一种应用于功能磁共振成像（fMRI）研究中全脑数据的改进型时间聚类分析方法。
An improved temporal clustering analysis method applied to whole-brain data in fMRI study.
作者信息
Lu Na, Shan Bao-Ci, Xu Jian-Yang, Wang Wei, Li Kun-Cheng
机构信息
Key Laboratory of Nuclear Analysis Techniques, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China.
出版信息
Magn Reson Imaging. 2007 Jan;25(1):57-62. doi: 10.1016/j.mri.2006.09.034. Epub 2006 Nov 28.
Temporal clustering analysis (TCA) has been proposed as a method to detect the brain responses of an fMRI time series when the time and location of the activation are completely unknown. But TCA is still incompetent in dealing with the time series of the whole brain due to the existence of many inactive pixels. If only active pixels are considered, the sensitivity of TCA will be improved greatly and it could be applied to the whole brain. In this study, some modifications were made to TCA to remove inactive pixels, and the applicability of the modified TCA to the whole brain was validated with a set of visual fMRI data. Based on the time series of the modified TCA, activations of the whole brain corresponding to the visual stimulation were detected. Compared with the previous TCA, the modified TCA method shows a significant improvement in the sensitivity to detect activation peaks of the whole brain.
时间聚类分析（TCA）已被提出作为一种在激活的时间和位置完全未知时检测功能磁共振成像（fMRI）时间序列大脑反应的方法。但由于存在许多非活动像素，TCA在处理全脑时间序列方面仍然无能为力。如果仅考虑活动像素，TCA的灵敏度将大大提高，并且可以应用于全脑。在本研究中，对TCA进行了一些修改以去除非活动像素，并使用一组视觉fMRI数据验证了修改后的TCA对全脑的适用性。基于修改后的TCA时间序列，检测到了与视觉刺激相对应的全脑激活。与之前的TCA相比，修改后的TCA方法在检测全脑激活峰值的灵敏度方面有显著提高。
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