False discovery rate analysis of brain diffusion direction maps

TL;DR

This paper introduces a statistical framework for analyzing diffusion direction maps in brain imaging, controlling false discoveries and improving detection power in group comparison studies.

Contribution

It develops a novel false discovery rate control method using empirical null modeling and spatial averaging for diffusion tensor imaging data.

Findings

Enhanced accuracy in identifying brain regions with differing diffusion directions.

Improved statistical power through local spatial averaging.

Applicability of methods to other large-scale spatial hypothesis testing problems.

Abstract

Diffusion tensor imaging (DTI) is a novel modality of magnetic resonance imaging that allows noninvasive mapping of the brain's white matter. A particular map derived from DTI measurements is a map of water principal diffusion directions, which are proxies for neural fiber directions. We consider a study in which diffusion direction maps were acquired for two groups of subjects. The objective of the analysis is to find regions of the brain in which the corresponding diffusion directions differ between the groups. This is attained by first computing a test statistic for the difference in direction at every brain location using a Watson model for directional data. Interesting locations are subsequently selected with control of the false discovery rate. More accurate modeling of the null distribution is obtained using an empirical null density based on the empirical distribution of the test statistics across the brain. Further, substantial improvements in power are achieved by local spatial averaging of the test statistic map. Although the focus is on one particular study and imaging technology, the proposed inference methods can be applied to other large scale simultaneous hypothesis testing problems with a continuous underlying spatial structure.