Vishwanathan Ashwin, Sood Alex, Wu Jingpeng, Ramirez Alexandro D, Yang Runzhe, Kemnitz Nico, Ih Dodam, Turner Nicholas, Lee Kisuk, Tartavull Ignacio, Silversmith William M, Jordan Chris S, David Celia, Bland Doug, Sterling Amy, Seung H Sebastian, Goldman Mark S, Aksay Emre R F
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
Center for Neuroscience, University of California, Davis, Davis, CA, USA.
Nat Neurosci. 2024 Dec;27(12):2443-2454. doi: 10.1038/s41593-024-01784-3. Epub 2024 Nov 22.
A long-standing goal in neuroscience is to understand how a circuit's form influences its function. Here, we reconstruct and analyze a synaptic wiring diagram of the larval zebrafish brainstem to predict key functional properties and validate them through comparison with physiological data. We identify modules of strongly connected neurons that turn out to be specialized for different behavioral functions, the control of eye and body movements. The eye movement module is further organized into two three-block cycles that support the positive feedback long hypothesized to underlie low-dimensional attractor dynamics in oculomotor control. We construct a neural network model based directly on the reconstructed wiring diagram that makes predictions for the cellular-resolution coding of eye position and neural dynamics. These predictions are verified statistically with calcium imaging-based neural activity recordings. This work demonstrates how connectome-based brain modeling can reveal previously unknown anatomical structure in a neural circuit and provide insights linking network form to function.
神经科学的一个长期目标是了解神经回路的形式如何影响其功能。在这里，我们重建并分析了幼体斑马鱼脑干的突触连接图，以预测关键功能特性，并通过与生理数据比较来验证这些特性。我们识别出强连接神经元模块，结果发现它们专门负责不同的行为功能，即眼睛和身体运动的控制。眼睛运动模块进一步组织成两个三模块循环，支持长期以来被认为是动眼控制中低维吸引子动力学基础的正反馈。我们直接基于重建的连接图构建了一个神经网络模型，该模型对眼睛位置的细胞分辨率编码和神经动力学进行预测。这些预测通过基于钙成像的神经活动记录进行了统计验证。这项工作展示了基于连接组的大脑建模如何能够揭示神经回路中以前未知的解剖结构，并提供将网络形式与功能联系起来的见解。
