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.

Neuronal avalanche dynamics and functional connectivity elucidate information propagation in vitro.

Cascading activity is commonly observed in complex dynamical systems, including networks of biological neurons, and how these cascades spread through the system is reliant on how the elements of the system are connected and organized. In this work, we studied networks of neurons as they matured over 50 days in vitro and evaluated both their dynamics and their functional connectivity structures by observing their electrophysiological activity using microelectrode array recordings. Correlations were obtained between features of their activity propagation and functional connectivity characteristics to elucidate the interplay between dynamics and structure. The results indicate that in vitro networks maintain a slightly subcritical state by striking a balance between integration and segregation. Our work demonstrates the complementarity of these two approaches-functional connectivity and avalanche dynamics-in studying information propagation in neurons in vitro, which can in turn inform the design and optimization of engineered computational substrates.

Early functional changes associated with alpha-synuclein proteinopathy in engineered human neural networks.

A patterned spread of proteinopathy represents a common characteristic of many neurodegenerative diseases. In Parkinson's disease (PD), misfolded forms of α-synuclein proteins accumulate in hallmark pathological inclusions termed Lewy bodies and Lewy neurites. Such protein aggregates seem to affect selectively vulnerable neuronal populations in the substantia nigra and to propagate within interconnected neuronal networks. Research findings suggest that these proteinopathic inclusions are present at very early time points in disease development, even before clear behavioral symptoms of dysfunction arise. In this study, we investigate the early pathophysiology developing after induced formation of such PD-related α-synuclein inclusions in a physiologically relevant in vitro setup using engineered human neural networks. We monitor the neural network activity using multielectrode arrays (MEAs) for a period of 3 wk following proteinopathy induction to identify associated changes in network function, with a special emphasis on the measure of network criticality. Self-organized criticality represents the critical point between resilience against perturbation and adaptational flexibility, which appears to be a functional trait in self-organizing neural networks, both in vitro and in vivo. We show that although developing pathology at early onset is not clearly manifest in standard measurements of network function, it may be discerned by investigating differences in network criticality states.

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation.

It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

µSpikeHunter: An advanced computational tool for the analysis of neuronal communication and action potential propagation in microfluidic platforms.

Understanding neuronal communication is fundamental in neuroscience, but there are few methodologies offering detailed analysis for well-controlled conditions. By interfacing microElectrode arrays with microFluidics (µEF devices), it is possible to compartmentalize neuronal cultures with a specified alignment of axons and microelectrodes. This setup allows the extracellular recording of spike propagation with a high signal-to-noise ratio over the course of several weeks. Addressing these µEF devices, we developed an advanced yet easy-to-use publically available computational tool, µSpikeHunter, which provides a detailed quantification of several communication-related properties such as propagation velocity, conduction failure, spike timings, and coding mechanisms. The combination of µEF devices and µSpikeHunter can be used in the context of standard neuronal cultures or with co-culture configurations where, for example, communication between sensory neurons and other cell types is monitored and assessed. The ability to analyze axonal signals (in a user-friendly, time-efficient, high-throughput manner) opens the door to new approaches in studies of peripheral innervation, neural coding, and neuroregeneration, among many others. We demonstrate the use of µSpikeHunter in dorsal root ganglion neurons where we analyze the presence of both anterograde and retrograde signals in µEF devices. A fully functional version of µSpikeHunter is publically available for download from https://github.com/uSpikeHunter .