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Journal article

Impact of automated ICA-based denoising of fMRI data in acute stroke patients

Abstract:

Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow foc...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1016/j.nicl.2017.06.033

Authors


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Institution:
University of Oxford
Department:
Oxford, MSD, RDM, Acute Vascular Imaging Centre
Licenik, R More by this author
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Institution:
University of Oxford
Department:
Oxford, MSD, Psychiatry
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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
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Institution:
University of Oxford
Department:
Oxford, MSD, Clinical Neurosciences
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Grant:
Oxford Biomedical Research Centre Programme; Clinical Research Network
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Grant:
Centre of Excellence for Personalized Healthcare WT088877/Z/09/Z
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Grant:
Centre of Excellence for Personalized Healthcare WT088877/Z/09/Z
Parkinson's UK More from this funder
Publisher:
Elsevier Publisher's website
Journal:
NeuroImage: Clinical Journal website
Volume:
16
Pages:
23-31
Publication date:
2017-06-30
Acceptance date:
2017-06-29
DOI:
EISSN:
2213-1582
ISSN:
2213-1582
Pubs id:
pubs:708855
URN:
uri:c2062fb1-c9db-4dfd-90be-8f551758dd7e
UUID:
uuid:c2062fb1-c9db-4dfd-90be-8f551758dd7e
Local pid:
pubs:708855

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