Meyer François G, Chinrungrueng Jatuporn
Department of Electrical Engineering, University of Colorado, Boulder, CO 80309, USA.
IEEE Trans Med Imaging. 2003 Aug;22(8):933-9. doi: 10.1109/TMI.2003.815869.
We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time series x(i) (t), indexed by the position i of a voxel inside the brain. The decision that a voxel i(o) is activated is based not solely on the value of the fMRI signal at i(o), but rather on the comparison of all time series x(i) (t) in a small neighborhood Wi(o) around i(o). We construct basis functions on which the projection of the fMRI data reveals the organization of the time series x(i) (t) into activated and nonactivated clusters. These clustering basis functions are selected from large libraries of wavelet packets according to their ability to separate the fMRI time series into the activated cluster and a nonactivated cluster. This principle exploits the intrinsic spatial correlation that is present in the data. The construction of the clustering basis functions described in this paper is applicable to a large category of problems where time series are indexed by a spatial variable.
我们探索了一种用于分析大脑活动的事件相关功能磁共振成像（fMRI）的新范式。我们将fMRI数据视为由大脑内体素位置i索引的非常大的时间序列集合x(i)(t)。体素i(o)被激活的判定不仅基于i(o)处fMRI信号的值，还基于i(o)周围小邻域Wi(o)内所有时间序列x(i)(t)的比较。我们构建基函数，fMRI数据在其上的投影揭示了时间序列x(i)(t)被组织成激活簇和未激活簇。这些聚类基函数是从小波包的大型库中根据它们将fMRI时间序列分离为激活簇和未激活簇的能力来选择的。这一原理利用了数据中存在的内在空间相关性。本文描述的聚类基函数的构建适用于一大类由空间变量索引时间序列的问题。
