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Yang L, Wang Z, Wang G, Liang L, Liu M, Wang J. Brain-inspired modular echo state network for EEG-based emotion recognition. Front Neurosci 2024; 18:1305284. [PMID: 38495107 PMCID: PMC10940514 DOI: 10.3389/fnins.2024.1305284] [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: 10/01/2023] [Accepted: 01/10/2024] [Indexed: 03/19/2024] Open
Abstract
Previous studies have successfully applied a lightweight recurrent neural network (RNN) called Echo State Network (ESN) for EEG-based emotion recognition. These studies use intrinsic plasticity (IP) and synaptic plasticity (SP) to tune the hidden reservoir layer of ESN, yet they require extra training procedures and are often computationally complex. Recent neuroscientific research reveals that the brain is modular, consisting of internally dense and externally sparse subnetworks. Furthermore, it has been proved that this modular topology facilitates information processing efficiency in both biological and artificial neural networks (ANNs). Motivated by these findings, we propose Modular Echo State Network (M-ESN), where the hidden layer of ESN is directly initialized to a more efficient modular structure. In this paper, we first describe our novel implementation method, which enables us to find the optimal module numbers, local and global connectivity. Then, the M-ESN is benchmarked on the DEAP dataset. Lastly, we explain why network modularity improves model performance. We demonstrate that modular organization leads to a more diverse distribution of node degrees, which increases network heterogeneity and subsequently improves classification accuracy. On the emotion arousal, valence, and stress/calm classification tasks, our M-ESN outperforms regular ESN by 5.44, 5.90, and 5.42%, respectively, while this difference when comparing with adaptation rules tuned ESNs are 0.77, 5.49, and 0.95%. Notably, our results are obtained using M-ESN with a much smaller reservoir size and simpler training process.
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Affiliation(s)
- Liuyi Yang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Zhaoze Wang
- School of Engineering and Applied Science, University of Pennsylvania, Pennsylvania, PA, United States
| | - Guoyu Wang
- Department of Auromation, Tiangong University, Tianjin, China
| | - Lixin Liang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Meng Liu
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Junsong Wang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
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2
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Aziz F, Slater LT, Bravo-Merodio L, Acharjee A, Gkoutos GV. Link prediction in complex network using information flow. Sci Rep 2023; 13:14660. [PMID: 37669983 PMCID: PMC10480459 DOI: 10.1038/s41598-023-41476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 08/27/2023] [Indexed: 09/07/2023] Open
Abstract
Link prediction in complex networks has recently attracted a great deal of attraction in diverse scientific domains, including social and biological sciences. Given a snapshot of a network, the goal is to predict links that are missing in the network or that are likely to occur in the near future. This problem has both theoretical and practical significance; it not only helps us to identify missing links in a network more efficiently by avoiding the expensive and time consuming experimental processes, but also allows us to study the evolution of a network with time. To address the problem of link prediction, numerous attempts have been made over the recent years that exploit the local and the global topological properties of the network to predict missing links in the network. In this paper, we use parametrised matrix forest index (PMFI) to predict missing links in a network. We show that, for small parameter values, this index is linked to a heat diffusion process on a graph and therefore encodes geometric properties of the network. We then develop a framework that combines the PMFI with a local similarity index to predict missing links in the network. The framework is applied to numerous networks obtained from diverse domains such as social network, biological network, and transport network. The results show that the proposed method can predict missing links with higher accuracy when compared to other state-of-the-art link prediction methods.
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Affiliation(s)
- Furqan Aziz
- School of Computing and Mathematical Sciences, University of Leicester, University Rd, Leicester, LE1 7RH, UK.
- Centre for Health Data Science, Birmingham, B15 2WB, UK.
| | - Luke T Slater
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK
- Centre for Health Data Science, Birmingham, B15 2WB, UK
| | - Laura Bravo-Merodio
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK
- Centre for Health Data Science, Birmingham, B15 2WB, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK
- MRC Health Data Research UK (HDR UK), London, UK
- Centre for Health Data Science, Birmingham, B15 2WB, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Institute of Translational Medicine, University of Birmingham, Birmingham, B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK
- MRC Health Data Research UK (HDR UK), London, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham, B15 2TT, UK
- Centre for Health Data Science, Birmingham, B15 2WB, UK
- Centre for Environmental Research & Advocacy, University of Birmingham, Birmingham, B15 2TT, UK
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3
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Where the genome meets the connectome: Understanding how genes shape human brain connectivity. Neuroimage 2021; 244:118570. [PMID: 34508898 DOI: 10.1016/j.neuroimage.2021.118570] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023] Open
Abstract
The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.
