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A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data

机译：一种贝叶斯压缩感知方法，用于从子采样的解剖数据中重建神经连接

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摘要

In recent years, the problem of reconstructing the connectivity in large neural circuits ("connec-tomics") has re-emerged as one of the main objectives of neuroscience. Classically, reconstructions of neural connectivity have been approached anatomically, using electron or light microscopy and histological tracing methods. This paper describes a statistical approach for connectivity reconstruction that relies on relatively easy-to-obtain measurements using fluorescent probes such as synaptic markers, cytoplasmic dyes, transsy-naptic tracers, or activity-dependent dyes. We describe the possible design of these experiments and develop a Bayesian framework for extracting synaptic neural connectivity from such data. We show that the statistical reconstruction problem can be formulated naturally as a tractable L-regularized quadratic optimization. As a concrete example, we consider a realistic hypothetical connectivity reconstruction experiment in C. elegans, a popular neuroscience model where a complete wiring diagram has been previously obtained based on long-term electron microscopy work. We show that the new statistical approach could lead to an orders of magnitude reduction in experimental effort in reconstructing the connectivity in this circuit. We further demonstrate that the spatial heterogeneity and biological variability in the connectivity matrix-not just the "average" connectivity-can also be estimated using the same method.
机译：近年来，在大型神经回路中重建连接性的问题（“连接体”）已经重新出现，成为神经科学的主要目标之一。经典地，已经使用电子或光学显微镜和组织学追踪方法在解剖学上探讨了神经连通性的重建。本文介绍了一种用于连通性重建的统计方法，该方法依赖于使用荧光探针（例如突触标记物，细胞质染料，反式突触示踪剂或活性依赖性染料）的相对容易获得的测量结果。我们描述了这些实验的可能设计，并开发了一种贝叶斯框架以从此类数据中提取突触神经连接。我们表明，统计重建问题可以自然地表述为可处理的L正规二次优化。作为一个具体的例子，我们考虑在秀丽隐杆线虫中进行的现实的假设连通性重建实验，秀丽隐杆线虫是一种流行的神经科学模型，该模型先前已基于长期电子显微镜工作获得了完整的接线图。我们表明，新的统计方法可以导致在重建此电路中的连通性方面的实验工作量减少一个数量级。我们进一步证明，连接矩阵中的空间异质性和生物学变异性（不仅仅是“平均”连接性）也可以使用相同的方法进行估算。

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