Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/30574
Title: Classification of antiseizure drugs in cultured neuronal networks using multielectrode arrays and unsupervised learning.
Austin Authors: Bryson, Alexander ;Mendis, Dulini;Morrisroe, Emma;Reid, Christopher A;Halgamuge, Saman;Petrou, Steven
Affiliation: The Florey Institute of Neuroscience and Mental Health
Seer Medical, Melbourne, Victoria, Australia..
Department of Mechanical Engineering, School of Electrical, Mechanical, and Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia..
Neurology
Issue Date: Jul-2022
Date: 2022
Publication information: Epilepsia 2022; 63(7): 1693-1703
Abstract: 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. 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 γ-aminobutyric 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. 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. 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/30574
DOI: 10.1111/epi.17268
ORCID: https://orcid.org/0000-0002-0033-8197
https://orcid.org/0000-0002-1457-8028
https://orcid.org/0000-0002-7145-1787
https://orcid.org/0000-0002-4960-6375
Journal: Epilepsia
PubMed URL: 35460272
PubMed URL: https://pubmed.ncbi.nlm.nih.gov/35460272/
Type: Journal Article
Subjects: antiseizure drugs
machine learning
multielectrode arrays
Appears in Collections:Journal articles

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