In this project, we have studied resting-state networks using Empirical Mode Decomposition (EMD) to obtain time-frequency-energy information. Intrinsic Mode Functions (IMFs) and associated spatial maps provide a data-driven decomposition of resting-state networks. We investigated the average energy-period relationship of IMFs of group independent components analysis (ICA) networks to better characterize temporal properties of networks and found that the IMFs of BOLD data provide inverted V-shaped energy-period signatures that allow a natural ranking of all resting-state networks when compared to signatures of pure noise.
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