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
|Title:||KCCA Feature Selection for fMRI Analysis|
|Keywords:||Correlation Analysis, Feature Selection, fMRI, KCCA|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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