Suppr超能文献

基于分区的连接组学:局限性与拓展。

The parcellation-based connectome: limitations and extensions.

机构信息

Department of Psychiatry, Rudolf Magnus Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.

出版信息

Neuroimage. 2013 Oct 15;80:397-404. doi: 10.1016/j.neuroimage.2013.03.053. Epub 2013 Apr 1.

Abstract

The human connectome is an intricate system of interconnected elements, providing the basis for integrative brain function. An essential step in the macroscopic mapping and examination of this network of structural and functional interactions is the subdivision of the brain into large-scale regions. Parcellation approaches used for the formation of macroscopic brain networks include application of predefined anatomical templates, randomly generated templates and voxel-based divisions. In this review, we discuss the use of such parcellation approaches for the examination of connectome characteristics. We specifically address the impact of the choice of parcellation scheme and resolution on the estimation of the brain's topological and spatial network features. Although organizational principles of functional and structural brain networks appear to be largely independent of the adopted parcellation approach, quantitative measures of these principles may be significantly modulated. Future parcellation-based connectome studies might benefit from the adoption of novel network tools and promising advances in connectivity-based parcellation approaches.

摘要

人类连接组是一个错综复杂的相互关联元素系统,为整体大脑功能提供了基础。在对这个结构和功能相互作用的网络进行宏观绘图和检查的过程中,一个重要的步骤是将大脑细分为大规模区域。用于形成宏观大脑网络的分割方法包括应用预先定义的解剖模板、随机生成的模板和基于体素的划分。在这篇综述中,我们讨论了使用这种分割方法来检查连接组特征。我们特别讨论了分割方案和分辨率的选择对大脑拓扑和空间网络特征估计的影响。尽管功能和结构大脑网络的组织原则似乎在很大程度上独立于所采用的分割方法,但这些原则的定量测量可能会受到显著调节。未来基于分割的连接组研究可能受益于采用新的网络工具和基于连接的分割方法的有前途的进展。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验