Incorporating Spatial Dependence into Bayesian Multiple Testing of Statistical Parametric Maps in Functional Neuroimaging

• Activity Number: 26
• Type: Topic Contributed
• Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Andrew Brown* and Nicole A. Lazar and Gauri S. Datta and Woncheol Jang and Jennifer E. McDowell
• Companies: Clemson University and University of Georgia and University of Georgia/U.S. Census Bureau and Seoul National University and University of Georgia
• Keywords: Multiple testing problem ; Bayesian statistics ; Conditional autoregressive model ; false discovery rate ; fMRI ; saccades

Abstract

The analysis of functional neuroimaging data often involves the simultaneous testing for activation at thousands of voxels, leading to a massive multiple testing problem. This is true whether the data analyzed are time courses observed at each voxel or a collection of summary statistics such as statistical parametric maps (SPMs). It is known that classical multiplicity corrections become strongly conservative in the presence of a massive number of tests. Some more popular approaches for thresholding imaging data tend to lose precision or power when the assumption of independence of the data does not hold. Bayesian approaches to large scale simultaneous inference also often rely on the assumption of independence. We introduce a spatial dependence structure into a Bayesian testing model for the analysis of SPMs. Increased power is demonstrated by using the dependence model to draw inference on a real dataset collected in a fMRI study of cognitive control. The model also is shown to lead to improved identification of neural activation patterns known to be associated with eye movement tasks.