Vieira Gilson, Amaro Edson, Baccalá Luiz A
Inter-institutional Grad Program on Bioinformatics, University of São Paulo, São Paulo, Brazil.
LIM-44, Department of Radiology, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
Brain Inform. 2015 Jun;2(2):53-63. doi: 10.1007/s40708-015-0011-5. Epub 2015 Feb 3.
To overcome the limitations of independent component analysis (ICA), today's most popular analysis tool for investigating whole-brain spatial activation in resting state functional magnetic resonance imaging (fMRI), we present a new class of local dimension-reduced dynamical spatio-temporal model which dispenses the independence assumptions that severely limit deeper connectivity descriptions between spatial components. The new method combines novel concepts of group sparsity with contiguity-constrained clusterization to produce physiologically consistent regions of interest in illustrative fMRI data whose causal interactions may then be easily estimated, something impossible under the usual ICA assumptions.
为克服独立成分分析（ICA）的局限性，ICA是当今用于研究静息态功能磁共振成像（fMRI）中全脑空间激活的最流行分析工具，我们提出了一类新的局部降维动态时空模型，该模型摒弃了严重限制空间成分之间更深层次连接描述的独立性假设。新方法将组稀疏性的新概念与邻接约束聚类相结合，以在示例性fMRI数据中生成生理上一致的感兴趣区域，然后可以轻松估计其因果相互作用，而这在通常的ICA假设下是不可能的。
