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Advanced Data Analysis methods to explore the neuronal dynamics in brain-on-a-chip models
No full textObjective
In the field of neuroengineering, the development of reliable in vitro neuronal networks is particularly significant, especially when supported by data analysis methods capable of exploring and dissecting the diverse dynamical regimes that may characterize neuronal assemblies under different conditions (e.g., spontaneous vs evoked/modulated activity). The aim of my PhD project was to develop and apply advanced methods specifically designed for electrophysiological analyses of in vitro models, enabling a deeper understanding of neuronal dynamics. In particular, I focused on three main topics: the implementation of user-friendly software for dose-response curve fitting, the creation of a custom detection method for the identification of electrophysiological signals from neurospheroids coupled to Micro-Electrode Arrays (MEAs), and the development of a computational model exploiting Evolutionary Game Theory to simulate and analyse neuronal communication. Each issue aimed at extracting quantitative metrics that describe the electrophysiological behavior of experimental neuronal models, providing tools to get insights into experimental outcomes.
Approach
The first goal involved the design of a software to generate dose-response curves and determine the drug concentrations at which a relevant effect on a specific metric (e.g., the spiking frequency) of in vitro neuronal cultures occurs. This software was tested on hippocampal and cortical networks, revealing differences between homogeneous (composed of a single neuronal type) and heterogeneous (with interacting neuronal populations) models.
The second topic required the development of a novel detection method tailored to the signals generated by neurospheroids. Detection methods employed for planar networks proved ineffective due to the summation of neuronal contributions in the spheroids, producing oscillatory signals. By employing wavelet analysis, I developed a method that explores both the temporal and frequency domain, through the computation of a spectrogram of the electrophysiological signal.
Finally, I developed a computational model using Hindmarsh-Rose equations to simulate individual neuron dynamics, while the connectivity between neurons was modeled exploiting principles from Evolutionary Game Theory. This model aims to replicate the neuronal activity observed in vitro and explore how different game-theory-derived parameters impact the emergent dynamics.
Main results
The dose-response curve software effectively demonstrated the importance of using heterogeneous neuronal models for drug testing, as the extracted IC50 values varied significantly between homogeneous (i.e., only one neuronal type) and heterogeneous networks (at least two different neuronal types).
The detection method for neurospheroids, designed to handle the complex, oscillatory signals of densely packed neuronal clusters, was able to identify electrophysiological activity in both time and frequency domains. This method was validated on a dataset comprising both neurospheroids and neuroassembloids (coupled spheroids), where it detected significant differences in the percentage of frequency band expression, depending on the neuronal type and the characteristics of the spheroids and the assembloids.
The Evolutionary Game Theory-based model proved to be not only a mathematical tool for generating electrophysiological patterns but also a computational approach capable of deriving game theory strategies present within a neuronal network. This feature highlights the potential of the model to classify network characteristics, such as distinguishing between cortical and hippocampal neuronal types, or between modular, heterogeneous, and three-dimensional networks, through the evaluation of the extracted Evolutionary Game Theory parameters.
Significance
The developed computational tools have broad implications for the analysis of electrophysiological data of in vitro neuronal models. The dose-response curve software aims at being used in preclinical studies. The detection method for neurospheroids enhances the study of three-dimensional neuronal models, which better mimic in vivo brain conditions due to their neuronal heterogeneity and structural complexity. The Evolutionary Game-Theory-based computational model holds the potential to be a powerful tool for classifying neuronal network types and understanding how connectivity and competition within neuronal populations shape emergent dynamics, providing insights into both healthy and pathological brain function
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
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
In vitro clustered cortical networks reveal NMDA-dependent modulation of repetitive activation sequences
No full textStimulus-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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A computational framework combining neuronal dynamics and evolutionary game theory for network-level synaptic interactions
No full textObjective.In this study, we present a novel computational framework that combines the Hindmarsh-Rose (HR) neuronal model with evolutionary game theory on networks to simulate and interpret synaptic-level interactions within neuronal populations. Our approach preserves the features of the HR model-capable of generating both spiking and bursting dynamics-while integrating game-theoretic principles that govern the balance between emulative and non-emulative behaviors across neurons.Approach.Neurons were modeled as strategic agents whose interactions evolve according to game-theoretic principles, allowing us to capture emergent network dynamics beyond classical electrophysiological analyses. A key innovation of our work is the formulation of a parameter estimation method based on adaptive observers, which enables the recovery of game-theoretic parameters solely from partial state observations. The proposed framework is validated through numerical simulations, demonstrating its ability to recover hidden parameters and accurately predict system behavior under diverse conditions.Main results.By applying the devised approach to synthetic datasets mimicking real electrophysiological recordings, we highlight its applicability in distinguishing neuronal populations based on their strategic interactions. In this context, the model is shown to faithfully reproduce both spiking and bursting behaviors, capturing the diverse electrophysiological patterns observed inin vitroexperimental settings. Furthermore, we explore the potential of this model in experimental data analysis by suggesting that the estimated parameters may serve as discriminative markers for different neuronal types and structural characteristics.Significance.The integration of dynamical systems theory, game-theoretic modeling, and adaptive estimation provides a robust quantitative tool for investigating complex neuronal network dynamics. Our results quantitatively demonstrate the scalability and accuracy of the method in parameter estimation, reinforcing its value for systematic analysis of synaptic interactions and advancing our understanding of neuronal network dynamics
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