Developmental conditions and culture medium influence the neuromodulated response of in vitro cortical networks.

Goal of this work is to show how the developmental conditions of in vitro neuronal networks influence the effect of drug delivery. The proposed experimental neuronal model consists of dissociated cortical neurons plated to Micro-Electrode Arrays (MEAs) and grown according to different conditions (i.e., by varying both the adopted culture medium and the number of days needed to let the network grow before performing the chemical modulation). We delivered rising amount of bicuculline (BIC), a competitive antagonist of GABAA receptors, and we computed the firing rate dose-response curve for each culture. We found that networks matured in BrainPhys for 18 days in vitro exhibited a decreasing firing trend as a function of the BIC concentration, quantified by an average IC50 (i.e., half maximal inhibitory concentration) of 4.64 ± 4.02 µM. On the other hand, both cultures grown in the same medium for 11 days, and ones matured in Neurobasal for 18 days displayed an increasing firing rate when rising amounts of BIC were delivered, characterized by average EC50 values (i.e., half maximal excitatory concentration) of 0.24 ± 0.05 µM and 0.59 ± 0.46 µM, respectively.Clinical Relevance- This research proves the relevance of the experimental factors that can influence the network development as key variables when developing a neuronal model to conduct drug delivery in vitro, simulating the in vivo environment. Our findings suggest that not considering the consequences of the chosen growing conditions when performing in vitro pharmacological studies could lead to incomplete predictions of the chemically induced alterations.

An efficient deep learning approach to identify dynamics in in vitro neural networks.

Understanding and discriminating the spatiotemporal patterns of activity generated by in vitro and in vivo neuronal networks is a fundamental task in neuroscience and neuroengineering. The state-of-the-art algorithms to describe the neuronal activity mostly rely on global and local well-established spike and burst-related parameters. However, they are not able to capture slight differences in the activity patterns. In this work, we introduce a deep-learning-based algorithm to automatically infer the dynamics exhibited by different neuronal populations. Specifically, we demonstrate that our algorithm is able to discriminate with high accuracy the dynamics of five different populations of in vitro human-derived neural networks with an increasing inhibitory to excitatory neurons ratio.

How 3D scaffolds with different mechanical properties affect the activity of neuronal networks in in vitro models.

Three-dimensionality has been proven extensively to be critical in the development of a reliable model for different anatomical compartments and for many diseases. Currently, we can produce implantable structures that help in the regeneration of different tissues such as bone and heart. Different is the situation when we consider the neuronal compartment. As it is still difficult to understand exactly how the brain computes, to conceive how the complex chain of neuronal events can generate conscious behavior, a comprehensive and workable model of neuronal tissue still has to be found. In this perspective, in the present work, we developed and compared different 3D scaffolds to understand the effects produced by the mechanical and material properties of four different scaffolds on a 3D neuronal network. To help in preclinical testing procedure, the scalability and ease-of-use of the different approaches were also taken into consideration.Clinical Relevance- By comparing different 3D scaffolds for the creation of neuronal constructs, the results in this paper move towards understanding the best strategy to develop functional 3D neuronal units for reliable pre-clinical studies.

Multiple neuron clusters on Micro-Electrode Arrays as an in vitro model of brain network.

Understanding the brain functioning is essential for governing brain processes with the aim of managing pathological network dysfunctions. Due to the morphological and biochemical complexity of the central nervous system, the development of general models with predictive power must start from in vitro brain network engineering. In the present work, we realized a micro-electrode array (MEA)-based in vitro brain network and studied its emerging dynamical properties. We obtained four-neuron-clusters (4N) assemblies by plating rat embryo cortical neurons on 60-electrode MEA with cross-shaped polymeric masks and compared the emerging dynamics with those of sister single networks (1N). Both 1N and 4N assemblies exhibited spontaneous electrical activity characterized by spiking and bursting signals up to global activation by means of network bursts. Data revealed distinct patterns of network activity with differences between 1 and 4N. Rhythmic network bursts and dominant initiator clusters suggested pacemaker activities in both assembly types, but the propagation of activation sequences was statistically influenced by the assembly topology. We proved that this rhythmic activity was ivabradine sensitive, suggesting the involvement of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and propagated across the real clusters of 4N, or corresponding virtual clusters of 1N, with dominant initiator clusters, and nonrandom cluster activation sequences. The occurrence of nonrandom series of identical activation sequences in 4N revealed processes possibly ascribable to neuroplasticity. Hence, our multi-network dissociated cortical assemblies suggest the relevance of pacemaker neurons as essential elements for generating brain network electrophysiological patterns; indeed, such evidence should be considered in the development of computational models for envisaging network behavior both in physiological and pathological conditions.

