使用微电极阵列模拟神经元-胶质细胞培养物中的癫痫发作网络。

Frontiers in network physiology Pub Date : 2024-09-03 eCollection Date: 2024-01-01 DOI:10.3389/fnetp.2024.1441345
Ujwal Boddeti, Jenna Langbein, Darrian McAfee, Marcelle Altshuler, Muzna Bachani, Hitten P Zaveri, Dennis Spencer, Kareem A Zaghloul, Alexander Ksendzovsky
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引用次数: 0

摘要

癫痫是一种常见的神经系统疾病,影响着全球 6500 多万人。不幸的是,尽管进行了切除手术,但仍有超过 30% 的耐药性癫痫患者会继续出现癫痫发作。利用神经监测过程中获得的颅内皮质电图(ECoG)考虑连通性的回顾性研究表明,治疗失败很可能是由于没有考虑癫痫发作网络的关键组成部分,这一观点于2002年首次正式提出。然而,目前的研究只能捕捉时间快照,无法考虑癫痫发作网络的发展。在过去几年中,多孔微电极阵列越来越多地被用于体外神经元网络研究。因此,我们试图开发一种新型体外 MEA 癫痫发作模型,以研究癫痫发作网络。具体来说,我们使用 4-氨基吡啶(4-AP)来捕捉过度兴奋的活动,然后在 2 天的慢性治疗后显示网络变化的增加。我们使用功能连通性测量和一种新的降维技术来描述网络变化。我们发现,4-AP 能成功捕捉到持续升高的平均发射率和基础连接模式的显著变化。我们认为这提供了一个稳健的体外癫痫发作模型,可以通过该模型研究纵向网络变化,为今后探索癫痫发作网络发展的研究奠定基础。
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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 % 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 in vitro. As such, we sought to develop a novel in vitro 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 in vitro seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

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