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4
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Zhang J, Xu L, Cui Z. Convergent developmental principles between Caenorhabditis elegans and human connectomes. Trends Cogn Sci 2021; 25:1015-1017. [PMID: 34657793 DOI: 10.1016/j.tics.2021.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 10/01/2021] [Indexed: 11/17/2022]
Abstract
A recent study by Witvliet et al. reconstructed the entire brain connectome for eight Caenorhabditis elegans spanning from birth to adulthood and described how synapse changes shape the connectome topology during development. Their data suggest some convergent developmental principles in connectome maturation between C. elegans and humans.
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Affiliation(s)
- Jinbo Zhang
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Longzhou Xu
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China.
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5
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Dvořáček J, Kodrík D. Drosophila reward system - A summary of current knowledge. Neurosci Biobehav Rev 2021; 123:301-319. [PMID: 33421541 DOI: 10.1016/j.neubiorev.2020.12.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 12/16/2020] [Accepted: 12/27/2020] [Indexed: 01/19/2023]
Abstract
The fruit fly Drosophila melanogaster brain is the most extensively investigated model of a reward system in insects. Drosophila can discriminate between rewarding and punishing environmental stimuli and consequently undergo associative learning. Functional models, especially those modelling mushroom bodies, are constantly being developed using newly discovered information, adding to the complexity of creating a simple model of the reward system. This review aims to clarify whether its reward system also includes a hedonic component. Neurochemical systems that mediate the 'wanting' component of reward in the Drosophila brain are well documented, however, the systems that mediate the pleasure component of reward in mammals, including those involving the endogenous opioid and endocannabinoid systems, are unlikely to be present in insects. The mushroom body components exhibit differential developmental age and different functional processes. We propose a hypothetical hierarchy of the levels of reinforcement processing in response to particular stimuli, and the parallel processes that take place concurrently. The possible presence of activity-silencing and meta-satiety inducing levels in Drosophila should be further investigated.
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Affiliation(s)
- Jiří Dvořáček
- Institute of Entomology, Biology Centre, CAS, and Faculty of Science, University of South Bohemia, Branišovská 31, 370 05 České Budějovice, Czech Republic.
| | - Dalibor Kodrík
- Institute of Entomology, Biology Centre, CAS, and Faculty of Science, University of South Bohemia, Branišovská 31, 370 05 České Budějovice, Czech Republic
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6
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Aziz F, Gul H, Uddin I, Gkoutos GV. Path-based extensions of local link prediction methods for complex networks. Sci Rep 2020; 10:19848. [PMID: 33199838 PMCID: PMC7670409 DOI: 10.1038/s41598-020-76860-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/02/2020] [Indexed: 02/08/2023] Open
Abstract
Link prediction in a complex network is a problem of fundamental interest in network science and has attracted increasing attention in recent years. It aims to predict missing (or future) links between two entities in a complex system that are not already connected. Among existing methods, local similarity indices are most popular that take into account the information of common neighbours to estimate the likelihood of existence of a connection between two nodes. In this paper, we propose global and quasi-local extensions of some commonly used local similarity indices. We have performed extensive numerical simulations on publicly available datasets from diverse domains demonstrating that the proposed extensions not only give superior performance, when compared to their respective local indices, but also outperform some of the current, state-of-the-art, local and global link-prediction methods.
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Affiliation(s)
- Furqan Aziz
- Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2TT, UK.
