Feature-space clustering for fMRI meta-analysis: Human Brain Mapping
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Feature-space clustering for fMRI meta-analysis : Human Brain Mapping. / Goutte, C.; Hansen, L.K.; Liptrot, Matthew George; Rostrup, E.
I: Human Brain Mapping, Bind 13, Nr. 3, 2001, s. 165-183.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Feature-space clustering for fMRI meta-analysis
T2 - Human Brain Mapping
AU - Goutte, C.
AU - Hansen, L.K.
AU - Liptrot, Matthew George
AU - Rostrup, E.
PY - 2001
Y1 - 2001
N2 - 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. © 2001 Wiley-Liss, Inc.
AB - 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. © 2001 Wiley-Liss, Inc.
KW - Clustering
KW - Feature extraction
KW - fMRI
KW - Gaussian mixture model
KW - Information criteria
KW - Meta analysis
KW - article
KW - brain mapping
KW - cluster analysis
KW - human
KW - nuclear magnetic resonance imaging
KW - priority journal
KW - signal processing
KW - visual stimulation
KW - Algorithms
KW - Brain Mapping
KW - Cluster Analysis
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Magnetic Resonance Imaging
KW - Meta-Analysis
KW - Models, Statistical
U2 - 10.1002/hbm.1031
DO - 10.1002/hbm.1031
M3 - Journal article
VL - 13
SP - 165
EP - 183
JO - Human Brain Mapping
JF - Human Brain Mapping
SN - 1065-9471
IS - 3
ER -
ID: 137009431