Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation

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• 作者：Peter Mondrup Rasmussen^a ; ^b ; ^{pmra@imm.dtu.dk} ; Trine Julie Abrahamsen^a ; Kristoffer Hougaard Madsen^a ; ^c ; Lars Kai Hansen^a
• 关键词：Multivariate analysis ; Classification ; Decoding ; Nonlinear modeling ; Kernel PCA ; Pre-image estimation ; NPAIRS resampling
• 刊名：NeuroImage
• 出版年：2012
• 期刊代码：83_10538119
• 类别：neu
• 出版时间：15 April, 2012
• 卷：60
• 期：3
• 页码：1807-1818
• 文件大小：1038 K

摘要

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 鈥?one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe.