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

共 49 条

[1]

Abrahamsen T.J., 2009, Machine Learning for Signal Processing, P1

[2] Regularized Pre-image Estimation for Kernel PCA De-noising Input Space Regularization and Sparse Reconstruction [J].

Abrahamsen, Trine Julie ;

Hansen, Lars Kai .

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2011, 65 (03) :403-412

[3]

Arias P., 2007, 18-23 jun, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, P1

[4] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].

BENJAMINI, Y ;

HOCHBERG, Y .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300

[5] Functional magnetic resonance image analysis of a large-scale neurocognitive network [J].

Bullmore, ET ;

RabeHesketh, S ;

Morris, RG ;

Williams, SCR ;

Gregory, L ;

Gray, JA ;

Brammer, MJ .

NEUROIMAGE, 1996, 4 (01) :16-33

[6] A tutorial on Support Vector Machines for pattern recognition [J].

Burges, CJC .

DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (02) :121-167

[7] Exploring predictive and reproducible Modeling with the single-subject FIAC dataset [J].

Chen, X ;

Pereira, F ;

Lee, W ;

Strother, S ;

MitcheI, T .

HUMAN BRAIN MAPPING, 2006, 27 (05) :452-461

[8]

Dambreville S., 2006, IS T SPIE S EL IM B, V6064(B)

[9] The human parietal operculum. II. Stereotaxic maps and correlation with functional imaging results [J].

Eickhoff, SB ;

Amunts, K ;

Mohlberg, H ;

Zilles, K .

CEREBRAL CORTEX, 2006, 16 (02) :268-279

[10]

Friston K. J., 1994, HUM BRAIN MAPP, V2, P189, DOI [10.1002/hbm.460020402, DOI 10.1002/HBM.460020402]

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