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Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers.

Abstract:

Many sources of fluctuation contribute to the fMRI signal, and this makes identifying the effects that are truly related to the underlying neuronal activity difficult. Independent component analysis (ICA) - one of the most widely used techniques for the exploratory analysis of fMRI data - has shown to be a powerful technique in identifying various sources of neuronally-related and artefactual fluctuation in fMRI data (both with the application of external stimuli and with the subject "at rest...

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Publication status:
Published

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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
Journal:
NeuroImage More from this journal
Volume:
90
Pages:
449-468
Publication date:
2014-04-01
DOI:
EISSN:
1095-9572
ISSN:
1053-8119
Language:
English
Keywords:
Pubs id:
pubs:445665
UUID:
uuid:acd876f1-5000-48cb-ab00-778625c1a396
Local pid:
pubs:445665
Source identifiers:
445665
Deposit date:
2014-02-08

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