Computational Neuroanatomy

项目5.摘要 计算神经解剖学（Yoav Freund，负责人; Friedman、Karten、克莱菲尔德） 解剖图谱通过“组件”的整理对电路的表征起着至关重要的作用 这又使得能够对这些电路进行逆向工程。口面动作的控制是由 不同的脑干前运动神经元群体，它们排列成相对较小的簇， 仅限于200至300 µm范围内的区域。此外，对于许多口面运动动作，运动前 神经元簇存在于脑干的多个水平， 以前由可用的地图集定义，包括Paxinos地图集和艾伦大脑通用坐标 框架图集。 我们建议从数字化的大脑图像堆栈中构建一个可训练的基于纹理的数字图谱 通过大脑连续冷冻切片的胶带转移获得（核心2 -精密组织学）， 映射的脑干运动前接口调制的orofacial运动动作。地图集的设计允许 标记的细胞、投射和记录位点在不同的大脑中准确自动地对齐。 我们的可训练的基于纹理的数字地图集利用基于纹理特征的地标识别 Nissl染色的细胞结构。这些标志由解剖学家专家识别并用于创建 用于机器学习的训练集。机器学习用于训练纹理检测器来区分 不同的细胞结构纹理，以自动化标志识别，这是一致的， 解剖学家的原始手动标志注释。这个过程和新大脑的自动排列 在三维空间中进行 可训练的基于纹理的数字地图集在计算机云服务器（Core 3 - Data）上实现 科学）。这使我们能够整合所有项目参与者的实验结果和来自 我们项目之外的其他人。因此，数字地图集是独立于平台的。我们的数据管理旨在 促进地图集的可访问性，描述实验输出的Meta数据，以及映射到所有 每个大脑中的切片，预计最多需要几个千兆字节。所有用户将能够高效地浏览 数字地图集和元数据。它也将有可能从完整的大脑堆栈中检索图像的子集， 验证原始数据。 我们特别关注脑干。然而，该系统是通用的，可以扩展到整个 中枢神经系统;事实上，一名新的研究生已经开始与艾曼博士合作开展一个项目。 Azim（Salk研究所），将寰椎延伸到脊髓，这是另一个具有挑战性的CNS区域。 细胞结构边界的亚区分组。

Project 5. Abstract Computational Neuroanatomy (Yoav Freund, lead; Friedman, Karten, Kleinfeld) Anatomical atlases play an essential role for characterization of circuitry by collation of “Components” which in turn enables reverse engineering of these circuits. Control of orofacial actions is coordinated by distinct populations of brain stem premotor neurons, which are arranged into relatively small clusters and can be limited to domains as small as 200 to 300 µm in extent. Further, for many orofacial motor actions, premotor neuronal clusters are present at multiple levels of the brainstem and do not conform to the boundaries previously defined by available atlases, including the Paxinos atlases and the Allen Brain Common Coordinate Framework atlas. We propose to construct a Trainable Texture-based Digital Atlas from digitized stacks of brain images obtained by tape-transfer of serial cryosections through the brain (Core 2 - Precision Histology) to enable mapping of the brainstem premotor interface modulation of orofacial motor actions. The atlas design allows labeled cells, projections and recording sites to be accurately and automatically aligned across different brains. Our Trainable Texture-based Digital Atlas makes use of identification of landmarks based on texture features of Nissl stained cytoarchitecture. The landmarks are identified by expert anatomists and are used to create training sets for machine learning. Machine learning is used to train texture detectors to distinguish between different cytoarchitectural textures in order to automate landmark identification that is consistent with the original manual landmark annotations by anatomists. This process and the automated alignment of new brains is performed in three dimensions The Trainable Texture-based Digital Atlas is implemented on a computer cloud server (Core 3 - Data Science). This enables us to integrate experimental results across all of the project participants and data from others outside our project. Thus the Digital Atlas is platform-independent. Our data management is designed to facilitate accessibility of the atlas, of meta data that describes experimental output, and of mappings back to all slices in each brain, which is expected to take at most a few Gbytes. All users will be able to efficiently browse the Digital Atlas and meta-data. It will also be possible retrieve subsets of images from full brain stacks for validation of raw data. Our particular focus is on the brainstem. Yet the system is general and can be expanded to the entire central nervous system; indeed, a new graduate student has begun work on a joint project with Dr. Einman Azim (Salk Institute), to extend the atlas to the spinal cord, another CNS region with challenging cytoarchitectural borders for subregion parcellation.