Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning

Abstract

Objective: Antiseizure drugs (ASDs) modulate synaptic and ion channel function to prevent abnormal hypersynchronous or excitatory activity arising in neuronal networks, but the relationship between ASDs with respect to their impact on network activity is poorly defined. In this study, we first investigated whether different ASD classes exert differential impact upon network activity, and we then sought to classify ASDs according to their impact on network activity. Methods:We used multielectrode arrays (MEAs) to record the network activity of cultured cortical neurons after applying ASDs from two classes: sodium channel blockers (SCBs) and γaminobutyric acid type A receptor-positive allosteric modulators (GABA PAMs). A two-dimensional representation of changes in network features was then derived, and the ability of this low-dimensional representation to classify ASDs with different molecular targets was assessed.Results: A two-dimensional representation of network features revealed a separation between the SCB and GABA PAM drug classes, and could classify several test compounds known to act through these molecular targets. Interestingly, several ASDs with novel targets, such as cannabidiol and retigabine, had closer similarity to the SCB class with respect to their impact upon network activity.Significance: These results demonstrate that the molecular target of two common classes of ASDs is reflected through characteristic changes in network activity of cultured neurons. Furthermore, a low-dimensional representation of network features can be used to infer an ASDs molecular target. This approach may allow for drug screening to be performed based on features extracted from MEA recordings.

Recent Progress and Perspectives on Neural Chip Platforms Integrating PDMS-Based Microfluidic Devices and Microelectrode Arrays

“…The development of in vitro MEAs aims to explore the potential mechanism of production, processing, and transmission of neural information in the brain [10]. In vitro MEAs have been used by scientists from different disciplines for drug screening [11,12], biosensors [13,14], and pathological mechanism research [15,16] because of their noninvasiveness, high biocompatibility, high controllability, and the ability to read and modulate signals of neural cultures bidirectionally [17]. In the next generation of artificial intelligence that combines artificial intelligence with the brain, a neural chip platform with in vitro MEAs as the core component also shines brightly.…”