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下行网络将命令信号转换为群体运动控制。

Descending networks transform command signals into population motor control.

机构信息

Neuroengineering Laboratory, Brain Mind Institute & Interfaculty Institute of Bioengineering, EPFL, Lausanne, Switzerland.

出版信息

Nature. 2024 Jun;630(8017):686-694. doi: 10.1038/s41586-024-07523-9. Epub 2024 Jun 5.

Abstract

To convert intentions into actions, movement instructions must pass from the brain to downstream motor circuits through descending neurons (DNs). These include small sets of command-like neurons that are sufficient to drive behaviours-the circuit mechanisms for which remain unclear. Here we show that command-like DNs in Drosophila directly recruit networks of additional DNs to orchestrate behaviours that require the active control of numerous body parts. Specifically, we found that command-like DNs previously thought to drive behaviours alone in fact co-activate larger populations of DNs. Connectome analyses and experimental manipulations revealed that this functional recruitment can be explained by direct excitatory connections between command-like DNs and networks of interconnected DNs in the brain. Descending population recruitment is necessary for behavioural control: DNs with many downstream descending partners require network co-activation to drive complete behaviours and drive only simple stereotyped movements in their absence. These DN networks reside within behaviour-specific clusters that inhibit one another. These results support a mechanism for command-like descending control in which behaviours are generated through the recruitment of increasingly large DN networks that compose behaviours by combining multiple motor subroutines.

摘要

为了将意图转化为行动,运动指令必须通过下行神经元(DN)从大脑传递到下游运动回路。这些包括一小部分命令样神经元,它们足以驱动行为——其电路机制尚不清楚。在这里,我们表明,果蝇中的命令样 DN 可以直接招募额外的 DN 网络,以协调需要主动控制众多身体部位的行为。具体来说,我们发现以前被认为单独驱动行为的命令样 DN 实际上会共同激活更大的 DN 群体。连接组分析和实验操作表明,这种功能募集可以通过命令样 DN 与大脑中相互连接的 DN 网络之间的直接兴奋性连接来解释。下行群体募集对于行为控制是必要的:具有许多下游下行伙伴的 DN 需要网络共同激活,才能驱动完整的行为,而在缺乏网络共同激活时,只能驱动简单的刻板运动。这些 DN 网络位于相互抑制的特定行为集群内。这些结果支持了一种命令样下行控制的机制,其中行为是通过招募越来越大的 DN 网络来产生的,这些网络通过组合多个运动子程序来构成行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2585/11186778/810993dcf56b/41586_2024_7523_Fig1_HTML.jpg

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