Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
MAGMA. 2010 Dec;23(5-6):289-307. doi: 10.1007/s10334-010-0228-5. Epub 2010 Oct 24.
Analytic tools for addressing spontaneous brain activity, as acquired with fMRI during the "resting-state," have grown dramatically over the past decade. Along with each new technique, novel hypotheses about the functional organization of the brain are also available to researchers. We review six prominent categories of resting-state fMRI data analysis: seed-based functional connectivity, independent component analysis, clustering, pattern classification, graph theory, and two "local" methods. In surveying these methods, we address their underlying assumptions, methodologies, and novel applications.
用于分析在“静息状态”下通过 fMRI 获得的自发性脑活动的分析工具在过去十年中得到了极大的发展。随着每种新技术的出现,研究人员也可以提出关于大脑功能组织的新假设。我们回顾了静息态 fMRI 数据分析的六个主要类别:基于种子的功能连接、独立成分分析、聚类、模式分类、图论和两种“局部”方法。在调查这些方法时,我们解决了它们的基本假设、方法和新的应用。