网络模块化揭示了在单细胞解决的神经活动记录中,随时间变化的功能连接的上下文和状态依赖的重组
Sihoon Moon1, Jingting Liang2, Hyun Jee Lee1
1Georgia Institute of Technology, School of Chemical & Biomolecular Engineering, Atlanta, GA.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
神经科学研究使用网络模块化来分析大脑活动. 这种方法揭示了功能连接如何在响应刺激时发生变化,并确定了影响网络组织的关键神经元.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
背景情况:
- 了解神经网络的跨规模组织对于破译复杂的信息处理和行为至关重要.
- 大规模的神经活动记录越来越常见,但将单个神经元连接与大脑范围的活动联系起来仍然具有挑战性.
研究的目的:
- 引入和应用基于模块化的社区检测到单细胞解决的神经活动.
- 在中间规模上分析时间变化的功能连接和网络组织.
主要方法:
- 应用社区检测和网络模块化到单细胞解决的多细胞和全脑活动记录.
- 分析功能连接性变化,以应对刺激 (C. elegans*的食物气味) 和自发活动.
主要成果:
- 食物气味改变了C. elegans*运动神经元内部的现有模块中的功能连接,而没有重新组织它们.
- 沉默关键枢纽神经元显著破坏了自发全脑活动中的模块分配的稳定性.
- 证明社区检测和模块化是分析上下文依赖的功能连接变化的有效工具.
结论:
- 模块化分析提供了一个可扩展的方法来总结时间变化的功能连接的复杂变化.
- 这种方法有助于在不同的时间窗口,记录和实验条件之间进行比较.
- 这些发现为研究神经活动组织在中间网络尺度下的机制铺平了道路.
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