Modeling seizure networks in neuron-glia cultures using microelectrode arrays

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>%</mml:mi></mml:math> of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks <i>in vitro</i>. As such, we sought to develop a novel <i>in vitro</i> MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust <i>in vitro</i> seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

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Publication Details

Journal
Frontiers in Network Physiology
Published
2024-09-03
DOI
https://doi.org/10.3389/fnetp.2024.1441345
Citations
1
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.27

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article

Modeling seizure networks in neuron-glia cultures using microelectrode arrays

Kareem A. Zaghloul, Alexander Ksendzovsky, Darrian McAfee, Hitten P. Zaveri et al.
1 citations
Frontiers in Network Physiology
Neuroscience and Neural Engineering
0.27
article

Modeling seizure networks in neuron-glia cultures using microelectrode arrays

Kareem A. Zaghloul, Alexander Ksendzovsky, Darrian McAfee, Hitten P. Zaveri, Marcelle Altshuler, Muzna Bachani, Ujwal Boddeti, Dennis D. Spencer, Jenna Langbein
article en
1 citations

Abstract

Epilepsy is a common neurological disorder, affecting over 65 million people worldwide. Unfortunately, despite resective surgery, over 30 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>%</mml:mi></mml:math> of patients with drug-resistant epilepsy continue to experience seizures. Retrospective studies considering connectivity using intracranial electrocorticography (ECoG) obtained during neuromonitoring have shown that treatment failure is likely driven by failure to consider critical components of the seizure network, an idea first formally introduced in 2002. However, current studies only capture snapshots in time, precluding the ability to consider seizure network development. Over the past few years, multiwell microelectrode arrays have been increasingly used to study neuronal networks <i>in vitro</i>. As such, we sought to develop a novel <i>in vitro</i> MEA seizure model to allow for study of seizure networks. Specifically, we used 4-aminopyridine (4-AP) to capture hyperexcitable activity, and then show increased network changes after 2 days of chronic treatment. We characterize network changes using functional connectivity measures and a novel technique using dimensionality reduction. We find that 4-AP successfully captures persistently elevated mean firing rate and significant changes in underlying connectivity patterns. We believe this affords a robust <i>in vitro</i> seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

Frontiers in Network PhysiologyVol. 4
University of Maryland, Baltimore (US), Brigham and Women's Hospital (US), National Institutes of Health (US), Yale University (US)
National Institutes of Health
Good health and well-being
Openalex Percentile: Top 45%
Neuroscience and Neural Engineering
0.27
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