Open AccessDealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets
Author(s) -
Thirion Bertrand,
Flandin Guillaume,
Pinel Philippe,
Roche Alexis,
Ciuciu Philippe,
Poline JeanBaptiste
Publication year - 2006
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20210
Subject(s) - spatial normalization , normalization (sociology) , voxel , computer science , artificial intelligence , pattern recognition (psychology) , smoothing , functional magnetic resonance imaging , cluster analysis , homogeneous , inference , computer vision , mathematics , psychology , neuroscience , combinatorics , sociology , anthropology
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 anatomo‐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. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.