Neuronal circuits on a chip for biological network monitoring

Cultured neuronal networks (CNNs) are a robust model to closely investigate neuronal circuits’ formation and monitor their structural properties evolution. Typically, neurons are cultured in plastic plates or, more recently, in microfluidic platforms with potentially a wide variety of neuroscience applications. As a biological protocol, cell culture integration with a microfluidic system provides benefits such as accurate control of cell seeding area, culture medium renewal, or lower exposure to contamination. The objective of this report is to present a novel neuronal network on a chip device, including a chamber, fabricated from PDMS, vinyl and glass connected to a microfluidic platform to perfuse the continuous flow of culture medium. Network growth is compared in chips and traditional Petri dishes to validate the microfluidic chip performance. The network assessment is performed by computing relevant topological measures like the number of connected neurons, the clustering coefficient, and the shortest path between any pair of neurons throughout the culture's life. The results demonstrate that neuronal circuits on a chip have a more stable network structure and lifespan than developing in conventional settings, and therefore this setup is an advantageous alternative to current culture methods. This technology could lead to challenging applications such as batch drug testing of in vitro cell culture models. From the engineering perspective, a device's advantage is the chance to develop custom designs more efficiently than other microfluidic systems.     

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.     

Copying and Evolution of Neuronal Topology

We propose a mechanism for copying of neuronal networks that is of considerable interest for neuroscience for it suggests a neuronal basis for causal inference, function copying, and natural selection within the human brain. To date, no model of neuronal topology copying exists. We present three increasingly sophisticated mechanisms to demonstrate how topographic map formation coupled with Spike-Time Dependent Plasticity (STDP) can copy neuronal topology motifs. Fidelity is improved by error correction and activity-reverberation limitation. The high-fidelity topology-copying operator is used to evolve neuronal topologies. Possible roles for neuronal natural selection are discussed.     

La plasticité neuronale : un nouveau paradigme entre neurosciences et psychanalyse

New neurobiological evidence for neuronal plasticity, demonstrating that experience leaves a structural and functional trace in the neuronal network, has raised questions about the organic and psychological causality of mental phenomena and calls in question our current views, suggesting that psychological events may have the potential to shape synaptic organization. Plasticity shows that the neuronal network remains open to change, to contingency: the brain must then be thought of as a highly dynamic organ constantly interacting with the environment as well as the psychological life of each person. Plasticity maintains the capacity to modify what has come before, allowing the person to respond to unpredictability, thereby constructing his or her individuality. Hence, plasticity entails moving on to a new paradigm. If neuronal networks are biologically determined, yet endowed with the capacity to be modified, and if the person participates in the emergence process, then it follows that neuroscience embodies, like psychoanalysis, the notions of both uniqueness and diversity. Thus, neuroscience and psychoanalysis come together around the question of the emergence of individuality, a process in which they both contribute to each other. What is at stake is not merely the logic of proof — validating psychoanalysis on the basis of neuroscience — but rather the realisation of the power of the paradigm shift brought about by the evidence of plasticity, through which contingent experience constantly modifies the brain of an evolving person.     

An introductory review of information theory in the context of computational neuroscience

This article introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience; information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.     

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。     

Studying the mechanisms of genomic and synaptic plasticity in neuronal cultures with microelectrode arrays

The plasticity of neural networks is a complex process determined by changes in physiological status, gene expression and phenotype of a cell. A detailed study of this process dynamics requires the simultaneous recording of electrical and genomic activities in networks of neurons. This sets up one of the tasks for modern neuroscience as development of integration of electrophysiology and molecular biology methods. In the paper we review the current approaches to such integration, as well as the choice of molecular markers for detection of genomic and synaptic plasticity of neurons by use of physiological micro-sensorial system based on neuronal cells cultured on the micro-electrode arrays.     

