Population Inference for Differential Functional Brain Connectivity

• Activity Number: 362
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Manjari Narayan*+ and Genevera Allen
• Companies: Rice University and Rice University
• Keywords: Graphical Models ; functional connectivity ; Large Scale Inference ; differential connectivity ; resampling ; population inference

Abstract

Neuroimaging studies based on fMRI, EEG, and MEG are often interested studying functional connectivity, or the relationships between remote brain regions during active or passive tasks. Beyond revealing the workings of the healthy brain, functional connectivity is used to illuminate functional and neurophysiological differences in various neurobiological conditions and diseases. Therefore, rigorous inferential procedures are needed to identify statistically significant differences in brain connectivity patterns across a population of subjects. Using Markov Networks to model functional connectivity patterns, we propose a novel resampling method to conduct large scale simultaneous inference over edges and nodes of a population of graphs. We demonstrate through simulations that our procedure possesses greater statistical power than existing approaches in identifying differences in distinct graph populations, while appropriately controlling the false discovery rate. We apply our methods to a two-group multi-subject fMRI study to discover objective and non-invasive biomarkers for synesthesia.