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KCCA Feature Selection for fMRI Analysis

KCCA Feature Selection for fMRI Analysis
KCCA Feature Selection for fMRI Analysis
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) and the more commonly used Ordinary Correlation Analysis (OCA) by localising “activity” on a simulated null data set. We also compare performance of the methods on the localisation of brain regions which control finger movement and regions that are involved in mental calculation. Finally we present an approach to reconstruct an activity signal from an “unknown” testing-set fMRI scans. This is used to validate the learnt semantics as non-trivial.
fMRI, KCCA
s.n.
Hardoon, David R
ba75cf10-43f2-4701-b648-3bb90edbabbf
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Friman, Ola
51d9c525-72c5-4580-82b6-ab240e6302df
Hardoon, David R
ba75cf10-43f2-4701-b648-3bb90edbabbf
Shawe-Taylor, John
b1931d97-fdd0-4bc1-89bc-ec01648e928b
Friman, Ola
51d9c525-72c5-4580-82b6-ab240e6302df

Hardoon, David R, Shawe-Taylor, John and Friman, Ola (2004) KCCA Feature Selection for fMRI Analysis s.n.

Record type: Monograph (Project Report)

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) and the more commonly used Ordinary Correlation Analysis (OCA) by localising “activity” on a simulated null data set. We also compare performance of the methods on the localisation of brain regions which control finger movement and regions that are involved in mental calculation. Finally we present an approach to reconstruct an activity signal from an “unknown” testing-set fMRI scans. This is used to validate the learnt semantics as non-trivial.

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More information

Published date: 2004
Keywords: fMRI, KCCA
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 260658
URI: http://eprints.soton.ac.uk/id/eprint/260658
PURE UUID: 35396e8a-b386-4b2e-a07c-e5afdbf35fc4

Catalogue record

Date deposited: 08 Mar 2005
Last modified: 23 Sep 2020 16:36

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