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.

中文翻译：

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用于静息状态网络估计的局部维数减少的动态时空模型。

为了克服独立成分分析（ICA）的局限性，这是当今研究静息状态功能磁共振成像（fMRI）中全脑空间激活的最流行分析工具，我们提出了一种新的局部维数减少的动态时空模型这消除了严重限制空间组件之间更深层次的连通性描述的独立性假设。该新方法将群体稀疏性的新概念与连续性约束的聚类相结合，以在说明性fMRI数据中产生感兴趣的生理上一致的区域，然后可以容易地估计其因果关系，而这在通常的ICA假设下是不可能的。