- MRC Health Data Research UK (HDR), Midlands, UK.
| | - Haji Gul
- City University of Science and Technology, Peshawar, Pakistan
| | - Irfan Uddin
- Kohat University of Science and Technology, Kohat, Pakistan
| | - Georgios V Gkoutos
- Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2TT, UK
- MRC Health Data Research UK (HDR), Midlands, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham, B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, Birmingham, B15 2TT, UK
- NIHR Biomedical Research Centre, Birmingham, B15 2TT, UK
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7
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Alicea B. Raising the Connectome: The Emergence of Neuronal Activity and Behavior in Caenorhabditis elegans. Front Cell Neurosci 2020; 14:524791. [PMID: 33100971 PMCID: PMC7522492 DOI: 10.3389/fncel.2020.524791] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 08/24/2020] [Indexed: 11/15/2022] Open
Abstract
The differentiation of neurons and formation of connections between cells is the basis of both the adult phenotype and behaviors tied to cognition, perception, reproduction, and survival. Such behaviors are associated with local (circuits) and global (connectome) brain networks. A solid understanding of how these networks emerge is critical. This opinion piece features a guided tour of early developmental events in the emerging connectome, which is crucial to a new view on the connectogenetic process. Connectogenesis includes associating cell identities with broader functional and developmental relationships. During this process, the transition from developmental cells to terminally differentiated cells is defined by an accumulation of traits that ultimately results in neuronal-driven behavior. The well-characterized developmental and cell biology of Caenorhabditis elegans will be used to build a synthesis of developmental events that result in a functioning connectome. Specifically, our view of connectogenesis enables a first-mover model of synaptic connectivity to be demonstrated using data representing larval synaptogenesis. In a first-mover model of Stackelberg competition, potential pre- and postsynaptic relationships are shown to yield various strategies for establishing various types of synaptic connections. By comparing these results to what is known regarding principles for establishing complex network connectivity, these strategies are generalizable to other species and developmental systems. In conclusion, we will discuss the broader implications of this approach, as what is presented here informs an understanding of behavioral emergence and the ability to simulate related biological phenomena.
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Affiliation(s)
- Bradly Alicea
- Orthogonal Research and Education Laboratory, Champaign, IL, United States
- OpenWorm Foundation, Boston, MA, United States
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8
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Pedersen M, Omidvarnia A, Shine JM, Jackson GD, Zalesky A. Reducing the influence of intramodular connectivity in participation coefficient. Netw Neurosci 2020; 4:416-431. [PMID: 32537534 PMCID: PMC7286311 DOI: 10.1162/netn_a_00127] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient (PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules.
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Affiliation(s)
- Mangor Pedersen
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Amir Omidvarnia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - James M Shine
- Brain and Mind Center, The University of Sydney, Sydney, New South Wales, Australia
| | - Graeme D Jackson
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia
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9
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Kaiser M. Computational models and fundamental constraints can inform the design of synthetic connectomes: Comment on "What would a synthetic connectome look like?" by Ithai Rabinowitch. Phys Life Rev 2019; 33:16-18. [PMID: 31416703 DOI: 10.1016/j.plrev.2019.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 08/05/2019] [Indexed: 10/26/2022]
Affiliation(s)
- Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, United Kingdom; Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.
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10
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Pospelov N, Nechaev S, Anokhin K, Valba O, Avetisov V, Gorsky A. Spectral peculiarity and criticality of a human connectome. Phys Life Rev 2019; 31:240-256. [PMID: 31353222 DOI: 10.1016/j.plrev.2019.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Accepted: 07/06/2019] [Indexed: 12/12/2022]
Abstract
We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. Our results indicate new peculiar features of connectomes of higher organisms. We found that the spectral density of adjacency matrices of human connectome has maximal deviation from the one of randomized network, compared to other organisms. Considering the network evolution induced by the preference of 3-cycles formation, we discovered that for macaque and human connectomes the evolution with the conservation of local clusterization is crucial, while for primitive organisms the conservation of averaged clusterization is sufficient. Investigating for the first time the level spacing distribution of the spectrum of human connectome Laplacian matrix, we explicitly demonstrate that the spectral statistics corresponds to the critical regime, which is hybrid of Wigner-Dyson and Poisson distributions. This observation provides strong support for debated statement of the brain criticality.
