Computational Neuroanatomy

项目5.摘要 计算神经解剖学(Yoav Freund，Lead；Friedman，Karten，Kleinfeld) 解剖图谱对于通过校对“元件”来描述电路起着至关重要的作用。 这又使得能够对这些电路进行反向工程。口面部动作的控制由以下人员协调 脑干前运动神经元的不同群体，这些神经元排列成相对较小的簇，可以 仅限于范围小至200至300微米的域。此外，对于许多口腔面部运动动作，运动前 神经元团存在于脑干的多个层面，与边界不符。 以前由可用的地图集定义，包括帕西诺斯地图集和艾伦大脑共同坐标 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 通过脑内连续冷冻切片的磁带传输获得(核心2-精密组织学)，以使 脑干前运动界面的标测对口面部运动动作的调节。地图集的设计允许 标记的细胞、投影和记录位置在不同的大脑中准确和自动地对齐。 我们的基于纹理的可训练数字地图集利用基于纹理特征的地标识别 尼氏染色的细胞结构。这些地标由专家解剖学家识别，并被用来创造 用于机器学习的训练集。机器学习用于训练纹理检测器以区分 不同的细胞架构纹理，以便自动识别符合 由解剖学家原创的手动地标注释。这一过程和新大脑的自动对齐 是在三维空间中进行的 基于纹理的可训练数字地图集在计算机云服务器(Core 3-Data)上实现 科学)。这使我们能够集成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、描述实验输出的元数据以及向所有人返回的映射的访问 每个大脑中的切片，预计最多只需几GB。所有用户都将能够高效地浏览 数字地图集和元数据。还可以从完整大脑堆栈中检索图像的子集 原始数据的验证。 我们特别关注的是脑干。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的一个联合项目的工作 Azim(Salk Institute)，将图谱扩展到脊髓，这是另一个具有挑战性的中枢神经系统区域 子区域分割的细胞结构边界。

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.