Delivery of Brain-Derived Neurotrophic Factor by 3D Biocompatible Polymeric Scaffolds for Neural Tissue Engineering and Neuronal Regeneration

Biopolymers are increasingly employed for neuroscience applications as scaffolds to drive and promote neural regrowth, thanks to their ability to mediate the upload and subsequent release of active molecules and drugs. Synthetic degradable polymers are characterized by different responses ranging from tunable distension or shrinkage to total dissolution, depending on the function they are designed for. In this paper we present a biocompatible microfabricated poly-ε-caprolactone (PCL) scaffold for primary neuron growth and maturation that has been optimized for the in vitro controlled release of brain-derived neurotrophic factor (BDNF). We demonstrate that the designed morphology confers to these devices an enhanced drug delivery capability with respect to monolithic unstructured supports. After incubation with BDNF, micropillared PCL devices progressively release the neurotrophin over 21 days in vitro. Moreover, the bioactivity of released BDNF is confirmed using primary neuronal cultures, where it mediates a consistent activation of BDNF signaling cascades, increased synaptic density, and neuronal survival. These results provide the proof-of-principle on the fabrication process of micropatterned PCL devices, which represent a promising therapeutic option to enhance neuronal regeneration after lesion and for neural tissue engineering and prosthetics.     

ECM concentration and cell-mediated traction forces play a role in vascular network assembly in 3D bioprinted tissue

Tissue vascularization is critical to enable oxygen and nutrient supply. Therefore, establishing expedient vasculature is necessary for the survival of tissue after transplantation. The use of biomechanical forces, such as cell-induced traction forces, may be a promising method to encourage growth of the vascular network. Three-dimensional (3D) bioprinting, which offers unprecedented versatility through precise control over spatial distribution and structure of tissue constructs, can be used to generate capillary-like structures in vitro that would mimic microvessels. This study aimed to develop an in vitro, 3D bioprinted tissue model to study the effect of cellular forces on the spatial organization of vascular structures and tissue maturation. The developed in vitro model consists of a 3D bioprinted polycaprolactone (PCL) frame with a gelatin spacer hydrogel layer and a gelatin–fibrin–hyaluronic acid hydrogel layer containing normal human dermal fibroblasts and human umbilical vein endothelial cells printed as vessel lines on top. The formation of vessel-like networks and vessel lumens in the 3D bioprinted in vitro model was assessed at different fibrinogen concentrations with and without inhibitors of cell-mediated traction forces. Constructs containing 5 mg/ml fibrinogen had longer vessels compared to the other concentrations of fibrinogen used. Also, for all concentrations of fibrinogen used, most of the vessel-like structures grew parallel to the direction the PCL frame-mediated tensile forces, with very few branching structures observed. Treatment of the 3D bioprinted constructs with traction inhibitors resulted in a significant reduction in length of vessel-like networks. The 3D bioprinted constructs also had better lumen formation, increased collagen deposition, more elaborate actin networks, and well-aligned matrix fibers due to the increased cell-mediated traction forces present compared to the non-anchored, floating control constructs. This study showed that cell traction forces from the actomyosin complex are critical for vascular network assembly in 3D bioprinted tissue. Strategies involving the use of cell-mediated traction forces may be promising for the development of bioprinting approaches for fabrication of vascularized tissue constructs.     

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.     

Epoxy-silane linking of biomolecules is simple and effective for patterning neuronal cultures

Surface chemistry is one of the main factors that contributes to the longevity and compliance of cell patterning. Two to three weeks are required for dissociated, embryonic rat neuronal cultures to mature to the point that they regularly produce spontaneous and evoked responses. Though proper surface chemistry can be achieved through the use of covalent protein attachment, often it is not maintainable for the time periods necessary to study neuronal growth. Here we report a new and effective covalent linking approach using (3-glycidoxypropyl) trimethoxysilane (3-GPS) for creating long term neuronal patterns. Micrometer scale patterns of cell adhesive proteins were formed using microstamping; hippocampal neurons, cultured up to 1 month, followed those patterns. Cells did not grow on unmodified 3-GPS surfaces, producing non-permissive regions for the long-term cell patterning. Patterned neuronal networks were formed on two different types of MEA (polyimide or silicon nitride insulation) and maintained for 3 weeks. Even though the 3-GPS layer increased the impedance of metal electrodes by a factor of 2-3, final impedance levels were low enough that low noise extracellular recordings were achievable. Spontaneous neural activity was recorded as early as 10 days in vitro. Neural recording and stimulation were readily achieved from these networks. Our results showed that 3-GPS could be used on surfaces to immobilize biomolecules for a variety of neural engineering applications.     

