The fly connectome reveals a path to the effectome
成果类型：
Article
署名作者：
Pospisil, Dean A.; Aragon, Max J.; Dorkenwald, Sven; Matsliah, Arie; Sterling, Amy R.; Schlegel, Philipp; Yu, Szi-chieh; McKellar, Claire E.; Costa, Marta; Eichler, Katharina; Jefferis, Gregory S. X. E.; Murthy, Mala; Pillow, Jonathan W.
署名单位：
Princeton University; Princeton University; MRC Laboratory Molecular Biology; University of Cambridge
刊物名称：
Nature
ISSN/ISSBN：
0028-6571
DOI：
10.1038/s41586-024-07982-0
发表日期：
2024-10-03
关键词：
摘要：
A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome1-3 specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a 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 linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to the nonlinear dynamics of more biophysically realistic simulations. We then analyse the connectome to propose circuits that dominate the dynamics of the fly nervous system. We discover that the dominant circuits involve only relatively small populations of neurons-thus, neuron-level imaging, stimulation and identification are feasible. This approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, we provide evidence that fly whole-brain dynamics are generated by a large collection of small circuits that operate largely independently of each other. This implies that a causal model of a brain can be feasibly obtained in the fly. Analysis of the whole-brain fly connectome reveals high-dimensional dynamics supported by many small independent circuits, motivating a proposal for optogenetic perturbation to efficiently learn a whole-brain causal neural dynamics model.