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Affiliation(s)
- N Pospelov
- Lomonosov Moscow State University, 119991, Moscow, Russia
| | - S Nechaev
- Interdisciplinary Scientific Center Poncelet (CNRS UMI 2615), 119002 Moscow, Russia; P.N. Lebedev Physical Institute RAS, Moscow, Russia.
| | - K Anokhin
- Lomonosov Moscow State University, 119991, Moscow, Russia; National Research Center "Kurchatov Institute", 123098, Moscow, Russia
| | - O Valba
- N.N. Semenov Institute of Chemical Physics RAS, 119991 Moscow, Russia; Department of Applied Mathematics, National Research University Higher School of Economics, 101000 Moscow, Russia
| | - V Avetisov
- N.N. Semenov Institute of Chemical Physics RAS, 119991 Moscow, Russia
| | - A Gorsky
- Institute for Information Transmission Problems RAS, 127051 Moscow, Russia; Moscow Institute of Physics and Technology, Dolgoprudny, 141700 Russia
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11
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12
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Arnatkevic̆iūtė A, Fulcher BD, Pocock R, Fornito A. Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome. PLoS Comput Biol 2018; 14:e1005989. [PMID: 29432412 PMCID: PMC5825174 DOI: 10.1371/journal.pcbi.1005989] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 02/23/2018] [Accepted: 01/19/2018] [Indexed: 11/18/2022] Open
Abstract
Studies of nervous system connectivity, in a wide variety of species and at different scales of resolution, have identified several highly conserved motifs of network organization. One such motif is a heterogeneous distribution of connectivity across neural elements, such that some elements act as highly connected and functionally important network hubs. These brain network hubs are also densely interconnected, forming a so-called rich club. Recent work in mouse has identified a distinctive transcriptional signature of neural hubs, characterized by tightly coupled expression of oxidative metabolism genes, with similar genes characterizing macroscale inter-modular hub regions of the human cortex. Here, we sought to determine whether hubs of the neuronal C. elegans connectome also show tightly coupled gene expression. Using open data on the chemical and electrical connectivity of 279 C. elegans neurons, and binary gene expression data for each neuron across 948 genes, we computed a correlated gene expression score for each pair of neurons, providing a measure of their gene expression similarity. We demonstrate that connections between hub neurons are the most similar in their gene expression while connections between nonhubs are the least similar. Genes with the greatest contribution to this effect are involved in glutamatergic and cholinergic signaling, and other communication processes. We further show that coupled expression between hub neurons cannot be explained by their neuronal subtype (i.e., sensory, motor, or interneuron), separation distance, chemically secreted neurotransmitter, birth time, pairwise lineage distance, or their topological module affiliation. Instead, this coupling is intrinsically linked to the identity of most hubs as command interneurons, a specific class of interneurons that regulates locomotion. Our results suggest that neural hubs may possess a distinctive transcriptional signature, preserved across scales and species, that is related to the involvement of hubs in regulating the higher-order behaviors of a given organism.
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Affiliation(s)
- Aurina Arnatkevic̆iūtė
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Ben D. Fulcher
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
| | - Roger Pocock
- Development and Stem Cells Program, Monash Biomedicine Discovery Institute and Department of Anatomy and Developmental Biology, Monash University, Clayton, VIC, Australia
| | - Alex Fornito
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
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13
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Studying the role of axon fasciculation during development in a computational model of the Xenopus tadpole spinal cord. Sci Rep 2017; 7:13551. [PMID: 29051550 PMCID: PMC5648846 DOI: 10.1038/s41598-017-13804-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 10/03/2017] [Indexed: 11/21/2022] Open
Abstract
During nervous system development growing axons can interact with each other, for example by adhering together in order to produce bundles (fasciculation). How does such axon-axon interaction affect the resulting axonal trajectories, and what are the possible benefits of this process in terms of network function? In this paper we study these questions by adapting an existing computational model of the development of neurons in the Xenopus tadpole spinal cord to include interactions between axons. We demonstrate that even relatively weak attraction causes bundles to appear, while if axons weakly repulse each other their trajectories diverge such that they fill the available space. We show how fasciculation can help to ensure axons grow in the correct location for proper network formation when normal growth barriers contain gaps, and use a functional spiking model to show that fasciculation allows the network to generate reliable swimming behaviour even when overall synapse counts are artificially lowered. Although we study fasciculation in one particular organism, our approach to modelling axon growth is general and can be widely applied to study other nervous systems.
