Cortical, striatal, and thalamic populations self-organize into a functionally connected circuit with long-term memory properties.

The human brain is a complex organ with an intricate neuronal connectivity and diverse functional regions. Neurological disorders often disrupt the delicate balance among these anatomical compartments, resulting in severe impairments. The available therapeutic options constitute an incomplete solution as many patients respond partially, highlighting the need for continued research into causes and treatments.

Investigating the interplay between segregation and integration in developing cortical assemblies.

Introduction: The human brain is an intricate structure composed of interconnected modular networks, whose organization is known to balance the principles of segregation and integration, enabling rapid information exchange and the generation of coherent brain states. Segregation involves the specialization of brain regions for specific tasks, while integration facilitates communication among these regions, allowing for efficient information flow. Several factors influence this balance, including maturation, aging, and the insurgence of neurological disorders like epilepsy, stroke, or cancer.

Electrophysiological features of cortical 3D networks are deeply modulated by scaffold properties.

Three-dimensionality (3D) was proven essential for developing reliable models for different anatomical compartments and many diseases. However, the neuronal compartment still poses a great challenge as we still do not understand precisely how the brain computes information and how the complex chain of neuronal events can generate conscious behavior. Therefore, a comprehensive model of neuronal tissue has not yet been found.

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.

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.

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.

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

Introduction: The goal of this work is to prove the relevance of the experimental model ( 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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles.

Objective: We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders.

Approach: We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane.

Main Results: We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime.

Soft chitosan microbeads scaffold for 3D functional neuronal networks.

The availability of 3D biomimetic in vitro neuronal networks of mammalian neurons represents a pivotal step for the development of brain-on-a-chip experimental models to study neuronal (dys)functions and particularly neuronal connectivity. The use of hydrogel-based scaffolds for 3D cell cultures has been extensively studied in the last years. However, limited work on biomimetic 3D neuronal cultures has been carried out to date.