Sato João Ricardo, Fujita André, Amaro Edson, Miranda Janaina Mourão, Morettin Pedro Alberto, Brammer Michal John
Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010, Cidade Universitria, CEP 05508-090, São Paulo, S.P., Brazil.
Biol Cybern. 2007 Jul;97(1):33-45. doi: 10.1007/s00422-007-0154-4. Epub 2007 May 30.
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.
自20世纪90年代初首次描述功能性磁共振成像（fMRI）技术以来，使用该技术的研究数量增长非常迅速。大多数已发表的研究都采用了基于体素的一般线性模型（GLM）应用的数据分析方法。另一方面，时间聚类分析（TCA）专注于通过测量时间共同特性来识别皮质区域之间的关系。在其最一般的形式中，TCA对血氧水平依赖（BOLD）信号的低信噪比敏感，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方法，并表明它可能对低信噪比数据集有用，允许自动选择最佳聚类数。我们还提供了该技术应用于模拟和真实fMRI数据集的示例。
