Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation.
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.
DTU Informatics, Technical University of Denmark, Kongens Lyngby, Denmark. pmra@imm.dtu.dk
Peter Mondrup
PM
Rasmussen
Trine Julie
TJ
Abrahamsen
Kristoffer Hougaard
KH
Madsen
Lars Kai
LK
Hansen
D000465:Algorithms; D016477:Artifacts; D002540:Cerebral Cortex / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio