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癫痫脑的多尺度建模:计算治疗探索的优势。

Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

机构信息

School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom.

UCB Biopharma SRL, Brussels, Belgium.

出版信息

J Neural Eng. 2024 Apr 25;21(2). doi: 10.1088/1741-2552/ad3eb4.

Abstract

: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

摘要

癫痫是一种复杂的疾病,涉及多个层面,从神经元中的离子通道到整个大脑的神经元回路。在过去的几十年中,计算模型已被用于从不同方面描述癫痫大脑的病理生理活动。传统上,每个计算模型都可以帮助优化治疗干预措施,因此,为治疗癫痫提供了特定的策略视角。因此,大多数研究都致力于生成特定的癫痫大脑模型,以帮助我们理解病理状态的某些机制。这些特定的模型在复杂性和生物学准确性方面存在差异,系统级模型通常缺乏生物学细节。

在这里,我们回顾了各种类型的癫痫计算模型,并讨论了它们在不同治疗方法和场景中的应用潜力,包括药物发现、手术策略、脑刺激和癫痫发作预测。我们提出,我们需要考虑采用跨多个尺度的统一建模框架的综合方法来理解癫痫大脑。我们的建议基于最近计算能力的提高,这为将那些特定的癫痫模型统一到具有前所未有的细节水平的模拟中开辟了可能性。

多尺度癫痫模型可以弥合用于解决分子和细胞问题的详细生物学模型与基于抽象模型的大脑全范围模型之间的差距,这些抽象模型可以解释复杂的神经和行为观察结果。

通过这些努力,我们朝着能够将细胞特征(如离子通道特性)与标准临床测量(如癫痫发作严重程度)联系起来的下一代癫痫大脑模型迈进。

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