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Abstract #0156

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

Dietmar Cordes1,2, 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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