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磁共振连接组学:原理与挑战。

MR connectomics: Principles and challenges.

机构信息

Department of Radiology, University Hospital Center and University of Lausanne (CHUV-UNIL), Switzerland; Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland.

出版信息

J Neurosci Methods. 2010 Dec 15;194(1):34-45. doi: 10.1016/j.jneumeth.2010.01.014. Epub 2010 Jan 22.

Abstract

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

摘要

磁共振连接组学是神经科学中的一个新兴框架,它将扩散磁共振成像和全脑束追踪方法与网络科学的分析工具相结合。在本工作中,我们回顾了目前用 MRI 实现结构连接图的方法,并展示了如何利用这些数据推断大脑结构和功能的新信息。我们还列出了未来需要解决的技术挑战,以实现结构连接的高分辨率图谱。从即将积累的大量数据中,我们讨论了在高级网络分析和可视化方法以及数据组织和分布方面必须应对的新挑战。这个新框架非常适合研究大脑复杂性的关键问题,我们试图预见哪些领域将最受益于这些方法。

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