Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6031991 | NeuroImage | 2012 | 12 Pages |
Abstract
We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We base these illustrations on two fMRI BOLD data sets - one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe.
Related Topics
Life Sciences
Neuroscience
Cognitive Neuroscience
Authors
Peter Mondrup Rasmussen, Trine Julie Abrahamsen, Kristoffer Hougaard Madsen, Lars Kai Hansen,