Amunts K, Hawrylycz M J, Van Essen D C, Van Horn J D, Harel N, Poline J-B, De Martino F, Bjaalie J G, Dehaene-Lambertz G, Dehaene S, Valdes-Sosa P, Thirion B, Zilles K, Hill S L, Abrams M B, Tass P A, Vanduffel W, Evans A C, Eickhoff S B
Institute of Neuroscience and Medicine, INM-1, Research Centre Jülich, Germany; C. and O. Vogt Institute for Brain Research, Heinrich Heine University, Düsseldorf, Germany.
Allen Institute for Brain Science, Seattle, WA, USA.
Neuroimage. 2014 Oct 1;99:525-32. doi: 10.1016/j.neuroimage.2014.06.010. Epub 2014 Jun 15.
The last two decades have seen an unprecedented development of human brain mapping approaches at various spatial and temporal scales. Together, these have provided a large fundus of information on many different aspects of the human brain including micro- and macrostructural segregation, regional specialization of function, connectivity, and temporal dynamics. Atlases are central in order to integrate such diverse information in a topographically meaningful way. It is noteworthy, that the brain mapping field has been developed along several major lines such as structure vs. function, postmortem vs. in vivo, individual features of the brain vs. population-based aspects, or slow vs. fast dynamics. In order to understand human brain organization, however, it seems inevitable that these different lines are integrated and combined into a multimodal human brain model. To this aim, we held a workshop to determine the constraints of a multi-modal human brain model that are needed to enable (i) an integration of different spatial and temporal scales and data modalities into a common reference system, and (ii) efficient data exchange and analysis. As detailed in this report, to arrive at fully interoperable atlases of the human brain will still require much work at the frontiers of data acquisition, analysis, and representation. Among them, the latter may provide the most challenging task, in particular when it comes to representing features of vastly different scales of space, time and abstraction. The potential benefits of such endeavor, however, clearly outweigh the problems, as only such kind of multi-modal human brain atlas may provide a starting point from which the complex relationships between structure, function, and connectivity may be explored.
在过去二十年中，人类脑图谱技术在各种空间和时间尺度上取得了前所未有的发展。这些技术共同提供了关于人类大脑许多不同方面的大量信息，包括微观和宏观结构分离、功能区域特化、连通性和时间动态。图谱对于以地形学上有意义的方式整合这些多样的信息至关重要。值得注意的是，脑图谱领域沿着几条主要路线发展，如结构与功能、死后与活体、大脑的个体特征与基于群体的方面，或慢动态与快动态。然而，为了理解人类大脑组织，似乎不可避免地要将这些不同的路线整合并组合成一个多模态人类大脑模型。为此，我们举办了一个研讨会，以确定多模态人类大脑模型的约束条件，这些条件需要实现：（i）将不同的空间和时间尺度以及数据模态整合到一个通用参考系统中，以及（ii）高效的数据交换和分析。如本报告所详述，要获得完全可互操作的人类大脑图谱，在数据采集、分析和表示的前沿仍需大量工作。其中，后者可能是最具挑战性的任务，特别是在表示空间、时间和抽象程度差异极大的特征时。然而，这种努力的潜在好处显然超过了问题，因为只有这种多模态人类大脑图谱才能为探索结构、功能和连通性之间的复杂关系提供一个起点。
