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
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Computer Science Department, Princeton University, Princeton, NJ, USA.
Nature. 2024 Oct;634(8032):201-209. doi: 10.1038/s41586-024-07982-0. Epub 2024 Oct 2.
A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome 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.
神经科学的一个目标是获得神经系统的因果模型。最近报道的全脑果蝇连接组确定了神经元之间相互影响的突触路径，但没有确定它们在体内相互影响的强度。为了克服这一局限性，我们引入了一种结合实验和统计的策略，用于有效地学习果蝇大脑的因果模型，我们将其称为“效应组”。具体来说，我们提出了一种果蝇大脑线性动力学模型的估计器，该估计器使用随机光遗传学扰动数据来估计因果效应，并将连接组作为先验信息以大大提高估计效率。我们在基于连接组的线性模拟中验证了我们的估计器，并表明它恢复了更具生物物理现实性模拟的非线性动力学的线性近似。然后，我们分析连接组以提出主导果蝇神经系统动力学的回路。我们发现，主导回路仅涉及相对较少的神经元群体——因此，神经元水平的成像、刺激和识别是可行的。这种方法还重新发现了已知回路，并生成了关于其动力学的可测试假设。总体而言，我们提供的证据表明果蝇全脑动力学是由大量相互之间基本独立运作的小回路产生的。这意味着在果蝇中可以切实获得大脑的因果模型。