Modularity and neuronal heterogeneity: Two properties that influence in vitro neuropharmacological experiments.

Introduction: The goal of this work is to prove the relevance of the experimental model (in vitro neuronal networks in this study) when drug-delivery testing is performed. Methods: We used dissociated cortical and hippocampal neurons coupled to Micro-Electrode Arrays (MEAs) arranged in different configurations characterized by modularity (i.e., the presence of interconnected sub-networks) and heterogeneity (i.e., the co-existence of neurons coming from brain districts). We delivered increasing concentrations of bicuculline (BIC), a neuromodulator acting on the GABAergic system, and we extracted the IC50 values (i.e., the effective concentration yielding a reduction in the response by 50%) of the mean firing rate for each configuration. Results: We found significant lower values of the IC50 computed for modular cortical-hippocampal ensembles than isolated cortical or hippocampal ones. Discussion: Although tested with a specific neuromodulator, this work aims at proving the relevance of ad hoc experimental models to perform neuropharmacological experiments to avoid errors of overestimation/underestimation leading to biased information in the characterization of the effects of a drug on neuronal networks.

Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays.

Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering.

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.

Stimulus-Evoked Activity Modulation of In Vitro Engineered Cortical and Hippocampal Networks.

The delivery of electrical stimuli is crucial to shape the electrophysiological activity of neuronal populations and to appreciate the response of the different brain circuits involved. In the present work, we used dissociated cortical and hippocampal networks coupled to Micro-Electrode Arrays (MEAs) to investigate the features of their evoked response when a low-frequency (0.2 Hz) electrical stimulation protocol is delivered. In particular, cortical and hippocampal neurons were topologically organized to recreate interconnected sub-populations with a polydimethylsiloxane (PDMS) mask, which guaranteed the segregation of the cell bodies and the connections among the sub-regions through microchannels. We found that cortical assemblies were more reactive than hippocampal ones. Despite both configurations exhibiting a fast (<35 ms) response, this did not uniformly distribute over the MEA in the hippocampal networks. Moreover, the propagation of the stimuli-evoked activity within the networks showed a late (35−500 ms) response only in the cortical assemblies. The achieved results suggest the importance of the neuronal target when electrical stimulation experiments are performed. Not all neuronal types display the same response, and in light of transferring stimulation protocols to in vivo applications, it becomes fundamental to design realistic in vitro brain-on-a-chip devices to investigate the dynamical properties of complex neuronal circuits.

Thalamocortical bistable switch as a theoretical model of fibromyalgia pathogenesis inferred from a literature survey.

Fibromyalgia (FM) is an unsolved central pain processing disturbance. We aim to provide a unifying model for FM pathogenesis based on a loop network involving thalamocortical regions, i.e., the ventroposterior lateral thalamus (VPL), the somatosensory cortex (SC), and the thalamic reticular nucleus (TRN). The dynamics of the loop have been described by three differential equations having neuron mean firing rates as variables and containing Hill functions to model mutual interactions among the loop elements. A computational analysis conducted with MATLAB has shown a transition from monostability to bistability of the loop behavior for a weakening of GABAergic transmission between TRN and VPL. This involves the appearance of a high-firing-rate steady state, which becomes dominant and is assumed to represent pathogenic pain processing giving rise to chronic pain. Our model is consistent with a bulk of literature evidence, such as neuroimaging and pharmacological data collected on FM patients, and with correlations between FM and immunoendocrine conditions, such as stress, perimenopause, chronic inflammation, obesity, and chronic dizziness. The model suggests that critical targets for FM treatment are to be found among immunoendocrine pathways leading to GABA/glutamate imbalance having an impact on the thalamocortical system.

