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 -