Banerjee Samik, Stam Caleb, Tward Daniel J, Savoia Steven, Wang Yusu, Mitra Partha P P
Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, New York, USA.
University of California San Diego, La Jolla, 92093, California, USA.
Res Sq. 2025 Jun 20:rs.3.rs-6642891. doi: 10.21203/rs.3.rs-6642891/v1.
To understand biological intelligence we need to map neuronal networks in vertebrate brains. Mapping mesoscale neural circuitry is done using injections of tracers that label groups of neurons whose axons project to different brain regions. Since many neurons are labeled, it is difficult to follow individual axons. Previous approaches have instead quantified the regional projections using the total label intensity within a region. However, such a quantification is not biologically meaningful. We propose a new approach better connected to the underlying neurons by skeletonizing labeled axon fragments and then estimating a volumetric length density. Our approach uses a combination of deep nets and the Discrete Morse (DM) technique from computational topology. This technique takes into account nonlocal connectivity information and therefore provides noise-robustness. We demonstrate the utility and scalability of the approach on whole-brain tracer injected data. We also define and illustrate an information theoretic measure that quantifies the additional information obtained, compared to the skeletonized tracer injection fragments, when individual axon morphologies are available. Our approach is the first application of the DM technique to computational neuroanatomy. It can help bridge between single-axon skeletons and tracer injections, two important data types in mapping neural networks in vertebrates.
为了理解生物智能，我们需要绘制脊椎动物大脑中的神经网络。绘制中尺度神经回路是通过注射示踪剂来完成的，这些示踪剂会标记那些轴突投射到不同脑区的神经元群。由于许多神经元被标记，因此很难追踪单个轴突。以往的方法是使用区域内的总标记强度来量化区域投射。然而，这种量化在生物学上并无意义。我们提出了一种新方法，通过将标记的轴突片段进行骨架化，然后估计体积长度密度，从而更好地连接到潜在的神经元。我们的方法结合了深度网络和计算拓扑学中的离散莫尔斯（DM）技术。该技术考虑了非局部连接信息，因此具有抗噪声能力。我们在全脑示踪剂注射数据上展示了该方法的实用性和可扩展性。我们还定义并说明了一种信息论度量，该度量量化了与骨架化示踪剂注射片段相比，当单个轴突形态可用时所获得的额外信息。我们的方法是DM技术在计算神经解剖学中的首次应用。它有助于在单轴突骨架和示踪剂注射之间架起桥梁，这是绘制脊椎动物神经网络时的两种重要数据类型。
