Bolaños-Puchet Sirio, Teska Aleksandra, Hernando Juan B, Lu Huanxiang, Romani Armando, Schürmann Felix, Reimann Michael W
Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland.
Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), Geneva, Switzerland.
Imaging Neurosci (Camb). 2024 Jun 28;2. doi: 10.1162/imag_a_00209. eCollection 2024.
Digital brain atlases define a hierarchy of brain regions and their locations in three-dimensional Cartesian space, providing a standard coordinate system in which diverse datasets can be integrated for visualization and analysis. Although this coordinate system has well-defined anatomical axes, it does not provide the best description of the complex geometries of layered brain regions such as the neocortex. As a better alternative, we proposethat consider the curvature and laminar structure of the region of interest. These coordinate systems consist of a principal axis aligned to the local vertical direction and measuring depth, and two other axes that describe a flatmap, a two-dimensional representation of the horizontal extents of layers. The main property of flatmaps is that they allow a seamless mapping between 2D and 3D spaces through structured dimensionality reduction where information is aggregated along depth. We introduce a general method to define laminar coordinate systems and flatmaps based on digital brain atlases and according to user specifications. The method is complemented by a set of metrics to characterize the quality of the resulting flatmaps. We applied our method to two rodent atlases. First, to an atlas of rat somatosensory cortex based on Paxinos and Watson's rat brain atlas, enhancing it with a laminar coordinate system adapted to the geometry of this region. Second, to the Allen Mouse Brain Atlas Common Coordinate Framework version 3, enhancing it with two flatmaps of the whole isocortex. We used one of these flatmaps to define new annotations of 33 individual barrels and barrel columns that are nonoverlapping and follow the curvature of the cortex, therefore, producing the most accurate atlas of mouse barrel cortex to date. Additionally, we introduced several applications highlighting the utility of laminar coordinate systems for data visualization and data-driven modeling. We provide a free software implementation of our methods for the benefit of the community.
数字脑图谱定义了脑区层次结构及其在三维笛卡尔空间中的位置,提供了一个标准坐标系,在该坐标系中可以整合各种数据集以进行可视化和分析。尽管这个坐标系有明确的解剖轴,但它并不能最好地描述诸如新皮层等分层脑区的复杂几何形状。作为一个更好的选择,我们建议考虑感兴趣区域的曲率和层状结构。这些坐标系由一条与局部垂直方向对齐并测量深度的主轴,以及另外两条描述平面地图的轴组成,平面地图是层水平范围的二维表示。平面地图的主要特性是它们允许通过结构化降维在二维和三维空间之间进行无缝映射,其中信息沿深度聚合。我们介绍了一种基于数字脑图谱并根据用户规范定义层状坐标系和平面地图的通用方法。该方法辅以一组度量来表征所得平面地图的质量。我们将我们的方法应用于两个啮齿动物图谱。首先,应用于基于帕西诺斯和沃森大鼠脑图谱的大鼠体感皮层图谱,用适应该区域几何形状的层状坐标系对其进行增强。其次,应用于艾伦小鼠脑图谱通用坐标框架版本3,用整个同型皮层的两个平面地图对其进行增强。我们使用其中一个平面地图定义了33个单独的桶状结构和桶状柱的新注释,这些注释不重叠且遵循皮层的曲率,因此生成了迄今为止最精确的小鼠桶状皮层图谱。此外,我们介绍了几个应用,突出了层状坐标系在数据可视化和数据驱动建模方面的实用性。为了社区的利益,我们提供了我们方法的免费软件实现。