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Complementary in vitro and computational modelling for the investigation of interacting neuronal networks
No full textObjective. Many higher brain functions are attributed to the cerebral cortex, characterized not only by many neurons but also by an extensive connectivity with other brain areas. The finely regulated interactions between them are suggested to be at the basis of the rise of complex patterns of activity. Due to the complexity of the system itself, unravelling the mechanisms underlying brain functions requires us to devise realistic but simplified models that allow understanding how cells of different brain circuits interact. The goal of my work fits in this framework. The objective is to create workable models of interacting neuronal networks which allow the investigation of the effects of the intrinsic features of the brain on its emerging dynamics.
Approach. To this end, I developed both experimental and computational models. First, I focused on dissecting the role of different brain key features, such as modularity, heterogeneity, and three-dimensionality, by creating different scaffolds. Then, I devised a new polymeric device that allows combining these elements for the study of 3D heterogeneous interacting populations with a realistic connectivity. Complementary, I developed a computational model to in part reproduce the in vitro experimental findings to try to infer the effect of the non-directly observable features and interaction between them thanks to the controllability of in silico systems.
Main results. Firstly, I studied the effect of a modular network organization by plating cortical and hippocampal neurons over Micro-Electrode Arrays: the results highlighted important differences in how electrical signals are transmitted in different brain regions. Therefore, it was not surprising that interconnecting heterogeneous cultures with these different intrinsic characteristics would produce a very different modulation of their activity. Cortical activity undergoes a complementary modification in the cortico-hippocampal and the cortico-thalamic microcircuit. And yet a different effect can be observed in cortical dynamics if three-dimensional structures are considered: the temporal distribution of cortical events is quite influenced by the type of 3D scaffold. Secondly,
my in silico results suggest that the introduction of three-dimensionality induces a global reduction in both firing and bursting rates compared to a 2D model. It also allowed to find out that the effects induced by a 3D organization of the cells is somewhat mitigated by different connectivity motifs of the network. Finally, the foundations for a more realistic model, comprehensive of all the listed key elements and a more in vivo-like connectivity, were laid out both experimentally and computationally.
Significance. The proposed in vitro and in silico models could help the study of the biological mechanisms responsible for cognitive capabilities and the breakdown of these mechanisms in brain diseases. In the framework of neuroscience, a great effort is focused on creating “data ladders” to link the information available at different scales of organization to have an increasingly complete picture of how the brain computes. Reliable in vitro models will be a powerful tool to add information at the microcircuit level. Complementarily, computational models will allow manipulations and recordings that are impossible and/or ethically problematic otherwise, enabling a thorough investigation of the causal mechanisms to the rise of brain’s activity and pathologies. The available data coupled to in silico models could in fact allow the reliable prediction of the key parameters underlying brain phenomena. Moreover, the knowledge acquired with these techniques could be exploited for brain-inspired technologies, with a positive repercussion on industry and society
Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays
No full text: 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
Developmental conditions and culture medium influence the neuromodulated response of in vitro cortical networks
No full textGoal 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 uM. 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 uM and 0.59 ± 0.46 uM, 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
How 3D scaffolds with different mechanical properties affect the activity of neuronal networks in in vitro models*
No full textThree-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
Cortical, striatal, and thalamic populations self-organize into a functionally connected circuit with long-term memory properties
No full text: 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. Bottom-up approaches, like in vitro models, offer insights into brain functions as they recreate the in vivo microenvironment that allows studying how specific features affect physiological and pathological conditions. In this work, we engineered the cortical-striatal-thalamic (CST) circuit, involved in many brain functions such as action initiation and selection, using a three-compartment polymeric device. We characterized the emerging spontaneous electrophysiological activity by using Micro-Electrode Arrays (MEAs). Cortical neurons exhibited complex bursting activity, which influenced the entire circuit. Striatal and thalamic neurons displayed predominantly tonic firing when isolated, while interconnections with the cortex synchronized and organized their neuronal activity, highlighting the cortical pivotal role in bursting activity and information processing. The CST circuit demonstrated self-organization abilities and displayed high entropy values, indicative of dynamic richness and information encoding potential. Furthermore, we proved the CST's involvement in learning and memory. Our CST model provides a platform for further exploration into brain circuitry and potential therapeutic interventions, underscoring the necessity of realistic in vitro models to fully understand neurological diseases' pathophysiology
Modularity and neuronal heterogeneity: Two properties that influence in vitro neuropharmacological experiments
No full textIntroductionThe 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. MethodsWe 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. ResultsWe found significant lower values of the IC50 computed for modular cortical-hippocampal ensembles than isolated cortical or hippocampal ones. DiscussionAlthough 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
Does the development context affect bankruptcy prediction models' general accuracy? A comparative analysis of four multivariate discriminant models in the Italian context.
