Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data.
Spatial Patterns and Functional Profiles for Discovering Structure in fMRI Data.
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DOI：
10.1109/acssc.2008.5074650
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发表时间：
2008-10
期刊：
影响因子：
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通讯作者：
Venkataraman A
中科院分区：
文献类型：
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作者：
Golland P;Lashkari D;Venkataraman A
我们探讨了无监督，无假设的方法在两种不同类型的实验中的功能磁共振成像分析。首先，我们采用聚类来识别大规模的功能同质系统。我们制定了一个生成的混合模型，推导出EM算法，并将其应用于描绘功能系统。我们还研究了谱聚类应用于这个问题，并证明了这两种方法产生了类似的分区的基础上休息状态的功能磁共振成像数据的大脑。其次，我们演示了如何扩展这种方法，包括有关实验协议的信息。具体来说，我们制定了一个混合物模型的空间可能的配置文件的大脑对刺激的反应。在这两个应用程序中，我们的方法证实了以前已知的结果，在大脑映射和点的fMRI数据的探索性分析的新的研究方向。
We explore unsupervised, hypothesis-free methods for fMRI analysis in two different types of experiments. First, we employ clustering to identify large-scale functionally homogeneous systems. We formulate a generative mixture model, derive the EM algorithm and apply it to delineate functional systems. We also investigate spectral clustering in application to this problem and demonstrate that both methods give rise to similar partitions of the brain based on resting state fMRI data. Second, we demonstrate how to extend this approach to include information about the experimental protocol. Specifically, we formulate a mixture model in the space of possible profiles of brain response to stimuli. In both applications, our methods confirm previously known results in brain mapping and point to new research directions for exploratory analysis of fMRI data.