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14
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Alcalde Cuesta F, González Sequeiros P, Lozano Rojo Á. A method for validating Rent's rule for technological and biological networks. Sci Rep 2017; 7:5378. [PMID: 28710373 PMCID: PMC5511203 DOI: 10.1038/s41598-017-05670-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 06/01/2017] [Indexed: 11/28/2022] Open
Abstract
Rent’s rule is empirical power law introduced in an effort to describe and optimize the wiring complexity of computer logic graphs. It is known that brain and neuronal networks also obey Rent’s rule, which is consistent with the idea that wiring costs play a fundamental role in brain evolution and development. Here we propose a method to validate this power law for a certain range of network partitions. This method is based on the bifurcation phenomenon that appears when the network is subjected to random alterations preserving its degree distribution. It has been tested on a set of VLSI circuits and real networks, including biological and technological ones. We also analyzed the effect of different types of random alterations on the Rentian scaling in order to test the influence of the degree distribution. There are network architectures quite sensitive to these randomization procedures with significant increases in the values of the Rent exponents.
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Affiliation(s)
- Fernando Alcalde Cuesta
- GeoDynApp - ECSING Group, Santiago de Compostela, Spain.,Departamento de Matemáticas, Universidade de Santiago de Compostela, E-15782, Santiago de Compostela, Spain
| | - Pablo González Sequeiros
- GeoDynApp - ECSING Group, Santiago de Compostela, Spain.,Departamento de Didácticas Aplicadas, Facultade de Formación do Profesorado, Universidade de Santiago de Compostela, Avda. Ramón Ferreiro s/n, E-27002, Lugo, Spain
| | - Álvaro Lozano Rojo
- GeoDynApp - ECSING Group, Santiago de Compostela, Spain. .,Centro Universitario de la Defensa Zaragoza, AGM, Ctra. Huesca s/n, E-50090, Zaragoza, Spain. .,Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza, E-50009, Zaragoza, Spain.
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15
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Kaiser M. Mechanisms of Connectome Development. Trends Cogn Sci 2017; 21:703-717. [PMID: 28610804 DOI: 10.1016/j.tics.2017.05.010] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/12/2017] [Accepted: 05/16/2017] [Indexed: 12/17/2022]
Abstract
At the centenary of D'Arcy Thompson's seminal work 'On Growth and Form', pioneering the description of principles of morphological changes during development and evolution, recent experimental advances allow us to study change in anatomical brain networks. Here, we outline potential principles for connectome development. We will describe recent results on how spatial and temporal factors shape connectome development in health and disease. Understanding the developmental origins of brain diseases in individuals will be crucial for deciding on personalized treatment options. We argue that longitudinal studies, experimentally derived parameters for connection formation, and biologically realistic computational models are needed to better understand the link between brain network development, network structure, and network function.
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Affiliation(s)
- Marcus Kaiser
- ICOS Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.
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16
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Liao X, Vasilakos AV, He Y. Small-world human brain networks: Perspectives and challenges. Neurosci Biobehav Rev 2017; 77:286-300. [PMID: 28389343 DOI: 10.1016/j.neubiorev.2017.03.018] [Citation(s) in RCA: 221] [Impact Index Per Article: 31.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Revised: 01/19/2017] [Accepted: 03/31/2017] [Indexed: 12/15/2022]
Abstract
Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.
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Affiliation(s)
- Xuhong Liao
- 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
| | - Athanasios V Vasilakos
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, 97187 Lulea, Sweden
| | - 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.
