing knowledge as new discovery. Here, I discuss that this problem can confound key results and estimate that it has affected more than three thousand studies in network neuroscience over the last decade. I suggest that future studies can reduce this problem by limiting the use of speculative evidence, integrating existing knowledge into benchmark models, and rigorously testing proposed discoveries against these models. I conclude with a summary of practical challenges and recommendations.
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Affiliation(s)
- Mika Rubinov
- Departments of Biomedical Engineering, Computer Science, and Psychology, Vanderbilt UniversityNashvilleUnited States
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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7
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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: 12] [Impact Index Per Article: 12.0] [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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8
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Kaiser M. Connectomes: from a sparsity of networks to large-scale databases. Front Neuroinform 2023; 17:1170337. [PMID: 37377946 PMCID: PMC10291062 DOI: 10.3389/fninf.2023.1170337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.
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Affiliation(s)
- Marcus Kaiser
- NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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9
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Bergoin R, Torcini A, Deco G, Quoy M, Zamora-López G. Inhibitory neurons control the consolidation of neural assemblies via adaptation to selective stimuli. Sci Rep 2023; 13:6949. [PMID: 37117236 PMCID: PMC10147639 DOI: 10.1038/s41598-023-34165-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/25/2023] [Indexed: 04/30/2023] Open
Abstract
Brain circuits display modular architecture at different scales of organization. Such neural assemblies are typically associated to functional specialization but the mechanisms leading to their emergence and consolidation still remain elusive. In this paper we investigate the role of inhibition in structuring new neural assemblies driven by the entrainment to various inputs. In particular, we focus on the role of partially synchronized dynamics for the creation and maintenance of structural modules in neural circuits by considering a network of excitatory and inhibitory [Formula: see text]-neurons with plastic Hebbian synapses. The learning process consists of an entrainment to temporally alternating stimuli that are applied to separate regions of the network. This entrainment leads to the emergence of modular structures. Contrary to common practice in artificial neural networks-where the acquired weights are typically frozen after the learning session-we allow for synaptic adaptation even after the learning phase. We find that the presence of inhibitory neurons in the network is crucial for the emergence and the post-learning consolidation of the modular structures. Indeed networks made of purely excitatory neurons or of neurons not respecting Dale's principle are unable to form or to maintain the modular architecture induced by the stimuli. We also demonstrate that the number of inhibitory neurons in the network is directly related to the maximal number of neural assemblies that can be consolidated, supporting the idea that inhibition has a direct impact on the memory capacity of the neural network.
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Affiliation(s)
- Raphaël Bergoin
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France.
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain.
| | - Alessandro Torcini
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CY Cergy Paris Université, CNRS, 2 Av. Adolphe Chauvin, 95032, Cergy-Pontoise, France
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
- Instituciò Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Spain
| | - Mathias Quoy
- ETIS, UMR 8051, ENSEA, CY Cergy Paris Université, CNRS, 6 Av. du Ponceau, 95000, Cergy-Pontoise, France
- IPAL, CNRS, 1 Fusionopolis Way #21-01 Connexis (South Tower), Singapore, 138632, Singapore
| | - Gorka Zamora-López
- Center for Brain and Cognition, Department of Information and Communications Technologies, Pompeu Fabra University, Carrer Ramón Trias i Fargas 25-27, 08005, Barcelona, Spain
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10
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Kringelbach ML, Perl YS, Tagliazucchi E, Deco G. Toward naturalistic neuroscience: Mechanisms underlying the flattening of brain hierarchy in movie-watching compared to rest and task. SCIENCE ADVANCES 2023; 9:eade6049. [PMID: 36638163 PMCID: PMC9839335 DOI: 10.1126/sciadv.ade6049] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 12/13/2022] [Indexed: 06/04/2023]
Abstract
Identifying the functional specialization of the brain has moved from using cognitive tasks and resting state to using ecological relevant, naturalistic movies. We leveraged a large-scale neuroimaging dataset to directly investigate the hierarchical reorganization of functional brain activity when watching naturalistic films compared to performing seven cognitive tasks and resting. A thermodynamics-inspired whole-brain model paradigm revealed the generative underlying mechanisms for changing the balance in causal interactions between brain regions in different conditions. Paradoxically, the hierarchy is flatter for movie-watching, and the level of nonreversibility is significantly smaller in comparison to both rest and tasks, where the latter in turn have the highest levels of hierarchy and nonreversibility. The underlying mechanisms were revealed by the model-based generative effective connectivity (GEC). Naturalistic films could therefore provide a fast and convenient way to measure important changes in GEC (integrating functional and anatomical connectivity) found in, for example, neuropsychiatric disorders. Overall, this study demonstrates the benefits of moving toward a more naturalistic neuroscience.
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Affiliation(s)
- Morten L. Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
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11
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Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: diffusion versus shortest path routing. Brain Struct Funct 2023; 228:651-662. [PMID: 36723674 PMCID: PMC9944050 DOI: 10.1007/s00429-023-02613-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 01/16/2023] [Indexed: 02/02/2023]
Abstract
The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted functional connectivity from structural connectivity than the shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.
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12
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Suarez LE, Yovel Y, van den Heuvel MP, Sporns O, Assaf Y, Lajoie G, Misic B. A connectomics-based taxonomy of mammals. eLife 2022; 11:e78635. [PMID: 36342363 PMCID: PMC9681214 DOI: 10.7554/elife.78635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.
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Affiliation(s)
- Laura E Suarez
- Montréal Neurological Institute, McGill UniversityMontrealCanada
- Mila - Quebec Artificial Intelligence InstituteMontrealCanada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana UniversityBloomingtonUnited States
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | | | - Bratislav Misic
- Montréal Neurological Institute, McGill UniversityMontrealCanada
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13
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Favaretto C, Allegra M, Deco G, Metcalf NV, Griffis JC, Shulman GL, Brovelli A, Corbetta M. Subcortical-cortical dynamical states of the human brain and their breakdown in stroke. Nat Commun 2022; 13:5069. [PMID: 36038566 PMCID: PMC9424299 DOI: 10.1038/s41467-022-32304-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01–0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome. Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.
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Affiliation(s)
- Chiara Favaretto
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy.,Department of Physics and Astronomy "Galileo Galilei", University of Padova, via Marzolo 8, 35131, Padova, Italy.,Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Catalonia, Spain
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA.,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy. .,Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, 35129, Padova, Italy.
