PDF(Pubmed)
Abstract：
In this exploratory study we compare and contrast two methods for deriving a laterality index (LI) from functional magnetic resonance imaging (fMRI) data: the weighted bootstrapped mean from the LI Toolbox (toolbox method), and a novel method that uses subtraction of activations from homologous regions in left and right hemispheres to give an array of difference scores (mirror method). Data came from 31 individuals who had been selected to include a high proportion of people with atypical laterality when tested with functional transcranial Doppler ultrasound (fTCD). On two tasks, word generation and semantic matching, the mirror method generally gave better agreement with fTCD laterality than the toolbox method, both for individual regions of interest, and for a large region corresponding to the middle cerebral artery. LI estimates from this method had much smaller confidence intervals (CIs) than those from the toolbox method; with the mirror method, most participants were reliably lateralised to left or right, whereas with the toolbox method, a higher proportion were categorised as bilateral (i.e., the CI for the LI spanned zero). Reasons for discrepancies between fMRI methods are discussed: one issue is that the toolbox method averages the LI across a wide range of thresholds. Furthermore, examination of task-related t-statistic maps from the two hemispheres showed that language lateralisation is evident in regions characterised by deactivation, and so key information may be lost by ignoring voxel activations below zero, as is done with conventional estimates of the LI.
摘要：
在这项探索性研究中，我们比较和对比了两种从功能磁共振成像（fMRI）数据中得出侧向指数（LI）的方法：LI工具箱中的加权自举平均值（工具箱方法），以及一种新颖的方法，该方法使用从左半球和右半球的同源区域中减去激活来给出一系列差异分数（镜像方法）。数据来自31个人，这些人在接受功能性经颅多普勒超声（fTCD）测试时被选中，其中包括高比例的非典型偏侧人群。在两个任务上，单词生成和语义匹配，与工具箱法相比，镜像法通常与FTCD侧向性具有更好的一致性，无论是针对单个感兴趣的区域，和对应于大脑中动脉的大区域。这种方法的LI估计的置信区间(CI)比工具箱方法的置信区间小得多；使用镜像方法，大多数参与者被可靠地侧向向左或向右，而使用工具箱方法，较高的比例被归类为双边(即，LI的CI跨越零)。讨论了fMRI方法之间差异的原因：一个问题是工具箱方法在广泛的阈值范围内平均LI。此外，对来自两个半球的任务相关t统计图的检查表明，语言偏侧化在以失活为特征的区域中是明显的，所以关键信息可能会因为忽略零以下的体素激活而丢失，正如传统的LI估计所做的那样。
