Voxel-Based Lesion-Symptom Mapping: A Nonparametric Bayesian Approach

The study of brain-injured patients (or lesion-based analysis) is a powerful paradigm for investigating structure-function relationships using neuroimaging techniques. Voxel-based Lesion-Symptom Mapping (VLSM) has been widely used to detect structure-function associations in neuroimaging studies. However this approach is based on Student t-test for which normality does not always hold. Our aim in the current study is twofold: 1) to confirm/refute the implication of the classical language areas using the Language Screening (LAST) test; and 2) to determine if it is possible to reduce the number of patients included in the VLSM study, using a different statistical approach. To achieve the second goal, we propose an alternative nonparametric and Bayesian test using Polya trees. The approach is Bayesian, assigning prior distributions and computing the Bayes factor of H-0 (null hypothesis) to H-1 (alternative) ; and it is nonparametric since the priors are put on the unknown distribution functions under H-0 and H-1. Our results highlight that the Polya tree prior provides a convenient and effective way for testing two sample differences in VLSM studies.