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14
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Khan S, Hashmi JA, Mamashli F, Hämäläinen MS, Kenet T. Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood. Front Neurol 2022; 13:814940. [PMID: 35812111 PMCID: PMC9259855 DOI: 10.3389/fneur.2022.814940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 05/10/2022] [Indexed: 11/25/2022] Open
Abstract
Cortical hubs identified within resting-state networks (RSNs), areas of the cortex that have a higher-than-average number of connections, are known to be critical to typical cognitive functioning and are often implicated in disorders leading to abnormal cognitive functioning. Functionally defined cortical hubs are also known to change with age in the developing, maturing brain, mostly based on studies carried out using fMRI. We have recently used magnetoencephalography (MEG) to study the maturation trajectories of RSNs and their hubs from age 7 to 29 in 131 healthy participants with high temporal resolution. We found that maturation trajectories diverge as a function of the underlying cortical rhythm. Specifically, we found the beta band (13–30 Hz)-mediated RSNs became more locally efficient with maturation, i.e., more organized into clusters and connected with nearby regions, while gamma (31–80 Hz)-mediated RSNs became more globally efficient with maturation, i.e., prioritizing faster signal transmission between distant cortical regions. We also found that different sets of hubs were associated with each of these networks. To better understand the functional significance of this divergence, we wanted to examine the cortical functions associated with the identified hubs that grew or shrunk with maturation within each of these networks. To that end, we analyzed the results of the prior study using Neurosynth, a platform for large-scale, automated synthesis of fMRI data that links brain coordinates with their probabilistically associated terms. By mapping the Neurosynth terms associated with each of these hubs, we found that maturing hubs identified in the gamma band RSNs were more likely to be associated with bottom-up processes while maturing hubs identified in the beta band RSNs were more likely to be associated with top-down functions. The results were consistent with the idea that beta band-mediated networks preferentially support the maturation of top-down processing, while the gamma band-mediated networks preferentially support the maturation of bottom-up processing.
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Affiliation(s)
- Sheraz Khan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- *Correspondence: Sheraz Khan
| | - Javeria Ali Hashmi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Anesthesia, Pain Management, and Perioperative Medicine, Dalhousie University, Halifax, NS, Canada
| | - Fahimeh Mamashli
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Matti S. Hämäläinen
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tal Kenet
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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15
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Xue X, Wu JJ, Huo BB, Xing XX, Ma J, Li YL, Wei D, Duan YJ, Shan CL, Zheng MX, Hua XY, Xu JG. Age-Related Changes in Topological Properties of Individual Brain Metabolic Networks in Rats. Front Aging Neurosci 2022; 14:895934. [PMID: 35645769 PMCID: PMC9136077 DOI: 10.3389/fnagi.2022.895934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Normal aging causes profound changes of structural degeneration and glucose hypometabolism in the human brain, even in the absence of disease. In recent years, with the extensive exploration of the topological characteristics of the human brain, related studies in rats have begun to investigate. However, age-related alterations of topological properties in individual brain metabolic network of rats remain unknown. In this study, a total of 48 healthy female Sprague–Dawley (SD) rats were used, including 24 young rats and 24 aged rats. We used Jensen-Shannon Divergence Similarity Estimation (JSSE) method for constructing individual metabolic networks to explore age-related topological properties and rich-club organization changes. Compared with the young rats, the aged rats showed significantly decreased clustering coefficient (Cp) and local efficiency (Eloc) across the whole-brain metabolic network. In terms of changes in local network measures, degree (D) and nodal efficiency (Enod) of left posterior dorsal hippocampus, and Enod of left olfactory tubercle were higher in the aged rats than in the young rats. About the rich-club analysis, the existence of rich-club organization in individual brain metabolic networks of rats was demonstrated. In addition, our findings further confirmed that rich-club connections were susceptible to aging. Relative to the young rats, the overall strength of rich-club connections was significantly reduced in the aged rats, while the overall strength of feeder and local connections was significantly increased. These findings demonstrated the age-related reorganization principle of the brain structure and improved our understanding of brain alternations during aging.
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Affiliation(s)
- Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yu-Jie Duan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Mou-Xiong Zheng,
| | - Xu-Yun Hua
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Xu-Yun Hua,
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- *Correspondence: Jian-Guang Xu,
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16
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Ke M, Li H, Liu G. The Local Topological Reconfiguration in the Brain Network After Targeted Hub Dysfunction Attacks in Patients With Juvenile Myoclonic Epilepsy. Front Neurosci 2022; 16:864040. [PMID: 35495041 PMCID: PMC9047017 DOI: 10.3389/fnins.2022.864040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 03/15/2022] [Indexed: 11/16/2022] Open
Abstract
The central brain regions of brain networks have been extensively studied in terms of their roles in various diseases. This study provides a direct measure of the brain's responses to targeted attacks on central regions, revealing the critical role these regions play in patients with juvenile myoclonic epilepsy (JME). The resting-state data of 37 patients with JME and 37 healthy subjects were collected, and brain functional networks were constructed for the two groups of data according to their Pearson correlation coefficients. The left middle cingulate gyrus was defined as the central brain region by the eigenvector centrality algorithm and was attacked by the CLM sequential failure model. The rich-club connection differences between the patients with JME and healthy controls before and after the attacks were compared according to graph theory indices and the number of rich-club connections. We found that the numbers of rich connections in the brain networks of the healthy control group and the group of patients with JME were significantly reduced [p < 0.05, false discovery rate (FDR) correction] before the CLM sequential failure attacks, and no significant differences were observed between the feeder connections and local connections. In the healthy control group, significant rich connection differences were obtained (p < 0.01, FDR correction), and no statistically significant differences were observed regarding the feeder connections and local connections in the brain network before and after CLM failure attacks on the central brain region. No significant differences were obtained between the rich connections, feeder connections, and local connections in patients with JME before and after CLM successive failure attacks on the central brain area. The rich connections, feeder connections, and local connections were not significantly different in the brain networks of the healthy control group and the group of patients with JME after CLM successive failure attacks on the central brain region. We concluded that the damage to the left middle cingulate gyrus is closely linked to various brain disorders, suggesting that this region is of great importance for understanding the pathophysiological basis of myoclonic seizures in patients with JME.
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Affiliation(s)
- Ming Ke
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Huimin Li
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
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17
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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18
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Faskowitz J, Betzel RF, Sporns O. Edges in brain networks: Contributions to models of structure and function. Netw Neurosci 2022; 6:1-28. [PMID: 35350585 PMCID: PMC8942607 DOI: 10.1162/netn_a_00204] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.
