Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRl datasets

被引:131
作者
Thirion, Bertrand
Flandin, Guillaume
Pinel, Philippe
Roche, Alexis
Ciuciu, Philippe
Poline, Jean-Baptiste
机构
[1] UNAF, Dept Rech Med, CEA, DSV,Serv Hosp Frederic Joliot, F-91401 Orsay, France
[2] CEA, Serv Hosp Frederic Joliot, DRM, DSV,Unite INSERM 562, F-91406 Orsay, France
[3] Wellcome Dept Imaging Neurosci, Funct Imaging Lab, London, England
关键词
parcellation; random effects analysis; spatial variability; spectral clustering; coregistration;
D O I
10.1002/hbm.20210
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页码:678 / 693
页数:16
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