Meeting Banner
Abstract #2323

Energy-Period Characteristics of Brain Networks using Empirical Mode Decomposition

Dietmar Cordes1,2, Muhammad Kaleem3, Xiaowei Zhuang1, Karthik Sreenivasan1, Zhengshi Yang1, Tim Curran2, and Virendra Mishra1

1Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, 2University of Colorado, Boulder, CO, United States, 3University of Management & Technology, Lahore, Pakistan

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here