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Affiliation(s)
- Joshua Faskowitz
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Richard F. Betzel
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
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19
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Czarnecki P, Lin J, Aton SJ, Zochowski M. Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:759131. [PMID: 35785148 PMCID: PMC9249096 DOI: 10.3389/fnetp.2021.759131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022]
Abstract
Sleep is indispensable for most animals' cognitive functions, and is hypothesized to be a major factor in memory consolidation. Although we do not fully understand the mechanisms of network reorganisation driving memory consolidation, available data suggests that sleep-associated neurochemical changes may be important for such processes. In particular, global acetylcholine levels change across the sleep/wake cycle, with high cholinergic tone during wake and REM sleep and low cholinergic tone during slow wave sleep. Furthermore, experimental perturbation of cholinergic tone has been shown to impact memory storage. Through in silico modeling of neuronal networks, we show how spiking dynamics change in highly heterogenous networks under varying levels of cholinergic tone, with neuronal networks under high cholinergic modulation firing asynchronously and at high frequencies, while those under low cholinergic modulation exhibit synchronous patterns of activity. We further examined the network's dynamics and its reorganization mediated via changing levels of acetylcholine within the context of different scale-free topologies, comparing network activity within the hub cells, a small group of neurons having high degree connectivity, and with the rest of the network. We show a dramatic, state-dependent change in information flow throughout the network, with highly active hub cells integrating information in a high-acetylcholine state, and transferring it to rest of the network in a low-acetylcholine state. This result is experimentally corroborated by frequency-dependent frequency changes observed in vivo experiments. Together, these findings provide insight into how new neurons are recruited into memory traces during sleep, a mechanism which may underlie system memory consolidation.
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Affiliation(s)
- Paulina Czarnecki
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States
| | - Jack Lin
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States
| | - Sara J. Aton
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI, United States
| | - Michal Zochowski
- Department of Physics and Biophysics Program, University of Michigan, Ann Arbor, MI, United States
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20
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López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Commun Biol 2021; 4:1037. [PMID: 34489535 PMCID: PMC8421429 DOI: 10.1038/s42003-021-02537-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 08/11/2021] [Indexed: 01/07/2023] Open
Abstract
Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.
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Affiliation(s)
- Ane López-González
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Rajanikant Panda
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Adrián Ponce-Alvarez
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Anira Escrichs
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Charlotte Martial
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Aurore Thibaut
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Olivia Gosseries
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
| | - Jitka Annen
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Steven Laureys
- GIGA-Consciousness, Coma Science Group, University of Liège, Liège, Belgium.,Centre du Cerveau2, University Hospital of Liège, Liège, Belgium
| | - Gustavo Deco
- Computational Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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21
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Li L, Smith DM. Neural Efficiency in Athletes: A Systematic Review. Front Behav Neurosci 2021; 15:698555. [PMID: 34421553 PMCID: PMC8374331 DOI: 10.3389/fnbeh.2021.698555] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/09/2021] [Indexed: 11/24/2022] Open
Abstract
According to the neural efficiency hypothesis (NEH), professionals have more effective cortical functions in cognitive tasks. This study is focusing on providing a systematic review of sport-related NEH studies with functional neuroimaging or brain stimulation while performing a sport-specific task, with the aim to answer the question: How does long-term specialized training change an athlete's brain and improve efficiency? A total of 28 studies (N = 829, Experimental Group n = 430) from 2001 to 2020 (Median = 2014, SD = 5.43) were analyzed and results were organized into four different sections: expert-novice samples, perceptual-cognitive tasks and neuroimaging technologies, efficiency paradox, and the cluster analysis. Researchers examined a wide range of sport-specific videos and multiple object tracking (MOT) specific to 18 different sports and utilized blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalogram (EEG). Expert-novice comparisons were often adopted into investigations about the variations in general about optimal-controlled performance, neurophysiology, and behavioral brain research. Experts tended to perform at faster speeds, more accurate motor behavior, and with greater efficiency than novices. Experts report lower activity levels in the sensory and motor cortex with less energy expenditure, experts will possibly be more productive. These findings generally supported the NEH across the studies reviewed. However, an efficiency paradox and proficient brain functioning were revealed as the complementary hypothesis of the NEH. The discussion concentrates on strengths and key limitations. The conclusion highlights additional concerns and recommendations for prospective researchers aiming to investigate a broader range of populations and sports.
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Affiliation(s)
- Longxi Li
- Department of Physical Education and Health Education, Springfield College, Springfield, MA, United States
| | - Daniel M Smith
- Department of Physical Education and Health Education, Springfield College, Springfield, MA, United States
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22
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23
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Deco G, Vidaurre D, Kringelbach ML. Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nat Hum Behav 2021; 5:497-511. [PMID: 33398141 PMCID: PMC8060164 DOI: 10.1038/s41562-020-01003-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 10/12/2020] [Indexed: 12/31/2022]
Abstract
A central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a 'global workspace', but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common 'functional rich club'. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
| | - Diego Vidaurre
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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24
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Rentzeperis I, van Leeuwen C. Adaptive Rewiring in Weighted Networks Shows Specificity, Robustness, and Flexibility. Front Syst Neurosci 2021; 15:580569. [PMID: 33737871 PMCID: PMC7960922 DOI: 10.3389/fnsys.2021.580569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 02/02/2021] [Indexed: 11/13/2022] Open
Abstract
Brain network connections rewire adaptively in response to neural activity. Adaptive rewiring may be understood as a process which, at its every step, is aimed at optimizing the efficiency of signal diffusion. In evolving model networks, this amounts to creating shortcut connections in regions with high diffusion and pruning where diffusion is low. Adaptive rewiring leads over time to topologies akin to brain anatomy: small worlds with rich club and modular or centralized structures. We continue our investigation of adaptive rewiring by focusing on three desiderata: specificity of evolving model network architectures, robustness of dynamically maintained architectures, and flexibility of network evolution to stochastically deviate from specificity and robustness. Our adaptive rewiring model simulations show that specificity and robustness characterize alternative modes of network operation, controlled by a single parameter, the rewiring interval. Small control parameter shifts across a critical transition zone allow switching between the two modes. Adaptive rewiring exhibits greater flexibility for skewed, lognormal connection weight distributions than for normally distributed ones. The results qualify adaptive rewiring as a key principle of self-organized complexity in network architectures, in particular of those that characterize the variety of functional architectures in the brain.
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Affiliation(s)
| | - Cees van Leeuwen
- Brain and Cognition Research Unit, KU Leuven, Leuven, Belgium
- Department of Cognitive and Developmental Psychology, University of Technology Kaiserslautern, Kaiserslautern, Germany
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25
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Abstract
Complex networks are abundant in nature and many share an important structural property: they contain a few nodes that are abnormally highly connected (hubs). Some of these hubs are called influencers because they couple strongly to the network and play fundamental dynamical and structural roles. Strikingly, despite the abundance of networks with influencers, little is known about their response to stochastic forcing. Here, for oscillatory dynamics on influencer networks, we show that subjecting influencers to an optimal intensity of noise can result in enhanced network synchronization. This new network dynamical effect, which we call coherence resonance in influencer networks, emerges from a synergy between network structure and stochasticity and is highly nonlinear, vanishing when the noise is too weak or too strong. Our results reveal that the influencer backbone can sharply increase the dynamical response in complex systems of coupled oscillators.
