From connectome to effectome: learning the causal interaction map of the fly brain.
From connectome to effectome: learning the causal interaction map of the fly brain.
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从连接组到效应组：学习果蝇大脑的因果相互作用图。
DOI：
10.1101/2023.10.31.564922
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Pillow,JonathanW
中科院分区：
文献类型：
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作者：
Pospisil,DeanA;Aragon,MaxJ;Dorkenwald,Sven;Matsliah,Arie;Sterling,AmyR;Schlegel,Philipp;Yu,Szi-Chieh;McKellar,ClaireE;Costa,Marta;Eichler,Katharina;Jefferis,GregorySXE;Murthy,Mala;Pillow,JonathanW
神经科学的一个长期目标是获得神经系统的因果模型。这将使神经科学家能够根据神经元之间的动态相互作用来解释动物行为。最近报道的全脑果蝇连接体[1-7]指定了神经元可以相互影响的突触路径，但不是它们是否或如何在体内相互影响。为了克服这一限制，我们引入了一种新的实验和统计相结合的策略，用于有效地学习苍蝇大脑的因果模型，我们称之为“effectome”。具体来说，我们提出了一个估计器的动态系统模型的苍蝇大脑，使用随机光遗传学扰动数据，以准确地估计因果关系的影响和连接体作为之前，大大提高估计效率。然后，我们分析连接体提出的电路，有最大的总影响的动力学的苍蝇神经系统。我们发现，幸运的是，占主导地位的电路显着涉及只有相对较小的神经元群体，因此成像，刺激和神经元识别是可行的。有趣的是，我们发现这种方法还重新发现了已知的电路，并生成了关于其动态的可测试假设。总的来说，我们对连接体的分析提供了证据，证明苍蝇大脑的全球动态是由大量小型且通常在解剖学上局部化的回路组成的，这些回路在很大程度上相互独立。这反过来又意味着，大脑的因果模型，系统神经科学的主要目标，可以在飞行中获得。
A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1–7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the “effectome”. Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons—thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly.