Thirion Bertrand, Faugeras Olivier
CEA/SHFJ, 4, Place du Général Leclerc, 91401, Orsay Cedex, France.
Med Image Anal. 2004 Dec;8(4):403-19. doi: 10.1016/j.media.2004.09.001.
Clustering is a well-known technique for the analysis of Functional Magnetic Resonance Imaging (fMRI) data, whose main advantage is certainly flexibility: given a metric on the dataset, it "summarizes" the main characteristics of the data. But intrinsic to this approach are also the problem of defining correctly the quantization accuracy, and the number of clusters necessary to describe the data. The Information Bottleneck (IB) approach to vector quantization, proposed by Bialek and Tishby, addresses these difficulties: (1) it deals with an explicit trade-off between quantization and data fidelity; (2) it does so during the clustering procedure and not post hoc; (3) it takes into account the full statistical distribution of the features within the feature space and not only their most likely value; last, it is principled through an information theoretic formulation, which is relevant in many situations. In this paper, we present how to benefit from this method to analyze fMRI data. Our application is the clustering of voxels according to the magnitude of their responses to several experimental conditions. The IB quantization provides a consistent representation of the data, allowing for an easy interpretation and comparison of datasets.
聚类是一种用于分析功能磁共振成像（fMRI）数据的著名技术，其主要优点无疑是灵活性：给定数据集上的一个度量，它“总结”了数据的主要特征。但这种方法本身也存在正确定义量化精度以及描述数据所需聚类数量的问题。Bialek和Tishby提出的信息瓶颈（IB）向量量化方法解决了这些困难：（1）它处理量化与数据保真度之间的明确权衡；（2）它在聚类过程中而非事后进行此操作；（3）它考虑了特征空间内特征的完整统计分布，而不仅仅是它们最可能的值；最后，它通过信息论公式有其原理依据，这在许多情况下都很重要。在本文中，我们展示了如何从该方法中受益以分析fMRI数据。我们的应用是根据体素对几种实验条件的响应幅度对其进行聚类。IB量化提供了数据的一致表示，便于对数据集进行解释和比较。
