Abstract #2496

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

Dietmar Cordes^1,2, 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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