DWT–CEM: an algorithm for scale-temporal clustering in fMRI
DWT–CEM: an algorithm for scale-temporal clustering in fMRI
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DOI：
10.1007/s00422-007-0154-4
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发表时间：
2007-07
影响因子：
1.9
通讯作者：
J. Sato;André Fujita;E. A. Júnior;J. Miranda;P. Morettin;M. Brammer
中科院分区：
文献类型：
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作者：
J. Sato;André Fujita;E. A. Júnior;J. Miranda;P. Morettin;M. Brammer
自20世纪90年代初首次描述该技术以来，使用功能性磁共振成像（fMRI）的研究数量增长非常迅速。大多数已发表的研究都利用了基于一般线性模型（GLM）的逐体素应用的数据分析方法。另一方面，时间聚类分析（TCA）侧重于通过测量时间共同属性来识别皮质区域之间的关系。在其最一般的形式中，TCA对BOLD的低信噪比敏感，并且依赖于滤波参数的主观选择。在本文中，我们介绍了一种基于小波的时间序列数据聚类的方法，并表明它可能是有用的，在低信噪比的数据集，允许自动选择的最佳数量的集群。我们还提供了应用于模拟和真实的fMRI数据集的技术的例子。
The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.