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,
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|
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science
|Date Deposited:||08 Mar 2005|
|Last Modified:||27 Mar 2014 20:03|
|Further Information:||Google Scholar|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Available Versions of this Item
- KCCA for fMRI Analysis. (deposited 08 Mar 2005) [Currently Displayed]
Actions (login required)