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

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  • Peter Mondrup Rasmussen
  • Trine Julie Abrahamsen, Cognitive Systems, Denmark
  • Kristoffer Hougaard Madsen, Cognitive Systems, Denmark
  • Lars Kai Hansen, Cognitive Systems, Denmark
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
Pages (from-to)1807-18
Number of pages12
Publication statusPublished - 2012

    Research areas

  • Algorithms, Artifacts, Cerebral Cortex, Evoked Potentials, Functional Neuroimaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Nonlinear Dynamics, Pattern Recognition, Automated, Principal Component Analysis, Reproducibility of Results, Sensitivity and Specificity, Signal-To-Noise Ratio

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