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Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses

Abstract:

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MSD
Department:
Clinical Neurosciences
Role:
Author
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Institution:
University of Oxford
Department:
Clinical Neurosciences
Role:
Author
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Name:
National Institute of Mental Health
More from this funder
Name:
National Institute on Drug Abuse
Publisher:
Frontiers Media
Journal:
Frontiers in Neuroscience More from this journal
Volume:
11
Pages:
115
Publication date:
2017-03-13
Acceptance date:
2017-02-23
DOI:
EISSN:
1662-453X
ISSN:
1662-4548
Pmid:
28348512
Language:
English
Keywords:
Pubs id:
pubs:689152
UUID:
uuid:d10d27a5-a1e5-49cd-802d-49435434388f
Local pid:
pubs:689152
Source identifiers:
689152
Deposit date:
2017-10-26

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