KCCA for fMRI Analysis


Hardoon, David R, Shawe-Taylor, John and Friman, Ola (2004) KCCA for fMRI Analysis. At Medical Image Understanding and Analysis, London, UK,

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Description/Abstract

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
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science
ePrint ID: 260655
Date Deposited: 08 Mar 2005
Last Modified: 27 Mar 2014 20:03
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/260655

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