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Bauer R, Kaiser M. Organisational Principles of Connectomes: Changes During Evolution and Development. DIVERSITY AND COMMONALITY IN ANIMALS 2017. [DOI: 10.1007/978-4-431-56469-0_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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18
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Livingston N, Bernatskiy A, Livingston K, Smith ML, Schwarz J, Bongard JC, Wallach D, Long JH. Modularity and Sparsity: Evolution of Neural Net Controllers in Physically Embodied Robots. Front Robot AI 2016. [DOI: 10.3389/frobt.2016.00075] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Azulay A, Itskovits E, Zaslaver A. The C. elegans Connectome Consists of Homogenous Circuits with Defined Functional Roles. PLoS Comput Biol 2016; 12:e1005021. [PMID: 27606684 PMCID: PMC5015834 DOI: 10.1371/journal.pcbi.1005021] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 06/15/2016] [Indexed: 12/15/2022] Open
Abstract
A major goal of systems neuroscience is to decipher the structure-function relationship in neural networks. Here we study network functionality in light of the common-neighbor-rule (CNR) in which a pair of neurons is more likely to be connected the more common neighbors it shares. Focusing on the fully-mapped neural network of C. elegans worms, we establish that the CNR is an emerging property in this connectome. Moreover, sets of common neighbors form homogenous structures that appear in defined layers of the network. Simulations of signal propagation reveal their potential functional roles: signal amplification and short-term memory at the sensory/inter-neuron layer, and synchronized activity at the motoneuron layer supporting coordinated movement. A coarse-grained view of the neural network based on homogenous connected sets alone reveals a simple modular network architecture that is intuitive to understand. These findings provide a novel framework for analyzing larger, more complex, connectomes once these become available.
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Affiliation(s)
- Aharon Azulay
- Department of Genetics, The Silberman Life Science Institute, Edmond J. Safra Campus, Hebrew University, Jerusalem, Israel
- Ph.D. Program in Brain Sciences, Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem, Israel
| | - Eyal Itskovits
- Department of Genetics, The Silberman Life Science Institute, Edmond J. Safra Campus, Hebrew University, Jerusalem, Israel
| | - Alon Zaslaver
- Department of Genetics, The Silberman Life Science Institute, Edmond J. Safra Campus, Hebrew University, Jerusalem, Israel
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20
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Bacik KA, Schaub MT, Beguerisse-Díaz M, Billeh YN, Barahona M. Flow-Based Network Analysis of the Caenorhabditis elegans Connectome. PLoS Comput Biol 2016; 12:e1005055. [PMID: 27494178 PMCID: PMC4975510 DOI: 10.1371/journal.pcbi.1005055] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 07/12/2016] [Indexed: 11/18/2022] Open
Abstract
We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. One of the goals of systems neuroscience is to elucidate the relationship between the structure of neuronal networks and the functional dynamics that they implement. An ideal model organism to study such interactions is the roundworm C. elegans, which not only has a fully mapped connectome, but has also been the object of extensive behavioural, genetic and neurophysiological experiments. Here we present an analysis of the neuronal network of C. elegans from a dynamical flow perspective. Our analysis reveals a multi-scale organisation of the signal flow in the network linked to anatomical and functional features of neurons, as well as identifying different neuronal roles in relation to signal propagation. We use our computational framework to explore biological input-response scenarios as well as exhaustive in silico ablations, which we relate to experimental findings reported in the literature.
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Affiliation(s)
- Karol A Bacik
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Michael T Schaub
- Department of Mathematics, Imperial College London, London, United Kingdom
- naXys & Department of Mathematics, University of Namur, Namur, Belgium
- ICTEAM, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | | | - Yazan N Billeh
- Computation and Neural Systems Program, California Institute of Technology, Pasadena, California, United States of America
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
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21
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Zhou M, Wang X, Shi H, Cheng L, Wang Z, Zhao H, Yang L, Sun J. Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer. Oncotarget 2016; 7:12598-611. [PMID: 26863568 PMCID: PMC4914307 DOI: 10.18632/oncotarget.7181] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Accepted: 01/24/2016] [Indexed: 12/14/2022] Open
Abstract
Accumulating evidence has underscored the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in cancer initiation and progression. In this study, we used an integrative computational method to identify miRNA-mediated ceRNA crosstalk between lncRNAs and mRNAs, and constructed global and progression-related lncRNA-associated ceRNA networks (LCeNETs) in ovarian cancer (OvCa) based on "ceRNA hypothesis". The constructed LCeNETs exhibited small world, modular architecture and high functional specificity for OvCa. Known OvCa-related genes tended to be hubs and occurred preferentially in the functional modules. Ten lncRNA ceRNAs were identified as potential candidates associated with stage progression in OvCa using ceRNA-network driven method. Finally, we developed a ten-lncRNA signature which classified patients into high- and low-risk subgroups with significantly different survival outcomes. Our study will provide novel insight for better understanding of ceRNA-mediated gene regulation in progression of OvCa and facilitate the identification of novel diagnostic and therapeutic lncRNA ceRNAs for OvCa.
