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Diffusion tensor imaging (DTI) is a novel modality of magnetic resonanceimaging that allows noninvasive mapping of the brain's white matter. Aparticular map derived from DTI measurements is a map of water principaldiffusion directions, which are proxies for neural fiber directions. Weconsider a study in which diffusion direction maps were acquired for two groupsof subjects. The objective of the analysis is to find regions of the brain inwhich the corresponding diffusion directions differ between the groups. This isattained by first computing a test statistic for the difference in direction atevery brain location using a Watson model for directional data. Interestinglocations are subsequently selected with control of the false discovery rate.More accurate modeling of the null distribution is obtained using an empiricalnull density based on the empirical distribution of the test statistics acrossthe brain. Further, substantial improvements in power are achieved by localspatial averaging of the test statistic map. Although the focus is on oneparticular study and imaging technology, the proposed inference methods can beapplied to other large scale simultaneous hypothesis testing problems with acontinuous underlying spatial structure.

False discovery rate analysis of brain diffusion direction maps