Department of Computer Science and Engineering, University of California, San Diego, CA, USA.
Department of Physics, University of California, San Diego, CA, USA.
Nat Methods. 2019 Apr;16(4):341-350. doi: 10.1038/s41592-019-0328-8. Epub 2019 Mar 11.
Brain atlases enable the mapping of labeled cells and projections from different brains onto a standard coordinate system. We address two issues in the construction and use of atlases. First, expert neuroanatomists ascertain the fine-scale pattern of brain tissue, the 'texture' formed by cellular organization, to define cytoarchitectural borders. We automate the processes of localizing landmark structures and alignment of brains to a reference atlas using machine learning and training data derived from expert annotations. Second, we construct an atlas that is active; that is, augmented with each use. We show that the alignment of new brains to a reference atlas can continuously refine the coordinate system and associated variance. We apply this approach to the adult murine brainstem and achieve a precise alignment of projections in cytoarchitecturally ill-defined regions across brains from different animals.
脑图谱使对不同大脑中的标记细胞和投射进行映射成为可能，并将其映射到一个标准的坐标系上。我们解决了在构建和使用图谱时的两个问题。首先，专家神经解剖学家确定脑组织的精细模式，即由细胞组织形成的“纹理”，以定义细胞构筑边界。我们使用机器学习和从专家注释中获得的训练数据来自动化定位地标结构和大脑对齐参考图谱的过程。其次，我们构建一个活跃的图谱，即随着每次使用而不断扩充。我们表明，将新大脑与参考图谱对齐可以不断改进坐标系和相关方差。我们将此方法应用于成年鼠脑干，并实现了来自不同动物的大脑中细胞构筑定义不明确区域的投射的精确对齐。
