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使用微电极阵列对神经胶质细胞培养物中的癫痫发作网络进行建模。

Modeling seizure networks in neuron-glia cultures using microelectrode arrays.

作者信息

Boddeti Ujwal, Langbein Jenna, McAfee Darrian, Altshuler Marcelle, Bachani Muzna, Zaveri Hitten P, Spencer Dennis, Zaghloul Kareem A, Ksendzovsky Alexander

机构信息

Surgical Neurology Branch, NINDS, National Institutes of Health, Baltimore, MD, United States.

Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, United States.

出版信息

Front Netw Physiol. 2024 Sep 3;4:1441345. doi: 10.3389/fnetp.2024.1441345. eCollection 2024.

DOI:10.3389/fnetp.2024.1441345
PMID:39290793
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11405204/
Abstract

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 . As such, we sought to develop a novel 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 seizure model from which longitudinal network changes can be studied, laying groundwork for future studies exploring seizure network development.

摘要

癫痫是一种常见的神经系统疾病,全球有超过6500万人受其影响。不幸的是,尽管进行了切除手术,但超过30%的耐药性癫痫患者仍继续发作。回顾性研究利用神经监测期间获得的颅内皮层脑电图(ECoG)来考虑连通性,结果表明治疗失败可能是由于未能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,目前的研究仅捕捉了时间上的快照,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用于研究神经元网络。因此,我们试图开发一种新型的微电极阵列癫痫模型,以便研究癫痫发作网络。具体而言,我们使用4-氨基吡啶(4-AP)来捕捉过度兴奋活动,然后显示在慢性治疗2天后网络变化增加。我们使用功能连通性测量和一种使用降维的新技术来表征网络变化。我们发现4-AP成功捕捉到持续升高的平均放电率以及潜在连通性模式的显著变化。我们相信这提供了一个强大的癫痫模型,从中可以研究纵向网络变化,为未来探索癫痫发作网络发展的研究奠定基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/c57a20de0cd2/fnetp-04-1441345-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/b08d2ac9d977/fnetp-04-1441345-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/0e3f283577c2/fnetp-04-1441345-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/36b7e31731e5/fnetp-04-1441345-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/e9a551ceea51/fnetp-04-1441345-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/c57a20de0cd2/fnetp-04-1441345-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/b08d2ac9d977/fnetp-04-1441345-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/0e3f283577c2/fnetp-04-1441345-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/36b7e31731e5/fnetp-04-1441345-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/e9a551ceea51/fnetp-04-1441345-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44b8/11405204/c57a20de0cd2/fnetp-04-1441345-g005.jpg

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Glioblastoma remodelling of human neural circuits decreases survival.
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