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26
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Deco G, Kringelbach ML. Turbulent-like Dynamics in the Human Brain. Cell Rep 2020; 33:108471. [PMID: 33296654 PMCID: PMC7725672 DOI: 10.1016/j.celrep.2020.108471] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Revised: 09/07/2020] [Accepted: 11/11/2020] [Indexed: 12/11/2022] Open
Abstract
Turbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of the brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1,003 healthy participants, we demonstrate turbulent-like human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed turbulent-like dynamics, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the exponential distance rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulent-like brain activity and optimal brain function. Overall, our results reveal a way of analyzing and modeling whole-brain dynamics that suggests a turbulent-like dynamic intrinsic backbone facilitating large-scale network communication.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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27
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Liu ZQ, Zheng YQ, Misic B. Network topology of the marmoset connectome. Netw Neurosci 2020; 4:1181-1196. [PMID: 33409435 PMCID: PMC7781610 DOI: 10.1162/netn_a_00159] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/21/2020] [Indexed: 12/11/2022] Open
Abstract
The brain is a complex network of interconnected and interacting neuronal populations. Global efforts to understand the emergence of behavior and the effect of perturbations depend on accurate reconstruction of white matter pathways, both in humans and in model organisms. An emerging animal model for next-generation applied neuroscience is the common marmoset (Callithrix jacchus). A recent open respository of retrograde and anterograde tract tracing presents an opportunity to systematically study the network architecture of the marmoset brain (Marmoset Brain Architecture Project; http://www.marmosetbrain.org). Here we comprehensively chart the topological organization of the mesoscale marmoset cortico-cortical connectome. The network possesses multiple nonrandom attributes that promote a balance between segregation and integration, including near-minimal path length, multiscale community structure, a connective core, a unique motif composition, and multiple cavities. Altogether, these structural attributes suggest a link between network architecture and function. Our findings are consistent with previous reports across a range of species, scales, and reconstruction technologies, suggesting a small set of organizational principles universal across phylogeny. Collectively, these results provide a foundation for future anatomical, functional, and behavioral studies in this model organism.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Ying-Qiu Zheng
- Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
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28
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Shadi K, Dyer E, Dovrolis C. Multisensory integration in the mouse cortical connectome using a network diffusion model. Netw Neurosci 2020; 4:1030-1054. [PMID: 33195947 PMCID: PMC7655044 DOI: 10.1162/netn_a_00164] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 08/03/2020] [Indexed: 01/05/2023] Open
Abstract
Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We find that a small number of brain regions–the claustrum and the parietal temporal cortex being at the top of the list–are involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks. Having a structural network representation of connectivity in the brain is instrumental in analyzing communication dynamics and neural information processing. In this work, we make steps towards understanding multisensory information flow and integration using a network diffusion approach. In particular, we model the flow of evoked activity, initiated by stimuli at primary sensory regions, using the asynchronous linear threshold (ALT) diffusion model. The ALT model captures how evoked activity that originates at a given region of the cortex “ripples through” other brain regions (referred to as an activation cascade). We apply the ALT model to the mouse connectome provided by the Allen Institute for Brain Science. A first result, using functional datasets based on voltage-sensitive dye (VSD) imaging, is that the ALT model, despite its simplicity, predicts the temporal ordering of each sensory activation cascade quite accurately. We further apply this model to study multisensory integration and find that a small number of brain regionsthe claustrum and the parietal temporal cortex being at the top of the listare involved in almost all cortical sensory streams. This suggests that the cortex relies on an hourglass architecture to first integrate and compress multisensory information from multiple sensory regions, before utilizing that lower dimensionality representation in higher level association regions and more complex cognitive tasks.
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Affiliation(s)
- Kamal Shadi
- School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Eva Dyer
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
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29
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Fukushima M, Sporns O. Structural determinants of dynamic fluctuations between segregation and integration on the human connectome. Commun Biol 2020; 3:606. [PMID: 33097809 PMCID: PMC7584581 DOI: 10.1038/s42003-020-01331-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 09/28/2020] [Indexed: 11/08/2022] Open
Abstract
While segregation and integration of neural information in the neocortex are thought to be important for human behavior and cognition, the neural substrates enabling their dynamic fluctuations remain elusive. To tackle this problem, we aim to identify specific network features of the connectome that are responsible for the emergence of dynamic fluctuations between segregated and integrated patterns in human resting-state functional connectivity. Here we examine the contributions of network features to dynamic fluctuations by constructing rewired surrogate connectome in which network features of interest are selectively preserved, and then by assessing the magnitude of fluctuations simulated with these surrogates. Our analysis demonstrates significant contributions from global geometry and topology of the connectome, as well as from localized structural connections involving visual areas. By providing structural accounts of dynamic fluctuations in functional connectivity, this study offers new insights into generative mechanisms driving temporal changes in segregation and integration in the brain.
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Affiliation(s)
- Makoto Fukushima
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
- Data Science Center, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.
- Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN, 47405, USA
- Indiana University Network Science Institute, 1001 IN-45, Bloomington, IN, 47408, USA
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30
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Moretti P, Hütt MT. Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks. Proc Natl Acad Sci U S A 2020; 117:18332-18340. [PMID: 32690716 PMCID: PMC7414146 DOI: 10.1073/pnas.1919785117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
In models of excitable dynamics on graphs, excitations can travel in both directions of an undirected link. However, as a striking interplay of dynamics and network topology, excitations often establish a directional preference. Some of these cases of "link-usage asymmetry" are local in nature and can be mechanistically understood, for instance, from the degree gradient of a link (i.e., the difference in node degrees at both ends of the link). Other contributions to the link-usage asymmetry are instead, as we show, self-organized in nature, and strictly nonlocal. This is the case for excitation waves, where the preferential propagation of excitations along a link depends on its orientation with respect to a hub acting as a source, even if the link in question is several steps away from the hub itself. Here, we identify and quantify the contribution of such self-organized patterns to link-usage asymmetry and show that they extend to ranges significantly longer than those ascribed to local patterns. We introduce a topological characterization, the hub-set-orientation prevalence of a link, which indicates its average orientation with respect to the hubs of a graph. Our numerical results show that the hub-set-orientation prevalence of a link strongly correlates with the preferential usage of the link in the direction of propagation away from the hub core of the graph. Our methodology is embedding-agnostic and allows for the measurement of wave signals and the sizes of the cores from which they originate.
