KCCA for fMRI Analysis
(2004) KCCA for fMRI Analysis At Medical Image Understanding and Analysis
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We use Kernel Canonical Correlation Analysis (KCCA) to infer brain activity in functional MRI by learning a semantic representation of fMRI brain scans and their associated activity signal. The semantic space provides a common representation and enables a comparison between the fMRI and the activity signal. We compare the approach against Canonical Correlation Analysis (CCA) by localising “activity” on a simulated null data set. Finally we present an approach to reconstruct an activity signal from a testing-set fMRI scans (both simulated and real), a method which allows us to validate our initial analysis.
|Item Type:||Conference or Workshop Item (Poster)|
|Additional Information:||Event Dates: 2004|
|Date Deposited:||08 Mar 2005|
|Last Modified:||31 Mar 2016 14:02|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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- KCCA for fMRI Analysis (deposited 08 Mar 2005) [Currently Displayed]
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