Dimensionality reduction methods for fMRI analysis and visualization

Kristian Nybo

Research output: ThesisLicenciate's thesisTheses

Abstract

The need to model and understand high-dimensional, noisy data sets is common in many domains these day, among them neuroimaging and fMRI analysis. Dimensionality reduction and variable selection are two common strategies for dealing with high-dimensional data, either as a pre-processing step prior to further analysis, or as an analysis step itself. This thesis discusses both dimensionality reduction and variable selection, with a focus on fMRI analysis, visualization, and applications of visualization in fMRI analysis. Three new algorithms are introduced. The first algorithm uses a sparse Canonical Correlation Analysis model and a high-dimensional stimulus representation to find relevant voxels (variables) in fMRI experiments with complex natural stimuli. Experiments on a data set involving music show that the algorithm successfully retrieves voxels relevant to the experimental condition. The second algorithm, NeRV, is a dimensionality reduction method for visualization high-dimensional data using scatterplots. A simple abstract model of the way a human studies a scatterplot is formulated, and NeRV is derived as an algorithm for producing optimal visualizations in terms of this model. Experiments show that NeRV is superior to conventional dimensionality reduction methods in terms of this model. NeRV is also used to perform a novel form of exploratory data analysis on the fMRI voxels selected by the first algorithm; the analysis simultaneously demonstrates the usefulness of NeRV in practice and offers further insights into the performance of the voxel selection algorithm. The third algorithm, LDA-NeRV, combines a Bayesian latent-variable model for graphs with NeRV to produce one of the first principled graph drawing methods. Experiments show that LDA-NeRV is capable of visualizing structure that conventional graph drawing methods fail to reveal.
Original languageEnglish
QualificationLicentiate's degree
Awarding Institution
  • Aalto University
Supervisors/Advisors
  • Kaski, Samuel, Supervising Professor
Publisher
Publication statusPublished - 2015
MoE publication typeG3 Licentiate thesis

Keywords

  • Functional MRI
  • Dimensionality reduction
  • Variable selection
  • Visualization
  • Graph drawing

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