Glial cells in neuronal network function

Numerous evidence demonstrates that astrocytes, a type of glial cell, are integral functional elements of the synapses, responding to neuronal activity and regulating synaptic transmission and plasticity. Consequently, they are actively involved in the processing, transfer and storage of information by the nervous system, which challenges the accepted paradigm that brain function results exclusively from neuronal network activity, and suggests that nervous system function actually arises from the activity of neuron–glia networks. Most of our knowledge of the properties and physiological consequences of the bidirectional communication between astrocytes and neurons resides at cellular and molecular levels. In contrast, much less is known at higher level of complexity, i.e. networks of cells, and the actual impact of astrocytes in the neuronal network function remains largely unexplored. In the present article, we summarize the current evidence that supports the notion that astrocytes are integral components of nervous system networks and we discuss some functional properties of intercellular signalling in neuron–glia networks.     

Molecular mechanisms of homeostatic plasticity in central pattern generator networks

Central pattern generator (CPG) networks rely on a balance of intrinsic and network properties to produce reliable, repeatable activity patterns. This balance is maintained by homeostatic plasticity where alterations in neuronal properties dynamically maintain appropriate neural output in the face of changing environmental conditions and perturbations. However, it remains unclear just how these neurons and networks can both monitor their ongoing activity and use this information to elicit homeostatic physiological responses to ensure robustness of output over time. Evidence exists that CPG networks use a mixed strategy of activity‐dependent, activity‐independent, modulator‐dependent, and synaptically regulated homeostatic plasticity to achieve this critical stability. In this review, we focus on some of the current understanding of the molecular pathways and mechanisms responsible for this homeostatic plasticity in the context of central pattern generator function, with a special emphasis on some of the smaller invertebrate networks that have allowed for extensive cellular‐level analyses that have brought recent insights to these questions.     

Bridging the gap - from ion channels to networks and behaviour

A major challenge for current research in neuroscience is to understand the intrinsic operation of the functional modules of the central nervous system, such as those formed by cortical columns and the neuronal networks controlling motor behaviour. Most vertebrate experimental models used in network analyses involve developing nervous systems, which are in rapid transition with regard to their cellular properties and the expression of different ion channels. Recent advances in our understanding of the cellular and circuit properties of motor networks are making it possible to decipher the mechanisms involved in vertebrate motor pattern generation.     

Central mechanisms of pathological pain

Chronic pain is a major challenge to clinical practice and basic science. The peripheral and central neural networks that mediate nociception show extensive plasticity in pathological disease states. Disease-induced plasticity can occur at both structural and functional levels and is manifest as changes in individual molecules, synapses, cellular function and network activity. Recent work has yielded a better understanding of communication within the neural matrix of physiological pain and has also brought important advances in concepts of injury-induced hyperalgesia and tactile allodynia and how these might contribute to the complex, multidimensional state of chronic pain. This review focuses on the molecular determinants of network plasticity in the central nervous system (CNS) and discusses their relevance to the development of new therapeutic approaches.     

Neuronal network analyses: premises, promises and uncertainties

Neuronal networks assemble the cellular components needed for sensory, motor and cognitive functions. Any rational intervention in the nervous system will thus require an understanding of network function. Obtaining this understanding is widely considered to be one of the major tasks facing neuroscience today. Network analyses have been performed for some years in relatively simple systems. In addition to the direct insights these systems have provided, they also illustrate some of the difficulties of understanding network function. Nevertheless, in more complex systems (including human), claims are made that the cellular bases of behaviour are, or will shortly be, understood. While the discussion is necessarily limited, this issue will examine these claims and highlight some traditional and novel aspects of network analyses and their difficulties. This introduction discusses the criteria that need to be satisfied for network understanding, and how they relate to traditional and novel approaches being applied to addressing network function.     

"Guilt by association" is the exception rather than the rule in gene networks

Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.     