Modeling of Neuronal Growth <Emphasis Type="Italic">In Vitro</Emphasis>: Comparison of Simulation Tools NETMORPH and CX3D

We simulate the growth of neuronal networks using the two recently published tools, NETMORPH and CX3D. The goals of the work are (1) to examine and compare the simulation tools, (2) to construct a model of growth of neocortical cultures, and (3) to characterize the changes in network connectivity during growth, using standard graph theoretic methods. Parameters for the neocortical culture are chosen after consulting both the experimental and the computational work presented in the literature. The first (three) weeks in culture are known to be a time of development of extensive dendritic and axonal arbors and establishment of synaptic connections between the neurons. We simulate the growth of networks from day 1 to day 21. It is shown that for the properly selected parameters, the simulators can reproduce the experimentally obtained connectivity. The selected graph theoretic methods can capture the structural changes during growth.     

Critical review on quantitative autoradiography of D1 and D2 dopaminergic receptors in the striatum of the mammalian brain: differential localization and plastic changes after pharmacological manipulation and dopaminergic input disruption

Major technical progress in the development of computer-based image analysis systems has made possible the entry of autoradiographic and immunohistochemical techniques into a new era where quantification via densitometry and morphometry has become easily accessible. In this context, quantitative biochemical data can be adapted to anatomical and histological resolution. This adaptation is most efficient in the neuroscience fields because of the huge importance of cellular communication via neuronal networks in the nervous system. Therefore, any experimental approach to the brain which considers the brain as a 'black box' appears now as very crude. In fact, subtle heterogeneity in the distribution of biochemical markers can now be demonstrated, as illustrated here by the use of quantitative autoradiography of D1 and D2 dopaminergic receptors in the striatum of the mammalian brain. Also, local adaptive changes resulting from chronic blockade of the dopaminergic input can be detected after repeated treatments with dopaminergic antagonists selective for D1 or D2 receptors or with surgical lesioning of the dopaminergic nigrostriatal pathway. The resulting plastic changes are unevenly distributed throughout the striatal target organ and vary according to the mode of suppressing the dopaminergic flow: direct destruction of the dopaminergic pathway or selective pharmacological manipulation without physical elimination of the dopaminergic cells themselves. All these results are discussed and reviewed in light of the most recent reports in this field.     

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Currently, large-scale networks derived from dissociated neurons growing and developing in vitro on extracellular micro-transducer devices are the gold-standard experimental model to study basic neurophysiological mechanisms involved in the formation and maintenance of neuronal cell assemblies. However, in vitro studies have been limited to the recording of the electrophysiological activity generated by bi-dimensional (2D) neural networks. Nonetheless, given the intricate relationship between structure and dynamics, a significant improvement is necessary to investigate the formation and the developing dynamics of three-dimensional (3D) networks. In this work, a novel experimental platform in which 3D hippocampal or cortical networks are coupled to planar Micro-Electrode Arrays (MEAs) is presented. 3D networks are realized by seeding neurons in a scaffold constituted of glass microbeads (30-40 µm in diameter) on which neurons are able to grow and form complex interconnected 3D assemblies. In this way, it is possible to design engineered 3D networks made up of 5-8 layers with an expected final cell density. The increasing complexity in the morphological organization of the 3D assembly induces an enhancement of the electrophysiological patterns displayed by this type of networks. Compared with the standard 2D networks, where highly stereotyped bursting activity emerges, the 3D structure alters the bursting activity in terms of duration and frequency, as well as it allows observation of more random spiking activity. In this sense, the developed 3D model more closely resembles in vivo neural networks.     

Recurrent fractal neural networks: a strategy for the exchange of local and global information processing in the brain

The regulation of biological networks relies significantly on convergent feedback signaling loops that render a global output locally accessible. Ideally, the recurrent connectivity within these systems is self-organized by a time-dependent phase-locking mechanism. This study analyzes recurrent fractal neural networks (RFNNs), which utilize a self-similar or fractal branching structure of dendrites and downstream networks for phase-locking of reciprocal feedback loops: output from outer branch nodes of the network tree enters inner branch nodes of the dendritic tree in single neurons. This structural organization enables RFNNs to amplify re-entrant input by over-the-threshold signal summation from feedback loops with equivalent signal traveling times. The columnar organization of pyramidal neurons in the neocortical layers V and III is discussed as the structural substrate for this network architecture. RFNNs self-organize spike trains and render the entire neural network output accessible to the dendritic tree of each neuron within this network. As the result of a contraction mapping operation, the local dendritic input pattern contains a downscaled version of the network output coding structure. RFNNs perform robust, fractal data compression, thus coping with a limited number of feedback loops for signal transport in convergent neural networks. This property is discussed as a significant step toward the solution of a fundamental problem in neuroscience: how is neuronal computation in separate neurons and remote brain areas unified as an instance of experience in consciousness? RFNNs are promising candidates for engaging neural networks into a coherent activity and provide a strategy for the exchange of global and local information processing in the human brain, thereby ensuring the completeness of a transformation from neuronal computation into conscious experience.     

