运动皮层神经元协作活动的动态结构携带着行为相关的信息
Marina Sundiang1, Nicholas G Hatsopoulos1,2,3, Jason N MacLean1,2,4
1Committee on Computational Neuroscience, University of Chicago, Chicago, IL, USA.
Network neuroscience (Cambridge, Mass.)
|July 3, 2023
概括
主要运动皮层 (M1) 中的神经网络结构反映了特定的运动. 功能网络 (FNs) 在任务期间发生变化,在指令线索之后变得可解码,表明用于运动控制的动态神经计算.
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.
- 发动机控制器的控制器
背景情况:
- 自愿运动取决于主要运动皮质 (M1) 中的神经计算.
- 神经活动模式,特别是神经元协作,代表了这些计算.
- 功能网络 (FN) 源自尖端时间统计,提供了一种描述神经元协同活动的方法.
研究的目的:
- 在运动任务期间调查M1中功能网络结构的行为特异性.
- 在整个试验过程中探索功能网络的时间动态.
- 了解外部信息如何影响M1网络结构.
主要方法:
- 构建功能网络 (FNs) 来自非人类灵长类动物执行指令延迟到达任务的双向峰值时间统计.
- 利用低维嵌入和图形对齐来分析FN结构.
- 在试验中的短时间间隔分析了时间FN.
- 在指令提示之后检查了互联连接的变化.
主要成果:
- 在行为上,FN结构是特定的,在网络空间中,FN用于更接近的范围方向更接近.
- 时间FN在低维子空间中表现出特定的轨迹.
- 在指令提示后不久,FNs变得可分离和可解码.
- 在指令符号之后,FN的互联连接暂时减少.
结论:
- M1功能网络的结构是特定于运动参数的.
- M1网络在运动任务期间动态演变,遵循特定到达轨迹.
- 外部线索暂时改变M1网络结构,影响信息处理.
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