Journal article
Feature-Space Clustering for fMRI Meta-Analysis
Clustering functional magnetic resonance imaging (fMRI) time series has emerged in recent years as a possible alternative to parametric modeling approaches. Most of the work so far has been concerned with clustering raw time series. In this contribution we investigate the applicability of a clustering method applied to features extracted from the data.
This approach is extremely versatile and encompasses previously published results [Goutte et al., 1999] as special cases. A typical application is in data reduction: as the increase in temporal resolution of fMRI experiments routinely yields fMRI sequences containing several hundreds of images, it is sometimes necessary to invoke feature extraction to reduce the dimensionality of the data space.
A second interesting application is in the meta-analysis of fMRI experiment, where features are obtained from a possibly large number of single-voxel analyses. In particular this allows the checking of the differences and agreements between different methods of analysis. Both approaches are illustrated on a fMRI data set involving visual stimulation, and we show that the feature space clustering approach yields nontrivial results and, in particular, shows interesting differences between individual voxel analysis performed with traditional methods.
Language: | English |
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Publisher: | John Wiley & Sons, Inc. |
Year: | 2001 |
Pages: | 165-183 |
ISSN: | 10970193 and 10659471 |
Types: | Journal article |
DOI: | 10.1002/hbm.1031 |
ORCIDs: | Hansen, Lars Kai and 0000-0002-3381-4380 |
Algorithms Brain Mapping Cluster Analysis Clustering Feature extraction Gaussian mixture model Humans Image Processing, Computer-Assisted Information criteria Magnetic Resonance Imaging Meta analysis Meta-Analysis Meta-Analysis as Topic Models, Statistical article brain mapping cluster analysis fMRI human nuclear magnetic resonance imaging priority journal signal processing visual stimulation