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Probabilistic independent component analysis for functional magnetic resonance imaging.

Abstract:

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically uniq...

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Publisher copy:
10.1109/tmi.2003.822821

Authors


Journal:
IEEE transactions on medical imaging
Volume:
23
Issue:
2
Pages:
137-152
Publication date:
2004-02-01
DOI:
EISSN:
1558-254X
ISSN:
0278-0062
Source identifiers:
116703

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