体外培养的神经元网络可塑性

神经元网络是大脑执行高级认知行为的结构基础,研究证明学习记忆及神经退行性疾病与神经元网络可塑性密切相关。因此,揭示调控和改变神经元网络可塑性的机制对理解神经系统信息交互以及疾病治疗具有重大意义。目前,基于微电极阵列（microelectrode array, MEA）培养的神经元网络是体外探究学习和记忆机制的理想模型,同时针对该模型的研究为预防和治疗神经退行性疾病提供了独特的视角。本文综述了基于MEA采集体外培养神经元网络的放电信号来构建功能网络的相关研究,分别从二维神经元网络和三维脑类器官发育,以及开环和闭环电刺激对神经元网络可塑性影响的角度,总结了体外培养神经元网络可塑性的相关研究,最后对该方向的应用前景进行了展望。     

Modification of hippocampal circuitry by adult neurogenesis

The adult hippocampus is one of the primary neural structures involved in memory formation. In addition to synapse-specific modifications thought to encode information at the subcellular level, changes in the intrahippocampal neuro-populational activity and dynamics at the circuit-level may contribute substantively to the functional capacity of this region. Within the hippocampus, the dentate gyrus has the potential to make a preferential contribution to neural circuit modification owing to the continuous addition of new granule cell population. The integration of newborn neurons into pre-existing circuitry is hypothesized to deliver a unique processing capacity, as opposed to merely replacing dying granule cells. Recent studies have begun to assess the impact of hippocampal neurogenesis by examining the extent to which adult-born neurons participate in hippocampal networks, including when newborn neurons become engaged in ongoing network activity and how they modulate circuit dynamics via their unique intrinsic physiological properties. Understanding the contributions of adult neurogenesis to hippocampal function will provide new insight into the fundamental aspects of brain plasticity, which can be used to guide therapeutic interventions to replace neural populations damaged by disease or injury.     

New moves in motor control

Motor behaviour results from information processing across multiple neural networks acting at all levels from initial selection of the behaviour to its final generation. Understanding how motor behaviour is produced requires identifying the constituent neurons of these networks, their cellular properties, and their pattern of synaptic connectivity. Neural networks have been traditionally studied with neurophysiological and neuroanatomical approaches. These approaches have been highly successful in particularly suitable 'model' preparations, typically ones in which the numbers of neurons in the networks were relatively small, neural network composition was unvarying across individual animals, and the preparations continued to produce fictive motor patterns in?vitro. However, analysing networks without these characteristics, and analysing the complete ensemble of networks that cooperatively generate behaviours, is difficult with these approaches. Recently developed molecular and neurogenetic tools provide additional avenues for analysing motor networks by allowing individual or groups of neurons within networks to be manipulated in novel ways and allowing experiments to be performed not only in?vitro but also in?vivo. We review here some of the new insights into motor network function that these advances have provided and indicate how these advances might bridge gaps in our understanding of motor control. To these ends, we first review motor neural network organisation highlighting cross-phylum principles. We then use prominent examples from the field to show how neurogenetic approaches can complement classical physiological studies, and identify additional areas where these approaches could be advantageously applied.     

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.     

Modulating effect of cytokines on mechanisms of synaptic plasticity in the brain

After accumulation of data showing that resident brain cells (neurons, astrocytes, and microglia) produce mediators of the immune system, such as cytokines and their receptors under normal physiological conditions, a critical need emerged for investigating the role of these mediators in cognitive processes. The major problem for understanding the functional role of cytokines in the mechanisms of synaptic plasticity, de novo neurogenesis, and learning and memory is the small number of investigated cytokines. Existing concepts are based on data from just three proinflammatory cytokines: interleukin-1 beta, interleukin-6, and tumor necrosis factor-alpha. The amount of information in the literature on the functional role of antiinflammatory cytokines in the mechanisms of synaptic plasticity and cognitive functions of mature mammalian brain is dismally low. However, they are of principle importance for understanding the mechanisms of local information processing in the brain, since they modulate the activity of individual cells and local neural networks, being able to reconstruct the processes of synaptic plasticity and intercellular communication, in general, depending on the local ratio of the levels of different cytokines in certain areas of the brain. Understanding the functional role of cytokines in cellular mechanisms of information processing and storage in the brain would allow developing preventive and therapeutic means for the treatment of neuropathologies related to impairment of these mechanisms.     

