Multidisciplinary reconstruction of the pan-network physiome generating spontaneous synchronized neural activity in the mammalian brain

哺乳动物大脑中产生自发同步神经活动的泛网络生理组的多学科重建

• 批准号: 215320970
• 负责人: Professor Dr. Swen Hülsmann
• 金额: --
• 依托单位: Klinik für Anästhesiologie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2015-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/215320970?language=en
• 关键词: Multidisciplinary; reconstruction; pan; network; physiome

Synchronized neuronal activity is fundamental in brain operation. Our goal is to reveal the pan-network physiome1 generating the synchronized activity of the respiratory network at different developmental stages using preparations that preserve spontaneous synchronous neuronal activity. In complex dynamic systems where numerous components interact mutually, we must use a computational model to propose testable predictions for differentiating opposing hypotheses. First, we will experimentally analyze network organizations of each component of the respiratory network using fluorescence imaging in transgenic mice preparations and optogenetic tools for cell specific modulation. With these experimental results, we shall set ground for a data-driven large-scale network model. Subsequently, we will convert the model into a neural mass model for describing the pan-network operation of the medullary respiratory centers. To complete the model, nontrivial predictions of functional roles of each component will be experimentally tested and iteratively fed back. The generation of such a pan-network model is essential for integrating still isolated physiological information. Our model will unveil innovative aspects of the network interaction and help us to better understand pathological states of the network in life-threatening diseases like Rett syndrome, sudden infant death syndrome or congenital central hypoventilation syndrome.

神经元的同步活动是脑部活动的基础。我们的目标是揭示在不同发育阶段产生呼吸网络同步活动的泛网络生理学1，使用保存自发同步神经元活动的准备。在许多组件相互作用的复杂动态系统中，我们必须使用计算模型来提出可测试的预测，以区分相反的假设。首先，我们将利用转基因小鼠的荧光成像准备和细胞特异性调节的光遗传学工具，对呼吸网络的每个组成部分的网络组织进行实验分析。有了这些实验结果，我们将为数据驱动的大规模网络模型奠定基础。随后，我们将该模型转换为用于描述延髓呼吸中心的泛网络操作的神经质量模型。为了完成模型，将对每个组件的功能角色的非平凡预测进行实验测试并迭代反馈。这种泛网络模型的生成对于集成仍然孤立的生理信息是必不可少的。我们的模型将揭示网络交互的创新方面，并帮助我们更好地了解网络在威胁生命的疾病中的病理状态，如Rett综合征、婴儿猝死综合征或先天性中枢低通气综合征。