Robust Spatial Extent Inference with a Semiparametric Bootstrap Joint Testing Procedure

• Activity Number: 297 - SPEED: Food, Environment, Biomedical Imaging and Physical System Visualization/Learning, Part 1
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Simon Vandekar* and Theodore Satterthwaite and Cedric K Xia and Azeez Adebimpe and Kosha Ruparel and Ruben C Gur and Raquel E Gur and Russell Shinohara
• Companies: Vanderbilt University and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
• Keywords: Neuroimaging; Spatial Inference; Hypothesis Testing; Semiparametric; Inference

Abstract

Spatial extent inference (SEI) is widely used across neuroimaging modalities to adjust for multiple comparisons when studying brain-phenotype associations. Recent studies have shown that the standard procedures can have inflated family-wise error rates (FWERs). This has led to substantial controversy as to which processing choices are necessary to control the FWER using these procedures. To date, the permutation procedure is the most robust SEI tool because it estimates the spatial covariance function from the imaging data. However, the permutation procedure can fail because its assumption of exchangeability is violated in many imaging modalities. We propose a semiparametric bootstrap joint (sPBJ) testing procedure designed for SEI of multilevel imaging data. The sPBJ procedure uses a robust estimate of the spatial covariance function, which yields consistent estimates of standard errors, even if the covariance model is misspecified. We use the methods to study the association between performance and executive functioning in a working memory fMRI study. We provide an R package to perform inference using the PBJ and sPBJ procedures.