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Affiliation(s)
- Paolo Moretti
- Institute of Materials Simulation, Department of Materials Science, Friedrich-Alexander-University Erlangen-Nürnberg, D-90762 Fürth, Germany;
| | - Marc-Thorsten Hütt
- Department of Life Sciences and Chemistry, Jacobs University Bremen, D-28759 Bremen, Germany
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31
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Chen Y, Zhang ZK, He Y, Zhou C. A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links. Cereb Cortex 2020; 30:4771-4789. [PMID: 32313935 PMCID: PMC7391281 DOI: 10.1093/cercor/bhaa060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 02/20/2020] [Accepted: 02/24/2020] [Indexed: 01/21/2023] Open
Abstract
As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.
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Affiliation(s)
- Yuhan Chen
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Department of Physics, Centre for Nonlinear Studies, and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
| | - Zi-Ke Zhang
- College of Media and International Culture, Zhejiang University, Hangzhou 310058, China
- Alibaba Research Center for Complex Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- Department of Physics, Zhejiang University, Hangzhou 310027, China
- Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China
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32
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Wang Z, Liu Z. A Brief Review of Chimera State in Empirical Brain Networks. Front Physiol 2020; 11:724. [PMID: 32714208 PMCID: PMC7344215 DOI: 10.3389/fphys.2020.00724] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 06/02/2020] [Indexed: 11/24/2022] Open
Abstract
Understanding the human brain and its functions has always been an interesting and challenging problem. Recently, a significant progress on this problem has been achieved on the aspect of chimera state where a coexistence of synchronized and unsynchronized states can be sustained in identical oscillators. This counterintuitive phenomenon is closely related to the unihemispheric sleep in some marine mammals and birds and has recently gotten a hot attention in neural systems, except the previous studies in non-neural systems such as phase oscillators. This review will briefly summarize the main results of chimera state in neuronal systems and pay special attention to the network of cerebral cortex, aiming to accelerate the study of chimera state in brain networks. Some outlooks are also discussed.
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Affiliation(s)
| | - Zonghua Liu
- School of Physics and Electronic Science, East China Normal University, Shanghai, China
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33
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Alonso Martínez S, Marsman JBC, Kringelbach ML, Deco G, Ter Horst GJ. Reduced spatiotemporal brain dynamics are associated with increased depressive symptoms after a relationship breakup. Neuroimage Clin 2020; 27:102299. [PMID: 32516738 PMCID: PMC7284067 DOI: 10.1016/j.nicl.2020.102299] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 05/14/2020] [Accepted: 05/17/2020] [Indexed: 12/24/2022]
Abstract
Depressive symptoms following a stressful life event, such as a relationship breakup, are common, and constitute a potent risk factor for the onset of a major depressive episode. Resting-state neuroimaging studies have increasingly identified abnormal whole-brain communication in patients with depression, but it is currently unclear whether depressive symptoms in individuals without a clinical diagnosis have reliable neural underpinnings. We investigated to what extent the severity of depressive symptoms in a non-clinical sample was associated with imbalances in the complex dynamics of the brain during rest. To this end, a novel intrinsic ignition approach was applied to resting-state neuroimaging data from sixty-nine participants with varying degrees of depressive symptoms following a relationship breakup. Ignition-based measures of integration, hierarchy, and metastability were calculated for each participant, revealing a negative correlation between these measures and depressive ratings. We found that the severity of depressive symptoms was associated with deficits in the brain's capacity to globally integrate and process information over time. Furthermore, we found that increased depressive symptoms were associated with reduced spatial diversity (i.e., hierarchy) and reduced temporal variability (i.e., metastability) in the functional organization of the brain. These findings suggest the merit of investigating constrained dynamical complexity as it is sensitive to the level of depressive symptoms even in a non-clinical sample.
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Affiliation(s)
- Sonsoles Alonso Martínez
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, 9713 AW Groningen, the Netherlands.
| | - Jan-Bernard C Marsman
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, 9713 AW Groningen, the Netherlands.
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, OX3 7JX Oxford, United Kingdom; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark; Life and Health Sciences Research Institute, School of Medicine, University of Minho, 4710-057 Braga, Portugal.
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, 08010 Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, 08010 Barcelona, Spain.
| | - Gert J Ter Horst
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, 9713 AW Groningen, the Netherlands.
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34
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Fischer FU, Wolf D, Fellgiebel A. Connectivity and morphology of hubs of the cerebral structural connectome are associated with brain resilience in AD- and age-related pathology. Brain Imaging Behav 2020; 13:1650-1664. [PMID: 30980275 DOI: 10.1007/s11682-019-00090-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The physiological basis of resilience to age-associated and AD-typical neurodegenerative pathology is still not well understood. So far, the established resilience factor intelligence has been shown to be associated with white matter network global efficiency, a key constituent of which are highly connected hubs. However, hub properties have also been shown to be impaired in AD. Individual predisposition or vulnerability of hub properties may thus modulate the impact of pathology on cognitive outcome and form part of the physiological basis of resilience. 85 cognitively normal elderly subjects and patients with MCI with DWI, MRI and AV45-PET scans were included from ADNI. We reconstructed the global WM networks in each subject and characterized hub-properties of GM regions using graph theory by calculating regional betweenness centrality. Subsequently, we investigated whether regional GM volume (GMV) and structural WM connectivity (WMC) of more hub-like regions was more associated with resilience, quantified as cognitive performance independent of amyloid burden, tau and WM lesions. Subjects with higher resilience showed higher increased regional GMV and WMC in more hub-like compared to less hub-like GM-regions. Additionally, this association was in some instances further increased at elevated amounts of brain pathology. Higher GMV and WMC of more hub-like regions may contribute more to resilience compared to less hub-like regions, reflecting their increased importance to brain network efficiency, and may thus form part of the neurophysiological basis of resilience. Future studies should investigate the factors leading to higher GMV and WMC of hubs in non-demented elderly with higher resilience.
