Exploring regions of interest with cluster analysis (EROICA) using a spectral peak statistic for selecting and testing the significance of fMRI activation time-series

  • Authors:

  • M. Jarmasz;R. L. Somorjai

  • Affiliations:

  • Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Man., Canada R3B 1Y6;Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Man., Canada R3B 1Y6

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

Much relevant information about activations and artifacts in a functional magnetic resonance imaging (fMRI) dataset can be obtained from an exploratory cluster analysis. In contrast to testing the significance of the measured experimental effect for a given model, unsupervised pattern recognition techniques, such as fuzzy clustering, often find unexpected behavior in addition to expected activations, allowing the exploitation of this element of surprise. The many artifact clusters often discovered might aid the experimenter in deciding whether the dataset is usable, whether some additional preprocessing step is required, or whether the one used has introduced spurious effects. However, clustering alone does not complete the analysis because the membership values that are generated are not indicative of the level of statistical significance with respect to the cluster activation patterns (centroids). This is of particular importance for fMRI datasets for which most time-series are ''noise'', with no activation patterns. We propose that an initial partition step should precede the clustering step. Only time-series that meet a certain statistical criterion (using a scaled version of Fisher's g-order statistic) are selected for clustering; this typically represents