Mesoscopic Organization Reveals the Constraints Governing Caenorhabditis elegans Nervous System

One of the biggest challenges in biology is to understand how activity at the cellular level of neurons, as a result of their mutual interactions, leads to the observed behavior of an organism responding to a variety of environmental stimuli. Investigating the intermediate or mesoscopic level of organization in the nervous system is a vital step towards understanding how the integration of micro-level dynamics results in macro-level functioning. The coordination of many different co-occurring processes at this level underlies the command and control of overall network activity. In this paper, we have considered the somatic nervous system of the nematode Caenorhabditis elegans, for which the entire neuronal connectivity diagram is known. We focus on the organization of the system into modules, i.e., neuronal groups having relatively higher connection density compared to that of the overall network. We show that this mesoscopic feature cannot be explained exclusively in terms of considerations such as, optimizing for resource constraints (viz., total wiring cost) and communication efficiency (i.e., network path length). Even including information about the genetic relatedness of the cells cannot account for the observed modular structure. Comparison with other complex networks designed for efficient transport (of signals or resources) implies that neuronal networks form a distinct class. This suggests that the principal function of the network, viz., processing of sensory information resulting in appropriate motor response, may be playing a vital role in determining the connection topology. Using modular spectral analysis we make explicit the intimate relation between function and structure in the nervous system. This is further brought out by identifying functionally critical neurons purely on the basis of patterns of intra- and inter-modular connections. Our study reveals how the design of the nervous system reflects several constraints, including its key functional role as a processor of information.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Phylogenetic,ontogenetic and adult adaptive plasticity of rhythmic neural networks: a common neuromodulatory mechanism?

Neuromodulatory inputs are known to play a major role in the adaptive plasticity of rhythmic neural networks in adult animals. Using the crustacean stomatogastric nervous system, we have investigated the role of modulatory inputs in the development of rhythmic neural networks. We found that the same neuronal population is organised into a single network in the embryo, as opposed to the two networks present in the adult. However, these adult networks pre-exist in the embryo and can be unmasked by specific alterations of the neuromodulatory environment. Similarly, adult networks may switch back to the embryonic phenotype by manipulating neuromodulatory inputs. During development, we found that the early established neuromodulatory population display alteration in expressed neurotransmitter phenotypes, and that although the population of modulatory neurones is established early, with morphology and projection pattern similar to adult ones, their neurotransmitter phenotype may appear gradually. Therefore the abrupt switch from embryonic to adult network expression occurring at metamorphosis may be due to network reconfiguration in response to changes in modulatory input, as found in adult adaptive plasticity. Strikingly, related crustacean species express different motor outputs using the same basic network circuitry, due to species-specific alteration in neuromodulatory substances within homologous projecting neurones. Therefore we propose that alterations within neuromodulatory systems to a given rhythmic neural network displaying the same basic circuitry may account for the generation of different motor outputs throughout development (ontogenetic plasticity), adulthood (adaptive plasticity) and evolution (phylogenetic plasticity).Abbreviations CoG Commissural ganglion - OG Oesophageal ganglion - STG Stomatogastric ganglion - STNS Stomatogastric nervous system     

Maturation of rhythmic neural network: role of central modulatory inputs.

Modulatory systems are well known for their roles in tuning the cellular and synaptic properties in the adult neuronal networks, and play a major role in the control of the flexibility of functional outputs. However far less is known concerning their role in the maturation of neural networks during the development. In this review, using the stomatogastric nervous system of lobster, we will show that the neuromodulatory system exerts a powerful influence on developing neural networks. In the adult the number of both motor target neurons and their modulatory neurons is restricted to tens of identifiable cells. They are therefore well characterized in terms of cellular, synaptic and morphological properties. In the embryo, these target cells and their neuromodulatory population are already present from mid-embryonic life. However, the motor output generated by the system is quite different: while in the embryo all the target neurons are organized into a single network generating unique motor pattern, in the adult this population splits into two distinct networks generating separate patterns. This ontogenetic partitioning does not rely on progressive acquisition of adult properties but rather on a switch between two possible network operations. Indeed, adult networks are present early in the embryonic life but their expression is repressed by central modulatory neurons. Moreover, embryonic networks can be revealed in the adult system again by altering modulatory influences. Therefore, independently of the developmental age, two potential network phenotypes co-exist within the same neuronal architecture: when one is expressed, the other one is hidden and vice versa. These transitions do not necessarily need dramatic changes such as growth/retraction of processes, acquisition of new intra-membrane proteins etc. but rather, as shown by modelling studies, it may simply rely on a subtle tuning of pre-existing intercellular electrical coupling. This in turn suggests that progressive ontogenetic alteration may not take place at the level of the target network but rather at the level of modulatory input neurons.     

