Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

Publication: Research - peer-reviewJournal article – Annual report year: 2012

NullPointerException

View graph of relations

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.
Original languageEnglish
JournalNeuroImage
Publication date2012
Volume60
Journal number3
Pages1807-1818
ISSN1053-8119
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 3

Keywords

  • Multivariate analysis, Classification, Decoding, Nonlinear modeling, Kernel PCA, Pre-image estimation, NPAIRS resampling
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 7660825