Chen Huafu, Yuan Hong, Yao Dezhong, Chen Lin, Chen Wufan
Center of Neuroinformatics, School of Life Science and Technology, School of Applied Math, University of Electronic Science and Technology of China, Chengdu 610054, China.
IEEE Trans Biomed Eng. 2006 Mar;53(3):452-8. doi: 10.1109/TBME.2005.869660.
Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.
聚类分析是一种很有前景的数据驱动方法，用于分析功能磁共振成像（fMRI）时间序列，然而，巨大的计算量使其难以实际应用。本文将邻域相关性（NC）和层次聚类（HC）方法整合为一种新方法，其中fMRI数据首先通过NC进行处理以获得大脑激活的初步图像，然后通过HC去除一些噪声。在HC中，为了更好地利用fMRI数据中的空间和时间信息，引入了一种新的时空度量。一项模拟研究以及对视觉fMRI数据的应用表明，可以有效地检测到大脑激活，并且可以区分不同的响应模式。这些结果表明，所提出的新的综合方法可能有助于检测微弱的fMRI信号。
