title: Voxel-based Bayesian lesion-symptom mapping.
abstract: Most existing voxel-based lesion-symptom mapping methods are based on the same statistical foundation: null hypothesis significance testing (NHST). The two major limitations of these methods are the inability to infer that there is no difference in lesion proportions, and a requirement for multiple-comparison correction. We propose a Bayesian approach that directly models the posterior distribution of lesion-proportion difference, and makes decisions based on inference on this posterior distribution. Compared to NHST-based approaches, our Bayesian approach yields inference results with clearer semantics, and does not require multiple-comparison correction. We evaluated our Bayesian method using simulated data, and data from a study of acute ischemic left-hemispheric stroke. Results of both experiments indicate that the Bayesian approach is sensitive in detecting regions that characterize group differences.
authors: Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA. rong.chen@uphs.upenn.edu
authors: Rong
authors: R
authors: Chen
authors: Edward H
authors: EH
authors: Herskovits
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002545:Brain Ischemia / Q000473:pathology; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging; D007839:Functional Laterality / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D020521:Stroke / Q000473:pathology*