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Conventionally, set-level inference on statistical parametric maps (SPMs) is based on the topological features of an excursion set above some threshold-for example, the number of clusters or Euler characteristic. The expected Euler characteristic-under the null hypothesis-can be predicted from an intrinsic measure or volume of the SPM, such as the resel counts or the Lipschitz-Killing curvatures (LKC). We propose a new approach that performs a null hypothesis omnibus test on an SPM, by testing whether its intrinsic volume (described by LKC coefficients) is different from the volume of the underlying residual fields: intuitively, whether the number of peaks in the statistical field (testing for signal) and the residual fields (noise) are consistent or not. Crucially, this new test requires no arbitrary feature-defining threshold but is nevertheless sensitive to distributed or spatially extended patterns. We show the similarities between our approach and conventional topological inference-in terms of false positive rate control and sensitivity to treatment effects-in two and three dimensional simulations. The test consistently improves on classical approaches for moderate (>20) degrees of freedom. We also demonstrate the application to real data and illustrate the comparison of the expected and observed Euler characteristics over the complete threshold range.

Original publication

DOI

10.1016/j.neuroimage.2012.11.046

Type

Journal article

Journal

Neuroimage

Publication Date

03/2013

Volume

68

Pages

133 - 140

Keywords

Algorithms, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging