An evaluation of spatial thresholding techniques in fMRI analysis

  • Logan B
  • Geliazkova M
  • Rowe D
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Many fMRI experiments have a common objective of identifying active voxels in a neuroimaging dataset. This is done in single subject experiments, for example, by performing individual voxel-wise tests of the null hypothesis that the observed time course is not significantly related to an assigned reference function. A voxel activation map is then constructed by applying a thresholding rule to the resulting statistics (e.g., t-statistics). Typically the task-related activation is expected to occur in clusters of voxels rather than in isolated single voxels. A variety of spatial thresholding techniques have been proposed to reflect this belief, including smoothing the raw t-statistics, cluster size inference, and spatial mixture modeling. We study two aspects of these spatial thresholding procedures applied to single subject fMRI analysis through simulation. First, we examine the performance of these procedures in terms of sensitivity to detect voxel activation, using receiver operating characteristic curves. Second, we consider the accuracy of these spatial thresholding procedures in estimation of the size of the activation region, both in terms of bias and variance. The findings indicate that smoothing has the highest sensitivity to modest magnitude signals, but tend to overestimate the size of the activation region. Spatial mixture models estimate the size of a spatially distributed activation region well, but may be less sensitive to modest magnitude signals, indicating that additional research into more sensitive spatial mixture models is needed. Finally, the methods are illustrated with a real bilateral finger-tapping fMRI experiment.

Author-supplied keywords

  • Benjamini-Hochberg procedure
  • Bonferroni procedure
  • Cluster size inference
  • ROC analysis
  • Random fields
  • Smoothing
  • Spatial mixture model

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  • Brent R. Logan

  • Maya P. Geliazkova

  • Daniel B. Rowe

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