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.

译文

自1990年代初对这项技术进行首次描述以来,使用功能磁共振成像(fMRI)的研究数量迅速增长。大多数已发表的研究都利用了基于一般线性模型(GLM)的体素化应用的数据分析方法。另一方面,时态聚类分析(TCA)侧重于通过测量时态共同属性来识别皮层区域之间的关系。在其最一般的形式中,TCA对BOLD的低信噪比敏感,并且取决于主观选择的滤波参数。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,并表明该方法在信噪比低的数据集中很有用,从而可以自动选择最佳聚类数。我们还提供了应用于模拟和真实fMRI数据集的技术示例。

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