Abstract #0156

Empirical Mode Decomposition and Frequency Characteristics of the Default Mode Network on Group fMRI Resting-State Data

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

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

In this project, high-frequency contributions to functional connectivity of the Default Mode Network (DMN) are studied. Rather than relying on user-defined frequency bands, Empirical Mode Decomposition (EMD) is used to decompose the natural occurring frequency bands of the DMN. The novelty of our approach lies in the data-adaptive and user-independent decomposition of fMRI data using EMD, and identification of a resting-state network based on the frequency characteristics of intrinsic modes in the data, instead of using wavelet- or windowed-Fourier-transform methods. Results are shown for multiband MB8 resting-state data of a group of 22 healthy subjects.

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