Lack of Epileptogenic Effects of the Creatine Precursor Guanidinoacetic Acid on Neuronal Cultures In Vitro.

The creatine precursor Guanidinoacetic Acid (GAA) accumulates in the genetic deficiency of the GuanidinoAcetate Methyl Transferase (GAMT) enzyme and it is believed to cause the seizures that often occur in this condition. However, evidence that it is indeed epileptogenic is scarce and we previously found that it does not cause neuronal hyperexcitation in in vitro brain slices. Here, we used Micro-Electrode Arrays (MEAs) to further investigate the electrophysiological effects of its acute and chronic administration in the networks of cultured neurons, either neocortical or hippocampal. We found that: (1) GAA at the 1 µM concentration, comparable to its concentration in normal cerebrospinal fluid, does not modify any of the parameters we investigated in either neuronal type; (2) at the 10 µM concentration, very similar to that found in the GAMT deficiency, it did not affect any of the parameters we tested except the bursting rate of neocortical networks and the burst duration of hippocampal networks, both of which were decreased, a change pointing in a direction opposite to epileptogenesis; (3) at the very high and unphysiological 100 µM concentration, it caused a decrease in all parameters, a change that again goes in the direction opposite to epileptogenesis. Our results confirm that GAA is not epileptogenic.

Functional Inhibitory Connections Modulate the Electrophysiological Activity Patterns of Cortical-Hippocampal Ensembles.

The brain is a complex organ composed of billions of neurons connected through excitatory and inhibitory synapses. Its structure reveals a modular topological organization, where neurons are arranged in interconnected assemblies. The generated patterns of electrophysiological activity are shaped by two main factors: network heterogeneity and the topological properties of the underlying connectivity that strongly push the dynamics toward different brain-states. In this work, we exploited an innovative polymeric structure coupled to Micro-Electrode Arrays (MEAs) to recreate in vitro heterogeneous interconnected (modular) neuronal networks made up of cortical and hippocampal neurons. We investigated the propagation of spike sequences between the two interconnected subpopulations during the networks' development, correlating functional and structural connectivity to dynamics. The simultaneous presence of two neuronal types shaped the features of the functional connections (excitation vs. inhibition), orchestrating the emerging patterns of electrophysiological activity. In particular, we found that hippocampal neurons mostly project inhibitory connections toward the cortical counterpart modulating the temporal scale of the population events (network bursts). In contrast, cortical neurons establish a larger amount of intrapopulation connections. Moreover, we proved topological properties such as small-worldness, degree distribution, and modularity of neuronal assemblies were favored by the physical environment where networks developed and matured.

Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks.

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the "noisy" connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.

On the road to the brain-on-a-chip: a review on strategies, methods, and applications.

The brain is the most complex organ of our body. Such a complexity spans from the single-cell morphology up to the intricate connections that hundreds of thousands of neurons establish to create dense neuronal networks. All these components are involved in the genesis of the rich patterns of electrophysiological activity that characterize the brain. Over the years, researchers coming from different disciplines developedin vitrosimplified experimental models to investigate in a more controllable and observable way how neuronal ensembles generate peculiar firing rhythms, code external stimulations, or respond to chemical drugs. Nowadays, suchin vitromodels are namedbrain-on-a-chippointing out the relevance of the technological counterpart as artificial tool to interact with the brain: multi-electrode arrays are well-used devices to record and stimulate large-scale developing neuronal networks originated from dissociated cultures, brain slices, up to brain organoids. In this review, we will discuss the state of the art of the brain-on-a-chip, highlighting which structural and biological features a realisticin vitrobrain should embed (and how to achieve them). In particular, we identified two topological features, namely modular and three-dimensional connectivity, and a biological one (heterogeneity) that takes into account the huge number of neuronal types existing in the brain. At the end of this travel, we will show how 'far' we are from the goal and how interconnected-brain-regions-on-a-chip is the most appropriate wording to indicate the current state of the art.

