Statistical inferences for complex dependence of multimodal imaging data

Understand

Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures.

• In this paper, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data.
• Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence among imaging modalities over brain regions, (b) testing independence between brain regions within imaging modalities, and (c) testing independence between brain regions across different modalities.
• Considering a general form for all the three tests, we develop a global testing procedure and a multiple testing procedure controlling the false discovery rate.