ICA of fMRI Group Study Data

This paper proposes to extend independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data from single subjects to simultaneous analysis of data from a group of subjects. This results in a set of time courses which are common to the whole group, together with an individual spatial response pattern for each of the subjects in the group. The method is illustrated using data from two fMRI experiments. The results show that: (a) ICA is capable of extracting nontrivial task related components without any a priori information about the fMRI experiment; (b) in analysis of group data, ICA identifies components common to the whole group as well as components manifested in single subjects only.

[1]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[2]  A. T. Smith,et al.  Attentional suppression of activity in the human visual cortex , 2000, Neuroreport.

[3]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[4]  Karl J. Friston.  Modes or models: a critique on independent component analysis for fMRI , 1998, Trends in Cognitive Sciences.

[5]  F Kruggel,et al.  Recording of the event‐related potentials during functional MRI at 3.0 Tesla field strength , 2000, Magnetic resonance in medicine.

[6]  Aapo Hyv.  Fast and Robust Fixed-Point Algorithms for Independent Component Analysis , 1999 .

[7]  M. McKeown.  Detection of Consistently Task-Related Activations in fMRI Data with Hybrid Independent Component Analysis , 2000, NeuroImage.

[8]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[9]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[10]  B. Biswal,et al.  Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. , 1999, Journal of computer assisted tomography.

[11]  G. Berns,et al.  Continuous Functional Magnetic Resonance Imaging Reveals Dynamic Nonlinearities of “Dose-Response” Curves for Finger Opposition , 1999, The Journal of Neuroscience.

[12]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[13]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[14]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[15]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[16]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[17]  D. Y. von Cramon,et al.  Auditory Sentence Comprehension: Different BOLD patterns modulated by task demands as revealed by a ‘single-trial’ fMRI-study , 1998, NeuroImage.

[18]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[19]  Seongjai Kim,et al.  Artificial Damping Techniques for Scalar Waves in the Frequency Domain , 1996 .

[20]  A. J. Bell,et al.  A Unifying Information-Theoretic Framework for Independent Component Analysis , 2000 .