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Abstract #2496

Empirical Mode Decomposition and Amplitude Characteristics of Resting-State Networks in Parkinson’s Disease

Dietmar Cordes1,2, Muhammad Kaleem3, Xiaowei Zhuang1, Karthik Sreenivasan1, Zhengshi Yang1, Virendra Mishra1, and Ryan R Walsh1

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, amplitudes of low-frequency fluctuations in resting-state fMRI data of subjects with Parkinson’s disease (PD) are studied and compared with matched normal controls. Empirical Mode Decomposition (EMD) is used to decompose the natural occurring frequency bands of major networks important in PD. The novelty of our approach lies in the data-adaptive decomposition of fMRI data using EMD, and identification of resting-state networks based on amplitude characteristics of intrinsic modes.

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