Three-dimensionality shapes the dynamics of cortical interconnected to hippocampal networks.

OBJECTIVE: The goal of this work is to develop and characterize an innovative experimental framework to design interconnected (i.e. modular) heterogeneous (cortical-hippocampal) neuronal cultures with a three-dimensional (3D) connectivity and to record their electrophysiological activity using micro-electrode arrays (MEAs). APPROACH: A two-compartment polymeric mask for the segregation of different neuronal populations (cortex and hippocampus) was coupled to the MEA surface. Glass microbeads were used as a scaffold to mimic the 3D brain micro-architecture. MAIN RESULTS: We built a fully functional heterogeneous 3D neuronal network. From an electrophysiological point of view, we found that the heterogeneity induces a global increase of the activity rate, while the 3D connectivity modulates the duration and the organization of the bursting activity. SIGNIFICANCE: In vivo, studies of network dynamics and interactions between neuronal populations are often time-consuming, low-throughput, complex, and suffer from reproducibility. On the other hand, most of the commonly used in vitro brain models are too simplified and thus far from the in vivo situation. The achieved results demonstrate the feasibility to build a more realistic and controllable experimental in vitro model of interconnected brain regions on-a-chip whose applications may have impacts on the study of neurological disorders that impair the connectivity between brain areas (e.g. Parkinson disease).

A Neuromorphic Prosthesis to Restore Communication in Neuronal Networks.

Recent advances in bioelectronics and neural engineering allowed the development of brain machine interfaces and neuroprostheses, capable of facilitating or recovering functionality in people with neurological disability. To realize energy-efficient and real-time capable devices, neuromorphic computing systems are envisaged as the core of next-generation systems for brain repair. We demonstrate here a real-time hardware neuromorphic prosthesis to restore bidirectional interactions between two neuronal populations, even when one is damaged or missing. We used in vitro modular cell cultures to mimic the mutual interaction between neuronal assemblies and created a focal lesion to functionally disconnect the two populations. Then, we employed our neuromorphic prosthesis for bidirectional bridging to artificially reconnect two disconnected neuronal modules and for hybrid bidirectional bridging to replace the activity of one module with a real-time hardware neuromorphic Spiking Neural Network. Our neuroprosthetic system opens avenues for the exploitation of neuromorphic-based devices in bioelectrical therapeutics for health care.

Reconstruction of Functional Connectivity from Multielectrode Recordings and Calcium Imaging.

In the last two decades, increasing research efforts in neuroscience have been focused on determining both structural and functional connectivity of brain circuits, with the main goal of relating the wiring diagram of neuronal systems to their emerging properties, from the microscale to the macroscale. While combining multisite parallel recordings with structural circuits' reconstruction in vivo is still very challenging, the reductionist in vitro approach based on neuronal cultures offers lower technical difficulties and is much more stable under control conditions. In this chapter, we present different approaches to infer the connectivity of cultured neuronal networks using multielectrode array or calcium imaging recordings. We first formally introduce the used methods, and then we will describe into details how those methods were applied in case studies. Since multielectrode array and calcium imaging recordings provide distinct and complementary spatiotemporal features of neuronal activity, in this chapter we present the strategies implemented with the two different methodologies in distinct sections.

High-frequency electrical stimulation promotes reshaping of the functional connections and synaptic plasticity in in vitro cortical networks.

High-frequency electrical stimulation (tetanus) promotes global synaptic potentiation in dissociated cortical networks coupled to multi-electrode arrays (MEAs). Since little is known about the functional changes induced by this protocol, this work aims to investigate the statistical dependences between the time series (i.e. functional links) of the network nodes involved pre- and post-tetanus. Specifically, we first show a strong reshaping of the functional connections induced by the stimulation and possibly associated with the global plasticity. Then, we find that about 30% of the nodes linked before and after electrical perturbation show high-connectivity degree (⩾9 links), occupying a central role in the neuronal communication. Finally, we observe that these functional units drive the global network plasticity showing more synaptic potentiation than the other nodes involved in the connectivity reshaping.

Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings.

Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.