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

被引：23

作者：

Rasmussen, Peter Mondrup ^{[1
,2
]}

Abrahamsen, Trine Julie ^{[1
]}

Madsen, Kristoffer Hougaard ^{[1
,3
]}

Hansen, Lars Kai ^{[1
]}

机构：

[1] Tech Univ Denmark, DTU Informat, Kongens Lyngby, Denmark

[2] Aarhus Univ Hosp, Danish Natl Res Fdn, Ctr Functionally Integrat Neurosci, Aarhus, Denmark

[3] Univ Copenhagen, Hvidovre Hosp, Danish Res Ctr Magnet Resonance, DK-1168 Copenhagen, Denmark

来源：

NEUROIMAGE | 2012年 / 60卷 / 03期

基金：

英国医学研究理事会;

关键词：

Multivariate analysis; Classification; Decoding; Nonlinear modeling; Kernel PCA; Pre-image estimation; NPAIRS resampling; FMRI DATA; QUANTITATIVE-EVALUATION; PREDICTION; NPAIRS; ACTIVATION; PATTERNS; REMOVAL; MODEL; PCA;

D O I：

10.1016/j.neuroimage.2012.01.096

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

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 - one from a simple finger tapping experiment and the other from an experiment on object recognition in the ventral temporal lobe. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：1807 / 1818

页数：12

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