KCCA for fMRI Analysis

(2004) KCCA for fMRI Analysis At Medical Image Understanding and Analysis

WarningThere is a more recent version of this item available.


Full text not available from this repository.


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
ePrint ID: 260655
Date Deposited: 08 Mar 2005
Last Modified: 31 Mar 2016 14:02
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/260655

Available Versions of this Item

Actions (login required)

View Item View Item