Structural properties of the Caenorhabditis elegans neuronal network

Despite recent interest in reconstructing neuronal networks, complete wiring diagrams on the level of individual synapses remain scarce and the insights into function they can provide remain unclear. Even for Caenorhabditis elegans, whose neuronal network is relatively small and stereotypical from animal to animal, published wiring diagrams are neither accurate nor complete and self-consistent. Using materials from White et al. and new electron micrographs we assemble whole, self-consistent gap junction and chemical synapse networks of hermaphrodite C. elegans. We propose a method to visualize the wiring diagram, which reflects network signal flow. We calculate statistical and topological properties of the network, such as degree distributions, synaptic multiplicities, and small-world properties, that help in understanding network signal propagation. We identify neurons that may play central roles in information processing, and network motifs that could serve as functional modules of the network. We explore propagation of neuronal activity in response to sensory or artificial stimulation using linear systems theory and find several activity patterns that could serve as substrates of previously described behaviors. Finally, we analyze the interaction between the gap junction and the chemical synapse networks. Since several statistical properties of the C. elegans network, such as multiplicity and motif distributions are similar to those found in mammalian neocortex, they likely point to general principles of neuronal networks. The wiring diagram reported here can help in understanding the mechanistic basis of behavior by generating predictions about future experiments involving genetic perturbations, laser ablations, or monitoring propagation of neuronal activity in response to stimulation.     

New concepts on the structure of the neuronal networks: The miniaturization and hierarchical organization of the central nervous system

The hypothesis is introduced that miniaturization of neuronal circuits in the central nervous system and the hierarchical organization of the various levels, where information handling can take place, may be the key to understand the enormous capability of the human brain to store engrams as well as its astonishing capacity to reconstruct and organize engrams and thus to perform highly sophisticated integrations. The concept is also proposed that in order to understand the relationship between the structural and functional plasticity of the central nervous system it is necessary to postulate the existence of memory storage at the network level, at the local circuit level, at the synaptic level, at the membrane level, and finally at the moIecular level. Thus, memory organization is similar to the hierarchical organization of the various levels, where information handling takes place in the nervous system. In addition, each higher level plays a role in the reconstruction and organization of the engrams stored at lower levels. Thus, the trace of the functionally stored memory (i.e. its reconstruction and organization at various levels of storage) will depend not only on the chemicophysical changes in the membranes of the local circuits but also on the organization of the local circuits themselves and their associated neuronal networks.Dedicated to Prof. R. Luft for his outstanding achievements in endocrinology and his provocative and inspiring discussions in biology.     

Small is beautiful: models of small neuronal networks

Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn.     

Cerebral cartography and connectomics

Cerebral cartography and connectomics pursue similar goals in attempting to create maps that can inform our understanding of the structural and functional organization of the cortex. Connectome maps explicitly aim at representing the brain as a complex network, a collection of nodes and their interconnecting edges. This article reflects on some of the challenges that currently arise in the intersection of cerebral cartography and connectomics. Principal challenges concern the temporal dynamics of functional brain connectivity, the definition of areal parcellations and their hierarchical organization into large-scale networks, the extension of whole-brain connectivity to cellular-scale networks, and the mapping of structure/function relations in empirical recordings and computational models. Successfully addressing these challenges will require extensions of methods and tools from network science to the mapping and analysis of human brain connectivity data. The emerging view that the brain is more than a collection of areas, but is fundamentally operating as a complex networked system, will continue to drive the creation of ever more detailed and multi-modal network maps as tools for on-going exploration and discovery in human connectomics.