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Affiliation(s)
- Florian U Fischer
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany. .,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany.
| | - Dominik Wolf
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany.,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany
| | - Andreas Fellgiebel
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Untere Zahlbacher Str. 8, 55131, Mainz, Germany.,Center for Mental Health in Old Age, Landeskrankenhaus (AöR), Hartmühlenweg 2-4, 55122, Mainz, Germany
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35
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Spanoudis G, Demetriou A. Mapping Mind-Brain Development: Towards a Comprehensive Theory. J Intell 2020; 8:E19. [PMID: 32357452 PMCID: PMC7713015 DOI: 10.3390/jintelligence8020019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/13/2020] [Accepted: 04/20/2020] [Indexed: 12/12/2022] Open
Abstract
The relations between the developing mind and developing brain are explored. We outline a theory of intellectual development postulating that the mind comprises four systems of processes (domain-specific, attention and working memory, reasoning, and cognizance) developing in four cycles (episodic, realistic, rule-based, and principle-based representations, emerging at birth, 2, 6, and 11 years, respectively), with two phases in each. Changes in reasoning relate to processing efficiency in the first phase and working memory in the second phase. Awareness of mental processes is recycled with the changes in each cycle and drives their integration into the representational unit of the next cycle. Brain research shows that each type of processes is served by specialized brain networks. Domain-specific processes are rooted in sensory cortices; working memory processes are mainly rooted in hippocampal, parietal, and prefrontal cortices; abstraction and alignment processes are rooted in parietal, frontal, and prefrontal and medial cortices. Information entering these networks is available to awareness processes. Brain networks change along the four cycles, in precision, connectivity, and brain rhythms. Principles of mind-brain interaction are discussed.
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Affiliation(s)
- George Spanoudis
- Psychology Department, University of Cyprus, 1678 Nicosia, Cyprus
| | - Andreas Demetriou
- Department of Psychology, University of Nicosia, 1700 Nicosia, Cyprus;
- Cyprus Academy of Science, Letters, and Arts, 1700 Nicosia, Cyprus
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36
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Carpentier SM, McCulloch AR, Brown TM, Faber SEM, Ritter P, Wang Z, Salimpoor V, Shen K, McIntosh AR. Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward. J Cogn Neurosci 2020; 32:734-745. [DOI: 10.1162/jocn_a_01508] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Abstract
Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we term “complexity matching.” Music is an ideal stimulus because it is a multidimensional signal with a rich temporal evolution and because of its emotion- and reward-inducing potential. When participants focused on acoustic features of music, we found that EEG complexity was lower and more closely resembled the musical complexity compared to an emotional task that asked them to monitor how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire correlated with less complexity matching but higher EEG complexity. Compared with perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those linked to the external stimulus. In other words, the brain adds something when judging the emotional valence of music.
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37
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Pei S. Influencer identification in dynamical complex systems. JOURNAL OF COMPLEX NETWORKS 2020; 8:cnz029. [PMID: 32774857 PMCID: PMC7391989 DOI: 10.1093/comnet/cnz029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 07/13/2019] [Indexed: 06/11/2023]
Abstract
The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e. the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g. independent cascading models) or discontinuous phase transitions (e.g. threshold models). We conclude the review with a summary and an outlook.
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Affiliation(s)
- Sen Pei
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY, USA
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38
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Tanaka G, Nakane R, Takeuchi T, Yamane T, Nakano D, Katayama Y, Hirose A. Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:24-38. [PMID: 30892239 DOI: 10.1109/tnnls.2019.2899344] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The development of hardware neural networks, including neuromorphic hardware, has been accelerated over the past few years. However, it is challenging to operate very large-scale neural networks with low-power hardware devices, partly due to signal transmissions through a massive number of interconnections. Our aim is to deal with the issue of communication cost from an algorithmic viewpoint and study learning algorithms for energy-efficient information processing. Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory. In the first approach following classical methods, we focus on sparse modular network structures inspired by biological brain networks and examine their storage capacity under an iterative learning rule. We show that incorporating long-range intermodule connections into purely modular networks can enhance the cost-performance ratio. In the second approach, we formulate for the first time an optimization problem where the network sparsity is maximized under the constraints imposed by a pattern embedding condition. We show that there is a tradeoff between the interconnection cost and the computational performance in the optimized networks. We demonstrate that the optimized networks can achieve a better cost-performance ratio compared with those considered in the first approach. We show the effectiveness of the optimization approach mainly using binary patterns and apply it also to gray-scale image restoration. Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.
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Saenger VM, Ponce-Alvarez A, Adhikari M, Hagmann P, Deco G, Corbetta M. Linking Entropy at Rest with the Underlying Structural Connectivity in the Healthy and Lesioned Brain. Cereb Cortex 2019; 28:2948-2958. [PMID: 28981635 DOI: 10.1093/cercor/bhx176] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Indexed: 01/06/2023] Open
Abstract
The brain is a network that mediates information processing through a wide range of states. The extent of state diversity is a reflection of the entropy of the network. Here we measured the entropy of brain regions (nodes) in empirical and modeled functional networks reconstructed from resting state fMRI to address the connection of entropy at rest with the underlying structure measured through diffusion spectrum imaging. Using 18 empirical and 18 modeled stroke networks, we also investigated the effect that focal lesions have on node entropy and information diffusion. Overall, positive correlations between node entropy and structure were observed, especially between node entropy and node strength in both empirical and modeled data. Although lesions were restricted to one hemisphere in all stroke patients, entropy reduction was not only present in nodes from the damaged hemisphere, but also in nodes from the contralesioned hemisphere, an effect replicated in modeled stroke networks. Globally, information diffusion was also affected in empirical and modeled strokes compared with healthy controls. This is the first study showing that artificial lesions affect local and global network aspects in very similar ways compared with empirical strokes, shedding new light into the functional nature of stroke.
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Affiliation(s)
- Victor M Saenger
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Adrián Ponce-Alvarez
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mohit Adhikari
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
| | - Patric Hagmann
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gustavo Deco
- Department of Information and Communication Technologies, Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain.,Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Clayton VIC, Australia
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
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Ktena SI, Schirmer MD, Etherton MR, Giese AK, Tuozzo C, Mills BB, Rueckert D, Wu O, Rost NS. Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke. Stroke 2019; 50:2761-2767. [PMID: 31510905 PMCID: PMC6756947 DOI: 10.1161/strokeaha.119.025738] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Background and Purpose- The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain's connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods- In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain's prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient's diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2-5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R2). Results- Of 41 patients (mean age [range]: 70 [45-89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R2 increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions- In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.
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Affiliation(s)
- Sofia Ira Ktena
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom (S.I.K., D.R.)
| | - Markus D Schirmer
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge (M.D.S.)
- Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany (M.D.S.)
| | - Mark R Etherton
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
| | - Anne-Katrin Giese
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
| | - Carissa Tuozzo
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
| | - Brittany B Mills
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom (S.I.K., D.R.)
| | - Ona Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown (O.W.)
| | - Natalia S Rost
- From the Stroke Division and Massachusetts General Hospital, J. Philip Kistler Stroke Research Center, Harvard Medical School, Boston (S.I.K., M.D.S., M.R.E., A.-K.G., C.T., B.B.M., N.S.R.)
