Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation.

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.

Bayesian supervised machine learning classification of neural networks with pathological perturbations.

Objective.Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of humanin vitroneural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform.Approach.We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbedin vitroneural networks.Main Results.We found a high degree of separability between the classes using DM point process features (p-value <0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (43 ± 16 ms versus 67 ± 31 ms,µIGfeature distribution).Significance.Our approach has been successful in extracting temporal features related to the neurons' behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation.

Formation of neural networks with structural and functional features consistent with small-world network topology on surface-grafted polymer particles.

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure-function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology.

A novel lab-on-chip platform enabling axotomy and neuromodulation in a multi-nodal network.

Lab-on-chip platforms, such as microfluidic chips and micro-electrode arrays (MEAs) are powerful tools that allow us to manipulate and study neurons in vitro. Microfluidic chips provide a controlled extracellular environment that structures neural networks and facilitates isolation and manipulation at a sub-cellular level. Furthermore, MEAs enable measurement of extracellular electrophysiological activity from single neurons to entire networks. Here, we demonstrate the design, fabrication and application of a 3-nodal microfluidic chip integrated with MEAs as a versatile study platform for neurobiology and pathophysiology. In this work, we evaluate the use of the microfluidic chip to structure a neural network into three separate nodes, interconnected through tunnels that isolate and guide axons into a channel, thus facilitating synaptic contacts between neurons originating from opposite nodes. Furthermore, we demonstrate the utility of the MEA for monitoring developing activity and intra-/inter nodal connectivity of the structured neural network. Finally, we demonstrate the versatility of the platform in two separate experiments. First, we demonstrate the ability to measure intra- and inter-nodal dynamic responses to a fluidically isolated chemical stimulation. Then, we demonstrate the feature of the microfluidic chip enabling the disruption of functional connectivity between nodes and examination of the immediate activity response of the neural network. The platform enables in vitro modelling of neural networks to study their functional connectomes in the context of neurodegenerative disease and CNS trauma, including spinal cord injury.