Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRl datasets
Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRl datasets
复制标题
DOI：
10.1002/hbm.20210
复制
发表时间：
2006-08-01
影响因子：
4.8
通讯作者：
Poline, Jean-Baptiste
中科院分区：
文献类型：
--
作者：
Thirion, Bertrand;Flandin, Guillaume;Poline, Jean-Baptiste
功能磁共振成像（fMRI）数据的分析记录在几个主题诉诸于所谓的空间归一化在一个共同的参考空间。这种归一化通常是在逐体素的基础上进行的，假设在Talairach参考系统中将功能图像与解剖模板图像共配后，可以跨受试者进行正确的基于体素的推断。这种方法的缺点通常是通过空间平滑数据来增加受试者特定激活区域之间的重叠来解决的。然而，该程序不能适应每个无功能主体配置。我们介绍了一种新的基于光谱聚类的主题内分割技术，该技术描绘了均匀和连接的区域。我们还提出了一种分层方法来派生跨主题的空间连贯和功能同质的组包裹。我们表明，我们可以获得包的组（或派系），很好地总结了主体间的激活。我们还表明，嵌入在我们程序中的空间松弛提高了随机效应分析的灵敏度。
The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so-called spatial normalization in a common reference space. This normalization is usually carried out on a voxel-by-voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel-based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject-specific activated regions. This procedure, however, cannot adapt to each anatorno-functional subject configuration. We introduce a novel technique for intra-subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter-subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random-effect analysis.