Adaptive Seeding for Resting-State Network Correlation Analysis with Empirical Mode Decomposition

Hsu-Lei Lee¹, Jürgen Hennig¹

The widely-used seed voxel correlation analysis for resting-state fMRI data requires priori seed ROI assumptions, and the result is strongly susceptible to the choice of this ROI. In this study we used empirical mode decomposition to separate low-frequency BOLD signals into different intrinsic mode functions before analyzing for underlying coherent networks. We also propose an adaptive weighted seeding scheme for generating the correlation map thats less susceptible to cut-off threshold and seed ROI selection, and can potentially provide a more reliable correlation map for further functional analyses.