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Activity Number:
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155
- Section on Statistics in Imaging Student Paper Award Winners
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Type:
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Topic-Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #317157
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Title:
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A Simple Permutation-Based Test of Intermodal Correspondence
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Author(s):
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Sarah M Weinstein* and Simon N Vandekar and Azeez Adebimpe and Tinashe M Tapera and Timothy Robert-Fitzgerald and Ruben C Gur and Raquel E Gur and Armin Raznahan and Theodore D Satterthwaite and Aaron F Alexander-Bloch and Russell Shinohara
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Companies:
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University of Pennsylvania and Vanderbilt University and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and Developmental Neurogenomics Unit, National Institute of Mental Health and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
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Keywords:
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neuroimaging;
intermodal correspondence;
permutation testing
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Abstract:
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Neuroimaging studies often seek to understand similarities across brain maps, but methods underlying these comparisons continue to vary and lack statistical rigor. We propose the simple permutation-based intermodal correspondence (SPICE) test, where we leverage subject-level data in a framework similar to traditional permutation tests. Our method differs from previous methods involving spherical rotations or spatial autocorrelation-preserving “surrogate” maps, in that we use subject-level data and do not assume stationarity of the covariance structure. We demonstrate in simulated data that the SPICE test is conservative in terms of type I error and has high power. Next, we illustrate that our method performs well for assessing intermodal relationships from multimodal magnetic resonance imaging data from the Philadelphia Neurodevelopmental Cohort. Notably, compared with previous methods, the SPICE test is the most flexible for analyzing correspondence within subregions of the brain and has the greatest potential to be used for generalizable statistical inference.
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Authors who are presenting talks have a * after their name.