Full text linkThis research starts from the study by Grice & Dugan (2001) which verified the sensitivity of bankruptcy prediction models to changings in the features of the investigated sample. The study showed that the investigated models’ accuracy was affected by the time-frame, the industry and the size of the firms which composed the investigated samples.
Given these premises, we hypothesized that models applied to samples similar to the one used in their development should reach higher degrees of accuracy than models developed within different contexts. In order to verify this hypotesis, we tested four multivariate discriminant models, one developed within the American context and the others developed in Italy: Altman’s Z’-Score model (1993); Alberici’s model (1975); Bottani, Cipriani and Serao’s model (2004) and Luerti’s model (1992).
We tested the four models twice: first on a sample of firms gone bankrupted within 2012 and 2014. Then on a sample equally composed by bankrupt and operating firms. Both samples were composed by firms located in the Emilia-Romagna region, in Northern Italy.
The first phase of the analysis aimed at verifying the models’ predictive capacity, while the second phase aimed at verifying theirs discriminant capacity. The accuracy of the models was then assessed comparing the results of their application with the real status of each firm.
The results show that even if the Italian models were developed using samples more similar to the one investigated in this research, Altman’s model reaches the highest degree of accuracy
Exploring the Contribution of Thalamic and Hippocampal Input on Cortical Dynamics in a Brain-on-a-Chip Model
No full textThe huge connectivity of the brain and the cellular diversity, which characterize the neuronal populations in the
different anatomical districts, are considered two of the main
sources originating the complex patterns of electrophysiological
activity. Despite the advancements in neurotechnologies, which
allowed investigating the brain complexity with a high level of
precision, the use of simplified in vitro brain-on-a-chip models
results to be a widespread alternative. In the present work,
we used an in vitro brain-regions-on-a-chip model to explore
the role of thalamic and hippocampal neurons in modulating
the dynamics of cortical ensembles. We recorded the emerging electrophysiological activity by means of Micro-Electrode
Arrays (MEAs) paired with ad hoc polymeric structures in
order to recreate interconnected heterogeneous networks. We
demonstrated that two specific neuronal inputs (thalamic and hippocampal) modulated cortical dynamics differently. The observed
variation in the cortical activity was sustained by a specific reorganization of the functional inhibitory connections with respect to
the cortical homogeneous controls. In perspective, the possibility
to design in vitro specific interconnected brain-regions-on-a-chip
and to record their electrophysiological activity could be an
alternative approach to investigate neurodegenerative pathologies
affecting the connectivity among different neuronal population
Stimulus-Evoked Activity Modulation of In Vitro Engineered Cortical and Hippocampal Networks
No full textThe 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
Electrophysiological features of cortical 3D networks are deeply modulated by scaffold properties
No full textThree-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. The present work was conceived in this framework: we aimed to contribute to what must be a collective effort by filling in some information on possible 3D strategies to pursue. We compared directly different kinds of scaffolds (i.e., PDMS sponges, thermally crosslinked hydrogels, and glass microbeads) in their effect on neuronal network activity recorded using micro-electrode arrays. While the overall rate of spiking activity remained consistent, the type of scaffold had a notable impact on bursting dynamics. The frequency, density of bursts, and occurrence of random spikes were all affected. The examination of inter-burst intervals revealed distinct burst generation patterns unique to different scaffold types. Network burst propagation unveiled divergent trends among configurations. Notably, it showed the most differences, underlying that functional variations may arise from a different 3D spatial organization. This evidence suggests that not all 3D neuronal constructs can sustain the same level of richness of activity. Furthermore, we commented on the reproducibility, efficacy, and scalability of the methods, where the beads still offer superior performances. By comparing different 3D scaffolds, our results move toward understanding the best strategies to develop functional 3D neuronal units for reliable pre-clinical studies
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