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Affiliation(s)
- Meng Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Xiaojun Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Hongbo Shi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Zhenzhen Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Hengqiang Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Lei Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
| | - Jie Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, PR China
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22
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Exploring the topological sources of robustness against invasion in biological and technological networks. Sci Rep 2016; 6:20666. [PMID: 26861189 PMCID: PMC4748249 DOI: 10.1038/srep20666] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 01/11/2016] [Indexed: 11/29/2022] Open
Abstract
For a network, the accomplishment of its functions despite perturbations is called robustness. Although this property has been extensively studied, in most cases, the network is modified by removing nodes. In our approach, it is no longer perturbed by site percolation, but evolves after site invasion. The process transforming resident/healthy nodes into invader/mutant/diseased nodes is described by the Moran model. We explore the sources of robustness (or its counterpart, the propensity to spread favourable innovations) of the US high-voltage power grid network, the Internet2 academic network, and the C. elegans connectome. We compare them to three modular and non-modular benchmark networks, and samples of one thousand random networks with the same degree distribution. It is found that, contrary to what happens with networks of small order, fixation probability and robustness are poorly correlated with most of standard statistics, but they depend strongly on the degree distribution. While community detection techniques are able to detect the existence of a central core in Internet2, they are not effective in detecting hierarchical structures whose topological complexity arises from the repetition of a few rules. Box counting dimension and Rent’s rule are applied to show a subtle trade-off between topological and wiring complexity.
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Papo D, Buldú JM, Boccaletti S, Bullmore ET. Complex network theory and the brain. Philos Trans R Soc Lond B Biol Sci 2015; 369:rstb.2013.0520. [PMID: 25180300 DOI: 10.1098/rstb.2013.0520] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- David Papo
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier M Buldú
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain Complex Systems Group, Universidad Rey Juan Carlos, Móstoles, Spain
| | | | - Edward T Bullmore
- Department of Psychiatry, Behavioural and Clinical Neurosciences Institute, University of Cambridge, Cambridge, UK GlaxoSmithKline, Alternative Discovery and Development, Addenbrooke's Centre for Clinical Investigations, Cambridge, UK
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Shih CT, Sporns O, Yuan SL, Su TS, Lin YJ, Chuang CC, Wang TY, Lo CC, Greenspan R, Chiang AS. Connectomics-Based Analysis of Information Flow in the Drosophila Brain. Curr Biol 2015; 25:1249-58. [PMID: 25866397 DOI: 10.1016/j.cub.2015.03.021] [Citation(s) in RCA: 100] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 03/05/2015] [Accepted: 03/13/2015] [Indexed: 01/30/2023]
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25
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"Knock once for yes, twice for no". J Cell Commun Signal 2015; 9:15-8. [PMID: 25711904 DOI: 10.1007/s12079-015-0273-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 02/02/2015] [Indexed: 10/23/2022] Open
Abstract
Previous studies have indicated that the expression of CCN3, a member of the CCN family of proteins, was tightly regulated during central nervous development and was associated with acquisition of cognitive functions in rats (Perbal, Mol Pathol 54(2):57-79, 2001; Su et al. Sheng Li Xue Bao 52(4):290-294, 2000) therefore suggesting that CCN3 might be involved in higher levels of physiological communication in the brain. In spite of the considerable amount of progress made into the understanding of neuronal organization and communication, reducing the knowledge gap between brain cellular biology and behavioral studies remains a huge challenge. Mind-to-mind communication has been the subject of numerous science fiction writings, intense research and emotional debates for many years. Scientists have tried for a long time to achieve transmission of messages between living subjects via non intrusive protocols. Thanks to the great progress made in imagery and neurosciences, another dimension of neuronal function in communication has now been documented. Two recent experimental demonstrations of direct brain to brain communication without physical contact (Grau et al. (2014) Conscious brain-to-brain communication in humans using non-invasive technologies. PLoS One. Aug 19;9(8)- - Rao et al. (2014) A direct brain-to-brain interface in humans. PLoS One. Nov 5;9(11)) pave the road to more sophisticated applications that could profoundly affect communications of humans with other humans, animals and machines. Although the wide use of such applications might seem a long way off, they raise quite a number of ethical, legal and societal issues.
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