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Seguin C, Razi A, Zalesky A. Inferring neural signalling directionality from undirected structural connectomes. Nat Commun 2019; 10:4289. [PMID: 31537787 PMCID: PMC6753104 DOI: 10.1038/s41467-019-12201-w] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 08/22/2019] [Indexed: 11/09/2022] Open
Abstract
Neural information flow is inherently directional. To date, investigation of directional communication in the human structural connectome has been precluded by the inability of non-invasive neuroimaging methods to resolve axonal directionality. Here, we demonstrate that decentralized measures of network communication, applied to the undirected topology and geometry of brain networks, can infer putative directions of large-scale neural signalling. We propose the concept of send-receive communication asymmetry to characterize cortical regions as senders, receivers or neutral, based on differences between their incoming and outgoing communication efficiencies. Our results reveal a send-receive cortical hierarchy that recapitulates established organizational gradients differentiating sensory-motor and multimodal areas. We find that send-receive asymmetries are significantly associated with the directionality of effective connectivity derived from spectral dynamic causal modeling. Finally, using fruit fly, mouse and macaque connectomes, we provide further evidence suggesting that directionality of neural signalling is significantly encoded in the undirected architecture of nervous systems.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, 3010, Australia.
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, 3800, Australia
- The Wellcome Trust Centre for Neuroimaging, University College London, London, WC1E 6BT, UK
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Sindh, 75270, Pakistan
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, 3010, Australia
- Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, 3010, Australia
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42
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de Lange SC, Ardesch DJ, van den Heuvel MP. Connection strength of the macaque connectome augments topological and functional network attributes. Netw Neurosci 2019; 3:1051-1069. [PMID: 31637338 PMCID: PMC6777983 DOI: 10.1162/netn_a_00101] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 06/14/2019] [Indexed: 12/22/2022] Open
Abstract
Mammalian brains constitute complex organized networks of neural projections. On top of their binary topological organization, the strength (or weight) of these neural projections can be highly variable across connections and is thus likely of additional importance to the overall topological and functional organization of the network. Here we investigated the specific distribution pattern of connection strength in the macaque connectome. We performed weighted and binary network analysis on the cortico-cortical connectivity of the macaque provided by the unique tract-tracing dataset of Markov and colleagues (2014) and observed in both analyses a small-world, modular and rich club organization. Moreover, connectivity strength showed a distribution augmenting the architecture identified in the binary network version by enhancing both local network clustering and the central infrastructure for global topological communication and integration. Functional consequences of this topological distribution were further examined using the Kuramoto model for simulating interactions between brain regions and showed that the connectivity strength distribution across connections enhances synchronization within modules and between rich club hubs. Together, our results suggest that neural pathway strength promotes topological properties in the macaque connectome for local processing and global network integration.
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Affiliation(s)
- Siemon C. de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Martijn P. van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam, The Netherlands
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43
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Hilgetag CC, Beul SF, van Albada SJ, Goulas A. An architectonic type principle integrates macroscopic cortico-cortical connections with intrinsic cortical circuits of the primate brain. Netw Neurosci 2019; 3:905-923. [PMID: 31637331 PMCID: PMC6777964 DOI: 10.1162/netn_a_00100] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 06/07/2019] [Indexed: 12/31/2022] Open
Abstract
The connections linking neurons within and between cerebral cortical areas form a multiscale network for communication. We review recent work relating essential features of cortico-cortical connections, such as their existence and laminar origins and terminations, to fundamental structural parameters of cortical areas, such as their distance, similarity in cytoarchitecture, defined by lamination or neuronal density, and other macroscopic and microscopic structural features. These analyses demonstrate the presence of an architectonic type principle. Across species and cortices, the essential features of cortico-cortical connections vary consistently and strongly with the cytoarchitectonic similarity of cortical areas. By contrast, in multivariate analyses such relations were not found consistently for distance, similarity of cortical thickness, or cellular morphology. Gradients of laminar cortical differentiation, as reflected in overall neuronal density, also correspond to regional variations of cellular features, forming a spatially ordered natural axis of concerted architectonic and connectional changes across the cortical sheet. The robustness of findings across mammalian brains allows cross-species predictions of the existence and laminar patterns of projections, including estimates for the human brain that are not yet available experimentally. The architectonic type principle integrates cortical connectivity and architecture across scales, with implications for computational explorations of cortical physiology and developmental mechanisms.
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Affiliation(s)
- Claus C Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
| | - Sarah F Beul
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
| | - Sacha J van Albada
- Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), and JARA-Institute of Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Germany
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Germany
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44
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Scholtens LH, Feldman Barrett L, van den Heuvel MP. Cross-Species Evidence of Interplay Between Neural Connectivity at the Micro- and Macroscale of Connectome Organization in Human, Mouse, and Rat Brain. Brain Connect 2019; 8:595-603. [PMID: 30479137 DOI: 10.1089/brain.2018.0622] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
The mammalian brain describes a multiscale system. At the microscale, axonal, dendritic, and synaptic elements ensure neuron-to-neuron communication, and at the macroscale, large-scale projections form the anatomical wiring for communication between cortical areas. Although it is clear that both levels of neural organization play a crucial role in brain functioning, their interaction is not extensively studied. Connectome studies of the mammalian brain in cat, macaque, and human have recently shown that regions with larger and more complex pyramidal cells to have more macroscale corticocortical connections. In this study, we aimed to further validate these cross-scale findings in the human, mouse, and rat brain. We combined neuron reconstructions from the NeuroMorpho.org neuroarchitecture database with macroscale connectivity data derived from connectome mapping by means of tract-tracing (rat, mouse) and in vivo diffusion magnetic resonance imaging (human). Across these three mammalian species, we show cortical variation in neural organization to be associated with features of macroscale connectivity, with cortical variation in neuronal complexity explaining significant proportions of cortical variation in the number of white matter projections of cortical areas. Our findings converge on the notion of a relationship between features of micro- and macroscale neural connectivity to form a central aspect of mammalian neural architecture.
