Abstract #3909

Empirical Mode Decomposition and Energy-Period Characteristics of Brain Networks in Group fMRI Resting-State Data

Dietmar Cordes^1,2, Zhengshi Yang¹, Xiaowei Zhuang¹, Muhammad Kaleem³, Tim Curran², Karthik Sreenivasan¹, Virendra Mishra¹, and Rajesh Nandy⁴

¹Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ²University of Colorado, Boulder, CO, United States, ³University of Management and Technology, Lahore, Pakistan, ⁴School of Public Health, University of North Texas, Fort Worth, TX, United States

In this project, we have studied resting-state networks using Empirical Mode Decomposition (EMD) to obtain energy-period information and compared results with the Maximal Overlap Discrete Wavelet Transform (MODWT) and the Short-Time Fourier Transform (STFT). We chose the STFT and MODWT for comparison with EMD, because the STFT is based on Fourier basis functions, the MODWT allows more adaptivity but still is model-based by wavelet functions, and EMD is model-free, adaptive and entirely data-driven. EMD showed the strongest relationship to frequency and energy content for different clusters of resting-state networks.

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