Laboratory of Neuro Imaging, Department of Neurology, University of California Los Angeles, CA, USA.
Front Neuroinform. 2009 Nov 6;3:38. doi: 10.3389/neuro.11.038.2009. eCollection 2009.
Large-archives of neuroimaging data present many opportunities for re-analysis and mining that can lead to new findings of use in basic research or in the characterization of clinical syndromes. However, interaction with such archives tends to be driven textually, based on subject or image volume meta-data, not the actual neuroanatomical morphology itself, for which the imaging was performed to measure. What is needed is a content-driven approach for examining not only the image content itself but to explore brains that are anatomically similar, and identifying patterns embedded within entire sets of neuroimaging data. With the aim of visual navigation of large- scale neurodatabases, we introduce the concept of brain meta-spaces. The meta-space encodes pair-wise dissimilarities between all individuals in a population and shows the relationships between brains as a navigable framework for exploration. We employ multidimensional scaling (MDS) to implement meta-space processing for a new coordinate system that distributes all data points (brain surfaces) in a common frame-of-reference, with anatomically similar brain data located near each other. To navigate within this derived meta-space, we have developed a fully interactive 3D visualization environment that allows users to examine hundreds of brains simultaneously, visualize clusters of brains with similar characteristics, zoom in on particular instances, and examine the surface topology of an individual brain's surface in detail. The visualization environment not only displays the dissimilarities between brains, but also renders complete surface representations of individual brain structures, allowing an instant 3D view of the anatomies, as well as their differences. The data processing is implemented in a grid-based setting using the LONI Pipeline workflow environment. Additionally users can specify a range of baseline brain atlas spaces as the underlying scale for comparative analyses. The novelty in our approach lies in the user ability to simultaneously view and interact with many brains at once but doing so in a vast meta-space that encodes (dis) similarity in morphometry. We believe that the concept of brain meta-spaces has important implications for the future of how users interact with large-scale archives of primary neuroimaging data.
神经影像学数据的大型档案库为重新分析和挖掘提供了许多机会，这可能会带来新的发现，可用于基础研究或临床综合征的特征描述。然而，这种档案库的交互往往是基于文本的，基于主题或图像体积元数据，而不是进行成像以进行测量的实际神经解剖形态本身。需要的是一种内容驱动的方法，不仅可以检查图像内容本身，还可以探索在解剖上相似的大脑，并识别整个神经影像学数据集内嵌入的模式。为了实现大规模神经数据库的可视化导航，我们引入了脑元空间的概念。元空间对群体中所有个体之间的成对差异进行编码，并将大脑之间的关系表示为可探索的框架。我们采用多维尺度（MDS）来实现元空间处理，以实现新的坐标系，该坐标系将所有数据点（脑表面）分布在共同的参考系中，解剖上相似的脑数据彼此靠近。为了在这个派生的元空间内进行导航，我们开发了一个完全交互式的 3D 可视化环境，允许用户同时检查数百个大脑，可视化具有相似特征的大脑集群，放大特定实例，并详细检查单个大脑表面的表面拓扑。可视化环境不仅显示了大脑之间的差异，还呈现了单个大脑结构的完整表面表示，允许即时查看解剖结构及其差异的 3D 视图。数据处理是在基于网格的设置中使用 LONI 管道工作流环境实现的。此外，用户还可以指定一系列基线脑图谱空间作为比较分析的基础尺度。我们方法的新颖之处在于用户能够同时查看和交互许多大脑，但同时在一个编码形态计量学（不）相似性的巨大元空间中进行。我们相信，脑元空间的概念对用户如何与大规模原始神经影像学数据档案库交互具有重要意义。
