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在气味诱发和自发肢体依赖行为期间下行神经元群体动力学。

Descending neuron population dynamics during odor-evoked and spontaneous limb-dependent behaviors.

机构信息

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

出版信息

Elife. 2022 Oct 26;11:e81527. doi: 10.7554/eLife.81527.

Abstract

Deciphering how the brain regulates motor circuits to control complex behaviors is an important, long-standing challenge in neuroscience. In the fly, , this is coordinated by a population of ~ 1100 descending neurons (DNs). Activating only a few DNs is known to be sufficient to drive complex behaviors like walking and grooming. However, what additional role the larger population of DNs plays during natural behaviors remains largely unknown. For example, they may modulate core behavioral commands or comprise parallel pathways that are engaged depending on sensory context. We evaluated these possibilities by recording populations of nearly 100 DNs in individual tethered flies while they generated limb-dependent behaviors, including walking and grooming. We found that the largest fraction of recorded DNs encode walking while fewer are active during head grooming and resting. A large fraction of walk-encoding DNs encode turning and far fewer weakly encode speed. Although odor context does not determine which behavior-encoding DNs are recruited, a few DNs encode odors rather than behaviors. Lastly, we illustrate how one can identify individual neurons from DN population recordings by using their spatial, functional, and morphological properties. These results set the stage for a comprehensive, population-level understanding of how the brain's descending signals regulate complex motor actions.

摘要

解析大脑如何调节运动回路以控制复杂行为是神经科学中的一个重要且长期存在的挑战。在果蝇中,这是由大约 1100 个下行神经元(DN)群体协调完成的。已知仅激活少数几个 DNs 就足以驱动复杂的行为,如行走和梳理。然而,更大的 DNs 群体在自然行为中扮演的额外角色在很大程度上仍是未知的。例如,它们可能调节核心行为指令,或者组成平行途径,根据感觉背景而被激活。我们通过在个体系绳果蝇中记录近 100 个 DNs 的群体来评估这些可能性,同时它们产生依赖于肢体的行为,包括行走和梳理。我们发现,记录到的 DNs 中最大的一部分编码行走,而在头部梳理和休息时活动的较少。很大一部分行走编码 DNs 编码转弯,而很少有微弱的速度编码。尽管气味环境并不决定招募哪些行为编码 DNs,但有几个 DNs 编码气味而不是行为。最后,我们展示了如何通过使用它们的空间、功能和形态特性来从 DN 群体记录中识别单个神经元。这些结果为全面了解大脑的下行信号如何调节复杂的运动动作奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43aa/9605690/874fbd61977a/elife-81527-fig1.jpg

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