Crinion Jenny, Ashburner John, Leff Alex, Brett Matthew, Price Cathy, Friston Karl
Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London WC1N 3BG, UK.
Neuroimage. 2007 Sep 1;37(3):866-75. doi: 10.1016/j.neuroimage.2007.04.065. Epub 2007 May 24.
A key component of group analyses of neuroimaging data is precise and valid spatial normalization (i.e., inter-subject image registration). When patients have structural brain lesions, such as a stroke, this process can be confounded by the lack of correspondence between the subject and standardized template images. Current procedures for dealing with this problem include regularizing the estimate of warping parameters used to match lesioned brains to the template, or "cost function masking"; both these solutions have significant drawbacks. We report three experiments that identify the best spatial normalization for structurally damaged brains and establish whether differences among normalizations have a significant effect on inferences about functional activations. Our novel protocols evaluate the effects of different normalization solutions and can be applied easily to any neuroimaging study. This has important implications for users of both structural and functional imaging techniques in the study of patients with structural brain damage.
神经影像数据的组分析的一个关键组成部分是精确且有效的空间归一化(即受试者间图像配准)。当患者存在结构性脑损伤,如中风时,这一过程可能会因受试者与标准化模板图像之间缺乏对应性而受到干扰。当前处理此问题的程序包括对用于将受损大脑与模板匹配的扭曲参数估计进行正则化,或“成本函数掩蔽”;这两种解决方案都有显著缺点。我们报告了三项实验,这些实验确定了针对结构受损大脑的最佳空间归一化方法,并确定归一化方法之间的差异是否对功能激活的推断有显著影响。我们新颖的方案评估了不同归一化解决方案的效果,并且可以轻松应用于任何神经影像研究。这对于在研究结构性脑损伤患者时使用结构和功能成像技术的人员具有重要意义。