McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.
Neuroimage. 2012 Aug 15;62(2):911-22. doi: 10.1016/j.neuroimage.2012.01.024. Epub 2012 Jan 10.
The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.
脑图谱领域的核心概念是使用标准化的（即“立体定向的”）3D 坐标框架进行数据分析，并报告神经影像学实验的结果。这种简单的结构使脑研究人员能够组合来自多个主体的数据，使得可以在背景噪声之上检测到组平均信号，无论是结构还是功能，从而淹没来自任何单个主体的微弱信号。在个体中信号足够强以被检测到时，它允许探索该信号在个体之间的位置差异。从更大的角度来看，它为比较和/或组合来自世界各地不同成像模式和实验室的脑图谱研究结果提供了强大的媒介。最后，它为创建大规模神经影像学数据库或“图谱”提供了一个框架，该图谱捕获了作为年龄或疾病函数的解剖学或生理学指标的人群平均值和方差。然而，虽然上述好处在第一级上没有问题，但存在一些概念和实际挑战，这些挑战在实验数据之间引入了二阶不兼容性。立体定向映射需要两个基本组件：（i）3D 立体定向坐标空间的规范，以及（ii）将 3D 脑图像从“原始”空间（即数据采集时扫描仪的坐标框架）转换为立体定向空间的映射函数。第一个组件通常通过选择代表性的 3D MR 图像来表示，该图像用作目标“模板”或图谱。在映射函数下，将原始图像从原始空间重新采样到立体定向空间，该映射函数可能具有少数或多个自由度，具体取决于实验设计。最佳选择图谱模板和映射函数取决于年龄、性别、半球不对称性、解剖对应、空间归一化方法和疾病特异性等因素的考虑。在定义立体定向空间时是否考虑这些各种因素，导致出现了为特定实验定制的、相互不兼容的、不断扩展的图谱集。这些困难继续困扰着脑图谱领域。本文综述了立体定向空间的演变，包括基本原理和相关概念挑战、人群图谱的创建以及图谱演变的未来趋势。
