Effects of thresholding on correlation-based image similarity metrics

Vanessa V. Sochat, Krzysztof J. Gorgolewski, Oluwasanmi Koyejo, Joke Durnez, Russell A. Poldrack

Research output: Contribution to journalArticle


The computation of image similarity is important for a wide range of analyses in neuroimaging, from decoding to meta-analysis. In many cases the images being compared have empty voxels, but the effects of such empty voxels on image similarity metrics are poorly understood. We present a detailed investigation of the influence of different degrees of image thresholding on the outcome of pairwise image comparison. Given a pair of brain maps for which one of the maps is thresholded, we show that an analysis using the intersection of non-zero voxels across images at a threshold of Z = ±1.0 maximizes accuracy for retrieval of a list of maps of the same contrast, and thresholding up to Z = ±2.0 can increase accuracy as compared to comparison using unthresholded maps. Finally, maps can be thresholded up to to Z = ±3.0 (corresponding to 25% of voxels non-empty within a standard brain mask) and still maintain a lower bound of 90% accuracy. Our results suggest that a small degree of thresholding may improve the accuracy of image similarity computations, and that robust meta-analytic image similarity comparisons can be obtained using thresholded images.

Original languageEnglish (US)
Article number418
JournalFrontiers in Neuroscience
Issue numberOCT
StatePublished - 2015


  • Functional magnetic resonance imaging
  • Human connectome project
  • Image classification
  • Image comparison
  • Neuroimaging
  • Thresholding

ASJC Scopus subject areas

  • Neuroscience(all)

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