The spectrum of covariance matrices of randomly connected recurrent neuronal networks with linear dynamics

A key question in theoretical neuroscience is the relation between the connectivity structure and the collective dynamics of a network of neurons. Here we study the connectivity-dynamics relation as reflected in the distribution of eigenvalues of the covariance matrix of the dynamic fluctuations of the neuronal activities, which is closely related to the network dynamics’ Principal Component Analysis (PCA) and the associated effective dimensionality. We consider the spontaneous fluctuations around a steady state in a randomly connected recurrent network of stochastic neurons. An exact analytical expression for the covariance eigenvalue distribution in the large-network limit can be obtained using results from random matrices. The distribution has a finitely supported smooth bulk spectrum and exhibits an approximate power-law tail for coupling matrices near the critical edge. We generalize the results to include second-order connectivity motifs and discuss extensions to excitatory-inhibitory networks. The theoretical results are compared with those from finite-size networks and the effects of temporal and spatial sampling are studied. Preliminary application to whole-brain imaging data is presented. Using simple connectivity models, our work provides theoretical predictions for the covariance spectrum, a fundamental property of recurrent neuronal dynamics, that can be compared with experimental data.     

A two-phase growth strategy in cultured neuronal networks as reflected by the distribution of neurite branching angles

Neurite outgrowth and branching patterns are instrumental in dictating the wiring diagram of developing neuronal networks. We study the self-organization of single cultured neurons into complex networks focusing on factors governing the branching of a neurite into its daughter branches. Neurite branching angles of insect ganglion neurons in vitro were comparatively measured in two neuronal categories: neurons in dense cultures that bifurcated under the presence of extrinsic (cellular environment) cues versus neurons in practical isolation that developed their neurites following predominantly intrinsic cues. Our experimental results were complemented by theoretical modeling and computer simulations. A preferred regime of branching angles was found in isolated neurons. A model based on biophysical constraints predicted a preferred bifurcation angle that was consistent with this range shown by our real neurons. In order to examine the origin of the preferred regime of angles we constructed simulations of neurite outgrowth in a developing network and compared the simulated developing neurons with our experimental results. We tested cost functions for neuronal growth that would be optimized at a specific regime of angles. Our results suggest two phases in the process of neuronal development. In the first, reflected by our isolated neurons, neurons are tuned to make first contact with a target cell as soon as possible, to minimize the time of growth. After contact is made, that is, after neuronal interconnections are formed, a second branching strategy is adopted, favoring higher efficiency in neurite length and volume. The two-phase development theory is discussed in relation to previous results.     

Detecting effective connectivity in networks of coupled neuronal oscillators

The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.     

Fabrication of artificial endothelialized tubes with predetermined three-dimensional configuration from flexible cell-enclosing alginate fibers

One possible strategy for creating three-dimensional (3D) tissue-engineered organs in vitro is to develop a vasculature for sufficient transport of oxygen and nutrients within these constructs. Here, we describe a novel technique to fabricate endothelialized tubes with predetermined 3D configuration, as a starting point for self-developing capillary-like networks in vitro. Calcium-alginate hydrogel fibers of ca. 250 and 500 mum in diameter, enclosing bovine carotid artery vascular endothelial cells (BECs), were used as templates for endothelialized tubes. Fibers were prepared by extruding a 2% (w/v) sodium alginate solution containing BECs into a 100 mM calcium chloride solution flowing in the same direction. Fibers were embedded in type I collagen gels and enzymatically degraded by alginate lyase, resulting in channels with predetermined 3D configuration filled with a BEC suspension. Cells attached to and covered the surfaces of the channels. Exposing the cells to medium containing basic fibroblast growth factor resulted in their migration into the ambient collagen gel and self-assembly into capillary-like structures. These results demonstrate that using artificial endothelialized tubes with predetermined 3D configuration, as a starting point for a self-developing capillary-like network, could be potentially useful for constructing 3D tissue-engineered organs.