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Affiliation(s)
- Lianne H Scholtens
- 1 Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lisa Feldman Barrett
- 2 Department of Psychology, Northeastern University, Boston, Massachusetts.,3 Department of Psychiatry and the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Martijn P van den Heuvel
- 1 Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,4 Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, The Netherlands
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45
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Faci-Lázaro S, Soriano J, Gómez-Gardeñes J. Impact of targeted attack on the spontaneous activity in spatial and biologically-inspired neuronal networks. CHAOS (WOODBURY, N.Y.) 2019; 29:083126. [PMID: 31472487 DOI: 10.1063/1.5099038] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 08/08/2019] [Indexed: 06/10/2023]
Abstract
We study the structural and dynamical consequences of damage in spatial neuronal networks. Inspired by real in vitro networks, we construct directed networks embedded in a two-dimensional space and follow biological rules for designing the wiring of the system. As a result, synthetic cultures display strong metric correlations similar to those observed in real experiments. In its turn, neuronal dynamics is incorporated through the Izhikevich model adopting the parameters derived from observation in real cultures. We consider two scenarios for damage, targeted attacks on those neurons with the highest out-degree and random failures. By analyzing the evolution of both the giant connected component and the dynamical patterns of the neurons as nodes are removed, we observe that network activity halts for a removal of 50% of the nodes in targeted attacks, much lower than the 70% node removal required in the case of random failures. Notably, the decrease of neuronal activity is not gradual. Both damage scenarios portray "boosts" of activity just before full silencing that are not present in equivalent random (Erdös-Rényi) graphs. These boosts correspond to small, spatially compact subnetworks that are able to maintain high levels of activity. Since these subnetworks are absent in the equivalent random graphs, we hypothesize that metric correlations facilitate the existence of local circuits sufficiently integrated to maintain activity, shaping an intrinsic mechanism for resilience.
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Affiliation(s)
- Sergio Faci-Lázaro
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
| | - Jordi Soriano
- Departament de Física de la Matèria Condensada, Universitat de Barcelona, E-08028 Barcelona, Spain
| | - Jesús Gómez-Gardeñes
- GOTHAM Lab, Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, 50018 Zaragoza, Spain
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46
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Arnatkevičiūtė A, Fulcher BD, Fornito A. Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks. Front Neural Circuits 2019; 13:47. [PMID: 31379515 PMCID: PMC6659348 DOI: 10.3389/fncir.2019.00047] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 07/04/2019] [Indexed: 12/04/2022] Open
Abstract
Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.
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Affiliation(s)
- Aurina Arnatkevičiūtė
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
| | - Ben D. Fulcher
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
- School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - Alex Fornito
- Monash Biomedical Imaging, School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Clayton, VIC, Australia
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47
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Oldham S, Fornito A. The development of brain network hubs. Dev Cogn Neurosci 2019; 36:100607. [PMID: 30579789 PMCID: PMC6969262 DOI: 10.1016/j.dcn.2018.12.005] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/24/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023] Open
Abstract
Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.
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Affiliation(s)
- Stuart Oldham
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia.
| | - Alex Fornito
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia
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48
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Human olfactory-auditory integration requires phase synchrony between sensory cortices. Nat Commun 2019; 10:1168. [PMID: 30858379 PMCID: PMC6411726 DOI: 10.1038/s41467-019-09091-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 02/21/2019] [Indexed: 12/22/2022] Open
Abstract
Multisensory integration is particularly important in the human olfactory system, which is highly dependent on non-olfactory cues, yet its underlying neural mechanisms are not well understood. In this study, we use intracranial electroencephalography techniques to record neural activity in auditory and olfactory cortices during an auditory-olfactory matching task. Spoken cues evoke phase locking between low frequency oscillations in auditory and olfactory cortices prior to odor arrival. This phase synchrony occurs only when the participant's later response is correct. Furthermore, the phase of low frequency oscillations in both auditory and olfactory cortical areas couples to the amplitude of high-frequency oscillations in olfactory cortex during correct trials. These findings suggest that phase synchrony is a fundamental mechanism for integrating cross-modal odor processing and highlight an important role for primary olfactory cortical areas in multisensory integration with the olfactory system.
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49
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Griffa A, Van den Heuvel MP. Rich-club neurocircuitry: function, evolution, and vulnerability. DIALOGUES IN CLINICAL NEUROSCIENCE 2018. [PMID: 30250389 PMCID: PMC6136122 DOI: 10.31887/dcns.2018.20.2/agriffa] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Over the past decades, network neuroscience has played a fundamental role in the understanding of large-scale brain connectivity architecture. Brains, and more generally nervous systems, can be modeled as sets of elements (neurons, assemblies, or cortical chunks) that dynamically interact through a highly structured and adaptive neurocircuitry. An interesting property of neural networks is that elements rich in connections are central to the network organization and tend to interconnect strongly with each other, forming so-called rich clubs. The ubiquity of rich-club organization across different species and scales of investigation suggests that this topology could be a distinctive feature of biological systems with information processing capabilities. This review surveys recent neuroimaging, computational, and cross-species comparative literature to offer an insight into the function and origin of rich-club architecture in nervous systems, discussing its relevance to human cognition and behavior, and vulnerability to brain disorders.
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Affiliation(s)
- Alessandra Griffa
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands
| | - Martijn P Van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, The Netherlands; Department of Clinical Genetics, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands
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50
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Beul SF, Goulas A, Hilgetag CC. Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle. PLoS Comput Biol 2018; 14:e1006550. [PMID: 30475798 PMCID: PMC6261046 DOI: 10.1371/journal.pcbi.1006550] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 10/06/2018] [Indexed: 12/31/2022] Open
Abstract
The architectonic type principle relates patterns of cortico-cortical connectivity to the relative architectonic differentiation of cortical regions. One mechanism through which the observed close relation between cortical architecture and connectivity may be established is the joint development of cortical areas and their connections in developmental time windows. Here, we describe a theoretical exploration of the possible mechanistic underpinnings of the architectonic type principle, by performing systematic computational simulations of cortical development. The main component of our in silico model was a developing two-dimensional cortical sheet, which was gradually populated by neurons that formed cortico-cortical connections. To assess different explanatory mechanisms, we varied the spatiotemporal trajectory of the simulated neurogenesis. By keeping the rules governing axon outgrowth and connection formation constant across all variants of simulated development, we were able to create model variants which differed exclusively by the specifics of when and where neurons were generated. Thus, all differences in the resulting connectivity were due to the variations in spatiotemporal growth trajectories. Our results demonstrated that a prescribed targeting of interareal connection sites was not necessary for obtaining a realistic replication of the experimentally observed relation between connection patterns and architectonic differentiation. Instead, we found that spatiotemporal interactions within the forming cortical sheet were sufficient if a small number of empirically well-grounded assumptions were met, namely planar, expansive growth of the cortical sheet around two points of origin as neurogenesis progressed, stronger architectonic differentiation of cortical areas for later neurogenetic time windows, and stochastic connection formation. Thus, our study highlights a potential mechanism of how relative architectonic differentiation and cortical connectivity become linked during development. We successfully predicted connectivity in two species, cat and macaque, from simulated cortico-cortical connection networks, which further underscored the general applicability of mechanisms through which the architectonic type principle can explain cortical connectivity in terms of the relative architectonic differentiation of cortical regions.
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Affiliation(s)
- Sarah F. Beul
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Neural Systems Laboratory, Department of Health Sciences, Boston University, Boston, Massachusetts, United States of America
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